These days, organizations are required to safeguard their customer data and comply with privacy regulations — a task that becomes even more challenging with the increase in third-party relationships. More data is being shared with third parties than ever before, which introduces a whole new set of risks to manage and mitigate.
What steps can you take to establish a third-party management (TPM) program that aligns both security and privacy objectives? This guide walks you through the changes in the security and privacy landscape and how you can best integrate privacy compliance into your TPM lifecycle.
Understand the rapid changes in the privacy and security landscape
Learn the importance of integrating privacy into your TPM program
Discover 10 proven practices for privacy compliance when working with third parties
Organizations around the globe are using third parties to accomplish business goals, and those vendors have become integral to creating a wider reach for those companies. As a result, they also bring risk and potential security issues to their partners. That’s why it’s imperative for security teams to implement a holistic approach to third-party management (TPM).
In this eBook, you’ll learn:
Why the shift from third-party risk to holistic management is a critical move
The ins and outs of the TPM lifecycle
How individual lines of business within your organization factor into TPM ownership
Download this resource to help your teams make the shift from analyzing risk to bringing enterprise-wide third-party management to the entire business.
Dive into this coauthored eBook by OneTrust and Protiviti to explore how organizations can establish a strong operating model for AI governance, addressing challenges around policies, development standards, and risk management.
What you’ll learn:
How to build a comprehensive AI inventory and develop governance structures that scale with your organization’s needs.
Best practices for identifying and managing AI risks, including shadow AI and regulatory compliance.
Practical steps to operationalize AI governance, ensuring continuous performance and ethical AI use.
Download the eBook now to ensure your organization stays ahead in the ever-evolving landscape of AI governance and risk management.
Establishing a clear, efficient AI project intake workflow can transform your team’s ability to assess and manage AI initiatives—balancing risk with speed and efficiency.
What you’ll learn:
How to create a streamlined intake process that integrates seamlessly with your current workflows
Ways to identify and evaluate risks early to prevent investing in high-stakes projects that don’t align with your goals
Leverage a comprehensive checklist at the end of the eBook, ensuring every essential detail is captured for informed project decisions
Download the eBook to equip your team with a user-friendly, effective intake process that helps you confidently scale your AI projects while upholding responsible governance.
Unlock the full potential of your enterprise data to drive business innovation, efficiency, and revenue. This essential book will show you how to transform your data integration processes from a technical headache into a strategic advantage.
Why you need this guide:
Vast amounts of data frequently lie untapped, scattered across various systems and applications. Effective data integration lets you turn data from diverse sources and various formats into actionable insights that propel your business forward. But it’s not always simple. This guide demystifies the process and offers practical solutions to help you overcome common challenges.
Key Takeaways:
Unlock Business Value from Data: Discover how effective data integration can uncover critical insights—from customer needs to sales opportunities—that drive innovation and give you a competitive edge.
Overcome Common Data Challenges: Learn about the most common obstacles in data integration projects and how to tackle them, ensuri
Artificial intelligence (AI) fluency, deeply rooted in robust data literacy, is increasingly critical. In fact, 60% of survey participants cite limited skills and resources as a barrier to AI success, and 82% of leaders say their employees will need new skills to be prepared for the growth of AI.
Predictive AI and generative AI (GenAI) are transformative forces for enterprises. They provide actionable insights from vast datasets, automate decision-making processes and personalize customer experiences — all leading to stronger growth, efficiency and competitive advantage.
With the adoption of generative AI (GenAI) systems surging across enterprises, new regulations are emerging worldwide. In fact, Emily Frolick, US Trusted Leader at KPMG, stated, “Executives need to proceed with vigilance as regulation around GenAI is built and implemented.”
Over the years, the National Roads and Motorists’ Association (NRMA) developed a siloed data environment. In order to rethink its data strategy, the NRMA partnered with Informatica to adopt a single data platform strategy and integrate all its data and applications on Google Cloud. The result? A remarkable 10X increase in productivity.
IT leaders and data management professionals are under constant pressure to enable greater efficiency, agility, and innovation at organizations in a variety of ways. Chief among them is ensuring that data is available when and where it’s needed to support business goals. As such, the database landscape continues to evolve with new offerings, features and capabilities, and tools and strategies to serve the expanding universe of modern data use cases and applications. Download this special report today to dive into the key technologies and best practices.
Managing fragmented data and manual processes can severely slow down your business, leading to costly delays and inefficiencies. Traditional integration methods often demand significant time, money, and technical expertise, making it challenging to keep up.
No-code data integration changes that. Our free eBook, “No-Code Data Integration,” provides step-by-step instructions and real-world examples to help you connect your systems and automate business data exchange effortlessly. Learn how no-code solutions can save you time, reduce errors, and enhance overall efficiency.
Businesses relying on traditional BI tools often face delays and inefficiencies due to manual data collection, which leads to inaccuracies. Additionally, consolidating and processing data from multiple sources can be time-consuming and technically challenging, leaving decision-makers without real-time insights.
With Embedded Analytics, You can integrate data into your existing IBM i applications, providing real-time insights and eliminating manual data processes. Our free eBook offers a complete guide to overcoming data integration challenges and enabling self-service analytics, allowing you to interpret and understand data easily without the need for technical expertise.
In database management, keeping pace with the ever advancing landscape of technologies and features isn’t just a choice; it’s essential. This holds especially true for MongoDB users, as it continuously evolves to deliver enhanced performance, heightened security, and unmatched scalability. However, upgrading MongoDB isn’t a matter of just clicking a button; it demands meticulous planning, exacting execution, and an intricate understanding of how upgrades work.
Whether you’re a seasoned DBA well-versed in MongoDB or a newcomer looking to harness its potential, this ebook provides the insights, strategies, and best practices to guide you through MongoDB upgrades, ensuring they go as smoothly as possible and your databases remain optimized and secure.
Databases are often subject to periods of high traffic, whether due to planned events like Black Friday, Cyber Monday, or other remarkable dates or unexpected traffic surges caused by content going viral or distributed denial-of-service (DDoS) attacks. No matter what causes them, during these hightraffic periods, your database infrastructure will be tested, and failure to adequately prepare can lead to performance issues, downtime, frustrated users, or worse.
This guide is designed to provide DBAs, developers, and IT professionals with the knowledge and tools to ensure your database is ready to handle an influx of traffic, minimizing disruptions and maintaining a positive user experience.
Essential Strategies for PostgreSQL Upgrade Success is your guidebook for navigating a PostgreSQL upgrade with confidence and expertise. Whether you’re an experienced database administrator, developer, or IT professional tasked with overseeing deployments — or just getting started in PostgreSQL – it’s crafted to be your companion for understanding the best practices for PostgreSQL upgrades. Inside, you’ll discover…
What to do before starting an upgrade: We provide a checklist that acts as a guide to ensure all essential measures and precautions are in place for a seamless and effective upgrade.
How to optimize performance and minimize downtime: Discover strategies for tuning and optimizing your PostgreSQL environment and ensuring uninterrupted access to your data after an upgrade.
Techniques for troubleshooting common challenges: Gain insights into common pitfalls and challenges during PostgreSQL upgrades and learn how to resolve them effectively.
Ways to safeguard your data: Und
With MongoDB Performance Tuning, Percona reaffirms its commitment to open source. We believe in making database management more straightforward, more efficient, and accessible to everyone. By sharing our expertise through this guide, we aim to help you enhance your database and application performance, ensuring that your MongoDB deployments run smoothly and efficiently.
So, dive in and prepare to unlock the full potential of MongoDB!
More than a third (36%) of business, data and artificial intelligence (AI) leaders say their lack of a defined architecture and roadmap is a top challenge, and another 29% cite cloud modernization and platform selection.
Data engineering hackathons aren’t easy to tackle – you need to think outside the box and solve new problems with cutting-edge technology. Discover how to conquer your next hackathon and strengthen your skill set.
To successfully guide artificial intelligence (AI) development and deployment, ethical frameworks are essential. Explore responsible AI principles to lay a solid foundation for building AI practices that are sustainable, compliant and meet societal expectations.
As the saying goes, you can't improve what you don't measure. To ensure that data is flowing properly through various pipelines, processes, and workflows -- and that data is accurate, consistent, and meets quality standards -- you need the ability to understand and monitor data throughout its lifecycle. It's from these challenges that the concept of data observability rose to prominence and is quickly gaining traction as a combination of tools, practices, and organizational culture. Download this special report to dive into key strategies for achieving effective data observability.
When embarking on new investment ventures, the top consideration is price point. That’s why we created a rundown of your flexible pricing options so you can quickly find a pricing plan that supports your data management goals, drives efficiency and reduces your total cost of ownership.
Whether you’re a functional department, global IT unit, data owner or application owner, there’s a plan tailored for every team’s needs.
Get our guide, “Flexible Pricing for Cloud Data Management,” to learn:
Pricing principles, definitions and examples
An overview of Informatica’s flexible pricing
Four pricing use cases
Quantifying ROI has become critical as organizations evaluate the direct impact of investments on efficiency, cost savings and business performance.
To better understand the ROI of Informatica Cloud Data Integration (CDI), Nucleus Research conducted an in-depth assessment of multiple customers across five industries. The results: 335% average ROI, 67% faster data processing and $3.9M+ average annual benefit.
Access more key findings in the report, “ROI Guidebook: Informatica Cloud Data Integration Services.” You’ll learn:
Research-backed ROI stats and top benefits
Strategies to refine AI with high-quality, trusted data
Analyses of costs and key financial metrics
A recent market study highlighted pivotal trends in data management and data fabric. The results proved that the adoption of cloud-based solutions and scalable data frameworks is increasing. In addition, priorities are shifting towards data quality, governance and integration platforms and 26% of respondents acknowledge the benefits of data fabric for IT data management.
Access more insights in the report, “2024 Modern Data Management and Data Fabric Study.” You’ll learn:
The current state of data management and data fabric
Top benefits and challenges to overcome
Informatica’s take on data architecture trends, including delivering trusted data for AI
A recent study revealed that the average organization uses 7.5 petabytes of storage. To effectively utilize growing data assets, IT leaders need a robust data engineering strategy and advanced data platform equipped with two critical capabilities: data loading and data replication.
While the benefits of data loading and replication are substantial, they're not always easy to quantify as part of a financial analysis. Unveil methods to calculate and communicate their value to business stakeholders.
Get our white paper, “Boosting Bottom Lines: 5 ROI Drivers from Data Loading and Replication,” to explore five opportunities for value (inspired by business value assessments), including:
Stronger productivity for data engineers and data consumers
Faster response to emerging opportunities and threats
Greater reliability and reduced downtime
Turn to the power of DataOps, MLOps and AI for faster time to insights, stronger agility and better cost efficiency. With DataOps and MLOps, you can improve machine learning accuracy and operationalize AI, ensuring robust data governance and effective model management.
Learn how to get started with our four-step quick guide, “How to Unlock Your Enterprise Data Potential with DataOps, MLOps and AI.” You’ll get tips and tricks on how to:
Improve data quality and delivery
Convert insights into real-time decision-making
Scale with precision and control costs
Facing slow manual processes that hindered data analysis, Banco ABC Brasil decided to make a change. The wholesale bank needed a new solution to enable a better understanding of their data and automation to handle growing data volume.
After implementing an AI-powered integration platform as a service (iPaaS), they saw huge improvements across the business.
Explore Banco ABC Brasil’s data success story to learn how the company:
Modernized data integration capabilities with AI
Improved operational efficiency
Reduced predictive model design and maintenance time by 70%
Data and analytics (D&A) leaders must continuously improve data quality to ensure the success of master data management, artificial intelligence (AI), machine learning (ML) and more.
As you evaluate various data quality solutions, we believe you should keep an eye out for a key driver: augmented data quality (ADQ). ADQ is driven by AI/ML, metadata, convergence and integrations across data management offerings for game-changing automation.
Explore the vendor landscape and make the best choice with the 2024 Gartner® Magic Quadrant™ for Augmented Data Quality Solutions. In fact, Informatica has been recognized as a Leader for the 16th time.
Get the report to learn:
What to look for in an enterprise data quality solution
An in-depth market evaluation and analysis of 13 vendors
Why Informatica is named a Leader
A data governance strategy for responsible AI outcomes should be at the forefront of your agenda. As countries develop new AI regulations, it’s critical to utilize an integrated, cloud-native data platform that aids the de-risking of AI initiatives and enhances compliance with emerging frameworks, like the EU AI Act.
Watch CEO of Immuta, Matthew Carroll’s fireside chat with Alessandra De Almeida, Executive Director of Data Management & Governance at Merck, exploring Merck’s transformative data management journey. Merck generates vast volumes of data across various functions—including R&D, manufacturing, and commercial—but siloed data use made it difficult to access and leverage effectively. This limited their potential to derive meaningful insights and deliver lifesaving innovations.
Recognizing the need for a cohesive approach, Merck developed a data marketplace to converge its data infrastructure to centralize access, enabling efficient data use from a single point while ensuring GxP compliance. Watch this on-demand webinar to hear Merck’s story, including:
How the company strategically transformed its data architecture to create a division-agnostic platform to find & access data in a one-stop approach.
The role of this marketplace in breaking down silos and enhancing the accessibility, av
Enterprise data is becoming increasingly diverse, dynamic, and distributed across cloud-native computing platforms. However, as organizations invest in modern cloud data platforms, they face a major challenge balancing the need to secure their data with providing fast, scalable access to internal and external users.
Concerns over data security and privacy can impede or completely stall projects. But, these concerns can be addressed and projects unblocked by implementing appropriate fine-grained data security and access control mechanisms.
Watch this on-demand webinar with Immuta’s Paul Myres, Field CTO, and Will Pearson, Senior Solutions Architect, and see how Immuta demonstrates some of the best practices for balancing data accessibility with security, enabling organizations to unlock the full potential of their data assets while safeguarding sensitive information. See a live demonstration of how Immuta’s data security and analytics approach is helping our customers automate and
The digital business era, driven by artificial intelligence (AI) and generative AI (gen AI), demands unified, interoperable data to overcome challenges like data integrity and control concerns. IDC’s January 2024 survey of 881 respondents reveals significant investment in gen AI, emphasizing the need for centralizing intelligence about data. Trends such as modern data architectures and treating data as a product underscore the importance of data interoperability.
Unlock AI-Powered Data Unification
Are you ready to remove data silos quickly and create unique customer profiles for accurate segmentation? The latest white paper from Ventana Research reveals how you can achieve these goals and more.
Download it now to discover how to:
Eliminate data silos and create reliable customer profiles for accurate segmentation, unlocking 360° customer views.
Balance robust data governance with the need for business agility, ensuring trusted data products.
Streamline data unification processes with AI for a low total cost of ownership and high return on investment.
If you have an older, traditional MDM system, it may be holding you back now. Not only do these systems often lack the scalability and performance needed for today’s massive data volumes, but they also can be expensive to manage, upgrade, or extend.
Modern MDM—cloud-native and SaaS-based—has become a must-have investment for organizations that need a reliable, rapid path to digital transformation without high costs and long deployment times. And this newer generation of MDM systems makes it easier for you to tap into the latest technologies and integrate with a variety of applications.
This white paper provides a comprehensive look at how you can leave the challenges of legacy MDM behind and realize the cost savings and productivity gains a modern MDM can deliver.
With high-quality, timely data for your business intelligence and AI/ML initiatives, you can improve business efficiency, mitigate risks, enhance the customer experience, and improve insights for better business outcomes.
Learn how our platform uses ML and gen AI to automate entity resolution, improve data quality, and boost data steward productivity—setting a new standard for efficiency and value in data unification.
Two major trends, the proven business value of analytics and the positive productivity impact of AI, drive organizations to collect, manage, and use more data. At the same time, the challenges of governing that data have only increased. Companies must comply with a growing patchwork of privacy laws and increasing cybersecurity risks while also facing increasing demands for fast data access and unprecedented data volumes.
These challenges are hard to meet with traditional data governance solutions that were built for the era of human-driven business intelligence and not the AI-driven era of human-machine collaboration. Most notable is the fact that traditional solutions focus on the data governance applications focus only on the discovery of structured data. While that may be sufficient for analytics use cases, it is not sufficient for AI, which rely heavily on unstructured data sources.
In today's rapidly evolving landscape of artificial intelligence, the need for responsible AI governance has never been more pressing. In this whitepaper, we’ll dive into the critical
drivers behind the surge in AI governance, highlighting the factors propelling its growth.
From the widespread adoption of AI and the rising number of AI incidents to the lack of organizational preparedness and increasing calls for regulation, these drivers underscore
the urgency of establishing a robust framework for the responsible use of AI.
Join us as we explore these drivers and the challenges organizations face, ultimately providing practical steps and strategies to navigate the complex terrain of AI governance.
Just in: 86% of data leaders say the key drivers that influence their decision-making the most when selecting a data architecture are avoiding vendor lock-in with a flexible data management platform, the need for improved data management and governance and improving efficiency and operational costs.
According to a recent study, data catalogs are seen as important or very important across most business functions. They’re key to improving data management, data governance, collaboration, scalability and strategic decision-making.
Frontier Communications is pioneering the path to digital transformation with a clear objective: “Building Gigabit America.” Discover how the organization fast-tracked digital transformation, upgraded to a cloud-first data stack and improved master data management.
It’s not surprising that 78% of data leaders expect their organization’s investment in data initiatives to increase in 2024 — especially when “firms with high data trust see an average revenue growth rate 7.8 percentage points higher than firms with low data trust levels.”
Evolving into a data-driven enterprise is no easy feat, faced with unique obstacles that demand strategic navigation. In fact, there are over 15 million losses due to poor data quality and less than 10% of AI/ML initiatives deliver.
Without question, data engineering needs the help of generative artificial intelligence (GenAI) and GenAI needs the help of data engineering. Here’s why: GenAI makes data engineers more productive, while data engineering provides GenAI new levels of innovation.
When it comes to implementing artificial intelligence (AI), keep your eyes on a critical piece of the puzzle: data integration. If your data foundation for AI isn’t strong enough, problems will emerge for AI, machine learning (ML) and data science projects.
The GDPR (General Data Protection Regulation) has been in effect since 2016. However, companies are still faced with taking technical and organizational measures to meet these high data protection requirements. Numerous court cases and lawsuits show that not every organization has been completely successful.
In the ever-evolving landscape of insurance, the challenge for agents to retain and grow their client base has intensified. The trend of customers "unbundling" their insurance products to explore diverse options creates both risks and opportunities.
This white paper explores the crucial role of clean CRM data in navigating this landscape. Discover a comprehensive five-step CRM hygiene plan to ensure your data is a powerful asset in the quest for customer retention and business growth.
A recent report shows a 149% increase in fraud attempts targeting financial services, and credit unions are no exception. Is your credit union constantly trying to keep up with fast-changing threats only for new tactics to make your security solutions obsolete? Discover five critical capabilities you should understand to ensure your fraud solution closes security gaps and the role complementary technologies can play.
From improving business decision-making to developing AI systems, fast access to reliable, actionable data is critical to the success of modern enterprises. However, a sizable gap still exists between the data aspirations of technology and business leaders and the execution of these goals. With data increasingly spread across different locations, file systems, databases, and applications at the average organization, effectively integrating and governing data remains a formidable challenge. Most organizations today have a mix of legacy infrastructure and new cloud-based solutions that have been adopted more recently. Download this special report to dive into key solutions and emerging best practices.
While the popularity of doing business in the cloud keeps growing, unfortunately, so does the cost. And not just the cost, but who is responsible for a platform costs and how it is billed. If we use history as a guide, the mainframe is much like the cloud—everything had a cost and was billed back in microscopic amounts, including CPU time (instructions), disk usage and even the number of pages that came off the printer. During the “open-system” days of real-time processing, this type of billing was simplified to a more basic charge-back model that is used today for paying for a certain number of CPU threads, memory and storage capacity. Read on to discover the benefits of FinOps, what’s needed to develop an effective FinOps strategy and how to assess the success of your investment.
EDB Postgres® AI equips you to overdeliver for your global user base and support the most demanding applications—anywhere, any time. Deploy multi-region clusters with five-nines availability to guarantee that data is consistent, timely, and complete, even during disruptions.
Intelligence, 88% of respondents reported that one hour of downtime cost more than $300,000. No wonder that the same survey found that 87% of respondents deemed an uptime of 99.99% to be the minimum acceptable level of reliability for mission-critical systems.
Working with third parties introduces new business risks, making it crucial to design a third-party risk management (TPRM) program that enables privacy compliance. What steps can you take to build a TPRM program that aligns with your privacy goals?
This checklist will help you:
Understand what to watch for when working with third parties
Find gaps in privacy compliance while growing your TPRM program
Futureproof your program for global opportunities
Increase cross-domain collaboration between privacy, security, and procurement & sourcing teams
In this video, Craig Mullins, IBM Gold Champion, overviews QuickSelect for Db2, a unique solution that accelerates Db2 online and batch workloads. Learn how this approach to Db2 result set caching delivers performance gains without the need for changes to JCL, application code or Db2.
Compliance, privacy, and ethics have always been top priorities for data-driven organizations. But as generative AI advances open new doors and opportunities for innovation, the stakes have never been higher than they are right now.
But for all of AI’s potential, it also presents significant uncertainty and risk. To understand the rapid pace of AI evolution and how organizations are adapting to harness the power of AI and machine learning, we surveyed 700+ data experts from around the globe. In this report, you’ll find out:
Why 80% of respondents say AI is making data security more challenging.
How data leaders are adjusting their strategies and processes in the face of AI adoption
The ways in which AI can actually help strengthen data security
Key considerations for leading AI-savvy teams in today’s fast-moving data ecosystem
In today’s fast-paced digital landscape, the ability to access and process data as quickly as possible is of utmost importance for businesses striving to gain a competitive edge. As such, a significant trend in the database world is utilizing memory for more types of data management and processing functions. If you can bypass disk I/O, you can achieve tremendous performance gains. There are many reasons for this, but of course, the most important reason is that disk access is much slower than memory access.
Caching is a critical component for most high-traffic applications. Caches can provide data access at high throughput with low latency while relieving load on your systems of record. When used correctly, caches can even provide for use cases that are near impossible to handle with a traditional database.
In this ebook, you will learn how to use Redis OSS as an application cache. We’ll cover some background on caches, their use cases, and some unique aspects about Redis OSS. Then, we’ll look at how to use Redis OSS well, including basic caching patterns, the core Redis OSS data types, and some advanced tips.
The world of database management keeps evolving with database types, new features, new tools, and new trends. Likewise, database management today is full of new challenges and opportunities. To get the most value of their data, businesses need greater speed, scalability, and flexibility in how data is processed, stored, and accessed. Download this special report to dive into the latest technologies and strategies essential to meeting current and emerging requirements.
Like any data product, when it comes to AI, garbage data in means garbage responses out. The success—and safety—of AI depends on the reliability of the data that powers it.
This Wakefield Research survey—which polled 200 data leaders and professionals—highlights the pressure data teams are under to deliver production-ready AI applications—and the risks that continue to expand in the process.
Among a variety of statistics that indicate the current state of the AI race—and professional sentiment about the emerging technology—this survey demonstrates that the data quality problem is evolving rapidly. And when data quality risks evolve, data quality methods need to evolve too.
Data observability is one of the hottest emerging data engineering technologies of the last several years, and the importance of data quality and data reliability only continues to rise.
But, with more data observability tools on the market than ever before, how does a data leader decide?
In this guide, we’ll share how the experts evaluate data observability tools, including the must-have features and benefits based on both analyst perspectives and our own experience building – and leading – the category.
We’ll go over:
The key components and features of data observability
Insights from leading analysts on data observability
An RFP template for evaluating data observability solutions
The evolving landscape of data observability and ways to select the best solution
Download the Data Observability Evaluation Guide and learn more!
A data strategy that enables organizations to reap the full benefits of data and analytics initiatives is required, one that enables them to be fearless with regard to providing governed, secure data quickly for business needs. This strategy should provide a comprehensive blueprint that outlines the necessary technology, procedures, personnel, and guidelines vital for overseeing an entity’s information resources over an extended period. It should operationalize the organization’s overarching vision concerning the acquisition, retention, dissemination, and utilization of its data resources.
Amazon Aurora is a fully managed relational database designed for unparalleled high performance and availability at global scale with full MySQL and PostgreSQL compatibility. Aurora provides managed high availability (HA) and disaster recovery (DR) capabilities in and across AWS Regions. In this whitepaper, explore Aurora HA and DR capabilities and discover design patterns that enable the development of globally resilient applications. Learn how to establish single Region and cross-Region HA and DR using Aurora features, including Multi-AZ deployments and Amazon Aurora Global Database.
PostgreSQL Performance Tuning is your expert guide to achieving exceptional database performance. Percona offers you the agility and knowledge to tackle your performance optimization challenges.
MySQL Performance Tuning will help you make informed decisions that ensure your databases are running at their best. Get ready to elevate your MySQL databases to new levels of efficiency and performance!
With MongoDB Performance Tuning, Percona reaffirms its commitment to open source. We believe in making database management more straightforward, more efficient, and accessible to everyone. By sharing our expertise through this guide, we aim to help you enhance your database and application performance, ensuring that your MongoDB deployments run smoothly and efficiently.
So, dive in and prepare to unlock the full potential of MongoDB!
In today's intricate data landscape, business users often grapple with the technical complexities of data management.
Datavid's semantic data platform resolves this challenge by utilising a semantic layer—a business-focused abstraction over the technical layer that seamlessly incorporates business logic.
This innovative approach frees users from grappling with the technical intricacies of data sources, offering a unified and user-friendly method for exploring, comprehending, and leveraging data.
Access the whitepaper to delve into the fundamental concepts, advantages, deployment strategies, and features of semantic layers.
You will also receive guidance on constructing a robust system, overcoming adoption hurdles, and real-world examples, including a case study showcasing the successful implementation of Datavid's semantic data platform. ACCESS NOW!
Unlock the power of your data with Datavid's Unified Data Layer (UDL).
In today's fast-paced world, making the most of your data is critical to staying ahead. Your organisation faces challenges in overcoming messy, unstructured, and siloed data. Datavid's UDL has the solution.
UDL helps you tackle data fragmentation, offering intelligent solutions to uncover valuable insights and boost your data strategy. Discover how UDL, using advanced AI like NLP, ML, and the integrative power of Knowledge Graphs and large language models (LLMs), transforms your data management.
Learn the shifts needed to improve data-driven decisions and grow sustainably. Adopt UDL and elevate your data game today!
Distributed PostgreSQL can be a game-changer for modern apps that demand constant availability, global access, and rapid response times. However, while many vendors today make claims about being "Postgres compatible" or "Postgres based," careful consideration should be given to the level of effort require to migrate application code and to what degree the product is outside the mainstream of the Postgres ecosystem. This guide outlines the six key considerations when evaluating a Postgres database as well as recommendations on how to assess what is right for your organization.
Even though Spark and Delta are the perfect basis for your future data pipeline, and the Data Lakehouse architecture allows us to enable a tremendous amount of new use cases, the usability and productivity still remain a challenge.
Leaders must take tangible and pragmatic steps to understand, address, and control data quality on an ongoing basis. This eBook will discuss the pervasive dysfunction within data ecosystems, the cost of bad data, and offers an actionable solution to rectify these issues, potentially saving millions in lost efficiencies and revenue.
Organizations today are hungry for information that can be acted on as events occur. In fact, when we asked DBTA subscribers what is driving their decisions around new data architecture patterns, platforms, and tools, the two biggest reasons they gave us were increasing real-time analytics and AI capabilities. Download this special report to dive into the latest technology solutions and evolving best practices.
Data sharing is essential for any growth-oriented business, helping to maintain a competitive edge and deliver business value. By allowing teams to deploy data products, collaborate with internal and external partners, and monetize insights, data sharing drives business outcomes.
So why are so few organizations actually sharing data at scale?
In this bundle, we’ve gathered resources to help identify the largest blockers to secure data sharing and examine how your team can overcome them to unlock additional value. You’ll find:
The Top 5 Barriers to Data Sharing and How to Overcome Them: See what CDOs identify as the top five challenges preventing efficient internal and external data sharing.
Enforce Compliance & Audit Reports for Data Sharing in Snowflake: Learn how Immuta closes the gap between data use agreements and policy enforcement with Snowflake.
Advancing Lifesaving Pharmaceutical Research & Development with Immuta: Explore how one global pharmaceutical company levera
Data quality is a central component of any successful modern data strategy. It powers everything, from business insights and product experiences to customer service and more.
With GenAI on the horizon, reliable data is more important than ever before. And that all starts with a focus on data quality. But where do you start when approaching data quality?
In this guide, we’ll walk you through the current state of data quality today, and we’ll deep dive into what to look for when choosing the right data quality tool for your organization. We’ll discuss:
How to choose the data quality tool that’s right for you
The benefits of data observability and data pipeline monitoring for data quality
How to scale data quality across your entire organization
How to measure data quality with key metrics, including data downtime, data reliability engineering, data satisfaction and adoption, and data health
Download the Data Quality Playbook and get started!
Choose the only distributed, fully managed, Oracle-compatible Postgres database - available on every cloud. EDB BigAnimal combines the world’s most-loved database with the freedom of a fully managed service–from one of the largest contributors to the open source PostgreSQL project.
Switching from using Oracle onsite to Postgres in the cloud can bring major benefits, reducing operational hassles from maintaining data centers, servers, and other infrastructure. Those headaches become the cloud service provider’s problem. Plus, the change can save money, provide access to advanced technology, and deliver organizations from their toxic relationship with Oracle.
FinOps calls for aligning your finance, developer and leadership teams to reach your cloud goals and inspire confidence in cloud adoption and ongoing usage. A chasm has grown between the development and database, where optimizing code to bring greater CPU and storage efficiency has ceased to exist. Where bad code against a database used to be more tolerated in the days of simple and fixed costs for components, it should now be less tolerated.
Read on to discover the benefits of FinOps, what’s needed to develop an effective FinOps strategy and how to assess the success of your investment.
Whether you're thinking about moving on from Oracle or migrating to open-source Postgres databases in the cloud, it's critical to identify ways to future-proof business performance. Essential considerations include assuring that the databases driving all cloud applications have the correct availability characteristics to retain customers, ensure uninterrupted revenue, and optimize business processes.
2024 will be the year of the data engineer.
Where 2023 saw teams scrambling to name drop their latest AI project, 2024 will see teams prioritizing real business problems— and the teams that will solve them.
That means a renewed focus on the priorities of data engineers, including data reliability and data quality.
In the third edition of this report, we’ve analyzed the data landscape and put the spotlight on 11 of the biggest trends poised to impact data engineers in 2024, including:
Cloud cost optimization
Self-serve data platforms
Rise of Apache Iceberg
And more!
We hope this report will help keep you up to speed with the organizational, industry, and technology trends most impacting our industry’s evolving landscape. Get your free copy today.
What does your data quality need?
Introducing the Data Quality Maturity Curve
Data quality is important—no doubt about it. But like any new data practice, developing a scalable data quality strategy doesn’t happen overnight. And what you need today may not be what you need tomorrow.
But where should your team start? Great Expectations? dbt tests? Data Observability?
Selecting the right data quality solution for your data platform isn’t about developing the ultimate strategy for forever—it’s about developing the right strategy for right now. In this piece, we examine the Data Quality Maturity Curve—a representation of how data quality works itself out at different stages of your organizational and analytical maturity—to offer some experienced perspective on where you should be right now—and where you’re headed.
As data teams shift their approach toward creating reliable data products, they are modernizing their team structure to place data product managers at the helm of these critical business assets. But what exactly does a good product manager do and do you really need one for your team?
Our latest eBook answers these questions, diving into important topics such as:
Defining the data product manager role and responsibilities
What a data product is, types of data products, and how to treat ""data like a product""
The differences and similarities between software and data product managers
The importance of basic analytics, AI, statistics, and ML concepts
The data product manager career path
How to measure success
And more!
Today, data teams cannot afford to be anything but lockstep with organizational goals. Access this guide today to see how data product managers are integral to establishing and maintaining this alignment.
At a time when more data is stored in – and headed to – the cloud than ever before and most organizations are diving into new data analytics and AI projects to reap greater business value from it, the protection and management of data is one of the toughest challenges facing IT and data leaders today. Download this special report to gain a deeper understanding of the emerging best practices and solutions enabling modern data governance and security.
As enterprises look to extend the value and functionality of their applications, a crucial aspect of modernization is the necessity for data to be as real time as possible. Download this special report to learn how Kourier Integrator from Kore Technologies facilitates the building, managing, and deployment of secure, scalable, real-time integrations to best-in-class applications via RESTful Web Services.
From predictive analytics and machine learning to generative AI, data is the lifeblood that fuels the development and efficacy of AI systems. At the same time, data-related issues remain a key obstacle across the training, deployment, scaling, and return on investment of initiatives at many enterprises. These issues include the availability and quality of data, the volume and speed at which data needs to be processed, as well as the protection of data.
Data democratization boosts decision-making speed and innovation by making data accessible to all, fostering a nimble and innovative corporate culture. It streamlines operations and cuts down on resource usage, enhancing efficiency. This openness improves organizational transparency and accountability, building trust. Additionally, it supports customer-centric approaches, directly influencing satisfaction and loyalty.
Amazon DocumentDB (with MongoDB compatibility) is a fully managed native JSON document database that makes it easy and cost effective to operate critical document workloads at virtually any scale without managing infrastructure. DocumentDB has native vector database capabilities, and provides built-in security best practices, continuous backups, and integrations with other AWS services. This data modeling book will help you quickly learn about the document model, and shares advanced tips for Amazon DocumentDB optimizations. Whether you are curious about the document model or an avid DocumentDB user hungry to gain expert knowledge, this Amazon DocumentDB data modeling book is for you!
As data ecosystems become more complex, organizations are looking for advanced tools and technologies to manage and derive value from diverse and interconnected data sources. Knowledge graphs provide a comprehensive view of complex data relationships, helping organizations gain insights and derive actionable intelligence from their data ecosystems. Download this special report to dive into how knowledge graphs are empowering data analytics and AI today.
Microservices-based applications have revolutionized the way applications are built by breaking down monolithic applications into smaller, independent services that communicate with each other. This modular approach brings benefits such as scalability, agility, and resilience. A best practice for microservices-based applications is to use in-memory caching to overcome data fragmentation and network latency challenges. This paper covers the advantages of microservices, the need for performance optimization, high availability, and how a cache-based messaging layer facilitates inter-microservice communication. Download this complimentary whitepaper today.
If your organization is still running SQL Server on-premises, this IDC InfoBrief is a must-read! As with other technologies, SQL Server users are looking to migrate to the cloud to enjoy more agility and scalability at a lower overall cost. This is becoming increasingly important as many organizations want to take advantage of generative AI, which needs the cloud to scale. In a recent survey of 2,259 organizations, a clear majority of those polled indicated that Amazon Web Services (AWS) is their chosen primary cloud provider for deploying SQL Server workloads. Download this special IDC InfoBrief to learn why, including key criteria for choosing a cloud provider, benefits and options for using AWS, and essential guidance.
Caching is an easy way to improve both database and application performance while reducing costs. This IDC Snapshot showcases the significant business value – including ROI, cost savings, and revenue gains – realized by other organizations from adopting Amazon ElastiCache, a fully managed in-memory data store service from Amazon Web Services (AWS). If you’ve ever wondered about the benefits of caching, you will learn how Amazon ElastiCache improves application performance, reduces operational costs, and enhances scalability and availability. By offloading read and write workloads from databases to ElastiCache, organizations can significantly enhance application responsiveness, leading to improved user experiences and increased productivity. Download this white paper today.
In a world where data is a valuable asset, efficient management of relational databases is essential for success. Amazon RDS is a collection of easy to manage relational database engines optimized for total cost of ownership. This IDC Snapshot provides valuable insights into the real-world business value impact – including ROI, cost savings, and revenue gains – realized by other organizations from adopting Amazon RDS, including cost savings, increased productivity, and enhanced business agility. By leveraging this cloud-based service, organizations can stay ahead of the curve and maintain a competitive edge in their respective industries. Download this complimentary IDC Snapshot today.
Amazon DynamoDB is a serverless, fully managed, NoSQL database with consistent single-digit millisecond performance at any scale. DynamoDB is the database that powers Amazon.com. This IDC Snapshot presents the business value – including ROI, cost savings, and revenue gains – realized by other organizations from adopting DynamoDB. With DynamoDB's serverless architecture and automatic scaling capabilities, organizations can scale their databases based on application’s demand without the need for manual provisioning or capacity planning. This is a must-read for any organization that is seeking a database that offers 1/ zero servers to provision, patch, or manage, 2/ no software to install, maintain, or operate, 3/ zero versions (major, minor, or patch) upgrades, and 4/ zero downtime maintenance! Download this complimentary IDC Snapshot today.
Today, most data is destined for the cloud -- not only for storage and processing, but, increasingly, analysis. Beyond potential cost savings, it’s the demand for greater speed, scalability, and flexibility that is changing how data delivery and analytics systems are being designed, implemented, and managed to meet current and future needs. Whether your organization is focused on supporting AI use cases, increasing real-time analytics, or improving self-service data access, being able to fully take advantage of modern cloud data platforms, tools, and strategies is essential. Download this special report for the key considerations and best practices.
In today’s fast-paced, rapidly changing world, businesses rely on line-of-business staff to make quick, intelligent decisions based on the latest information. Agility, precision, and fast responses all rest on the decisions and actions made by business-line members, whether they are responding to an online query, offering a customer discount, or selecting a supplier to reduce supply chain risk. Agile, data-driven decision-making, across all levels of the organization, is critical to improving business outcomes.
Every modern organization relies on data to operate. And along with that data comes the need to protect, monitor, and use it responsibly.
While data discovery solutions offer a range of features, there are some critical points to cover when evaluating one for your own organization. These include:
Why you should use a tool for data discovery
Key elements of an effective data discovery solution
The different data discovery use cases
Hybrid and multi-cloud computing open new possibilities for data management. The cloud -- whether linked to onsite resources in a hybrid fashion or manifested across multiple services -- offers a cost-effective and responsive approach to managing and making data available to end users and next-generation applications. At the same time, moving to hybrid and multi-cloud data architectures may create new levels of complexity. Download this special report today for key strategies and emerging best practices.
Are you navigating the evolving landscape of database management? Discover how to balance business metrics while efficiently handling complex environments. Learn about the changing role of database management, the rise of multiple databases, and essential features in third-party monitoring tools.
Discover industry expert insights on active-active replication's considerations for achieving high availability. Learn strategies to optimize database uptime and performance critical for business operations.
Unlock insights into the latest trends and benchmarks for cloud adoption and modernization with the 2024 report. Based on a survey of 220 IT and business professionals, it provides valuable insights for organizations.
Securing a Changing Data Landscape
One of the most exciting aspects of our data-driven world is its dynamic nature. The data landscape is subject to the constant generation, innovation, and iteration of new ideas from experts and newcomers alike.
The most impactful of these ideas are those that take hold across industrial and geographical lines, adopted by and experimented with by organizations. With this in mind, it’s critical that we connect with peers across industries about the trends they are experiencing so we can work together to ensure secure and accessible data use this year and beyond.
In our 2024 Trendbook, data leaders share their thoughts on:
The growing adoption of distributed architectures like data mesh and data fabric
The immediate and long term effects of rapidly evolving artificial intelligence (AI) tools
The reprioritization of resources and budgets to revitalize modern data access and data security
As enterprises increase the expanse and range of SQL Server databases via VMware virtual machines, they need to ensure that the data within is protected without disrupting their business operations. While the volume of data and demands for faster data backups and restores grow, IT teams are also tasked with reducing costs to maximize efficiency. This is forcing many to look for ways to consolidate and simplify their infrastructure -- including data protection. Download this special white paper to learn about a new approach to addressing common challenges and ensuring best practices are followed at your organization.
The restrictions and drawbacks of legacy proprietary databases like Oracle have never been clearer—especially in contrast to the freedom and innovation offered by Postgres. As a result, more and more organizations are saying goodbye to Oracle and migrating to greener Postgres pastures—both enterprise and open source. Consistently, those who make this change find themselves with more flexibility, better control of their data and an enhanced ability to modernize. In this eBook, you’ll learn about the experience of three such businesses:
a remote telemetry leader that reduced licensing costs and accelerated customer experiences
a telecommunications business support provider that harnessed Postgres for agile and cost-efficient application modernization
an automotive repair juggernaut who underwent a major transformation project without the fear of vendor lock-in or excessive downtime
How can Oracle migration empower you? Let’s find out.
This white paper focuses on the most popular source and target for database migrations: moving from Oracle to Postgres. Oracle is the #1 legacy database, and its extremely onerous license policies are driving the majority of migration demand.
Postgres is the logical target for the migrations. With a constant stream of innovations reflected in annual releases, Postgres has achieved three major database of the year awards from DBengines.com, recognition as the #1 database in StackOverflow’s annual developer survey, and the position of #1 database on the Cloud Native Computing Foundation’s tech radar. Not only is it clear that Postgres is winning the hearts and minds of innovation drivers, but its small footprint makes it an ideal solution in containers too (see Datadog survey).
The principles and approaches described in this paper are applicable to other source/target combinations as well. You’ll find:
A quick review of the business drivers and migration approaches;
A dive in
Given today’s economic uncertainty, many organizations are taking a closer look at their budgets. Lowering the total cost of ownership (TCO) factors into every decision companies make.
See how three leading organizations today are escaping legacy databases and using EDB’s migration tools and support to break free from restrictive legacy databases, increase performance, accelerate innovation, and decrease TCO.
Today’s financial service providers are under pressure from multiple angles. Increased customer expectations, growing digitization, expanding data volumes, regulation complexity, security threats, rising costs, and competitive challenges are requiring banking, financial services, and insurance (BFSI) organizations to modernize and future-proof their technology.
Business leaders are constantly searching for ways to minimize expenses and optimize the total cost of ownership (TCO) of their technology. One way is by taking a good hard look at their database and database software. This eBook walks through how CIOs and CTOs are successfully decreasing TCO via ensuring their database technology is running at optimal levels, avoiding hidden costs and keeping pace with digital transformation.
Read this eBook to learn how EDB customers ensured their user experience and improved their database availability, flexibility, reliability and security with Postgres.
This document presents a framework and a series of recommendations to secure and protect a Postgres database. We discuss a layered security model that addresses physical security, network security, host access control, database access management and data encryption. While all of these aspects are equally important, the document focuses on Postgres-specific aspects of securing the database and the data. For our discussion of the specific security aspects relating to the database and the data managed in the database, we use an AAA (Authentication, Authorization and Auditing) approach common to computer and network security.
Most of the recommendations in this document are applicable to PostgreSQL (the Community edition) and to EDB Postgres™ Advanced Server (EPAS), the enterprise-class, feature-rich commercial distribution of Postgres from EnterpriseDB® (EDB™). EPAS provides additional relevant security enhancements such as Transparent Data Encryption, password profiles, auditing, data
Today’s financial service providers are under pressure from multiple angles. Increased customer expectations, growing digitization, expanding data volumes, regulation complexity, security threats, rising costs, and competitive challenges are requiring banking, financial services, and insurance (BFSI) organizations to modernize and future-proof their technology.
Read this eBook to learn how EDB customers ensured their user experience and improved their database availability, flexibility, reliability and security with Postgres.
In today’s rapidly evolving, data-driven world, C-level executives in banking, financial services, and insurance (BFSI) are under tremendous pressure to ensure their database systems are operating as they should be.
Download this eBook to discover how database stability and resiliency are helping CIOs overcome their challenges and sleep more soundly.
The banking and financial services (BFSI) industry has entered a new era spearheaded by disruptive, tech-savvy, and well-funded fintechs, expanding the boundaries of open banking.
This is why a growing number of BFSI companies are turning to open source technologies. Open source databases, such as Postgres, offer the greatest flexibility in how enterprises unlock the power of data. In this guide, we’ll explore why open source is such a good fit for BFSI organizations that are committed to digital transformation, and outline the key factors that can ensure your success.
For real time streaming and queuing technology, Apache Kafka® is truly unrivaled, but it can be an inscrutable beast for newcomers.
Without proper guidance, it’s easy to miss out on Kafka’s full capabilities. While not the easiest technology to optimize, Kafka rewards those willing to explore its depths. Under the hood, it is an elegant system for stream processing, event sourcing, and data integration.
In this white paper, we cover the 10 critical rules that will help you optimize your Kafka system and unlock its full potential.
The open source movement has taken center stage in software development, and its influence echoes through other areas of life such as open culture and open data. Many software companies hope to cement both their revenue sources and their status in open source communities by offering a mixture of open source (also called free) and closed (proprietary) software. The combination is generally called open core.
Ensuring that data is readily available, secure, and accessible to data scientists, data applications, and other stakeholders across the enterprise is no easy task. In today’s organizations, data engineers wear many hats and the role continues to grow in importance. Download this special report for an overview of the top trends, challenges, and opportunities moving forward.
When it comes to building data and AI platforms at scale, few companies work at the scale and speed of personal finance application Intuit CreditKarma. Vishnu Ram, VP of Data Science & Engineering at CreditKarma, joins us to walk through how his team designed and implemented their modern data and AI platform to power 35B financial predictions daily for over 120 million members. He’ll discuss the technologies, processes, and team structure required to build a data, ML, and AI function from scratch, and the role of data observability in this equation.
The Forrester Wave™: for Streaming Data Platforms, Q4 2023 is a key source of information for any organization looking to deploy real-time applications that deliver instant action on streaming data. In this report, you get a comprehensive view of the most significant streaming data platforms in the market today and their respective strengths.
In this report, Forrester evaluated 14 vendors and reviewed them against 21 different criteria related to a company’s current offering, strategy, and market presence.
Hazelcast was evaluated as a Strong Performer in this report and received the top scores possible in the following criteria:
Throughput
Enrichment
Latency
Vision
Innovation
Roadmap
Why the Wave Matters
Streaming data is becoming a priority for enterprises with real-time aspirations and the goal to use it as a competitive advantage. It’s not only a snapshot of what is happening across the business, streaming data opens new possibilities for real-time applications tha
Many businesses are already taking advantage of real-time data. But the challenge today is that they are predominantly focusing on faster access to stored data, and thus not on taking faster action when data is created, and when opportunities arise. Many “streaming” technologies today must first store data and then rely on humans to gain insights via manual analysis and then respond in human time. A complementary approach that leverages stream processing engines will address business problems that need immediate, automated responses.
This paper covers:
Leveraging stream processing technologies to respond in real-time for use cases like “right offer at the right time” (i.e., real-time offers) and transaction fraud detection.
Using real-time data to drive better business outcomes via recommendations or reduced loss from fraud.
How to better identify the real-time use cases that you can deploy to gain competitive advantage.
Register now to get your digital copy of this free ebo
Instead of a feature-by-feature comparison between these two popular technologies, there is a more meaningful way to determine which is right for you. After all, comparing Hazelcast to Redis is almost like comparing a sports utility vehicle (SUV) to a pickup truck. Picking one of these vehicles over the other depends on how you plan on using them. It’s the same with Hazelcast versus Redis.
The main difference between Hazelcast and Redis is that as a unified real-time data platform, Hazelcast provides stream processing capabilities that you don’t get in Redis. So if you are looking to take instant action on real-time data, in which your applications help you respond to changes in data when it matters most, Hazelcast is your technology.
Read this paper to learn how Hazelcast covers all your bases for your stream processing needs, and hopefully, you won’t need to spend so much time figuring out how to differentiate Hazelcast from Redis or any other storage-only data platforms.
Modern data architectures require real-time capabilities that will support the emerging AI-infused enterprise. Download this special report for the latest best practices and emerging technologies to keep in mind on your journey.
Leading global organizations like J.B. Hunt and Swedbank have long relied on Databricks to unlock deep insights and power their data and AI workloads. The arrival of Databricks Unity Catalog broadened this scope by providing unified governance for data and AI assets, which is essential as data usage, regulations, and threats continue to grow.
To deliver trusted data and harness a broad range of data sources, organizations require an advanced data management platform that’s easy to use and cost-effective.
According to McKinsey, generative AI and other technologies have the potential to automate work activities that absorb 60% to 70% of employees’ time. Work smarter with generative AI-powered iPaaS and transform the way you design, test, deploy, manage and scale your workflows.
The value of cloud data pipelines is clear: They help enterprises build analytics quickly, automate ingestion and data processing workflows and more. However, pinpointing the right data pipeline solution — one that supports complex use cases and incorporates generative AI — is another story.
According to Gartner, “Poor data quality destroys business value. Recent research shows organizations estimate the average cost of poor data quality at $10.8 million per year.”
Take a modern approach to data integration with the right data integration solution that can help unify, govern and share data.
Get started with the 2023 Gartner® Magic Quadrant™ for Data Integration Tools. In fact, Informatica has been named a Leader — placed highest in Ability to Execute and furthest in Completeness of Vision for 10 years straight.
Currently, 97.2% of organizations invest in big data and artificial intelligence (AI). In addition, the generative AI (genAI) market is poised to explode, growing to $1.3 trillion over the next 10 years from a market size of just $40 billion in 2022.
The sports industry has seen a significant rise in the adoption of data and analytics to gain a competitive advantage and improve the fan experience. The Texas Rangers are at the forefront of this movement, as they analyze terabytes of in-game data to optimize player performance and scouting. Join this webinar to learn how the Rangers’ data team overcame the challenges of technical resource constraints and the complexities of scaling real-time data pipelines with Prophecy’s low-code data engineering platform.
Software license compliance audits are a big business for enterprise vendors -- and they're on the rise right now. Now is not the time to ignore the risk of a potential audit of your organization. This survival guide will help you understand Oracle’s audit process and navigate through it when they come knocking.
Security, agility, and visibility – how are data leaders prioritizing these initiatives to protect and strengthen their businesses in 2024?
Generative AI has opened up a vast new world of productivity, possibility, and most critically, risk. AI models and use cases are being built, deployed, and restructured within a span of just months. The rate of technological innovation has left many data leaders weighing the balance between substantial gains and frightening setbacks. In an effort to develop an authentic understanding of the current moment,
we surveyed 700+ data platform and security leaders who are asking today’s most pressing questions:
How should I allocate my tech and human resources to optimize our data security?
How can I improve my data team reporting structure?
How are others navigating organizational, technological, and process-level security risks?
How can my actions as a data leader directly drive better business outcomes?
What pressing needs and data security p
There is a growing disconnect between enterprises seeking greater data-driven capabilities and the actual data that is on the ground of their business units. That’s because bottlenecks, silos, over-centralization, and organizational layers are hindering the access and capabilities needed by rapidly expanding userbases. Download this special report for best practices associated with successful data mesh design and development.
Get ready to revolutionize your database development game. This eBook is your ticket to an agile approach that spells efficiency and innovation. Uncover the keys to adapt, optimize, and keep your data projects agile.
Growing in popularity in recent years, data mesh architectures promise a wide range of benefits for modern organizations: increased data democratization, elimination of data silos, and the enablement of more widespread, business-driving data access and use.
In practice, however, operationalizing a scalable and secure data mesh can be a complicated process. With the right practices, platforms, and people, modern organizations can achieve a data mesh implementation that meets their needs without compromising speed or security.
In this eBook, you’ll learn:
What a data mesh is and what benefits teams have come to expect
The complexity of data mesh security, including common challenges
3 key steps to achieving a secure data mesh implementation
How the Roche team is achieving results with data mesh
In the face of increasing demands for speed and adaptability, the challenge of maintaining the performance and availability of critical systems and applications is intensifying. To accommodate the realities of modern data and applications -- highly distributed, modular, portable -- new technologies and strategies are essential. Download this special report to stay ahead of the curve when it comes to database performance.
The term sounds impressive when you say it, and a lot of industry analysts extol its virtues, but what does it take to become part of a modern analytics ecosystem? Unlike installing an application or populating a data warehouse, becoming part of an analytics ecosystem involves many moving parts across not only your own enterprise, but others as well. Download this special report key considerations to get started.
From legacy infrastructures to public clouds, the average enterprise has data spread across different locations, file systems, databases, and applications—and the volume and sources of that data is constantly growing. As a result, while most organizations want rapid access to meaningful, actionable information, effectively integrating and governing data is becoming more difficult. Download this special report for best practices in developing a forward-looking data integration and governance strategy.
Business-critical applications have very strict requirements when it comes to availability, performance, recoverability, scalability, and security -- and Oracle shops are no exception. An important step in successfully modernizing these applications is finding the right cloud that matches your requirements. The ability to mix and match providers without code or configuration changes through a consistent multi-cloud infrastructure offers significant advantages. Watch this special podcast to learn how you can rapidly migrate Oracle workloads to VMware Hybrid Multi-Clouds with license compliance.
Cloud data warehouse, data lakehouse, data fabric, data mesh? And what about real-time analytics and streaming IoT data? Right now, IT leaders and data architects have a plethora of architecture patterns and enabling technologies to consider when evaluating strategies for modernizing their data infrastructure.
From the adoption of hybrid and multi-cloud architectures to ongoing advancements in machine learning, automation and security, the world of database management continues to evolve. Download this special report for key enabling technologies and evolving best practices today.
It only seems like yesterday that batch processing was the norm. Now, decision makers want almost instantaneous glimpses of their business and its performance—and the tools that enable such capabilities. There are many data management strategies and tools for achieving real-time capabilities. Download this special report to dive into the key approaches and technologies to succeed.
Are you feeling overwhelmed and lost in the rapidly evolving database world? Fear not, this e-book is here to help. Using an entertaining zombie apocalypse analogy, you'll learn how to avoid becoming a "database zombie" and instead thrive in your career. The book covers topics such as the impact of emerging technologies, the importance of collaboration between teams, and how to stay ahead of the curve in your skillset. Whether you're a seasoned database professional or just starting out, this guide is a must-read for anyone looking to survive and thrive in the changing world of databases.
The digital era now upon us calls for new approaches to data governance and security. Download this special report for best practices to design and develop data governance and security for a modern data ecosystem.
Data fabric is a term used to describe a set of technologies, services, and strategies that provide ‘a unified and reliable view’ of data spanning hybrid and multi-cloud environments. Eliminating data silos, surfacing insights more effectively, and increasing the productivity and value of data are some of the ways data fabric can improve data management at organizations. Download this special report to dive into building a data fabric, including key components, challenges, and emerging best practices.
As organizations seek to design, build, and implement data and analytics capabilities, they are pressed to reinvent and reorient their data architectures—as well as justify these activities with ROI measures. From the cloud-native data warehouse and data lakehouse to data mesh and data fabric, a range of architecture patterns and enabling technologies have come to the forefront of modernization discussions. Likewise, many organizations right now are eyeing new strategies and solutions to enable more agile and responsive data and analytics systems. Ultimately, moving to a next-generation architecture is a journey; not a sprint. Download this special report for key considerations to succeed along the way.
Cloud deployments are an ever-moving, ever-changing target, so it’s important to continuously assess and improve data management processes and procedures accordingly. Download this special report to dive into best practices for managing data within today’s cloud environments.
As data pipelines grow in size and complexity, DataOps continues to gain a bigger foothold in enterprises hungry for fast, actionable insights. DataOps brings people, processes, and technology together to orchestrate the effective, efficient, and secure flow of data. To do that, organizations must have in place key components in all three areas. Download this special report for a better understanding of the key technologies, strategies, and use cases that are unlocking the power of DataOps at enterprises today.
Software license compliance mistakes can quickly cost millions of dollars. Therefore, SAM software is a crucial component of proactively managing your software deployment. This guide will provide insight and practical tips for purchasing a software asset management solution and the criteria to consider when hiring a software license consultant. We will also discuss a new, more efficient, hybrid approach to dealing with the challenges of software license compliance called the SAM Managed Service.
Data-driven enterprises do not exist on data on their own; they require an advanced, responsive data architecture to gain ground within their markets. Download this special report for best practices of leaders in data-driven architecture who have established high-producing data architectures.
TODAY’S ENTERPRISES are more distributed than ever before—both in terms of employees working across different geographical locations and the dispersal of data across different departments, applications, databases, on-prem data centers, and clouds. This expansion of enterprise data landscapes offers opportunities and challenges for IT leaders and data management professionals. Succeeding in this increasingly distributed, complex world requires rethinking traditional approaches to data architecture and key data management processes around integration, governance, and security. Download this special report for key considerations to achieving real data democratization.
The database world has been changing dramatically over the past decade, and the pace of change has been accelerating in recent years. Ensuring a smooth-running, high-performing database environment today means rethinking how data flows, where it flows, and who oversees the flow. Download this special report to learn about the new technologies and strategies for managing database performance in the modern enterprise.
The future of databases is in the cloud, but achieving higher levels of growth and agility can be hampered by persistent myths. Oracle Corporation, which offers its own proprietary cloud platform, has promoted fear, uncertainty, and doubt about the viability of running Oracle databases on robust, competitive cloud platforms such as Amazon Web Services (AWS). As a result, it is understandable when some IT leaders and database teams hesitate to migrate their Oracle databases. This paper explores and debunks the leading myths that inhibit organizations from migrating, and the realities of how they benefit from moving databases and applications to more flexible and scalable cloud environments.
To examine how database environments and roles are changing within enterprises – as well as how deeply new modes of collaboration and technology are being adopted – Unisphere Research recently conducted a survey of DBTA subscribers in partnership with Quest. From cloud and automation to the rise of "Ops" culture, the world of database management is evolving with new challenges and opportunities emerging for IT leaders and database professionals. Download this special study for a first-hand look at the results and learn where database management is heading.
The challenge for multi-cloud and hybrid environments is to live up to their promise of enabling organizations to be more flexible and agile without the overhead incurred from system complexity. Data management needs to achieve this as well. Developing a well-functioning, hybrid, multi-cloud management strategy requires a number of considerations. Download this report today to dive into key strategies.
As data environments continue to grow in size and complexity, spanning on-premises and cloud sites, effective data integration and governance processes are paramount. Download this special report for the latest trends and strategies for success.
For database managers and users, moving to the cloud means breaking through the confines imposed by capacity ceilings, speed limits, performance issues, and security concerns. At the same time, moving to the cloud does not mean outsourcing control or responsibility for data to an outside provider, and care must be taken to ensure migrations take place with as little disruption to the business as possible. In addition, organizations need to be prepared with the specific skills required to migrate to and manage databases in the cloud. Download this white paper for the questions you need to ask before undertaking a cloud migration.
Deploying today’s flexible technology services and components— containers, microservices, and cloud-based infrastructure—may bring measures of improved agility to IT and data management operations, but translating this into business agility is what makes these technologies impactful. Here’s where an agile technology architecture demonstrates its true value. Download this special report for key capabilities and best practices for success.
Database management today is flush with new challenges and opportunities. More than ever before, businesses today desire speed, scalability, and flexibility from their data infrastructure. At the same time, the complexity of data environments continues to grow – spread across different database types and brands, applications, and on-premise and cloud sites. This makes new technologies and strategies critical. Download this special report today for key practices to managing databases in this highly diverse, emerging landscape.
Meeting the demands of the rapid evolution to real-time business requires new perspectives and approaches. Download this report for seven recommendations to make the most of real-time capabilities today.
Many organizations’ data assets are hidden away in silos or proprietary applications, which can take great amounts of time and resources to locate. This is made more complicated as the amount of data flowing through, into, and out of enterprises keeps growing exponentially. Data catalogs can enable self-service analytics and data lake modernization, as well as support data governance, privacy, and security initiatives. Download this special report, sponsored by Sandhill Consultants, to dive into best practices for harnessing the power of data catalogs in the enterprise today.
Despite greater awareness of threats, protecting data has not become easier in recent years. Data is being created at a rapid clip and there are more ways than ever before to store it. Understanding today’s data security and governance problems is the first step to solving them. Download this special report for 10 key tenets for improving data security and governance in today’s complex data environments.
There is one clear direction data management has been going in as of late – to the cloud. The availability of cloud resources provides new options for building and leveraging enterprise-scale data environments. Support for hybrid and multi-cloud data warehousing is becoming mainstream, edge analytics adoption is rising, and the demand for real-time analytics capabilities is soaring. As more organizations invest in data warehouse and data lake modernization, these areas are also converging with concepts such as the “data lakehouse” and the “data mesh.” Download this special report to navigate the growing constellation of technologies and strategies emerging in modern data management and analytics today.
At a time when enterprises are seeking to leverage greater automation and intelligence, many are becoming acquainted with the advantages of using knowledge graphs to power real-time insights and machine learning. In fact, Gartner predicts that, by 2023, graph technology will play a role in the decision-making process for 30% of organizations worldwide. Download this special report to understand how knowledge graphs are accelerating analytics and AI in the enterprise today.
There’s no turning back from cloud as an enterprise data platform, and adoption continues to expand rapidly. The question is not whether to pursue a cloud strategy, but how to do so to meet your organization’s business requirements most efficiently and cost-effectively. Download this special report to gain a deeper understanding of emerging best practices, key enabling technologies, challenges, and solutions in the evolving world of cloud and data management.
DataOps helps to improve processes throughout the data lifecycle – from initial collection and creation to delivery to the end user, but implementing the methodology requires effort. Download this special report to learn the ten key tenets for a successful DataOps implementation.
Now, more than ever, businesses want scalable, agile data management processes that can enable faster time to market, greater self-service capabilities, and more streamlined internal processes. Download this report for seven key steps to designing and promoting a modern data architecture that meet today’s business requirements.
With all that’s happened in the past 2 years, it is often observed that there may be more risk in staying with the status quo than moving forward and trying something new. Today, agility, enabled by modern methodologies, such as DataOps and DevOps, and the use of new technologies, like AI and machine learning, is critical for addressing new challenges and flexibly pivoting to embrace new opportunities. As we get further into 2022, the annual Data Sourcebook issue puts the current data scene in perspective and looks under the covers of the key trends in data management and analytics. Download your copy today.
In keeping up with the demands of a digital economy, organizations struggle with availability, scalability, and security. For users of the world’s most popular enterprise database, Microsoft SQL Server, this means evolving to deliver information in a hybrid, multi-platform world. While the data platform has long been associated with the Windows Server operating system, many instances can now be found running within Linux environments. The increasing movement toward cloud, which supports greater containerization and virtualization, is opening platform independence in ways not previously seen, and enterprises are benefitting with greater flexibility and lower costs. Download this special white paper today to learn about the era of the amplified SQL Server environment supported by the capabilities of Linux and the cloud.
From the rise of hybrid and multi-cloud architectures to the impact of machine learning, automation, and containerization, database management today is rife with new opportunities and challenges. Download this special report today for the top 9 strategies to overcome performance issues.
In a hybrid, multi-cloud world, data management must evolve from traditional, singular approaches to more adaptable approaches. This applies to the tools and platforms that are being employed to support data management initiatives – particularly the infrastructure-as-a-service, platform-as-a-service, and SaaS offerings that now dominate the data management landscape. Download this special report today for new solutions and strategies to survive and thrive in the evolving hybrid, multi-cloud world.
Data management is changing. It’s no longer about standing up databases and populating data warehouses; it’s about making data the constant fuel of the enterprise, accessible to all who need it. As a result, organizations need to be able to ensure their data is viable and available. Download this special report for the key approaches to developing and supporting modern data governance approaches to align data to today’s business requirements.
Today’s emerging architecture is built to change, not to last. Flexible, swappable systems, designed to be deployed everywhere and anywhere, and quickly dispensed at the end of their tenure, are shifting the dynamics of application and data infrastructures. The combination of containers and microservices is delivering a powerful one-two punch for IT productivity. At the same time, the increasing complexity of these environments brings a new set of challenges, including security and governance, and orchestration and monitoring. Download this special report for guidance on how to successfully leverage the flexibility and scalability that containers and microservices offer while addressing potential challenges such as complexity and cultural roadblocks.
The database is no longer just a database. It has evolved into the vital core of all business activity; the key to market advancement; the essence of superior customer experience. In short, the database has become the business. What role does the new data environment—let’s call it “the era of data races”—play in moving enterprises forward? Download this special report to learn about the ways emerging technologies and best practices can support enterprise initiatives today.
Companies embarking on significant Hadoop migrations have the opportunity to advance their data management capabilities while modernizing their data architectures with cloud platforms and services. Consequently, having a comprehensive approach reduces the risk of business disruption and/or the potential for data loss and corruption. Download this special eGuide today to learn the do’s and don’ts for migrating Hadoop data to the cloud safely, securely, and without surprises, and key architecture strategies to follow.
To successfully make the journey to a data-driven enterprise, businesses are under pressure to extract more value from their data in order to be more competitive, own more market share and drive growth. This means they have to make their data work harder by getting insights faster while improving data integrity and resiliency, leverage automaton to short cycle times and reduce human error, and adhere to data privacy regulations. DataOps opens the path to delivering data through the enterprise as its needed, while maintaining its quality and viability. In this thought leadership paper, we will provide perspectives on the advantages DataOps gives to stakeholders across the enterprise, including database administrators, data analysts, data scientists, and c-level executives.
With more data than ever flowing into organizations and stored in multiple cloud and hybrid scenarios, there is greater awareness of the need to take a proactive approach to data security. Download this special report for the top considerations for improving data security and governance from IT and security leaders today.
There are many types of disruption affecting the data management space, but nowhere will the impact be more substantial than at the edge. Leading operations moving to the edge include smart sensors, document and data management, cloud data processing, system backup and recovery, and data warehouses. Download this special report for the key transformational efforts IT leaders need to focus on to unlock the power of IoT and the edge.
For organizations with growing data warehouses and lakes, the cloud offers almost unlimited capacity and processing power. However, transitioning existing data environments from on-premises systems to cloud platforms can be challenging. Download this special report for key considerations, evolving success factors and new solutions for enabling a modern analytics ecosystem.
Bringing knowledge graph and machine learning technology together can improve the accuracy of the outcomes and augment the potential of machine learning approaches. With knowledge graphs, AI language models are able to represent the relationships and accurate meaning of data instead of simply generating words based on patterns. Download this special report to dive into key uses cases, best practices for getting started, and technology solutions every organization should know about.
From the rise of hybrid and multi-cloud architectures, to the impact of machine learning and automation, the business of data management is constantly evolving with new technologies, strategies, challenges, and opportunities. The demand for fast, wide-ranging access to information is growing. At the same time, the need to effectively integrate, govern, protect, and analyze data is also intensifying. Download this special report for the top trends in data management to keep on your radar for 2021.
DataOps is now considered to be one of the best ways to work toward a data-driven culture and is gaining ground at enterprises hungry for fast, dependable insights. Download this special report to learn about the key technologies and practices of a successful DataOps strategy.
The move to modern data architecture is fueled by a number of converging trends – the rise of advanced data analytics and AI, the Internet of Things, edge computing, and cloud. Both IT and business managers need to constantly ask whether their data environments are robust enough to support the increasing digitization of their organizations. Over the past year, requirements for data environments have been driven largely by cost considerations, efficiency requirements, and movement to the cloud. Download this special report for emerging best practices and key considerations today.
Now, more than ever, the ability to pivot and adapt is a key characteristic of modern companies striving to position themselves strongly for the future. Download this year’s Data Sourcebook to dive into the key issues impact enterprise data management today and gain insights from leaders in cloud, data architecture, machine learning, data science and analytics.
This study, sponsored by Quest Software, includes the views and experiences of 285 IT decision makers, representing a fairly broad sample of company types and sizes. The survey found that databases continue to expand in size and complexity, while at the same time, more enterprises are turning to cloud-based resources to keep information highly available.
The critical role of data as fuel for the growing digital economy is elevating data managers, DBAs, and data analysts into key roles within their organizations. In addition, this rapid change calls for a three-pronged approach that consists of expanding the use of more flexible cloud computing strategies, growing the automation of data environments, and increasing the flow of data and collaboration through strategies such as DevOps and DataOps. Download this special report today to better understand the emerging best practices and technologies driving speed and scalability in modern database management.
A strong data management foundation is essential for effectively scaling AI and machine learning programs to evolve into a core competence of the business. Download this special report for the key steps to success.
Today’s enterprises rely on an assortment of platforms and environments, from on-premise systems to clouds, hybrid clouds and multi-clouds. This calls for modern data management practices that leverage emerging technologies, providing enterprise decision managers with the tools and insights they need to improve and transform their businesses. Download this special report for best practices in moving to modern data management standards to ensure the integration and governance of valuable data sources within today’s diverse environments.
Emerging agile technologies and techniques are leading to new ways of accessing and employing data. At the same time, the increasing complexity of these environments is creating additional challenges around security and governance, and orchestration and monitoring, which is particularly evident with the rise of hybrid, multi-cloud enterprise environments. Welcome to the era of the digitally enriched platform. Download this special report today to dive into emerging technologies and best practices.
AIOps market is set to be worth $11B by 2023 according to MarketsandMarkets. Originally started as automating the IT operations tasks, now AIOps has moved beyond the rudimentary RPA, event consolidation, noise reduction use cases into mainstream use cases such as root causes analysis, service ticket analytics, anomaly detection, demand forecasting, and capacity planning. Join this session with Andy Thurai, Chief Strategist at the Field CTO ( thefieldcto.com) to learn more about how AIOps solutions can help the digital business to run smoothly.
A challenge of ML is operationalizing the data volume, performance, and maintenance. In this session, Rashmi Gupta explains how to use tools for orchestration and version control to streamline datasets. She also discusses how to secure data to ensure that production control access is streamlined for testing.
As market conditions rapidly evolve, DataOps can help companies produce robust and accurate analytics to power the strategic decision-making needed to sustain a competitive advantage. Chris Bergh shares why, now more than ever, data teams need to focus on operations, not the next feature. He also provides practical tips on how to get your DataOps program up and running quickly today.
Traditional methodologies for handling data projects are too slow to handle the teams working with the technology. The DataOps Manifesto was created as a response, borrowing from the Agile Manifesto. This talk covers the principles of the DataOps Manifesto, the challenges that led to it, and how and where it's already being applied.
The ability to quickly act on information to solve problems or create value has long been the goal of many businesses. However, it was not until recently that new technologies emerged to address the speed and scalability requirements of real-time analytics, both technically and cost-effectively. Attend this session to learn about the latest technologies and real-world strategies for success.
Each week, 275 million people shop at Walmart, generating interaction and transaction data. Learn how the company's customer backbone team enables extraction, transformation, and storage of customer data to be served to other teams. At 5 billion events per day, the Kafka Streams cluster processes events from various channels and maintains a uniform identity of each customer.
To support ubiquitous AI, a Knowledge Graph system will have to fuse and integrate data, not just in representation, but in context (ontologies, metadata, domain knowledge, terminology systems), and time (temporal relationships between components of data). Building from ‘Entities’ (e.g. Customers, Patients, Bill of Materials) requires a new data model approach that unifies typical enterprise data with knowledge bases such as industry terms and other domain knowledge.
We are at the juncture of a major shift in how we represent and manage data in the enterprise. Conventional data management capabilities are ill equipped to handle the increasingly challenging data demands of the future. This is especially true when data elements are dispersed across multiple lines of business organizations or sourced from external sites containing unstructured content. Knowledge Graph Technology has emerged as a viable production ready capability to elevate the state of the art of data management. Knowledge Graph can remediate these challenges and open up new realms of opportunities not possible before with legacy technologies.
Knowledge Graphs are quickly being adopted because they have the advantages of linking and analyzing vast amounts of interconnected data. The promise of graph technology has been there for a decade. However, the scale, performance, and analytics capabilities of AnzoGraph DB, a graph database, is a key catalyst in Knowledge Graph adoption.
Though MongoDB is capable of incredible performance, it requires mastery of design to achieve such optimization. This presentation covers the practical approaches to optimization and configuration for the best performance. Padmesh Kankipati presents a brief overview of the new features in MongoDB, such as ACID transaction compliance, and then move on to application design best practices for indexing, aggregation, schema design, data distribution, data balancing, and query and RAID optimization. Other areas of focus include tips to implement fault-tolerant applications while managing data growth, practical recommendations for architectural considerations to achieve high performance on large volumes of data, and the best deployment configurations for MongoDB clusters on cloud platforms.
Just as in real estate, hybrid cloud performance is all about location. Data needs to be accessible from both on-premise and cloud-based applications. Since cloud vendors charge for data movement, customers need to understand and control that movement. Also, there may be performance or security implications around moving data to or from the cloud. This presentation covers these and other reasons that make it critical to consider the location of your data when using a hybrid cloud approach.
What if your business could take advantage of the most advanced AI platform without the huge upfront time and investment inherent in building an internal data scientist team? Google’s Ning looks at end-to-end solutions from ingest, process, store, analytics, and prediction with innovative cloud services. Knowing the options and criteria can really accelerate the organization's AI journey in a quicker time frame and without significant investment.
After 140+ years of acquiring, processing and managing data across multiple business units and multiple technology platforms, Prudential wanted to establish an enterprise wide data fabric architecture to allow data to be available where and when its needed. Prudential chose data virtualization technology to create the logical data fabric that spans their entire enterprise.
The pace of technology change is continuing to accelerate and organizations have no shortage of tool and application options. But while many are modernizing tool infrastructure and ripping out legacy systems, the data that powers new tools still presents difficult and seemingly intractable problems. Seth Earley discusses approaches for bridging the gap between a modernized application infrastructure and ensuring that quality data is available for that infrastructure.
As business models become more software driven, the challenge of maintaining reliable digital services and delightful customer experiences, as well as keeping those services and customer data safe is a "continuous" practice. It’s particularly important now, when the COVID-19 global pandemic has created a discontinuity in digital transformation and many industries have been forced entirely into a digital business model due to social distancing requirements. Bruno Kurtic discusses the impact of the pandemic on industries and digital enterprises leverage continuous intelligence to transform how they build, run, and secure their digital services and use continuous intelligence to outmaneuver their competition.
In this session, Lee Rainie discusses public attitudes about data, machine learning, privacy, and the role of technology companies in society—including in the midst of COVID-19 outbreak. He covers how these issues will be factors shaping the next stages of the analytics revolution as politicians, regulators, and civic actors start to focus their sights on data and its use.
From the rise of hybrid and multi-cloud architectures, to the impact of machine learning and automation, database professionals today are flush with new challenges and opportunities. Now, more than ever, enterprises need speed, scalability and flexibility to compete in today’s business landscape. At the same time, database environments continue to increase in size and complexity; crossing over relational and non-relational, transactional and analytical, and on-premises and cloud sites. Download this report to dive into key enabling technologies and evolving best practices today.
With constantly evolving threats and an ever-increasing array of data privacy laws, understanding where your data is across the enterprise and properly safeguarding it is more important today than ever before. Download this year’s Cybersecurity Sourcebook to learn about the pitfalls to avoid and the key approaches and best practices to embrace when addressing data security, governance, and regulatory compliance.
Today’s organizations want advanced data analytics, AI, and machine learning capabilities that extend well beyond the power of existing infrastructures, so it’s no surprise that data warehouse modernization has become a top priority at many companies. Download this special report to under how to prepare for the future of data warehousing, from increasing impact of cloud and virtualization, to the rise of multi-tier data architectures and streaming data.
Rapid data collection is creating a tsunami of information inside organizations, leaving data managers searching for the right tools to uncover insights. Knowledge graphs have emerged as a solution that can connect relevant data for specific business purposes. Download this special report to learn how knowledge graphs can act as the foundation of machine learning and AI analytics.
It’s no surprise then that adoption of data lakes continues to rise as data managers seek to develop ways to rapidly capture and store data from a multitude of sources in various formats. However, as the interest in data lakes continues to grow, so will the management challenges. Download this special report for guidelines to building data lakes that deliver the most value to enterprises.
While cloud is seen as the go-to environment for modernizing IT strategies and managing ever-increasing volumes of data, it also presents a bewildering array of options. Download this special report for the nine points to consider in preparing for the hybrid and multi-cloud world.
DataOps is poised to revolutionize data analytics with its eye on the entire data lifecycle, from data preparation to reporting. Download this special report to understand the key principles of a DataOps strategy, important technology, process and people considerations, and how DataOps is helping organizations improve the continuous movement of data across the enterprise to better leverage it for business outcomes.