This is an introduction, overview, and update for professionals who interact with data privacy functions and want to understand privacy and data security better. We walk through not just the regulatory environment and the ways in which businesses are responding (and failing to respond), but why privacy is important and what's changed to make that so relevant today. Learn about fair information practices, the legislative landscape, and how businesses can best secure their data. Don't get caught short when it comes to protecting the data stored by your organization. Join privacy professional Jeff Jockisch in this interactive workshop to understand how to better protect the sensitive data in your business.

Knowledge graphs are a valuable tool that organizations can use to manage the vast amounts of data they collect, store, and analyze. Enterprise knowledge graphs’ representation of an organization’s content and data creates a model that integrates structured and unstructured data. Knowledge graphs have semantic and intelligent qualities to make them “smart.” Attend this workshop to learn what a knowledge graph is, how it is implemented, and how it can be used to increase the value of your data. This is a very interactive workshop, so be prepared not only to learn about knowledge graphs but to actually build one.

Machine learning is revolutionizing the process of complex decision-making by enabling the analysis of bigger, more complex datasets and the delivery of faster, more accurate results. Although the technology is developing rapidly, many projects are still in their early phases while other have hit a wall because they can’t keep up with the volume and variety of data. From selecting data sets and data platforms, to architecting and optimizing data pipelines, to evaluating commercial and open source frameworks, there are many success factors to keep in mind. Most ML models are trained over examples collected at different points in time, and often are trained to predict the future. Machine learning needs the ability to forget — to learn what’s relevant now. Attend this workshop to learn how to iterate on ML models with event-based data, and develop scalable, real-world machine learning pipelines and applications.

The starting point in developing and launching an enterprise data and analytics strategy is to understand the interrelationships that are necessary to deliver analytics capabilities. These relationships also account for skills and roles of everyone who works with data, from business executives to business analysts to data scientists. To measure and drive success, an actionable road map is needed, with each phase focused on being lean with a business impact. Attend this workshop to learn how to designate business drivers into analytic capabilities and data priorities, create and implement a road map, and deliver a compelling executive briefing.

Sharing insights from his bestselling book, Infonomics, Laney discusses why information both is and is not an asset. He covers issues around information ownership, rights, and privileges; explores how best to monetize assets and measure realized value; and explains his set of generally accepted information principles culled from other asset management disciplines. Learn why he believes we should stop talking about data as the new oil and concentrate on acting on its true importance.

When it comes to data analytics infrastructure, there are a wealth of options for storing and querying information. New technological approaches allow for more flexibility in cloud data management and are democratizing data for use across the organization. By stripping away data engineering complexity and lowering total cost of infrastructure ownership and maintenance, more and more organizations are unlocking the value of analytics at scale. Join Thomas Hazel as he dives into the latest and demystifies them, so you can determine which is right for your organization. Sponsored by Chaos Search. 

Digital transformation efforts today are targeted not only at how data is managed and stored, but also at how it is leveraged to deliver insights and competitive advantage to businesses across a range of industries.

To transform your organization, it is essential to have management support. The insurance industry is changing rapidly due to changing customer expectations of digitally enhanced and tailored interactions with insurers as well as the emergence of tech-savvy new entrants into the space that are building the types of experiences customers are seeking. Learn how Arbella proposed and crafted a solution that addressed immediate concerns and showed its relevance to meeting the challenges of the envisioned future.

The pandemic has accelerated the digital transformation journey for many organizations, regardless of maturity and readiness. As they continue to face an explosion of data, organizations need to be very thoughtful in utilizing data for its intended use. According to various analyst research, many data management programs fail in the first 3–6 months. Why does this happen? Often, it is due to readiness. Organizations are not prepared to tackle the full data management program. One tactic that can help is to break up a project into smaller chunks so that you learn to walk before running.

Data projects that are completed on time, address changing requirements, and deliver value in the real world require a combination of skills and technologies, as well as the right people.

Team management in data careers is a balancing act between providing the support and clarity that team members need to get the job done and keeping them engaged to create innovative solutions and improve on existing ones. This interactive session offers 10 strategies for building and sustaining high-performing data teams that draw from lessons learned on the job as a data professional.

Lam describes Office Depot's journey from a legacy data warehouse to the cloud, including how real-time standards-based connectivity allowed Office Depot to immediately leverage existing analytics tools and processes without having to rebuild its infrastructure or disrupt its business during this move.

Data professionals have a wide array of choices to help them deal with the growth and diversity of their data. But with so many technology options that can be deployed on-prem, in the cloud, or a combination thereof, the complexity is also increasing.

The CIO for one of the 25 largest property and casualty insurance companies in the world was faced with the challenge of its business units running their business intelligence and analytics reporting on an ever-growing number of disparate tools and systems. From Informatica to Business Objects, SAS to Power BI, OBIEE to Hadoop, the multinational company needed a way to not only reduce its costs in managing these disparate tool sets but also sought to develop an integrated data strategy that would align the business units with one source of the truth and lead the company into the future. Sasso discusses the challenges of running the business currently on the myriad of tools while planning the data strategy for the future and the road map to get to that strategy.

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 when new technologies emerged that the speed and scalability requirements of high quality analytics could be addressed both technically and cost-effectively by organizations on a large scale.

Wayfair’s massive, petabyte scale clickstream data environment consists of processes and data sources designed to capture and represent external customer activity while they browse one of our storefront sites/apps. To gain important insights to inform our strategic direction, we process clickstream data sets daily using Google BigQuery. Our intent is to capture all activity from legitimate external customers and to create actionable site data for marketing and storefront analytics. Taking a deep dive into the data processing architecture, Viswanathan discusses methods, technology, and processes used at Wayfair to build data processing and analytics at scale. She showcases the next gen data modeling practices, with special emphasis on how data processing has advanced with the advent of cloud computing.

Real time analytics is the new frontier for data management and analytics. This shifts from focusing on “big data, which tends to be siloed, slow, and looking backwards, to “fast data,” where data is captured and analyzed in real time. Schneider describes how to make “in the moment” business decisions that increase performance and ROI while decreasing costs and failures.

To turn data into insights and leverage the wealth of information that they are collecting, organizations need to ensure that their data is up-to-date and trustworthy. There is no magic answer. It’s a combination of technology and processes.

The pressures on organizations keep increasing—to speed up the pace of insights, improve time to market, fine-tune personalization, and create usable products and services. Central to these efforts is a data architecture and design crafted for speed and scale.

Around 85% of analytics, big data, and AI projects will fail, despite massive investments of money. It’s not new news, but it still reflects on how powerfully design affects speed, scale, and usage. Why are customers and employees not engaging with these data products and services? Often, they weren’t designed around user needs, wants, and behavior. A "people first, technology second" approach can minimize the chance of failure and drive your analytics/AI/data/product team to create innovative and indispensable software solutions. Don't be that designer.

Providing a look at hype versus reality, this presentation offers a data practitioner’s view of the latest and greatest design structure for big data, including the current problems that need to be addressed with data mesh.

From data warehouses to data lakes to data lakehouses, there is a growing array of choices when it comes to data platforms, deployment models, and features. At the same time, many challenges remain the same, including data integration and governance, performance, and management and monitoring.

There are so many new buzzwords lately, including the data lakehouse, data mesh, and data fabric, just to name a few. But what do all these terms mean, and how do they compare to a data warehouse? This presentation covers all of them in detail and explains the pros and cons of each, with suggested use cases so attendees can see what approach will really work best for their big data needs.

Data fabrics are emerging as the most effective means of integrating knowledge throughout the enterprise, and many experts agree that this approach represents the future of enterprise analytics and AI.

The data management world is not standing still; it is constantly evolving as new technologies and new requirements emerge. The need for agility is driving the need for logical, rather than physical architectures. Data ownership has shifted to the domain experts and business teams. The data sharing culture is driving the needs for business oriented data access. We need to empower the analysts, data scientists, and data stewards with real-time trusted data. Let's expand our horizons beyond the traditional integration. Let's talk about the logical approach, let's talk about the Logical Data Fabric.

With rapid business change, ongoing market volatility, and enterprise data collection expected to increase at a 42% CAGR over the next 2 years, organizations need to automate manual processes. Proactive data management is key to responding well to unexpected volume and market disturbances. It arms organizations with a single view of accurate, consistent, and trusted real-time data for analytics that can be applied to meet operational and strategic challenges. Fried discusses the traditional patterns that must be re-examined in order to adopt a proactive data management approach that enables real-time analytics.

In this era of exploding data volumes, CDOs must find the perfect balance between offering a good user experience to analytics experts, having a data pipeline as agile as possible, and keeping the data budget under control. Given the current set of data tools they leverage, this is almost an impossible task. Together let's dive deeper into the CDO's dilemma, and how to solve it. 

Different approaches led by various groups within organizations can lead to a sprawling mess, with duplicated effort and wasted opportunities. What’s needed is unified analytics.

The data warehouse has been an analytics workhorse for decades for business intelligence teams. But unprecedented volumes and new types of data, plus the need for advanced analyses, brought on the age of the data lake. Now, many companies have a data lake for data science, a data warehouse for BI, or a mishmash of both—possibly combined with a mandate to go to the cloud. Find out how technical and spiritual unification of the two camps can have a powerful impact on the effectiveness of analytics for the business overall.

Getting started with DataOps doesn’t need to be overly complicated, if you just follow a few basic guidelines.

DataOps is a must-have for any successful data and analytics team because it is the most effective way to deliver better analytics faster. Increasingly, those companies that neglect to invest in process-driven innovation will be at a competitive disadvantage. But how do you convince other organizational stakeholders to prioritize this important investment today? DataOps supercharges—and does not replace—your existing staff and technology investment. Learn how to build your internal business case and to collect small wins that illustrate DataOps’ impact at your organization.

DataOps is arguably the most powerful data management practice available today for enhancing operational effectiveness. By easing and speeding the exchange of real-time information, it enables improvements throughout the entire value chain, across every industry.

Monolithic data supply chains built based on loosely coupled, waterfall-oriented methodologies inevitably end up with pathologically high degrees of inefficiency and poor integration. DataOps aims to fix this. Lugo discusses the techniques of DataOps, their relevance to both data and analytics teams, and their ability to unify architectures in a powerful way. A robust DataOps culture can address challenges through an agile, collaborative, and self-service data marketplace, making it essential for today’s highly data-driven enterprises.

Every technology presents challenges, and DataOps is no exception. But challenges exist to be overcome.

 Those of us who administer production databases have had to endure significant change relative to how the databases and applications we support are managed. While it isn’t mainstream yet, Data Ops is maturing into a promising automated, process-oriented methodology for higher-quality data extraction and input into your analytics platforms. Hall discusses the impact of DataOps on database professionals, including administrators, and how they can maximize efficiency and performance.

The cloud is touted as the answer to a multitude of data conundrums, from storage to analytics. It has advantages for DataOps as well.

Today’s modern, data-driven enterprise is at a crossroads: On the one hand, IT leaders want to take advantage of the flexibility and raw compute power of public cloud services. On the other hand, not all data workloads are created equal, and migrating big data workloads between clouds and on-prem environments can introduce more complexity to an already convoluted process. Agarwal details the common obstacles that data teams encounter in data migration and explains why next-generation data tools must evolve beyond simple observability to provide prescriptive insights, shares best practices for optimizing big data costs, and demonstrates through real-world case studies how a mature DataOps practice can accelerate even the most complex cloud migration projects.

DataOps have many applications in today’s business environment. This panel looks ahead to what the future might bring.

Technology without strategy is doomed to fail. Looking at MLOps and Robotic Processes Automation exemplifies the need for clarity and strategic thinking.

Driving the AI strategy in one of the oldest French retailers is a real challenge in and of itself. Using AI to make educated guesses about which customers to target, what products to recommend, and where to display ads is even trickier. Aquarone explains how, as the company’s AI portfolio grew, a standardized canvas to go from a few dozen models to hundreds or even thousands of models became necessary. His team created a set of tools, guidelines, flows, and processes to drive this growth and make sure every prediction is understood, maintained, and used. He details the building blocks of the MLOps Platform and the milestones in transforming the organization to become AI-driven.

For too long, enterprises have lacked the capability to make use of unstructured data. While new technology is the obvious first tool that most organizations look to, savvy automation leaders know that the existing tech stack can be rife with opportunity to yield even more benefit. Wilde identifies ways that enterprises can leverage technology that integrates the existing tech stack to provide a richer, more comprehensive view of enterprise knowledge, while adding structure to their unstructured data. In turn, this strategy unlocks far greater value from existing automation solutions, such as Robotic Process Automation (RPA), and other business-critical technologies, illustrated through use cases in commercial real estate, financial services, insurance, and shared enterprise services.

Machine learning has evolved considerably over the past few years, and best practices have changed in tandem with that evolution.

On average, it takes 7 to 18 months, to go from idea to ML model in production. But things are changing. Data platforms are maturing and success is getting within reach of many organizations. Come hear about some top-level trends in machine learning.

Graph technology has become a main driver of AI and machine learning advances within a wide variety of industries.

The pandemic accelerated the pace of digital transformation across all industries. Organizations are looking for ways to accelerate their analytics, AI, and machine learning projects to increase revenue, manage risks, and improve customer experience. Join us to learn about the three core capabilities necessary to drive the business outcomes: connecting internal and external datasets and pipelines with a distributed graph database, analyzing connected data to discover insights with advanced analytics, and learning from the connected data with in-database machine learning.

Theories about machine learning have their place, but applying methodologies to real-world issues give practitioners a leg up.

Money laundering impacts society in a number of ways. Banks must adhere to regulatory guidelines to counter them. To combat money laundering, banks use data analytics to gain a complete understanding of transactions data accuracy, completeness, and timeliness from various sources within the bank. Maheshwari explains how extraction, transformation, and loading of data from various systems are critical. Machine learning methodologies are also critical to determine how these transactions (which are millions of dollars on a given day) can be analyzed for any money-laundering activity.

In this session, Hodeghatta addresses the challenges of protecting data while providing data for AI and machine learning projects and why data privacy is a concern and can be a hindrance for these types of projects.

One of the biggest organizational obstacles to data quality management is basic pessimism about the possibility of managing the quality of data. This is due to lack of clarity—the goals and processes for data quality management have not been defined or have not been understood—and disbelief that the quality of data could be subject to control. Sebastian-Coleman describes the forms of data quality denial and what any organization facing data quality issues can learn from them. She addresses how to get beyond denial to a place where organizations improve the quality of their data to more effectively leverage its value.

Using a semantic layer makes data accessible and accelerates the business impact of AI and BI at your organization. Youssef offers practical advice, and real-life enterprise examples on how to modernize your data and analytics stack and achieve quantifiable results with an order of magnitude better query performance, increased productivity, lower query compute costs, and improved Speed to Insights.

From powering NASA’s mission to Mars to driving business innovation for Fortune 500 companies, graph database tech[1]nology is delivering value to organizations across the globe. Hear how companies are using graph database platforms to outpace their competitors and power business-critical appli[1]cations, along with real-world use cases that include fraud detection, analytics, AI/ML, and supply chain management.

A recent survey of IT leaders found that the major­ity view hybrid or multi-cloud data warehousing as one of the most important data warehousing-re­lated trends. Today, the question is not whether to move to the cloud, but rather, which cloud platform is best for each organization’s needs.

The Internet of Things (IoT), once an emerging space, is quickly transforming industries by enabling machinery and products with network connectivity. From optimizing industrial machinery and manufacturing processes to powering connected cars and healthcare equipment, IoT-centered innovation is bringing about a future powered by data.

As sensor technology becomes more affordable, companies of all sizes will have the ability to embrace IoT strategies to build innovative products and services and establish new revenue streams. Yet, as with any promising technology, challenges remain. To reap the full value of IoT devices, companies need to migrate petabytes of IoT data quickly and securely to the cloud. As organizations across industries explore IoT offerings to strengthen consumer experiences and build new revenue streams, they must first understand its unique data management and orchestration challenges. This presentation explores best practices for activating data from the edge to cloud environments and activating this data to build new revenue streams.

It is the goal of many organizations to continuously leverage intelligence from fast-moving event streams to help automate business decisions. Learn the best practices for utilizing streaming data to support modern applications.

To deliver continuously useful insights, apps always need to have an answer from the latest data. Algorithms have to analyze, train, and predict continuously, and each new event must be analyzed in real time as soon as it arrives. As a result, insights and predictions are necessarily “given data thus far,” and the outputs therefore also form a real-time stream. This session provides a working example using streaming events from Apache Kafka and show attendees how to build applications that analyze, learn, and predict on-the-fly. This approach enables applications to self-assemble from streaming data, and applications are millions of times faster than “microservice plus database” architectures. It also gives developers new ways to ensure timely responses and to manage the effects of partitioning on event-driven applications.

Product teams must realize the need for instrumenting the product to collect accurate data. Most teams tend to neglect the instrumentation process and later scramble to gather insights from data. If product teams don’t include or prioritize instrumentation as part of their road map, they run a risk of not capturing user behavior.

App Store ratings aren't very helpful when it comes to better understanding customers. In-app ratings and feedback, however, can provide valuable insights into the behaviors and preferences of your users. In this session, you'll learn how to use in-app feedback to personalize and improve your user experience, including through the development of Artificial Intelligence models.

The coming decade is going to require a modern data warehouse to meet demanding new requirements for machine learning, data variety, and real-time analytics—while still satisfying the more traditional need for analysis of structured data at scale.  

Vendor claims to the contrary, data warehouse scale, performance, and operational issues are trickier than ever in the cloud. Choosing the wrong cloud database can result in millions of dollars of excess cost, unacceptable performance problems, or a drastically compromised database design. Winter provides a concise, strategic view of the cloud data warehouse landscape, highlighting how cloud database engines differ and how to choose one that will work for you. This will help avoid platform mistakes that can get you into deep trouble in the coming years.

Modern data analytics platforms that fuel enterprise-wide data hubs are critical for decision making and information sharing. The problem? 

As data storage and analytics in the cloud continue to grow, organizations are evaluating the essentials for success, such as scalability, efficiency, affordability, and security.

New ways to store data and leverage it in different ways are being utilized by data-driven organizations hungry for flexibility and scale.

For data to be useful, it must be trusted. It is important to standardize data, track its lineage, ensure that it is high quality, and verify that it is appropriate for the organizational roles or applications that leverage it.

A common pattern in data lake and lakehouse design is structuring data into zones, with bronze, silver, and gold being typical labels. Each zone is suitable for different workloads and different consumers. For instance, machine learning algorithms typically process against bronze or silver, while analytic dashboards often query gold. This prompts the question: Which layer is best suited for applying data quality rules and actions? The answer: All of them. This presentation delves deeper into this answer, describing the purposes of the different zones and mapping the categories of data quality relevant for each by assessing its qualitative requirements. Bryson also covers data enrichment—the practice of making observed anomalies available as inputs to downstream data pipelines.

DevOps and databases share many common characteristics. They shouldn’t be positioned as being at odds with each other.

You may have heard the term “DevOps” a lot lately, but is this just one of those buzzwords that gets thrown around and means something different depending on who’s talking? While traditional software methodologies pit developers and operations folks against each other, DevOps requires that they work together for a common goal. And, ultimately, shouldn’t the software project’s success be everyone’s goal? Come and learn how DevOps is changing the DBAs world for the better.

Those of us who administer production databases have had to endure significant change relative to how the databases and applications we support are managed. This session introduces DevOps concepts and explain what their impact is on how database administrators and developers manage their infrastructures.

The care and feeding (aka management) of databases takes on new meaning in the Internet of Things era.

Popular wisdom is that cache is king and you can easily scale by fronting your database with caching services like Redis. Moreover, you can scale out your relational database with read replicas. Finally, if that doesn't do it, you can choose a NoSQL database. However, what do you do when you have a lot of writes and a lot of reads, data integrity is critical, and downtime is a nonstarter? A new technology called distributed SQL borrows the best from both relational and NoSQL databases giving you both read and write scale while also ensuring the data is correct. As critical systems, financial systems, and the entire back office moves to the cloud, distributed SQL is key to ensuring data is consistent, available and scalable.

Licensing has always been tricky but the rise of trolling has created many new things to guard against.

Any organization that has gone through a vendor software licensing audit knows firsthand how expensive and draining a process can be for an organization. No one disputes that companies like Oracle, Microsoft, or IBM have a right to be fairly compensated for the use of their software. The same safeguards these organizations put in place to protect their intellectual property can easily be distorted by a software troll against an unsuspecting company to extort millions of dollars. Learn about software license trolls and simple steps to take to ensure your organization is not their next victim.

[This session has been canceled due to unforeseen circumstances. We apologize for any inconvenience.]

The notion of data exhaust, all that peripheral data floating around in your organization, can have value to your business operations. It could help with predictive analytics, for example, or customer analysis. But as big data gets even bigger, are we exhausted by all the data at our fingertips? At what point does technology step in to help and which technologies are best suited to deal with data exhaustion? That’s what this panel considers.

Data within the enterprise is useless if it can’t be found and used. AI and ML provide some pathways to data discovery that give companies competitive advantage.

Machine learning is both a buzzword and the Holy Grail, depending on how you use it. Enterprise cloud companies use machine learning to accelerate or supercharge their data journey—it helps them work at a faster pace, with more efficiency and greater accuracy. Once AI is fully in use, teams need to be able to answer this question: How do we know if this is working correctly? In order to do this, teams must extend their existing observability approaches to cover their AI and ML capabilities. Today, there’s a big gap there, and that creates risk as organizations can’t manage those assets properly. This session takes a deep dive into how companies utilize machine learning to take every competitive advantage to advance their ability to use all the data at their disposal.

A single view of the customer, powered by AI and ML, can help identify fraud, personalize recommendations, and contribute to outstanding customer care encounters.

With the advent of omnichannel, leveraging customer data has become paramount. In a behemoth such as Walmart, each customer’s identity exists as a silo rather than a single customer from the company perspective. Handling customer data for privacy, regulations, and deprecation becomes challenging with every new ID introduced in the system. A streaming ML platform that seamlessly combines data belonging to the same customer on a single box and runs ML models, which use this data as features, leads to a single view of a customer. Brar discusses the platform (built on Kafka ecosystem) and its important aspects.

As the world becomes increasingly data-driven, AI/ML algorithms are being incorporated in most business applications.

Historically, data in AI architectures was moved to a central location to perform both model training and inference. This centralized approach is becoming untenable due to cost, performance, and privacy reasons. In this talk, Kaladhar shares his thoughts on next-generation distributed AI architectures and presents the concepts of “AI Marketplaces” and “Federated AI” to demonstrate how these concepts are an integral part of distributed AI architectures.

Putting AI to work to improve healthcare requires having technology and infrastructure coupled with the right people.

Healthcare innovators are innovators are prioritizing AI and operationalizing it for better performance, better outcomes, and better patient experience. But getting into predictive and prescriptive analytics takes many of them out of their comfort zones. Yet with every opportunity for improvement, there is a risk that organizations won’t have what it takes to successfully develop, implement, or operationalize AI. Having the technology and infrastructure for AI is not enough. Having the right people—with the technical capability and healthcare expertise—is enormously important. In this talk, Mehrotra and Fernando provide insight into strategies and plans to attract and retain the talent needed to unlock the potential of AI.

Data architecture-as-a-service is a verbal twist on cloud processing environments, such as software-as-a-service or platform-as-a-service. This moniker conveys that it’s possible to abstract architecture and build it into easy-to-use, customer-facing tools. When we abstract data architecture, we solve the most enduring data pain point in the data world: the proliferation of data silos and pipelines that wreak havoc on data consistency and trustworthiness.