Knowledge graphs are a valuable tool that organizations can use to manage the vast amounts of data they collect, store, and analyze. An Enterprise knowledge graph’s 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.

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 and business analysts to data scientists. To measure and drive success, an actionable road map, with each phase focused on being lean with a business impact, is required. 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.

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.

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.

New approaches are available to modernize, streamline, and accelerate data management processes. It is critical to stay up-to-date with the latest ways to extract value from data with a modern data strategy. Attend this session for key insights on game-changing technologies and tips on how you can use them to succeed.

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.

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. Attend this session to learn how data virtualization:

• Has become a strategic component in their enterprise data architecture.

• Has democratized data within Prudential while providing robust security and governance.

• Is seen as a critical component in Prudential’s journey to the cloud.

The heterogeneous nature of data requires technologies that are best-suited to enable its many use cases. As a result, most data architectures at enterprises today feature a variety of solutions in a hybrid environment where the utilization of multiple cloud computing and storage services is fast becoming the go-to strategy. Attend this session for information about solutions and strategies to help you survive and thrive in the growing hybrid, multi-cloud world.

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.

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.

Data-driven enterprises use data and insights to fundamentally change the value proposition they offer to customers. The best among them catalyze a virtuous cycle in which more customers generate more data that is used to further enrich products and services, creating a sustainable competitive advantage. This requires orchestrating process, culture, and technology around making the most of data’s distinctive--and disruptive--economics. Attend to explore the key concepts that inspire data-driven enterprises, see how your organization compares with benchmark data, and learn how Apache Cassandra can accelerate your progress.

Database technologies are constantly changing and adding new options for enterprises. To take advantage of the new world of database technologies, enterprise and database managers need to be open to new possibilities. Attend this session for the inside scoop on key enabling technologies and evolving best practices in the new world of databases today.

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.

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.

For decades, companies have made large investments in enterprise data warehouses to fuel their BI systems. Increasingly, organizations are turning to cloud platforms for their modern data warehouses, which are enabling next-generation initiatives. Attend this session to learn about the changing technology landscape, migration strategies, and the pitfalls to avo

Join us to hear about how Highmark Health uses the Yellowbrick platform for data warehouse modernization and for data lake augmentation.  Highmark Health is a national health and wellness organization as well as the second largest integrated delivery and financing system (IDFS) in America.

In this webinar Highmark Health will share details about:

• Consolidation of their disparate database systems 

• Quick data migration from legacy data warehouses to Yellowbrick 

• Meaningful results achieved with Yellowbrick in less than 2 days

Don’t miss this opportunity to see for yourself how Highmark Health took advantage of Yellowbrick’s innovations to help modernize their data warehouse environment and accelerate insights from their data lake.

Knowledge graphs are on the rise at enterprises hungry for more effective ways to connect the dots between the data world and the business world. Paired with complimentary AI technologies like machine learning and NLP, knowledge graphs are enabling new opportunities to leverage data not possible before and are quickly becoming a fundamental component of modern data systems. Attend this session to learn how you can use these technologies to change the game at your company.

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.

This presentation will describe the operational capabilities and benefits of Knowledge Graph technology: “The Future of Enterprise Data". We will discuss how ontologies are the way forward to better represent enterprise definitions and data. The Financial Industry Business Ontology (FIBO) will be described as an exemplar of a semantically modeled knowledge graph for finance. We will describe how semantic graphs can be further enriched with probabilistic associations from machine learning and data mining algorithms. We will discuss the evolution of data from strings and numbers, to first class semantic objects to distributed representations of concept embeddings in vector space. We will also describe how Knowledge Graph technology can provide a layer of “knowledge” over legacy data structures to obtain maximum understanding of data inventory and provide the foundational building blocks for powerful data catalogs and hubs. Knowledge Graph also positions organizations to better support customer 360, risk management, regulatory compliance, technology asset management and many other use cases.

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.

The Entity-Event Data Model we present puts core entities of interest at the center and then collects several layers of knowledge related to the entity as ‘Events’. Using this novel data model approach, organizations gain a holistic view of customers, patients, students, or important entities and the ability to discover deep connections, uncover new patterns and attain explainable results.

During this presentation we explain and demonstrate how Entity-Event Knowledge Graphs are the future of AI in the Enterprise.

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.

Full presentation details will be posted shortly.

DataOps has emerged as an agile methodology to improve the speed and accuracy of analytics through new data management practices and processes—from data quality and integration to model deployment and management. Attend this session to hear about the key supporting technologies, strategies, real-world success stories, and how to get started on your DataOps journey.

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.

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.

Machine learning is on the rise at businesses hungry for greater automation and intelligence. At the same time, most projects remain in the early phases. Machine learning is still the new kid on the block. From data quality issues to architecting and optimizing models and data pipelines, there are many considerations to keep in mind. Attend this session to gain a deeper understanding of the enabling technologies, techniques, and applications you need to know to achieve your goals.

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.

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.