Increasingly, IT business executives talk about information as one of their most important assets. But few behave as if it is. Executives report to the board on the health of their workforce, their financials, their customers, and their partnerships, but rarely the health of their information assets. And corporations typically exhibit greater discipline in managing and accounting for their office furniture than their data.

Laney shares insights from his best-selling book, Infonomics, about how organizations can actually treat information as an actual enterprise asset. He discusses why information both is and isn’t an asset and property, along with what this means to organizations and the investment community. He covers the issues of information ownership, rights, and privileges, along with external data opportunities and challenges, and his set of generally accepted information principles culled from other asset management disciplines.

This session will be beneficial for those looking to help their organization move beyond the trite “data is an asset” or “data is the new oil” lip-service to actually begin acting that way. Participants will learn and have an opportunity to discuss:

• How to monetize information assets in a wide variety of ways, including a number of real world examples

• How to manage information as an actual asset by apply asset management principles and practices from other asset domains

• How to measure information’s potential and realized value to help budget for and prove data management benefits

• How classic microeconomic concepts can be applied to information for improved data architecture & management, and economic benefits

Data governance teams attempt to apply manual control at various points for consistency and quality of the data. By thinking of our machine learning data pipelines as compilers that convert data into executable functions and leveraging data version control, data governance and engineering teams can engineer the data together, filing bugs against data versions, applying quality control checks to the data compilers, and other activities. This talk illustrates how innovations are poised to drive process and cultural changes to data governance, leading to order-of-magnitude improvements.

The 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.

Carr explains how to get the most out of Edge Analytics with the right hybrid data management and integration.

We are in the midst of an unprecedented convergence of events accelerating digital transformation. From COVID-19, work-from-home initiatives, the adoption of cloud and more, technology is helping companies reinvent their business models and how they operate. Now, more than ever, amidst this transformation, enterprises can leverage their data and real-time analytics to gain competitive advantage and gain market share. Real-time analytics will play an ever-increasing role in measuring key business and performance indicators across all aspects of a digital business operation, such as development, operations, security, marketing, finance and other business use cases. Reliance on real-time analytics and data will help accelerate and improve decision-making, drive great customer experiences and enable differentiation.

Bruno Kurtic outlines the key trends driving what he calls, “continuous competitive advantage” and how real-time data generated by operations, security and business use cases will increase in relevance to business leaders. Joining him is Kal Patel of Genesys, who shares how the company leverages real-time analytics generated from machine data to support its cloud-based, omnichannel contact center platform. Together, Kal and Bruno discuss how real-time analytics has influenced Genesys'  "continuous" improvement mindset to drive outstanding customer experiences.

O'Brien provides guidance about the agile methodology and templates that project delivery teams can follow to build modern data infrastructures (on-prem, hybrid, and multi-cloud). With this approach, delivery teams can follow, initiate, and leverage data and integration design patterns while working to build an enterprise data and analytics platform. This approach also defines the teams and individual roles, along with expectations for working in a prioritized and governed manner to evolve the data platform in alignment with business priorities.

Most companies face challenges in storing and managing their increasing volumes of data, let alone performing analytics to learn patterns and trends in that data. Vertica offers an advanced unified analytical warehouse that enables organizations to keep up with the size and complexity of enormous data volumes. Whether you are starting out on your journey of building an enterprise and analytics platform or you are looking to switch from a legacy system which has speed and scalability limitations, Vertica can offer many advantages like built-in analytics and machine learning functions, linear scaling, native high availability and hybrid deployment options.

Hudi(Hadoop Upserts Deletes and Incrementals) is a storage abstraction library that improves data ingestion. Uber's Nishith Agarwal explains what Hudi offers and why it is needed, including how Hudi can provide ACID semantics to a data lake and some of the basic primitives—such as upsert and delete—that are required to achieve acceptable latencies in ingestion, while also providing high-quality data by enforcing schematization on datasets. Additionally, he discusses more advanced primitives, such as restore, delta-pull, compaction and file sizing required for reliability, efficient storage management, and building incremental ETL pipelines. He reveals how to easily onboard your existing dataset to Hudi format while keeping the same open-source formats so you can start utilizing all the features provided by Hudi without needing to make any drastic changes to your data lake.

As Data Lakes grow, the traditional and cloud sources in the enterprise have not disappeared. Most companies have a hybrid environment where the data resides across Data Lakes, traditional on-prem sources, and in the cloud. It is also common for Data Lakes to be used for gathering all types of data, as such the quality and consistency of the data at times is questionable. Hybrid systems and data quality make Data Virtualization an essential and critical component for providing and managing data access services for Consumers, Analytics, and Presentation Layers. Data Virtualization ensures trusted and governed data access. Inessa Gerber discusses how Data Virtualization ensures consistency, quality, and governance of all your data across the enterprise, making your Data Lake a key source of quality data. Data Virtualization is an essential component for a modern deployment across Data Lakes and Hybrid environments.

As software infrastructure is stretched between on premise, public clouds, and hybrid clouds, keeping software in compliance is a significant challenge. Sorting through all the FUD and getting straight answers from vendors on the proper way to license software in this complicated world is not easy. Some vendors have turned to Software License Audits as an easy way to generate additional revenues. In this presentation, we will discuss current Software License trends, the difference between Oracle policy and your contractual obligations, Licensing Oracle on a virtualized environment and licensing best practices. Lesson learned will apply to all major software vendors.

As customers with existing data consider moving that data and applications to “the cloud”, it rapidly becomes apparent that, just as in real estate, it’s all about location. A move to the cloud is most typically a journey accomplished over time, and not a “big bang”. For at least a time, customer data will need to be accessible from both legacy 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 will cover these and other reasons why it’s critical to consider location of your data during your cloud journey. We’ll discuss tools and techniques that can help make your move easier; and why a hybrid approach to the cloud may be best.

Craig Mullins looks at the industry trends and issues that are impacting the job of database administration, fundamentally transforming the duties of the job. He reviews the current landscape for database management systems and DBAs and examines the impact of the following dynamics on DBA: Big Data and Data Growth, The Cloud, Regulatory Compliance, Data Breaches, AI and Machine Learning, Heterogeneity, DevOps and Agile, and the IoT. What are the needs for further automation and orchestration of DBA practices and procedures? Find out from this presentation.

In today’s heterogeneous, hybrid database environment, having the right tools is more important than ever. Learn how Quest’s award-winning monitoring and management tools can provide valuable insight and help you better manage your complex environments.

Enterprises waste millions of dollars on failed data initiatives because they apply outdated thinking to new data problems. Trying to fix these issues with technology just makes it worse. Deploying new self-service BI and data science tools and adding a layer of governance over the top typically results in overly complex, rigid processes that benefit the few and make everyone else less productive. Agile data governance is a new practice that applies the best of agile and open software development to data and analytics. It iteratively captures knowledge as data producers and consumers work together so that everyone can benefit.

Comprehending natural language text with its first-hand challenges of ambiguity, synonymity and co-reference has been a long-standing problem in Natural Language Processing. Transfer Learning uses some of the models that have been pre-trained on terabytes of data and fine-tunes them based on the problem at hand. It's the new way to efficiently implement machine learning solutions without spending months on data cleaning pipeline. The general idea is to transfer learned feature representations from the pre-trained model (trained on the big dataset) and fine-tune the task specific data in the last layers, so as to create a new output layer. Jayeeta Putatunda highlights ways of implementing language model BERT and fine tuning the base model to build an efficient text classifying model. This also ensures that, by using the available Open Code platforms, we are able to Better Science as well as Better Environment because training a single AI model contributes to 5 cars' lifetime worth of carbon emissions.

Ben Sharma will take you on a journey of the technology evolution that’s enabled today’s DataOps and how to streamline your data supply chain to efficiently and securely deliver analytics-ready data while reducing costs. He will also share Zaloni’s recommended zone-based data governance architecture.