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It's been more than three decades since organizations began building data warehouses to improve reporting, analysis, and decision making. And ever since those early days, poor quality data has hampered these initiatives. It seems the state of data quality has changed little from the early years to today.

This webinar will review findings from a recent report on the state of data quality in 2025. It will discuss the most common data quality problems, the causes of those problems, the cost of poor quality data, the remedies companies today use to address data quality issues, and criteria for evaluating data quality tools.

You will also see how companies are using one data quality tool to get a handle on their data quality issues. Tune in to learn about the state of data quality and what your organization can do to address this perennial problem.

You Will Learn:

● The most common data quality problems

● The causes of those problems

● The cost of poor quality data

● Criteria for evaluating data quality tools

● A quick demo of one data quality tool 

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Every once in a while companies select data & analytics products, such cloud data platforms, ML/AI tools, and data catalogs. Organizations often bet hundreds of thousands, if not millions, of dollars on these decisions. And staff have to interact with these tools and platforms for years, if not decades. Even tools purchased for a single department have long lifespans. So it's imperative that organizations make the right choices.

Given the stakes, data leaders need a rigorous methodology for evaluating and selecting data & analytics products.

Unfortunately, some data leaders rush the process and pay the consequences later when they discover the tools fail to perform as expected, they lack critical integrations, or teams refuse to use them.

In this webinar, Eckerson Group consultants step through the methodology and templates they use with clients to evaluate and select products. They discuss who should be involved in the process, how to avoid common pitfalls, and how to ensure adoption.

You Will Learn:

● When a formal product evaluation is required and when it's not. 

● Who should participate in a product evaluation, internally and externally.

● Pitfalls to avoid when evaluating and selecting products.

● The best way to compile evaluation criteria and score products.

● Whether and how to conduct a proof of concept.

● How to implement a product to ensure adoption.

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Data teams can design the most elegant data architectures, deploy leading-edge tools, and design exquisite data models, but if individuals and teams won't change the way they do business, your expensive investments will be all for naught.

In this webinar, Wayne Eckerson will present a framework he uses with clients to facilitate organization change. Although most companies aren't fortunate enough to hire professionally trained change managers, they can still effect positive change by adhering to a few core principles and enlisting help from program managers and inhouse marketing and communications professionals.

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Are you a curious data or analytics leader? Do you want to know what will change in 2025? Our experts believe the answer is simple: everything! Generative AI is poised to dominate, but that’s not the whole story. From AI governance to quantum computing risks, the future of data and analytics holds exciting challenges and opportunities.

This webinar will enable you and your team to stay abreast of the emerging trends, technologies, and techniques that are shaping our industry and will soon affect how you and your team do what you do. This webinar will host a panel of experts from all parts of our industry: practitioners, consultants, and industry analysts. It will even feature a prediction from ChatGPT!! Join the conversation and critique our predictions and add your own to the mix.

You Will Learn about:

● Chatbot Revolution: AI-powered chatbots will dominate structured data analytics, providing conversational interfaces that simplify insights for business users.

● Governance Challenges: Companies will implement AI chatbot gateways to manage inconsistent “chat truths” and enhance governance across AI systems.

● AI Governance Priorities: Data bias will emerge as a focal point, with organizations redefining data quality to include bias detection and mitigation.

● The Decline of ML Models: Pre-trained AI models will increasingly replace traditional ML approaches, enabling faster results without extensive data science expertise.

● Neuro-Symbolic Models: These hybrid models will gain traction for their reasoning and explainability, complementing or even challenging transformer models.

● Quantum Threats: Quantum computing will challenge existing encryption methods, driving innovation in cybersecurity strategies.

● Data Contracts: These will become standard in data pipelines, enhancing trust and accountability in data flows.

● And more!!

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Our industry loves new data management paradigms. Even before we’ve mastered one, we discard it in favor of the newest approach to managing data. Every approach has its downsides: the data warehouse can easily become too monolithic and costly; a data lake too ungoverned and unwieldy. But will the newest paradigms—the data mesh and data fabric—prove any better? And are these paradigms convergent or divergent with each other as well as prior paradigms?

Today, it’s generally accepted that it’s impossible to consolidate all corporate information into a data warehouse or data lake. Therefore, we need a new architectural paradigm that queries data where it resides without having to move and consolidate it. It’s also true that many business units have accrued deep data expertise, either through a merger & acquisition or sheer data survival due to enterprise data bottlenecks. As a result, many companies are turning to data fabric to query data in place and the data mesh to empower business domains to generate data products.

It's Eckerson Group’s view that all these paradigms are accretive. Just because you need a data mesh or data fabric, doesn’t mean you forsake core principles of a data warehouse or data lake. And a data fabric and data mesh are inextricably connected, the former serving as the data infrastructure for the latter.

This webinar will explain the core concepts of a data mesh and data fabric, show how they are related, and explain how they augment data warehousing and data lake architectural models.

You Will Learn:

● Core concepts of a data mesh and data fabric

● How data mesh and data fabric can coexist

● How data mesh and data fabric augment existing data paradigms

● Best practices for implementing data architectures