Secrets of Data Analytics Leaders

Chief Data Officers are expanding data sharing to drive productivity, innovation, and better decisions. Data marketplaces simplify access by connecting users to ready-to-use, high-value data through a centralized, self-service platform. Published at: https://www.eckerson.com/articles/how-data-product-marketplaces-enable-seamless-data-consumption-and-generate-value

All data management activities—whether internally or externally focused—should either reduce costs or grow earnings. Published at: https://www.eckerson.com/articles/book-review-how-to-make-money-with-data

This blog offers five guiding principles to help CIOs, CDOs, and team leaders optimize hybrid data environments. Published at: https://www.eckerson.com/articles/balancing-act-five-principles-to-optimize-hybrid-cloud-environments

This blog defines the governance requirements that streaming data pipelines must meet to make artificial intelligence/machine learning (AI/ML) initiatives successful. Published at: https://www.eckerson.com/articles/streaming-data-governance-three-must-have-requirements-to-support-ai-ml-innovation

Anthony Cosgrove, Co-founder at Harbr, tells us how successful data products focus on clear value propositions, tailored placement, intuitive packaging, and appropriate permissions. Published at: https://www.eckerson.com/articles/what-is-a-data-product

This blog, the second in a series, explores the mix of infrastructure types that support modern AI. Published at: https://www.eckerson.com/articles/cloud-on-prem-hybrid-oh-my-where-ai-adopters-host-their-projects-and-why

Despite $180 billion spent on big data tools and technologies, poor data quality remains a significant barrier for businesses, especially in achieving Generative AI goals. Published at: https://www.eckerson.com/articles/poor-data-quality-is-a-full-blown-crisis-a-2024-customer-insight-report

This blog discusses how GenAI and natural language processing are transforming SQL query generation, allowing non-technical users to access and query data easily. Published at: https://www.eckerson.com/articles/from-4gl-to-genai-how-sql-automation-has-evolved

This article by Piotr Czarnas, founder of DQOps, outlines a proven, team-based approach to tackling persistent issues like invalid data, delayed reporting, and inconsistent formats. Published at: https://www.eckerson.com/articles/overcoming-the-challenge-of-low-data-quality

This article explores how Product Information Management (PIM) helps manufacturers and retailers manage rich product data through phased attribution and collaborative workflows. Published at: https://www.eckerson.com/articles/keys-to-succeeding-in-your-product-information-management-pim-journey

As cloud adoption accelerates, not all analytics workloads are heading in the same direction. This blog explores three strategic options for data and IT leaders. Published at: https://www.eckerson.com/articles/are-you-cloud-bound-the-case-for-migration-repatriation-or-keeping-your-analytics-projects-on-premises

This article breaks down the evolving landscape of AI/ML platforms, from AutoML to full-stack AI workbenches, and provides a structured tool evaluation framework to cut through vendor ambiguity. Published at: https://www.eckerson.com/articles/the-ai-ml-tool-evaluation-template-a-guide-to-smarter-selection

This guide provides a step-by-step framework to assess vendors, align priorities, and make informed decisions about enterprise data and analytics tools. Published at: https://www.eckerson.com/articles/the-buyer-s-guide-to-selecting-the-right-enterprise-data-analytics-tool

This blog, the second in a series, recommends six criteria to help data leaders evaluate tools for managing streaming data pipelines. Published at: https://www.eckerson.com/articles/ultimate-guide-to-streaming-data-pipelines-six-criteria-to-evaluate-and-select-the-right-tool

This blog defines streaming data, explains why companies need it, and explores how streaming data pipelines feed multi-faceted GenAI applications. Published at: https://www.eckerson.com/articles/why-and-how-streaming-data-drives-the-success-of-generative-ai

Change is inevitable, but adoption is key. From AI/ML tools to leadership shifts, success depends on aligning people, not just technology. Published at: https://www.eckerson.com/articles/managing-change-in-the-age-of-ai-the-head-heart-and-herd-framework

This blog covers integrating SAP and third-party systems to build a unified data foundation for analytics and AI in conversational use cases. Published at: https://www.eckerson.com/articles/analytics-and-ai-for-sap-environments-build-a-unified-data-foundation-to-drive-advanced-use-cases

This blog recommends four questions to help data and AI leaders compare homegrown and commercial options for retrieval augmented generation. Published at: https://www.eckerson.com/articles/build-or-buy-rag-four-questions-to-guide-your-approach-to-retrieval-augmented-generation-for-genai

What will change next year? In the age of generative AI, the answer is simple: Everything! Published at: https://www.eckerson.com/articles/predictions-2025-everything-is-about-to-change

Exploring data observability's limits in data migration, integrity audits, and the need for specialized tools for reliability. Published at: https://www.eckerson.com/articles/the-shiny-allure-of-data-observability-its-limits-in-data-migration-integrity-audits-and-certification

A strategic approach to data monetization covering key processes, risks, and organizational shifts needed for success. Published at: https://www.eckerson.com/articles/unlocking-data-monetization-success

A fresh perspective on data architecture, advocating for an 'anti-monolith' approach inspired by engineering best practices. Published at: https://www.eckerson.com/articles/good-data-architecture-anti-monolith

Many practitioners view data mesh and data fabric as mutually exclusive approaches to data strategy. However, these paradigms complement each other. Data mesh focuses on decentralization and autonomy; Data fabric ensures centralized integration and governance. Let's dive into how blending elements of both can offer flexibility and control to create the right fit for your organization's data strategy. Published at: https://www.eckerson.com/articles/blending-data-mesh-and-data-fabric-crafting-a-balanced-data-strategy-2118cd34-e463-4468-b150-bdaf9e1c541d

As organizations grapple with data spread across various storage locations, solutions like Coginiti Hybrid Query offer a much-needed alternative to fragmented tools. Published at: https://www.eckerson.com/articles/a-novel-approach-for-reducing-cloud-data-warehouse-expenses-from-coginiti

This blog post explores the evolving landscape of data catalogs, highlighting ten key market trends driving the adoption of next-generation solutions. Published: https://www.eckerson.com/articles/ten-key-market-trends-in-next-generation-data-catalogs

Data teams must filter, blend, and refine raw data inputs to create the high-octane fuel that drives innovation with artificial intelligence and machine learning (AI/ML). Published at: https://www.eckerson.com/articles/refining-the-right-fuel-how-data-integration-drives-the-ai-ml-model-lifecycle

With numerous data catalog options available, all claiming to be the best, how do you make an informed decision without exhaustive research? Published at: https://www.eckerson.com/articles/unveiling-the-future-of-data-catalogs

This blog describes the need for data teams to establish a flexible yet well-governed data architecture to support dynamic AI/ML projects. Published at: https://www.eckerson.com/articles/multi-style-data-integration-for-ai-ml-three-use-cases

Many data leaders want to implement self-service, but don't realize that they first have to implement the right architecture, governance, operating model, project delivery approach, data, and change management plan. Published at: https://www.eckerson.com/articles/self-service-is-the-outcome-not-the-driver-of-a-data-driven-organization

Explore the essential characteristics to choose the right conversational query tool for your needs and environment. Published at: https://www.eckerson.com/articles/modernizing-analytics-with-conversational-query-tools-five-must-have-characteristics

Data analytics is a balance of flexibility for innovation and governance to control risks. This blog discusses its implications for artificial intelligence (AI), including machine learning (ML) and generative AI (GenAI). Published at: https://www.eckerson.com/articles/ai-ml-innovation-requires-a-flexible-yet-governed-data-architecture

Non-profit organizations are more mission-driven, consensus-driven, and resource-constrained than commercial organizations. As a result, it's imperative that non-profits develop a data strategy before plunging into building data solutions. It will save them time, money, and burnout in the long run. Published at: https://www.eckerson.com/articles/why-non-profits-need-a-data-strategy

Explore the reasons for data engineers to collaborate with data scientists, machine learning (ML) engineers, and developers on DataOps initiatives that support GenAI. Published at: https://www.eckerson.com/articles/dataops-for-generative-ai-data-pipelines-part-iii-team-collaboration

This blog explores three criteria to evaluate tools that manage unstructured data pipelines for GenAI. Published at: https://www.eckerson.com/articles/data-engineering-for-genai-three-criteria-to-evaluate-pipeline-tools

If your data team wants to implement data products, it would be wise to avoid these 12 pitfalls that can torpedo an initiative. Published at: https://www.eckerson.com/articles/12-pitfalls-to-avoid-when-implementing-data-products

This article compares data catalogs and data marketplaces and argues that you need both and will soon have both as vendors add data marketplace extensions. Published at: https://www.eckerson.com/articles/why-do-i-need-a-data-marketplace-when-i-have-a-data-catalog

This blog defines conversational BI, why companies should consider it, and how their power and casual users can best get the desired results. Published at: https://www.eckerson.com/articles/driving-results-with-conversational-bi-best-practices-for-power-and-casual-users

Data engineering is now considered a crucial job in IT as Generative AI, the hottest technology of this decade, relies on data engineers to provide accurate inputs. Published at: https://www.eckerson.com/articles/data-engineering-for-genai-how-to-optimize-data-pipelines-and-governance

Data engineers and data scientists must manage pipelines for unstructured data to ensure healthy inputs for language models. Published at: https://www.eckerson.com/articles/why-and-how-data-engineers-will-enable-the-next-phase-of-generative-ai

Companies that adopt DataOps increase the odds of success by making GenAI data pipelines what they should be: modular, scalable, robust, flexible, and governed. Published: https://www.eckerson.com/articles/dataops-for-generative-ai-data-pipelines-part-ii-must-have-characteristics

Most data leaders want to deliver data products, but few are doing it. Let's face it: most data teams today function as internal service bureaus that fulfill customer requests that arrive via ticketing systems, email, handwritten notes, or calls from colleagues looking for a favor. Most work double time to keep their request backlogs from ballooning from weeks to months. In this environment, few data leaders have time or capacity to switch from a project management approach to a product management one. Even if data leaders had time, most wouldn't know how to make this transition. Most have no experience in product management, nor do they have a good idea of a data product. So asking data leaders to deliver data products is like asking them to build a rocket ship that can travel to the moon. In this episode, Wayne Eckerson interviews Henrik Strandberg, a strong proponent of running data teams using product management principles. Henrik Strandberg is a seasoned data transformation leader who, for the past 25 years, has helped numerous organizations bridge gaps between business and technology. In stints at publishing and gaming companies, Henrik has developed a unique understanding of building and delivering data products at scale that delight customers.

GenAI can help data engineers become more productive, and data engineering can help GenAI drive new levels of innovation. Published at: https://www.eckerson.com/articles/achieving-fusion-how-genai-and-data-engineering-help-one-another

Discover how master data management (MDM) provides language models with high-quality enterprise data to improve their response accuracy. Published at: https://www.eckerson.com/articles/improving-genai-accuracy-with-master-data-management

Explore our four primary criteria for evaluating conversational BI products. Published at: https://www.eckerson.com/articles/genai-driven-analytics-product-evaluation-criteria-for-conversational-bi

The success of Generative AI depends on fundamental disciplines like DataOps. Published at: https://www.eckerson.com/articles/dataops-for-generative-ai-data-pipelines-part-i-what-and-why

With the increasing adoption of Generative AI, learn how data governance will add value to and benefit from Generative AI. Published at: https://www.eckerson.com/articles/data-governance-in-the-era-of-generative-ai

"Meet the business where it is." If you're on the data team, that's what you're expected to do to empower stakeholders with data. But how far should you go to meet the business? And shouldn't the business be expected to move a little toward meeting the data where it is? Published at: https://www.eckerson.com/articles/meeting-the-data-where-it-is-time-for-the-business-to-step-up

The European Union recently passed the first of its kind legal framework on the development, use, and governance of artificial intelligence. It lays out rules and standards with the aim of ensuring technologies are safe and transparent, and do not violate the fundamental rights of an individual. Published at: https://www.eckerson.com/articles/the-eu-ai-act-and-the-emergence-of-new-global-standards

Most organizations are committed to responsible and ethical use of AI. Yet anticipating unintended consequences before designing and implementing AI can be challenging. This framework and process helps evaluate short-term and long-term impacts across multiple dimensions so you can mitigate AI's unintended consequences. Published at: https://www.eckerson.com/articles/mitigating-ai-s-unintended-consequences

It's not easy being the head of data & analytics at a large organization. You must align a large team across multiple disciplines; you must deal with oodles of legacy systems and tools that hamper innovation; and you must deliver business value fast to keep executives at bay and your job intact. You also need to recruit dynamic managers who can push the envelope while meeting operational objectives. And when you falter--which you inevitably will-you have to rebound fast. No one knows these lessons better than Tiffany Perkins-Munn. She currently runs a 275-person data & analytics team at JP Morgan Chase that consists of data engineers, data scientists, behavioral economists, and business intelligence experts. She thrives on versatility, having earned a Ph.D. in Social-Personality Psychology with an interdisciplinary focus on Advanced Quantitative Methods. Building on this foundation, she has accumulated vast experience in the art of managing data & analytics teams during her 23 years in technical and managerial roles in the financial services industry. In this interview, you'll learn: 1. Tiffany's secret for aligning a large data & analytics team and keep them from splitting into silos of specialization 2. Her favorite techniques for recruiting the right people to her team. 3. How to wade through the thicket of legacy systems and deliver innovative solutions quickly. 4. The impact of GenAI on her operations and the financial services industry. 5. How to advance your careers in data & analytics.

Adopting community of practice principles, along with coaching and mentoring, is a practical approach to fostering and cultivating data literacy. Published at: https://www.eckerson.com/articles/a-people-first-approach-to-developing-data-literacy