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 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 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

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

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

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.

Companies need to invest heavily in teams and people, both at corporate and in the field, if they want to become a data-driven organization. Published at: https://www.eckerson.com/articles/organizing-for-success-part-iii-how-to-organize-and-staff-data-analytics-teams

Conventional data governance conflicts with today’s world of self-service analytics and agile projects. Published at: https://www.eckerson.com/articles/modern-data-governance-problems

Data democratization is the buzzword to describe empowering enterprise stakeholders with data. While there have been advances in data management, governance, and analytics, something keeps getting in the way of achieving data democratization. Published at: https://www.eckerson.com/articles/data-democratization-and-the-duties-of-data-citizenship

Dan and Wayne discussed the concept of data and analytics operating models, which refers to how organizations organize their data and analytics resources for alignment and efficiency.

Generative AI brings a promise to improve lives in a blistering innovation race, but also a threat to people, corporations, and even nations. Data analytics leaders must understand the risks of generative AI, both societal and business-related, to use it positively and avoid the destructive consequences seen with nuclear energy development. Published at: https://www.eckerson.com/articles/the-opportunity-and-risk-of-generative-ai-part-i-a-nuclear-explosion

Dan O'Brien and Kevin Petrie discuss FinOps, which is a cost governance discipline for cloud-based analytics and operational projects.

An Analytics Center of Excellence empowers business teams to meet their own data needs by changing the role of IT from developer to facilitator. The reality, however, is that IT needs be both a facilitator and a developer. Published at: https://www.eckerson.com/articles/analytics-center-of-excellence-part-i-how-to-shape-the-organization

Despite innovations in data architecture, infrastructure, and analytics, most organizations today still struggle to realize the promised value of data. Learn how the data mesh principle of data as a product can help, as part of a data mesh initiative or as a stand-alone strategy. Published at: https://www.eckerson.com/articles/data-products-part-of-a-data-mesh-initiative-or-a-stand-alone-strategy

I got energized walking the show floor at the Gartner Data & Analytics event last month and learned a few things about the future of our industry. Published at: https://www.eckerson.com/articles/quick-recap-of-gartner-conference-2023

The modern data stack is a loose collection of technologies, often cloud-based, that collaboratively process and store data to support modern analytics. It must be automated, low code/no code, AI-assisted, graph-enabled, multimodal, streaming, distributed, meshy, converged, polyglot, open, and governed. Published at: https://www.eckerson.com/articles/twelve-must-have-characteristics-of-a-modern-data-stack

One version of the truth is the holy grail of data and analytics. However, the promise of one version of the truth still evades us because even with consistent data, the truth is, as the film My Cousin Vinny demonstrates, a matter of perspective and context. Published at: https://www.eckerson.com/articles/one-version-of-the-truth-according-to-my-cousin-vinny

Over the past 20 years or more, data architecture practices have focused almost exclusively on managing data for analytics. Operational data is much more than source data for analytics. We must give attention to operational data architecture or pay the price in data disparity, data friction, and technical debt. Published at: https://www.eckerson.com/articles/operational-data-architecture

We enter 2023 in a haze of uncertainty. Enterprises must rationalize analytics projects, shift to lower-risk use cases, and control cloud costs. They also must measure the ROI of analytics projects and use data governance to reduce business risk. Published at: https://www.eckerson.com/articles/analyzing-a-downturn-five-principles-for-data-analytics-in-2023

As enterprises grow more dependent on the cloud and as the economy convulses, FinOps will soon become mandatory. Published at: https://www.eckerson.com/articles/the-rise-of-finops-cost-governance-for-cloud-based-analytics

Business domains have a range of data & analytics capabilities that enterprise data teams must support. The key is to ensure domain activity aligns with enterprise standards and best practices to ensure data consistency and avoid silos. Published at: https://www.eckerson.com/articles/an-operating-model-for-data-analytics-part-iv-red-team-composition

There are many models for bridging business and technical teams. These models can be more centralized or decentralized in nature, depending on the culture of the organization and nature of the business domain. Each requires a strong enterprise data teams comprised of multiple departments and roles. Published at: https://www.eckerson.com/articles/an-operating-model-for-data-analytics-part-iii-team-composition-and-dynamics

Hybrid development teams are critical to the success of a data & analytics program. Data leaders must invest time, energy, and thought to the creation of these teams and how best to support them. It’s critical that they allocate staff time to nurture knowledge flows between component groups. Published at: https://www.eckerson.com/articles/an-operating-model-for-data-analytics-part-ii-knowledge-flows

Data orchestration uses caching, APIs, and centralized metadata to help compute engines access data in hybrid or multi-cloud environments. Data platform engineers can use data orchestration to gain simple, flexible, and high-speed access to distributed data for modern analytics and AI projects. Published at: https://www.eckerson.com/articles/data-orchestration-simplifying-data-access-for-analytics