Modern Data Strategies: People, Processes, Technology, and Teams at Data Summit 2024

As enterprises continue to digitize, implementing a robust, actionable data strategy is paramount. Data is the currency—and lifeblood—of business, where an effective data strategy acts as a blueprint and enterprise-wide plan to leverage data and analytics for greater business achievement. Such a strategy helps to streamline operations, reduce costs, improve business decisions and plans, and grow revenues.

Wayne Eckerson, president, Eckerson Group, led the annual Data Summit pre-conference workshop, “Constructing Your Data Strategy: A Business & Technical Foundation for Success at Data Summit 2024,” to explore the fundamentals of crafting an actionable, executable data strategy that aligns with businesses’ unique needs, culture, and data maturity.

The annual Data Summit conference returned to Boston, May 8-9, 2024, with pre-conference workshops on May 7.

Eckerson explained that a data strategy encompasses a variety of components—ranging from a logical data architecture to a data and analytics portfolio, enterprise data model and flows, a data governance plan, operating model, data literacy program, and more.

In a more conceptual manner, Eckerson broke down a basic data strategy into six components:

  • Optimize the operating model.
  • Modernize the data architecture.
  • Deploy data governance.
  • Standardize development.
  • Improve self-service.
  • Apply change management practices throughout.

Of all the many things that a data strategy is composed of, Eckerson explained that part of its purpose is to act as a communications vehicle that communicates with:

  • Executives by showing how data helps the business and secure buy-in
  • Department heads by showing how data supports their area and secure buy-in to avoid sabotage
  • Your team to get everyone on the same page and gain direction and focus

Additionally, data and analytics should be run as a business, complete with a mission, goals, critical success factors, key stakeholders, target customers, and success metrics, according to Eckerson. Importantly, it should exist with a roadmap and priorities; initiatives and tasks; and a budget and plans.

Whether due to a new data-driven CEO, a business transformation, modernization efforts, or to resolve a disagreement, a data strategy should be refreshed annually or after a major change, such as a merger, a new corporate strategy, or a technological paradigm shift.

Embarking on a data strategy refresh begins with a current state assessment, examining every facet of your enterprise—from the business to the culture, users, data analysts, team, capabilities, and architecture.

Some examples of assessing business culture include examining if your company:

  • Has business leaders that view data as a critical investment
  • Competes by giving workers better, more timely access to information
  • Competes by using algorithms to optimize and automate key business processes
  • Strives to be a leader in adopting new technology
  • Shares data and collaborates on data and analytics activities
  • Embraces new initiatives, processes, and technologies to improve productivity and effectiveness
  • Has executives that use data to justify or validate critical decisions

Knowing your architecture—as well as its challenges—is equally as important as understanding your business culture. Whether your enterprise has multiple data silos, no data catalog, lack of advanced analytics, or no support for diverse data types informs the sort of strategy that may prove successful.

This leads itself into data governance, or the act of overseeing how data is managed and used to reduce risks and optimize business outcomes. According to Eckerson, data governance takes shape as a framework that addresses the why, who, where, how, what, and when:

  • Why—Vision, Mandate, Strategy: Defines scope and expectations for the program and how it will improve value and reduce risk, asserting authorization to set and enforce policies
  • Who—Roles: Encompasses data governance program managers, data owners, data stewards, data administrator, and data citizens
  • Where—Organization: A data governance program office, executive committee, or a working council
  • How—Processes: Define data standards, processes, issue management and escalation, as well as curate datasets and drives communication and awareness
  • What—Data Policies: Spanning data quality, security, privacy, data catalogs, master and reference data, data modeling and design, data architecture, and document management
  • When—Monitoring: Success metrics, data governance reports, and maturity assessments

A data strategy, ultimately, consists of people, processes, technology, and teams; it’s not just a data architecture, noted Eckerson. A data strategy should be business driven, understanding of an enterprise’s people and culture, and builds support for change—from the top and bottom.

Many Data Summit 2024 presentations are available for review at