A Guide to Data Strategy: Key Questions and Framework for Executives

Wayne Eckerson’s recent report “Data Strategy Guide: What Every Executive Needs to Know” answers many questions about data strategy, its purpose, and timing. Because data strategy can be a large and complex undertaking, the author wonders how to create an environment, achieve business alignment, and drive good data‑management practices during formulation and implementation. Figure 1 shows a global view that helps understand and visualize the scope and complexity of data strategy.

Figure 1. Data Strategy Big Picture

Data strategy enables data discovery, maximizing the ability to understand what data can tell us. Data discovery drives business discovery, creating opportunities to learn new things about the business. In turn, business discovery creates new demand for data exploration. Data strategists must work at the intersection of data discovery and business discovery. Data strategy cannot view data in isolation; it must be seen within both business and management environments. This big‑picture framework allows us to develop key questions data strategists should ask.

Business Pressure

Business dynamics and volatility are the main reasons for data dependence. Numerous external forces—political, economic, social, technological, competitive, legal, ethical, and environmental—pressurize enterprises, creating a need for action and continuous business adjustment. Responses occur in four ways: predict pressure when possible, proactively adapt when change is evident, react quickly when change is imminent, and respond in unexpected situations. Data analytics plays a crucial role at each stage. Considering this, data strategists should ask:

What dynamics are the external drivers for your enterprise? How can data help address these forces?

Business Value

Adapting to change is essential to maintain and grow business value. Companies that cannot adapt will struggle and eventually fail. Those who adapt survive; those who master adaptability thrive. Data and analytics are vital for continuous adaptation. At the most basic level, they provide insight into business performance. When they go beyond insight to drive process, product, and business‑model innovation, they deliver greater value. From a value perspective, data strategists should ask:

What are the main data‑driven value opportunities for our business? How do we use analytics to drive innovation?

Business Management

Adaptation does not become reality until business people take action. Discussing management actions can sound cliché, yet actions are needed at strategic, tactical, and operational levels to achieve real change. Coordination across all levels is critical. Strategy must be implemented as tactics, tactics must be executed operationally, without distortion or sub‑optimal local optimization. Data analytics provides the necessary feedback loops to monitor and manage alignment. From a business‑management viewpoint, data strategists should ask:

What do and need management require regarding data and analytics? How does it affect decision‑making and action? What metrics are needed to measure strategic‑tactical‑operational alignment?

Data Management

Relevant, trustworthy, well‑managed data is essential for effective and successful business management. High‑quality data and modern data‑management practices must be a goal of any data strategy. Extracting the right data, improving it to increase value and usability, effectively managing and protecting sensitive data are key to maintaining trustworthy data assets. Trustworthy data is the raw material for descriptive, diagnostic, predictive, and prescriptive analytics, capable of answering questions about business management, causes, hypotheses, and methods. From a data‑management perspective, data strategists should ask:

How will we continuously and rapidly adjust data content, services, and practices? How will we provide end‑to‑end analytics capabilities?

Business Discovery and Data Discovery

The business‑discovery and data‑discovery cycles at the center of the diagram are collaborative. Each discovery process drives the other in an endless new‑learning loop. When data strategists raise demands, this creates huge value opportunities for data:

How do we use data to discover new patterns and relationships? How do we communicate data discovery through visualization and storytelling? How do we use data discovery to drive dialogue and collaboration? How do we encourage data analysts and data scientists to explore data regularly? How do we encourage business analysts and managers to explore data regularly? How do we leverage business discovery to drive communication, collaboration, and action?

Making Data Strategy Work

Once developed, a data strategy should not become valueless “shelf‑ware.” As the business world and data world continuously evolve, the strategy must evolve and be applied to shape data dimensions relevant to day‑to‑day business. (See Figure 2)

Figure 2. Connecting Data Strategy

Define your data strategy and put it into use. Use it to shape data architecture, build a collaborative data culture, identify and develop needed data‑management and analytics capabilities, and guide technology selection and implementation.