The Scope and Complexities of Data Strategy
Wayne Eckerson’s recent report, Data Strategy Guidebook: What Every Executive Needs to Know, answers many questions about the what, why, and when of data strategy. But data strategy, as with all strategy efforts, can be a large and complex effort. As I read the report I found myself wondering how to establish context, achieve business alignment, and drive good data management practices as the strategy is developed and put into action. Figure 1 illustrates my big picture view that helps to understand and visualize the scope and complexities of data strategy.
Figure 1. The Big Picture for Data Strategy
Data strategy enables data discovery, maximizing the ability to learn what data can tell us. Data discovery drives business discovery, creating opportunities to learn new things about the business. Business discovery, in turn, drives data discovery by creating a new desire for data exploration. Data strategists must work at the intersection of data discovery and business discovery. Data strategy can’t look at data in isolation. It must be viewed in a business context and in a management context. With that big picture framing, we can begin to develop some of the key questions that data strategists should ask.
The dynamic and volatile nature of business is the primary cause of data dependency. Many external forces—political, economic, sociological, technological, competitive, legal, ethical, and environmental—put pressure on business and create the need to take action and continuously adapt the business. Response to these pressures occurs in four ways: anticipating pressures when possible, proactively adapting when change is apparent, responding quickly when change is imminent, and reacting when taken by surprise. Data analysis has an important role at every stage from anticipation to reaction. With this in mind, data strategists should ask:
Which dynamics are the external driving forces of your business?
How can data help to respond to those forces?
Adapting to change is necessary to sustain and grow business value. Businesses that fail to adapt will struggle and eventually fail. Those who adapt to change well are the survivors, and those who master adaptation will thrive. Data and analytics are essential for continuous adaptation. At the most basic level, they provide insight into business performance. They deliver even greater value when applied beyond insight to drive the process, product, and business model innovation. From a value perspective, data strategists should ask:
What are the major data-driven value opportunities for our business?
How can we use analytics to drive innovation?
Adapting to change doesn’t become a reality until people in the business take action. It is almost cliché to talk about management action, but we need action at all levels—strategic, tactical, and operational—to bring about real change. Alignment across all levels is critical. The strategy must be implemented as tactics, and tactics must be executed operationally, all without distortion or local sub-optimization. Data analysis provides necessary feedback loops to monitor and manage alignment. From a business management perspective, data strategists should ask:
What does management want and need in data and analytics?
How does it influence decision making and action taking?
What metrics are needed to measure strategic-tactical-operational alignment?
Relevant, trusted, and well-managed data is essential for effective and successful business management. High-quality data and modern data management practices must be among the goals of data strategy. Ingesting the right data, refining it to increase value and usability, governing effectively, and protecting sensitive data are all critical factors to maintain a resource of trusted data. Trusted data is the raw material for descriptive, diagnostic, predictive, and prescriptive analytics that answer the what, why, what-if, and how-to questions of business management. From a data management perspective, data strategists should ask:
How will we continuously and quickly adapt data content, services, and practices?
How will we deliver the full range of analytics capabilities?
Business Discovery and Data Discovery
The business discovery and data discovery cycles at the center of the diagram are synergistic. Each discovery process drives the other in a never-ending process of new learning. This offers tremendous value opportunities with data when data strategists ask:
How will we use data to discover new patterns and relationships?
How can we communicate data discoveries with visualization and storytelling?
How do we use data discoveries to drive conversation and collaboration?
How do we encourage data analysts and data scientists to routinely explore data?
How do we encourage business analysts and managers to routinely explore data?
How do we use business discoveries to drive communication, collaboration, and action?
Putting Data Strategy to Work
Once developed, data strategy should not become “shelfware” that returns little value. As the business world and the data world continue to change the strategy must evolve, and it must be applied to shape data related dimensions of day-to-day business. (See figure 2.)
Figure 2. Connecting the Data Strategy
Define your data strategy and then put it to work. Use it to help shape data architecture, to build a collaborative data culture, to identify and develop the data management and analysis competencies that you need, and to guide technology selection and implementation.