What Does Someone Mean When They Say You Need a Data Strategy? (DS1)

Part 1 of a multi-part series on building effective data strategies

"We need a data strategy."

If you've spent any time in corporate meetings over the past few years, you've probably heard this phrase. It gets thrown around in boardrooms, strategy sessions, and planning meetings with the confidence of someone ordering coffee. But what does it actually mean?

The problem is that most people who say "we need a data strategy" can't clearly articulate what that strategy should accomplish or how it should work. They know data is important. They've heard competitors are "data-driven." They see the potential, but the path forward remains frustratingly unclear.

This confusion isn't surprising. The term "data strategy" has become corporate buzzword bingo, used to describe everything from buying new analytics software to hiring data scientists to "becoming more data-driven." But a real data strategy is much more specific—and much more powerful—than these tactical approaches suggest.

To understand what people really mean when they say you need a data strategy, we need to start with some fundamentals. Let's break down the core concepts that will shape everything else in this series.

What Is Strategy, Really? A Brief History

Before we can understand data strategy, we need to understand strategy itself. This might seem obvious, but strategy is one of the most misunderstood concepts in business—partly because we've forgotten its origins and evolution.

The Military Roots of Strategy

Strategy originated in military thinking, where life-and-death decisions required clear thinking about how to win against adversaries. The ancient Chinese military strategist Sun Tzu captured this essence in The Art of War:

"Strategy without tactics is the slowest route to victory. Tactics without strategy is the noise before defeat."

Sun Tzu understood that strategy wasn't about individual battles—it was about the overarching plan to achieve victory. Similarly, the Prussian military theorist Carl von Clausewitz defined strategy in On War as "the use of engagements for the object of war." In other words, strategy is about connecting individual actions to ultimate objectives.

From Battlefields to Playing Fields

Sports adopted strategic thinking early, recognizing that winning required more than just individual skill. The best coaches understand that strategy means making choices about how to deploy limited resources (players, time, energy) to maximize the chance of victory. A basketball team might choose to play fast-paced offense and aggressive defense, accepting that this approach requires exceptional fitness but creates competitive advantage through tempo.

Strategy Enters Business

Business strategy emerged as companies grew larger and competition intensified. Early business strategists borrowed heavily from military thinking, recognizing that companies, like armies, needed clear plans for deploying scarce resources to achieve objectives in competitive environments.

The modern understanding of business strategy was crystallized by strategy expert Richard Rumelt in Good Strategy Bad Strategy:

"Good strategy is a coherent response to an important challenge. It includes a diagnosis of the challenge, a guiding policy for dealing with the challenge, and coherent actions designed to carry out the guiding policy."

What Strategy Actually Is

Across all these domains—military, sports, business—strategy serves the same fundamental purpose: it's a set of choices about how you will win given limited resources and real competition.

Strategy is not a plan. It's not a goal. It's not a vision statement plastered on conference room walls. Strategy is a set of choices about how you will win.

Good strategy answers three core questions:

• Where will we play? (What markets, customers, or problems will we focus on?)
• How will we win? (What will be our competitive advantage?)
• What capabilities must we have? (What do we need to be excellent at to execute this approach?)

The critical element that makes something a strategy rather than a wish list is trade-offs. Strategy requires saying "we will do this, not that" and "we will excel at X, even if it means being mediocre at Y." Without these conscious trade-offs, you don't have strategy—you have a collection of good intentions.

This foundation is crucial because it shapes how we think about every other type of strategy, including data strategy.

What Is a Business Strategy?

Business strategy applies these strategic principles to the fundamental question of how your organization will create and capture value in the marketplace. As Michael Porter wrote in Competitive Strategy:

"The essence of strategy is choosing what not to do. Without trade-offs, there would be no need for choice and thus no need for strategy."

The Four Pillars of Business Strategy

Your business strategy defines:

• What customers you will serve (and which ones you won't)
• What problems you will solve for those customers
• How you will solve those problems better than alternatives
• What makes your approach sustainable and defensible over time

Strategic Frameworks for Business Strategy

Over decades, strategists have developed frameworks to help organizations think through these choices systematically:

Porter's Five Forces: Analyzes competitive dynamics by examining the bargaining power of suppliers and buyers, the threat of new entrants and substitutes, and the intensity of competitive rivalry. This framework helps identify where you can create sustainable competitive advantage.

Blue Ocean Strategy: Focuses on creating uncontested market space rather than competing in existing markets. Companies using this approach look for opportunities to make competition irrelevant by creating new value propositions.

Resource-Based View: Emphasizes building competitive advantage through unique resources and capabilities that are valuable, rare, inimitable, and non-substitutable (VRIN). This framework focuses internally on what you can do better than anyone else.

Ansoff Growth Matrix: Helps organizations think about growth strategies by examining four quadrants: market penetration (existing products, existing markets), market development (existing products, new markets), product development (new products, existing markets), and diversification (new products, new markets).

BCG Growth-Share Matrix: Categorizes business units based on market growth rate and relative market share, helping organizations allocate resources across different business areas.

Real-World Examples

Let's see how these concepts work in practice:

Southwest Airlines built their strategy around serving price-conscious customers with low-cost, point-to-point air travel. They made clear trade-offs: no first-class seating, no meals, no assigned seats, and limited routes. These aren't failures—they're strategic choices that enable their cost advantage.

Amazon's early business strategy focused on being "Earth's most customer-centric company" by prioritizing customer experience and long-term thinking over short-term profits. This led to strategic choices about reinvesting profits, building infrastructure, and tolerating losses in pursuit of market position.

Netflix evolved their business strategy from DVD-by-mail to streaming to content creation, always centered on giving customers a superior entertainment experience with maximum convenience. Each evolution involved clear choices about what to prioritize and what to abandon.

Notice that each of these strategies involves clear choices about what the company will and won't do. They're not trying to be everything to everyone—they're making focused bets about how to win in their chosen markets.

As A.G. Lafley and Roger Martin emphasize in Playing to Win:

"Strategy is about making specific choices to win. Strategy is not about best practices. It is not about being better. It is about being different and making choices that competitors cannot or will not make."

What Is a Data Strategy?

Now we can define data strategy properly, building on our understanding of strategy and business strategy. As Thomas Davenport writes in Competing on Analytics:

"Most companies that are building their competitive strategies around analytics have one or more senior executives who are true believers in fact-based decision making."

A data strategy is a set of choices about how you will use data and analytics capabilities to execute your business strategy more effectively.

Data strategy is not separate from business strategy—it's a critical component of it. Your data strategy should directly support your business strategy by defining how information and analytics will help you win in your chosen markets.

This means your data strategy must be grounded in your business strategy. If your business strategy is to provide superior customer service, your data strategy might focus on creating a unified customer view, predictive analytics for proactive support, and real-time personalization. If your business strategy is operational excellence and cost leadership, your data strategy might emphasize process optimization, predictive maintenance, and supply chain analytics.

Core Components of a Data Strategy

A comprehensive data strategy addresses five critical components:

1. Data Foundation

• What data will you collect, from where, and how?
• How will you ensure data quality, governance, and security?
• What infrastructure will store, process, and move your data?

2. Analytics Capabilities

• What types of analytics will you build (descriptive, diagnostic, predictive, prescriptive)?
• What tools and technologies will enable these capabilities?
• How will you develop and deploy analytical models?

3. Data Products and Applications

• How will insights reach decision-makers when they need them?
• What customer-facing features will be powered by data?
• How will data enable automation and optimization?

4. Organization and Skills

• What roles and skills do you need across the organization?
• How will data teams collaborate with business units?
• What governance structures will ensure effective data use?

5. Value Creation

• How will you measure the business impact of data investments?
• What use cases will deliver value in the short, medium, and long term?
• How will you prioritize competing data initiatives?

Key Questions Every Data Strategy Must Answer

A well-developed data strategy provides clear answers to these strategic questions:

Strategic Alignment Questions:

• How does our data strategy support our business strategy?
• What competitive advantages will our data capabilities create?
• Which business processes will be transformed by data?

Value Creation Questions:

• What specific business outcomes will our data investments drive?
• How will we measure success and ROI from data initiatives?
• What use cases will deliver value across different time horizons?

Capability Questions:

• What data do we need to collect, and where will we get it?
• What analytics capabilities do we need to build or buy?
• What technology infrastructure will support our data strategy?
• What skills and roles do we need in our organization?

Operating Model Questions:

• How will data teams collaborate with business units?
• What governance structures will ensure data quality and compliance?
• How will we prioritize competing data initiatives?
• What change management is needed to become truly data-driven?

Risk and Sustainability Questions:

• How will we ensure data privacy and security?
• What regulatory requirements must we address?
• How will we manage data quality and reliability risks?
• What ethical considerations guide our data use?

Just like business strategy, data strategy requires trade-offs. You can't do everything at once, and trying to be great at all aspects of data will likely result in being mediocre at everything. Good data strategy involves conscious choices about where to focus and where to accept limitations.

What Is the Goal of a Data Strategy?

The ultimate goal of any data strategy is to create sustainable competitive advantage through better decision-making and superior business outcomes. As Bernard Marr explains in Data Strategy:

"A data strategy is not about the technology. It's about identifying the questions that matter to your business and then leveraging data to answer them."

This breaks down into four specific objectives:

1. Enable Better Decisions

Data strategy should improve the quality and speed of decisions across your organization. This means identifying the most critical decisions your business makes, understanding how those decisions are currently made, and designing data capabilities that make them faster and more accurate.

Better decisions might involve:

• Providing sales teams with customer insights at the point of engagement
• Giving product managers real-time usage data to guide development priorities
• Enabling executives to monitor business performance with predictive indicators rather than lagging metrics

2. Create Competitive Advantage

Your data strategy should leverage your unique position, data assets, or capabilities to create advantages that competitors can't easily replicate. This might involve:

• Proprietary data sources that give you unique insights into customer behavior
• Superior analytics capabilities that enable better predictions or optimizations
• Data-driven products and services that create new value for customers

The key is that these advantages must be sustainable—difficult for competitors to copy or neutralize.

3. Drive Operational Excellence

Data should improve how your business operates—reducing costs, increasing efficiency, minimizing risks, and optimizing processes. This includes:

• Supply chain optimization that reduces inventory costs while improving service levels
• Predictive maintenance that minimizes equipment downtime
• Automated quality control that catches defects before they reach customers
• Fraud detection that protects both the business and customers

4. Enable Innovation and Growth

Data strategy should fuel new opportunities for growth, whether through new products, services, markets, or business models. This might involve:

• Using data to identify unmet customer needs or market opportunities
• Creating personalized offerings that command premium pricing
• Developing entirely new data-driven revenue streams
• Enabling new business models through data monetization

The Critical Connection to Business Strategy

The key insight is that these goals must directly connect to your business strategy. If your data initiatives don't clearly support your business objectives, you're building capabilities that don't matter.

This is why so many data initiatives fail to deliver value. Organizations collect data because they can, build dashboards because they seem useful, and implement analytics tools because they're available. But without a clear connection to business strategy, these activities become expensive distractions rather than competitive advantages.

Why This Matters: The Cost of Not Having a Data Strategy

When someone says "we need a data strategy," they're usually expressing a real need—but often without understanding what they're actually asking for.

They might be recognizing that their organization makes too many decisions based on intuition rather than evidence. They might see competitors gaining advantages through better use of data. They might have invested in data tools and teams without seeing the expected business impact.

The cost of not having a clear data strategy is significant:

• Wasted investments in tools and technologies that don't align with business needs
• Missed opportunities to create competitive advantage through data
• Poor decision-making based on incomplete or irrelevant information
• Organizational frustration as data teams struggle to demonstrate value
• Competitive disadvantage as rivals develop superior data capabilities

The solution isn't to jump immediately into tactical questions about what tools to buy or how to organize data teams. The solution is to start with strategy: understanding how data can support your specific business objectives and making conscious choices about where to focus.

The Foundation for Everything Else

This strategic foundation determines everything else. It shapes what data you collect, what analytics capabilities you build, how you organize your teams, and how you measure success. Without this foundation, you'll end up with a collection of data projects rather than a coherent strategy.

As Rumelt emphasizes in Good Strategy Bad Strategy:

"A good strategy has coherence, coordinating actions, policies, and resources so as to accomplish an important end."

Your data strategy should have this same coherence—all elements working together to support your business objectives rather than pursuing independent goals.

Everything builds on the foundation we've established here. Data strategy is about making strategic choices to support your business objectives. It requires the same disciplined thinking as business strategy: clear goals, conscious trade-offs, and relentless focus on what will drive the most value.

The Bottom Line

When someone says "we need a data strategy," what they really mean is: "We need to make conscious, strategic choices about how data will help us win in our market."

This is both simpler and more complex than most people realize. Simpler because it starts with your existing business strategy and builds from there. More complex because it requires the same rigorous strategic thinking that good business strategy demands.

The organizations that succeed with data aren't those with the most sophisticated tools or the largest data science teams. They're the ones that think strategically about how data supports their business objectives and make focused choices about where to invest.

That's what a real data strategy looks like. And that's what we'll help you build in this series.

Ready to develop your data strategy? Start by clearly articulating your business strategy and identifying the 3-5 most critical decisions your organization makes. Then ask yourself: how could data make those decisions faster, better, or more profitable? Everything else builds from there.