6 key components of a successful data strategy

Why do you need a data strategy?

A data strategy defines long-term objectives for how an organization uses data, along with the policies and practices that support them. To be successful, a data strategy must cover all data use cases – not just technical processes for data management and analytics, but also the human element.

No modern business can leave the management, security and use of such an important corporate asset to individual data architects or developers. A comprehensive data strategy, with broad involvement and support, ensures data is managed well and used effectively.

Data priorities differ across organizations, shaped by management strategies and business goals, so there's no generic template to follow. But there are six critical components every data strategy must include.

1. Data

This is the most fundamental component, of course. But all the advice that follows will be of no help if your data isn't safely stored and secured, well-maintained and ready for use. The strategic value of your data must be built on a solid base of enterprise data management. That includes integrating and processing your data, validating its quality, governing its use and auditing the processes that affect it.

Once these basics are in place, I always recommend an enterprise data catalog as a critical component of a data strategy. You can't strategize around data if you don't know what data you have. Data catalog tools are particularly useful for making data available to business users by providing detailed, descriptive metadata. Sometimes IT managers want to map their systems -- to know what data they have and where it resides. The IT team can create its own simplified data catalog for such needs.

The key questions are always the same. What data do I have? Where is it? Who can use it?

2. Tools

Data catalog tools are provisioned by IT and data management teams who know how to use the various features in data catalog software, set them up and deploy them. We can make a useful distinction between tools provided by IT and tools adopted by end users. Both play an important role in a data strategy, complementing rather than contradicting each other.

Data management tools are almost always the domain of IT. There are some lightweight data quality and data integration tools designed for business users, but data management remains largely a behind-the-scenes function.

IT often also deploys the BI tools used to create data visualizations, dashboards and reports. But data and business analysts might have their own preferences and choose different tools. That can work well so long as we put controls in place to govern data access and usage. Likewise, data scientists might feel most comfortable using tools they already mastered or that support certain analytics methodologies.

In the past, most IT teams tried to prevent the use of unsanctioned, non-standard tools. Now, just as we've adapted to bring-your-own-device, analytics specialists commonly bring their own favored applications. A good data strategy embraces that diversity but with sensible limits. In this case, we can ask another question: What tools are appropriate to use? Enabling a data analyst to use a self-service BI application to build some dashboards is reasonable; allowing someone to build their own data warehouse beyond their skills and authority is not.

3. Analytics techniques

Just as we use various analytics tools depending on our needs, we also employ a variety of analytics techniques. Data visualization is a common example. We might also find uses for predictive analytics, text analytics, sentiment analysis and cluster analysis, to name a few advanced analytics techniques. They can be powerful and useful, but also need careful oversight. Without it, we might run afoul of data governance and privacy laws.

Predictive analytics, for example, might show business value in optimizing equipment maintenance cycles. That's an uncontroversial use. But predictive techniques could also be used to help automate hiring or manage marketing promotions. In those cases, employees and consumers might have concerns about the reliability, fairness or openness of the process.

A data strategy must recognize that governing only data and tools might not suffice. We need to understand -- and train people to understand -- that not all analytics techniques are neutral. Some use cases, especially those involving personally identifiable information, won't be justified by their business value alone.

4. Collaboration

In modern businesses, data use is typically more collaborative than in the past. Increased data literacy and easier-to-use tools mean more people can participate in analytics, as well as technical fields like data preparation and data quality.

Even closely controlled processes, such as data governance and primary data definition development, can be crowdsourced. For example, doing so can ensure that product names, error codes and managed processes reflect reality on the shop floor in a manufacturing company. Collaboration on primary data can also avoid that most frustrating customer service response: "There's no code for that."

Collaborative tools are also being used more, including file sharing, enterprise chat, messaging and video conferencing. Human beings are compulsive collaborators. We constantly share, discuss and debate with others. If collaboration isn't planned for, it will happen anyway -- unplanned.

Consider the role of data and analytics in your organization's business decisions and identify processes that involve engagement within and beyond teams. Use that insight to support the ability to share and comment on dashboards, reports and data visualizations.

For example, some BI and analytics tools enable multiple users to annotate visualizations. Increasingly, they also integrate with chat and messaging apps. Even simple file sharing can be effective, especially when supported by enterprise-class scalability and security features.

5. Documentation and auditing

In describing these data strategy components, I've emphasized the need to balance IT control with end users' freedom to do self-service when appropriate.

To find this balance, our strategic goals must be well documented. Successful data strategies are built on the ability to answer four questions about any element of the plan and any resource -- data, tools, etc. -- it incorporates.

• What is appropriate?
• What is approved?
• What is the purpose?
• What is the governance policy?

With good documentation of both the data strategy and the underlying data architecture, we can answer these questions before any new project or initiative. We should also be able to look back at any project and answer them retrospectively. By doing so, we put ourselves in a good position to audit how the data strategy is working. It can also help us assess compliance with data governance policies and other internal data standards.

6. People

The two most important elements of your data strategy are the bookends of this list: data and people. Organizations increasingly look for data literacy and analytics skills in new business hires. Almost every business school now teaches basic data analytics.

Data scientists remain in high demand, though the role has evolved significantly in recent years. AI and machine learning have reshaped what organizations require. The priority now is professionals who can not only analyze data, but also build and govern the systems that act on it.

You should also think carefully about IT and data management in your staffing and hiring. With so much technology running in the cloud and systems more robust than ever, it's tempting to think IT merely has to keep the lights on. It's not true. High availability, disaster recovery, meeting service-level agreements, supporting new business requirements and regulatory demands all fall into IT's domain.

Data architects, data integration developers, data engineers, database administrators and other data management professionals also play key roles in meeting business needs. An IT staff that is savvy about the business is a great strategic advantage. That caliber of IT staff needs recognition and leadership support as much as any other role.

How to implement an effective data strategy

These six key components aren't a complete guide to developing a data strategy. You also must consider broader concerns, such as budgets, competition, innovation, marketing plans, staffing policies and legal frameworks.

But you can apply this thinking broadly. For example, your staffing plan could include guidelines for making better use of data and analytics based on strategic priorities. Product innovation is increasingly driven by data on customer feedback, user behavior and market trends.

Implementing a data strategy requires understanding your entire organization's strategic goals. From there, break down the role of data, how it will be managed and used and apply it consistently across production, finance, marketing and HR. The result will be a data strategy that is workable and flexible for ever-changing business pressures and needs.

Editor's note: This article was republished in April 2026 to improve the reader experience. 

Donald Farmer is a data strategist with 30+ years of experience, including as a product team leader at Microsoft and Qlik. He advises global clients on data, analytics, AI and innovation strategy, with expertise spanning from tech giants to startups. He lives in an experimental woodland home near Seattle.