What Is a Data Strategy and Why Do You Need One?

Key takeaways

• Think of a data strategy as a blueprint that guides how you gather, maintain, interpret, and secure data, outlining the steps you need to take and how it all aligns with your company’s goals and objectives.

• Having a data management strategy can improve decision-making and ensure everyone understands what data your organization collects and how that data is handled, helping everyone work with data consistently and reducing the likelihood of incompatible data and other problems.

• When building your data strategy roadmap, consider your company’s goals, its existing systems and practices for handling data, and how you’ll approach the overall data-related workflow.

A data strategy is essential for managing data responsibly and gaining optimal benefits from it. Read on to learn more about how you can create a data strategy that helps you meet your company’s objectives, the ways a strategy produces data insights, and the overall importance of data analytics.

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What is data strategy, and why is it important?

A company’s data strategies encompass its plans for managing, storing, collecting, and completing data analytics to further its business objectives. If you run a business in today’s market, you’re already collecting ample volumes of different kinds of data. Your data strategy gives you a roadmap, defining the kinds of data that are important to you and what will happen to the data after you collect it in terms of utilizing it both efficiently and effectively. For example, your data strategy might outline that all data collected, regardless of its source, gets organized and stored in a central database or location accessed by specified team members charged with working with that data.

Having this plan in place allows you to manage your data, even as complexity and volume grow. In turn, it helps you:

• Get more value from your data, uncovering insights you may not otherwise find

• Provide more personalized experiences to build customer trust

• Enhance your ability to adopt emerging technologies, such as artificial intelligence

In addition, your data strategy can define how you will keep your data safe, how your data will abide by regulations in your industry, and how data fits into the greater goals and objectives of your company.

Data strategy roadmap: Components of a data strategy

Since a data strategy can define so many different aspects of your company’s relationship with data, it can be helpful to break it down into three main components: your company's goals and objectives, the systems and practices for how your team will interact with data, and lastly, the potential challenges around data in terms of company culture and human resources.

Goals and objectives

The first part of a data strategy is deciding how you want to use your data based on your overarching goals. How will it aid you in reaching the organization’s objectives? Having a clear definition of how data will help your company makes it easier to wade through a mountain of data and make sense of what you need.

Some ways to think about the goals and objectives of your data strategy include:

• Perform a SWOT (strength, weakness, opportunity, threat) analysis to help you understand the strengths and weaknesses of both your current data operations and your business overall.

• Identify your objectives and what success looks like, and define metrics to help you measure the progress of your data strategy.

• You can standardize procedures, empowering all team members to understand their role in data and what company expectations will be.

Systems and practices

The second part of your data strategy is to lay out the systems and practices your team will use for collecting, storing, and interacting with your data. This includes considering how long the data will remain relevant and how different departments within your company will use it. For example, marketing and sales departments will use similar data points but draw different insights from them.

Some tools to help you build your data systems include:

• Data catalog tools like Apache Atlas or Snowflake Horizon can help you create a catalog of your data types, similar to a library's card catalog, making it easier to synchronize business operations with data operations.

• Tools like Power BI and Tableau can help you visualize and create reports from data.

• Data governance tools like MineOS or the Databricks Data + AI Platform help you determine who makes data decisions and ensure all data complies with your industry's regulations.

Read more: Understanding Data Governance Strategy

Culture and HR

The last component to consider in your data strategy is the people in your organization who will help you bring your plan to life. For this step, you’ll need to think about the data workflow and how different people must interact with it. You will need to consider how your company culture contributes to or hinders your data strategy. You’ll also need to consider the technical skills your company needs to accomplish its data plan and whether your current workforce can meet those needs.

To think about how your culture and team contribute to your data strategy, consider:

• Programs designed to increase data literacy throughout the company to help your team work more effectively with data

• Training to empower everyone on the team and help them understand the organization’s expectations and data policies

• You can establish clear roles and responsibilities to remove ambiguity from tasks.

What are three pillars of data strategy?

The three pillars of modern data strategy, data experience, data economy, and data ecosystem, can help businesses get the most out of their data strategy. Think of it like a three-point framework. Data experience focuses on ensuring everyone can access relevant data and enhances data literacy across teams. Data economy establishes the principles your company operates under to ensure data reliability. A data ecosystem refers to the platform you choose that provides appropriate access and tools for users’ needs.

What is the purpose of a company's data strategy?

A data strategy can benefit your company in many ways, such as allowing you to make quicker, better-informed decisions. Firstly, it offers a clear set of procedures so that every member of your team understands what data the company will collect, how to store it, and all other relevant processes involving that data. That way, everyone understands the company's objective, and you won’t encounter problems like incomplete or incompatible data.

A data strategy also gives you a big-picture view of how your company manages data, giving you the perspective to see what’s working and what processes your team can improve. In this manner, developing a data strategy can help you create more effective and less expensive processes while helping you avoid issues such as data duplication, ambiguity regarding data priorities, and the inefficient transfer of data between departments within your company.

Ultimately, you make business decisions based on the data you have. A data strategy helps ensure you have uncovered sound insights from the data to make the best decisions possible.

Centralized vs. decentralized data governance strategy

The three main types of data strategies are centralized, decentralized, and balanced between the two.

With a centralized data strategy, you aim to store all of your data in one location so that every person in your company, with permission, can access it. For example, every employee needing to access the year's total sales will find it in the same location.

A decentralized data strategy takes a different approach, allowing each department to consider how they use data and develop its own strategies. This doesn’t mean you want to accept a data free-for-all; instead, each department develops the best practices for using, compiling, and storing data effectively.

A balanced approach blends the components of centralized and decentralized data strategies in a manner that best fits the organization's needs and reaps the benefits of each. With a centralized data strategy, data becomes standardized, and you can save labor hours by having only one person compile and manage it.

Additionally, a primarily centralized approach typically fosters a more secure system for your data, emphasizing regulatory compliance, procedures for preventing data theft, and fraud detection in analytics. In a decentralized data strategy, rather than having one person handle the data, each team that needs to work with it will need to collect and organize it. On the other hand, decentralized data strategies offer flexibility and the chance for data in real-time, which is more difficult to do with a centralized strategy.

Who uses data strategies?

Data is important for every industry because it can provide data insights to even the least tech-reliant company, which makes data strategies essential for businesses of all kinds. Three potential careers for you to consider in this field include data engineers, data scientists, and data analysts.

Data engineers

Median total salary in the US: $135,000 [1]

Job outlook (projected growth from 2025 to 2035): 35 percent [2]

Education requirements: To become a data engineer, you will likely need to earn a bachelor’s degree in computer science or a similar area of study and get additional training in programming languages, such as Java and Python, or a non-degree certification in data engineering.

As a data engineer, you design and develop the pathways through which data will enter your company’s network or infrastructure. You typically collect and store the company’s data in a system you design to meet your and the industry's needs. In this role, you may also work with data scientists to create algorithms that help them interact with data.

Data analyst

Median total salary in the US: $94,000 [3]

Job outlook (projected growth from 2025 to 2035): 12 percent [4]

Education requirements: To become a data analyst, you will likely need to earn a bachelor’s degree in business, computer science, or a related field. Sometimes, your employer will prefer that you earn a master’s degree.

As a data analyst, you collect and interpret data to extract data insights that your company or organization can use to make better-informed business decisions. In this role, you could work in various industries, even sometimes for a smaller business, on a team with fewer analysts. It would be best to convey your findings to senior decision-makers non-technically. Furthermore, presenting your findings as a narrative can make them more impactful.

Data scientist

Median total salary in the US: $158,000 [5]

Job outlook (projected growth from 2025 to 2035): 35 percent [2]

Education requirements: Although you may be able to begin working as a data scientist with a bachelor’s degree in math, computer science, business, or a related field, many employers will prefer that you earn a master’s degree or even a doctoral degree.

As a data scientist, you use the insights gleaned from data to improve the company’s operations. This includes gathering data, categorizing and organizing the data, creating algorithms to interact with the data, creating visualizations to demonstrate your findings, and making recommendations based on your conclusions. In this position, you may work as part of a team with a specialized role, such as writing or testing algorithms.

*All salary information represents the median total pay from Glassdoor as of September 2026. These figures include base salary and additional pay from other compensation forms, such as profit-sharing, commissions, or bonuses.

How to choose the right data strategy

If you intend to implement AI or already work with the technology, you should take this into account as you build your data strategy, since your data will need to meet AI models' needs to deliver the most value. If you are ready to begin drafting your company’s data strategy, the following tips can help provide a starting point.

• Start with the end goal in mind: Determine what success will look like first, then you can plot the path you’ll take to get there. For example, you might want to develop a data strategy to reduce supply chain costs or better understand how marketing and finance interact.

• Experiment with AI: You can use artificial intelligence to collect and analyze data to improve your product's marketing approach.

• Make a security plan: Remember to incorporate data security as a part of your plan to keep your company’s data and customers’ information safe.

• Standardize your terms: Create a data glossary or other resources so everyone on the team uses the same language to discuss data, helping eliminate ambiguity and miscommunication.

Explore free resources to build data skills

Prepare to craft effective data strategies and build in-demand data skills with free resources like Career Chat, our weekly newsletter that explores everything from emerging technologies to career possibilities. Other resources include the following:

• Find your career path: Data Science Career Path Quiz

• Watch a YouTube video: What is Data Science - A Day in the Life of a Data Scientist by IBM

• Bookmark a glossary: Data Science Terminology and Definitions

You can also develop your skills and become more familiar with in-demand technologies to keep growing, both personally and professionally, with a Coursera Plus subscription. You’ll get access to over 10,000 flexible courses.