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

Data is an important resource for assisting companies in making informed decisions, but you need a comprehensive data strategy to manage it effectively. A data strategy is like a blueprint for how your company gathers, maintains, and interprets data and how your company will approach other important aspects of data like governance and security. Additionally, implementing a precise data strategy can raise your company’s revenue. For example, in 2022, MGM Resorts International partnered with a data analytics company, and with the new information, MGM was able to increase its annual revenue by $2.4M [1].

Discover more about how you can create a data strategy that helps you meet your company goals, the ways a strategy produces data insights, and the overall importance of data analytics. 

What are data strategies? 

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. 

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. 

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 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. 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 tools to help you 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 laying out the systems and practices your team will use when collecting, storing, or interacting with your data. This includes considering how long the data is relevant and how different departments within your company will use the data. For example, marketing and sales departments will use similar data points but will use that data to draw different insights. 

Some tools to help you build your data systems include: 

• Data catalog tools help you create a database of the data types you have, similar to the card catalog system at a library, making it easier for you to synchronize business operations with data operations. 

• Certain tools can help you visualize and create reports from data. 

• Data governance tools aid you in determining who makes decisions about data to ensure all data complies with regulations in your industry. 

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. 

Some tools you can use to help you think about how your culture and team contribute to your data strategy include: 

• 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. 

Why is a data strategy important?

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 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 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 their 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 only needing one person to compile and manage the data. Additionally, a primarily centralized approach typically fosters a more secure system for your data, emphasizing regulation compliance, procedures for preventing data theft, and identifying fraud when it comes to analytics. In a decentralized data strategy, rather than having one person handling the data, each team that needs to work with data will need to take the time 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. Three potential careers for you to consider in this field include data engineers, data scientists, and data analysts. 

Data engineers

Average annual salary in the US: $106,894 [2]

Job outlook (projected growth from 2022 to 2032): 35 percent [3]

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 non-degree certification in data engineering. 

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

Data analyst

Average annual salary in the US: $76,995 [4]

Job outlook (projected growth from 2022 to 2032): 23 percent [5]

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 will 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

Average annual salary in the US: $129,767 [6]

Job outlook (projected growth from 2022 to 2032): 35 percent [3]

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. 

As a data scientist, you will 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.

How to choose the right data strategy

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, maybe you would like to formulate a data strategy for lowering 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 that everyone on the team uses the same language to discuss data, helping eliminate any ambiguity or miscommunication. 

Learn more with Coursera.

Data plays a critical role in modern business operations, and you can optimize it with effective data strategies. These roadmaps outline the who, what, where, why, and how of your company’s data used to support its overall business strategy. 

One resource that can help you create an effective data strategy is Data Privacy Fundamentals, offered on Coursera by Northeastern University. This eight-hour course can help you learn how and why to keep your company’s data secure.  You might also opt to gain more foundational skills and knowledge about business strategies as a whole with courses like the Business Strategy Specialization from the University of Virginia, which provides a comprehensive look at evaluating evolving industries, creating business strategies, and aligning various efforts to the business's overarching strategy.