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

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Learn how a data strategy helps you streamline your efforts company-wide for a cohesive and planned approach to gathering insights from data analytics, plus discover how to create your own data strategy.
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.
If you’re ready to go deeper or begin building a career in data, consider enrolling in the IBM Data Science Professional Certificate. In as little as four months, you can complete this beginner-friendly 12-course series, which offers opportunities to build your data science skill set, learn more about importing and working with data, and apply your new skills to projects you can use to build your portfolio.
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.
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.
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.
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
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.
The three pillars of modern data strategy, data