Data Strategy Explained - Framework, Examples, and Tools

If you have a business, you have data — but data by itself won't let you optimize and improve your business. You need a data strategy if you want to turn data into value.

Data strategy refers to the tools, processes, and rules that define how to manage, analyze, and act upon business data. A data strategy helps you to make informed decisions based on your data. It also helps you keep your data safe and compliant.

The importance of data strategy

Virtually every business collects data in multiple forms, and a data strategy enables a business to manage and interpret all of that data. It also puts a business in a strong position to solve challenges such as:

• Slow and inefficient business processes
• Data privacy, data integrity, and data quality issues that undercut your ability to analyze data
• Lack of deep understanding of critical parts of the business (customers, supply chain, competitive landscape, etc.) and the processes that make them tick
• A lack of clarity about current business needs (a problem that descriptive analytics can help solve) and goals (which predictive and prescriptive analytics can help identify)
• Inefficient movement of data between different parts of the business, or duplication of data by multiple business units

In short, a business without a data strategy is poorly positioned to operate efficiently and profitably or to grow successfully.

Data strategy goals

To overcome these challenges and craft an effective data strategy, you need to work toward several goals:

• Innovation: Any successful business creates new value or efficiency through innovation. Innovation should be a central goal as you create and implement data strategy.
• Addressing the needs of users: Your data strategy must support and empower your users — anyone within your organization who helps power the business.
• Addressing risk and regulations: An effective data strategy must address your business's data security risks and compliance requirements, which can vary widely between different types of industries.

And, because your IT department will be responsible for implementing and overseeing the tools and infrastructure that power your data strategy, consider IT resources and capabilities when setting your goals.

Data strategy: 4 components

Although no two data strategies are identical, all successful data strategies include four key components, each of which plays a role in putting your data strategy into practice.

1. Business strategy

Your data strategy should reinforce and advance your overall business strategy, which refers to the processes you use to operate and improve your business.

To that end, establish clear goals and measurable objectives for your data strategy that serve your larger business strategy. For example, your data strategy could include a goal of keeping data storage costs below a certain threshold. To achieve this goal, the strategy might define storage tools or services that meet your cost requirements, as well as best practices that can help users optimize storage costs. And it should establish metrics, such as average cost per gigabyte of storage, to help you track your success in achieving this goal.

Set both long-term and short-term goals. While you might set a short-term goal of performing data quality reviews once every month, for example, a long-term goal might be to achieve continuous data quality, meaning that you identify and address data quality problems continually, rather than relying on periodic checks.

Your business already may have a data strategy; however, as the Stitch data strategy guide explains, many companies' data strategies were written years ago when the toolsets and timelines associated with data management and analysis were different. Periodically review your data strategy to assess whether it aligns with your current business goals.

2. Organizational roles

A data strategy should include attention to organizational roles by documenting who does what with the data, in order to facilitate collaboration and avoid duplication. Not everyone in an organization uses data the same way, and their roles in data collection, management, and analytics will vary.

Three main types of users typically implement and enforce data strategy:

• Data engineers, who oversee the data pipeline and are responsible for building an efficient, reliable data architecture
• Data scientists, who work with data that the pipeline delivers
• Data analysts, who specialize in analyzing and interpreting data
• Business managers, who help to manage data operations and review data reports

When coordinating roles, consider everyone in the organization who uses data in any way, even if working with data is not a primary part of their job responsibilities. For example, an account manager who records customer information has a role to play in data collection, and a sales manager may need data analytics to help plan the next marketing campaign. Your data strategy should document the roles of each team member or group.

In addition, when a business maintains multiple data sets, its data strategy should specify who "owns" which data, meaning who is responsible for storing, safeguarding, and interpreting the different data sets.

3. Data architecture

Your data architecture consists of the tools and processes that allow you to work with and analyze data. These elements may include various kinds of on-premises and cloud-based hardware and software.

A first step in defining your data architecture is determining what datasets exist among business units across the company. Data catalogs are useful tools for this purpose. If you don't have a data catalog, review data sources with your team and the users who work with the data.

To analyze your data, you need to store it in a single repository, such as a data warehouse or data lake. You may also want to integrate or transform it to put it in a format that lends itself better to analysis.

You need a data pipeline to ingest raw data from disparate sources and replicate it to a destination for storage and analysis.

Data identification, ingestion, storage, and analysis are all parts of a data architecture. Documenting and implementing your data architecture is essential for a consistent, predictable data strategy. It also makes it easier to scale your data operations as your needs change.

4. Data management

Data management encourages all team members to think of data as a business asset, rather than a byproduct of business operations. It encourages everyone in your organization to follow policies when working with data.

The foundation for effective data management is data governance, which establishes the processes and responsibilities that ensure the quality and security of the data used across an organization. For example, data governance might specify that a manager must archive data in an offline location if it's no longer in daily use. Or a data governance policy may require data encryption to bolster security.

You should update data governance policies as your business needs change. You might store all of your data on-premises today, but if you move your data to the cloud, you may need to update your data governance rules to accommodate cloud-based data management. For example, data that is stored in the cloud might require stricter encryption rules.

Bring data strategy together with Stitch

You can run a business without a data strategy; however, most businesses thrive only when they adopt a systematic approach to collecting, storing, analyzing, and managing their data. That requires a data strategy that serves the entire organization.

Stitch helps on this front by piping all of your data into one central, cloud-based location, where you can analyze it seamlessly. It allows you to derive insights from data in minutes, and it's free to try — so sign up today.