What is a data strategy?

Data strategies typically connect and coordinate many different functional areas that surround an organization’s data. These areas include disciplines such as data collection, data management, data governance, data analytics, data quality and data security.

In many organizations, a chief data officer (CDO) is responsible for creating and guiding a data strategy. In other organizations, senior executives, data scientists and data engineers might collaborate on building a data strategy.

A successful data strategy can help a business identify market opportunities, improve products and services, increase customer satisfaction and gain a competitive advantage.

There are different methodologies for creating and implementing an enterprise data strategy. Most include some version of these six basic components:

• Understanding business objectives
• Assessing obstacles and challenges
• Creating a data strategy roadmap
• Establishing controls
• Focusing on small wins
• Scaling the strategy

The first step for building a data strategy is gaining a clear understanding of business objectives across the entire organization. C-suite leaders and stakeholders from business units typically work together to identify goals and explore how the use of data can help them achieve these goals.

Based on this discussion of goals, the organization selects specific use cases on which the data strategy focuses. For example, an organization might want to focus on using data processes to lower supply chain costs, automate risk and compliance challenges or better understand evolving customer needs.

IT professionals can then weigh in on the tools and technologies the organization might need to help achieve these outcomes.

Organizations then identify the barriers that might block the successful execution of the data strategy. These barriers might include technical obstacles such as data silos that prevent easy data access, a lack of data governance policies or an outdated data architecture that doesn’t support modern data operations.

There might also be human challenges. Business users might need to be educated on the pillars of a data-driven culture, and IT team members might need training to acquire specific technical skills.

A data strategy roadmap defines how the data strategy is implemented. This roadmap includes details on the business goals, current and proposed technologies, processes and people involved. It also establishes a timeline for completion and the metrics that measure the strategy’s success.

To keep a data strategy on track, organizations often implement controls to monitor data activity and maintain proper performance in data processes. For example, data governance policies can help ensure data quality, privacy, security and compliance with regulatory mandates.

There are also human controls, such as data advocates who meet regularly to review standards, use cases and progress across multiple lines of business. Another important control is standardized terminology so that everyone is speaking the same language when discussing the company’s data strategy.

When launching a new data strategy, organizations often aim for small wins in a short amount of time. Prioritizing data processes that show value early can help encourage adoption of the strategy across the business.

Simplifying data consumption and empowering data consumers is another tactic for gaining buy-in for the data strategy. For example, organizations sometimes create a central catalog where new data insights can be easily accessed and shared through a self-service model.

To get buy-in, organizations typically provide teams with frequent updates and reports on how the strategy is meeting milestones such as driving revenue.

Organizations might also provide continuous training and support to encourage stakeholders throughout the business to adopt the data strategy.

For example, an organization might invest in data literacy efforts to help stakeholders access and analyze datasets to generate their own results. Or the organization might prioritize the hiring and upskilling of technical talent to support and expand the capabilities of its data infrastructure.

The motivation behind these actions is to build strong partnerships across the business that expand the reach and use of the data strategy. 

Data strategies typically weave together people, processes and tools from various data disciplines, such as:

• Data management
• Data governance
• Data integration
• Data architecture
• Data analytics
• Data security
• Data quality

Data management is the discipline of managing data through every stage of its lifecycle, from data collection, processing, storage, sharing and usage to archival and deletion.

Data governance focuses on the quality, security and availability of an organization’s data. The goal of data governance is to maintain safe, high-quality data that is easily accessible for data discovery and business intelligence initiatives.

Data integration is the process of combining and harmonizing data from multiple sources into a unified, coherent format that can be used for various analytical, operational and decision-making purposes.

A data architecture describes how data is managed, from collection through to transformation, distribution and consumption. It sets the blueprint for data and the way that it flows through data storage systems.

Data analytics uses data science to extract actionable insights from an organization’s data. These insights can then be used to create data visualizations that help business users understand patterns, trends and anomalies. 

Data security is the practice of protecting digital information from unauthorized access, corruption or theft throughout its entire lifecycle. It includes measures to protect data such as encryption, firewalls, authentication, antivirus and antimalware tools. 

Data quality measures how well a dataset meets criteria for accuracy, completeness, consistency and fitness for purpose. If data issues such as duplication, missing values or outliers aren’t properly addressed, there is an increased risk for negative business outcomes.

For businesses that collect, prepare, store, analyze and share massive volumes of information from multiple data sources, a data strategy is an essential resource. It provides a step-by-step blueprint of the policies and processes for generating business value from all of these data assets.

A data strategy helps an organization attain its business objectives by empowering it to:

• Make informed decisions
• Launch artificial intelligence (AI) initiatives
• Increase productivity
• Reduce costs
• Innovate
• Outperform competitors

There are several challenges that a business might face when implementing a data strategy. These challenges can include:

• Insufficient data maturity
• Low-quality data
• Data silos
• Lack of data governance
• Company culture

A business that can't use data as a strategic asset must start from the beginning. It can be a costly and time-consuming effort because it requires creating and implementing new policies, processes, technologies and training.

High-quality data is crucial for an effective data strategy. Data that is inconsistent, incomplete or inaccurate produces unreliable results and negative business outcomes.

A data strategy requires accessibilty to multiple sources of data to generate positive results. Data that is spread across disconnected silos can be difficult, costly and time-consuming to process. 

A data strategy typically requires clear policies regarding data ownership, access, security and regulatory compliance. If these policies are not in place, the data strategy might not be able to move forward.

A data strategy requires a data-driven company culture to succeed. If executives, business users and IT professionals are not aligned on data processes and goals, the data strategy can stall.