What is data strategy?

Definition of data strategy

A data strategy encompasses the people, processes, policies, and technology required to support the effective and efficient acquisition, analysis, management, storage, and use of an organization’s data throughout its lifecycle. Developing, implementing, and maintaining a data strategy allows organizations to make optimal use of all their data resources to achieve specific objectives and fuel analytics-driven strategies.

With a data strategy, all users, from individual contributors and leadership teams to applications and analytic engines, benefit from high-quality, easily accessible data. The strategy delivers this by ensuring that data is treated with the utmost care to protect it from corruption and misuse, preserving its confidentiality, integrity, and availability (i.e., the CIA triad).

Key elements of a data strategy include identification, storage, provisioning, processing, and governance.

Identification
A data strategy begins with identifying and understanding all data, irrespective of its domain value, origin, location, or structure, to ensure that it can be manipulated and processed. In some cases, this requires creating and applying naming and value conventions to establish the consistency necessary to share and use data. It also requires appending metadata to facilitate and expedite referencing and accessing data.

Governance
Data governance is a crucial element of a data strategy. Policies for data usage are established, managed, and communicated through a data governance framework. The strategy includes the implementation of the framework and support for driving the adoption of the related policies. Data strategy is not effective without the structure and data quality guardrails provided by governance.

Processing
Part of the processing piece of a data strategy addresses how data is moved and combined from disparate systems. Before it can be used, raw data needs to be prepared, transformed, and checked for quality. Data strategy directs this work that converts raw data into useful information and gives users a unified and consistent view of the data.

Provisioning
A data strategy includes guidelines for how data is packaged and prepared for sharing between people and applications. Guidelines include rules for data packaging, access, and sharing. This allows data to be more easily used across multiple applications, systems, and teams.

For a company’s data to become an asset, a data strategy should address data provisioning as a standard business procedure and present guidelines dictating how the data must be packaged and prepared for sharing.

Storage
Storage is also a vital part of a data strategy.

While storage is a fundamental part of organizations’ technology stack, it often centers on the data creation phase and fails to give adequate consideration to downstream usage and sharing requirements, especially with regard to bulk data sharing.

A data strategy needs to consider data holistically and take a centralized approach to sharing stored data to eliminate fragmented copies of source data.

Why a data strategy is important

A data strategy helps organizations overcome challenges that are driven by data limitations, such as:

• Data privacy risks
• Data silos
• Excessive data replication
• Inability to make timely, data-driven decisions
• Inefficient business processes
• Lack of predictive analytics to inform decision-making
• Limitations on profitability
• Limited access to data
• Manual data integration for disparate sources
• No single source of truth for data
• Operational inefficiencies
• Poor data quality
• Reliance on historical information for decision-making
• Slower adoption of technology
• Time wasted preparing raw data
• Users being overly reliant on IT
• Visibility limitations

Advantages of a data strategy

The advantages of a comprehensive data strategy revolve around its core principle of driving value from data. A data strategy can provide long-term solutions to a range of data challenges by providing specific guidance on addressing or avoiding common pitfalls, such as poor data quality, silos, and vulnerabilities.

The advantages of a data strategy include the following.

Enable a data culture.
A data strategy reflects an organization’s prioritization of data as an asset and helps develop its value. When teams see this and the derivative results of quality data, the organization sees the data culture grow.

Enhance customer experience and loyalty.
Analytics can enhance customer experiences and drive loyalty only if the data is accurate and provides a holistic view. A strong data strategy ensures that organizations have access to a rich pool of data related to customers to enable targeted, relevant communications that engage and deepen relationships.

Enforce data management directives.
A data management strategy includes setting and enforcing rules for how data is handled. This is especially helpful for maintaining consistent formatting and keeping data well organized, which makes it more accessible and useful for analytics and other applications.

Ensure data consistency.
The data management directives that are part of a data strategy ensure consistent data formatting across an organization, facilitating access and improving overall data quality.

Get the most from artificial intelligence (AI) and machine learning (ML).
The efficacy of AI and ML applications is dependent on data. A data strategy helps organizations collect and process the data these applications need to ingest to train models for image recognition, predictive analytics, and natural language processing.

Grow analytic maturity.
Analytic maturity progress is facilitated with a data strategy. It helps organizations transition from simple reviews to understand what happened and why to sophisticated predictive modeling that can effectively direct strategy based on what is expected to happen.

Help monetize data.
With a data strategy, organizations gain visibility into how it can be monetized by providing actionable insights and enabling increased productivity.

Improve data architecture.
A data strategy includes directions on the technical architecture to support data collection, storage, transformation, distribution, and consumption. This includes directing how various systems and tools interact, including databases, applications, file systems, and data models.

Meet compliance requirements.
An effective data strategy includes implementing data security measures to protect information from unauthorized access. This addresses the requirements of various government and industry regulations for data security, privacy, integrity, and accessibility.

Prevent data silos. With a data strategy, data management best practices, such as centralized storage, are implemented, eliminating data silos that result from different groups storing and managing data independently.

Provide users with guidance.
A data strategy provides users with guidance on how to extract and maintain the value of data by ensuring that they know how to use, manage, and share data safely and effectively.

Streamline data workflows.
A data strategy creates processes for how data is collected, stored, accessed, and shared to make it more visible and accessible within the parameters of approved usage protocols. This also reduces data duplication that occurs without data management.

Data strategy components

While storage is at the heart of anything related to data, an effective data strategy needs to go beyond this and consider adjacent areas related to how data is collected, managed, shared, and used. Following is a review of these important data strategy components.

Alignment with organizational objectives
For a data strategy to be effective and permeate an organization, it must align with its overall objective. Done correctly, a data strategy must prioritize needs in a way that makes sense to users and managers and helps them achieve their goals. This means taking time to understand the micro prioritize across an organization and how the overarching data strategy can support them.

Data analytics
The output of a data strategy is instantiated with data analytics, which derive insights from data using analytics tools, such as AI and ML models. Data analytics uncover patterns and trends in data that can be used to support decision-making.

Data catalog tools
A data catalog tool facilitates data collection, organization, and governance by identifying and categorizing data assets. This supports a data strategy by providing detailed metadata to make data more searchable and accessible.

Data governance
Data governance plays a key role in a data strategy by establishing the processes for creating roles and rules that ensure data quality and security. The most effective data governance rules are practical, feasible, and proportionate to the actual requirements.

Data management
Data management is the practice of storing, organizing, maintaining, and using the data an organization collects securely, efficiently, and cost-effectively. It is a foundational part of any data strategy.

Data management tools
Tools used to support data management programs include those used for data integration, visualization, reporting, and dashboards. It also includes the systems required to store and process data, such as a data warehouse and extract, transform, and load (ETL) tools. A data strategy directs which tools should be used to meet the needs of IT and business users.

Data maturity assessments
The development and implementation of a data strategy must be tied to the organization’s current data maturity level, include baseline metrics, and set expectations and objectives for moving through the maturity model (i.e., starting with descriptive, moving to diagnostic, advancing to predictive, and topping out at prescriptive).

Inventory of data-related architecture, infrastructure, and workflows
A data strategy should thoroughly assess an organization’s inventory of data-related architectures, workflows, tools, technologies, systems, and supporting processes. This comprehensive view of the organization’s data architecture, infrastructure, and workflows helps direct the allocation of resources to replace, update, optimize, or add components to support the data strategy goals. Considerations when reviewing these elements include:

1. Accessibility—do users and systems have access to the data they need when needed?
2. Performance—is data able to be processed and run through analytics tools to give results in a timely fashion?
3. Relevance—who and how many people use the systems, and are their needs being met?
4. Scope—do the systems address the data management needs at each stage of the data lifecycle (e.g., data identification, ingestion, storage, analysis, and usage to support decision-making).

Protocols for data security, privacy, and ethics
At a minimum, a data strategy plan should include protocols for meeting data security and privacy compliance requirements for protecting data from unauthorized access. Beyond this, organizations should include an ethics component that ensures data all data usage is protected to a degree that aligns with its ethical standards.

Data strategy approaches

Organizations use one of two data strategy approaches—centralized or decentralized. Some organizations opt for a hybrid data strategy approach.

Centralized
A centralized data strategy follows a highly structured model that takes a top-down approach with a designated executive overseeing data management and data governance. This approach establishes a single source of truth. Data is collected from disparate sources, then cleaned and stored centrally.

Decentralized
A decentralized data strategy allows data repositories to reside in functional areas. The source is drawn from a single source of truth, but groups are allowed to pull data and perform data transformations for their specific needs. In a decentralized model, there is a more collaborative approach to data management, data strategy, and business intelligence.

Hybrid
A hybrid data strategy approach combines the other two approaches. Data management is handled in a centralized fashion, but groups have the flexibility of a decentralized approach for data analytics work.

Data strategy roles and responsibilities

The data strategy team includes representatives from senior management, business analytics, IT, and data security teams. It solicits input from key stakeholders across the enterprise.

The following is a summary of the key roles and responsibilities within a data strategy team. These people work together to develop, implement, and enforce data strategy plans.

Data engineers
Data engineers are responsible for designing, building, and maintaining the data architecture. In addition, data engineers support the execution of the data strategy by managing key data pipeline functions, including data collection, processing, storage, and analytics. Data engineers are also responsible for developing and managing data security and data governance systems.

Data scientists
Once data engineers have collected and processed data, data scientists ingest it into AI and ML models developed for analysis. They use the resulting analytics to find actionable insights.

Data analysts work with data scientists to ensure the data models align with the organization’s requirements. They analyze and interpret the information data scientists provide to create reports to support decision-making.

Business managers
Business managers use and share the data in the reports in day-to-day operations and to inform strategic planning. In addition, they monitor the work done by the other members of the team to ensure data strategy aligns with the organization’s overall objectives.

Steps to create a data strategy

Creating a comprehensive data strategy can be complex. The following is an outline of the basic steps. The order of these steps will vary based on the organization, available resources, and team members.

Create a data strategy plan and use it to gain buy-in from leadership and key stakeholders.

1. Demonstrate how the data strategy will provide value with examples and an economic report.
2. Obtain approval for the plan.
3. Secure resources needed to implement the strategy.

Refine data strategy goals and detail plans to achieve them.

1. Create a data strategy roadmap that shows the path to achieve goals.
2. Outline specific tactics to achieve data strategy goals.
3. Assign owners who will ensure that everything is done to achieve goals.

Build data management and governance teams to execute the data strategy.
Under each of the owners of parts of the data strategy plan, individuals and teams are assigned to execute the plan. These can be aligned with a specific group or play a cross-team role.

The roles for executing a data strategy include:

1. Senior-level managers and department heads
2. Data analysts
3. Data collectors
4. Data governance manager
5. Data model designer
6. Data quality management
7. Data security manager
8. Data steward
9. Database architect
10. Master data management

Identify the data to be collected and their sources.

1. Data strategy goals dictate the types of data that should be collected.
2. Use cases define the sources of data.
3. Plans should direct how the data is collected from the various sources.

Align data collection practices with applicable regulations.

1. Identify which regulations apply to the organization.
2. Determine what rules apply to data collection.
3. Ensure that data collection practices and processes adhere to the applicable regulations.

Establish rules for data storage, organization, and usage.

1. Data management policies related to the storage and organization of data need to be created.
2. Methods for data storage and access should be defined.
3. Systems and processes need to be implemented to ensure that data is stored in a consistent format and data silos are avoided.

Optimize the data architecture.

1. Ensure that the right tools and technologies are deployed.
2. Identify and resolve gaps in the data architecture that could impact access, storage, and security.
3. Consider the following elements of the data architecture:
4. Data volume and type as related to access and storage
5. Data quality requirements
6. Scope of analytics (e.g., business intelligence AI, and ML)
7. Security and compliance
8. Data lifecycle

Data strategy framework as a path to increased value

The value that can be extracted from data spans every aspect of an organization. It drives profitability, increases productivity, and improves customer satisfaction. The trick is developing and maintaining the value of data.

A data strategy framework, even a simple one, creates and keeps a clear path to high-quality, value-driving data. It provides guidelines and processes that help organizations effectively manage data despite expanding quantities and changing use cases for it. A data strategy framework gives organizations the structure necessary to extract the maximum value from data.

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