What Is a Data Strategy Plan?

A data strategy plan contains an organization’s vision of the collection, storage, management, sharing, and utilization of data. The Massachusetts Institute of Technology, Center for Information Systems Research (MIT CISR) Data Board defines it as a central and integral concept that articulates the function of data to inspire business strategies. The data strategy plan of an organization will look different from one another, depending on the industry. However, these plans still perform accordingly. The plan must define how data contributes to business development, meet business objectives through different activities, describe possible changes to organizational needs to maximize the value of data activities, and outline other plans to apply these changes. The document must also establish a workable timeline to complete activities, define vital milestones and priorities, and describe necessary procedures to move work forward. The data strategy must also relay necessary financial justifications for the data activities and the benefits it brings to the company, using the information to increase business profitability and data monetization. Remember that the data strategy plan must follow the SMART methodology, making it specific and actionable yet open to revision and changes as circumstances can change.

According to an article from the Harvard Business Review entitled What’s Your Data Strategy, published in the magazine from May-June 2017, cross-industry investigations show an average of less than half of structured data from organizations actively make decisions for the company, with less than one percent of unstructured data analysis and use. Data also shows that 70 percent of employees have access to information they should not have access to, while 80 percent of the time spent on data analysis only involves identifying and preparing data.

Components of a Data Strategy Plan

Each company has varying levels of technical needs and analytical maturity. The crafting of data strategy plans greatly depends on the needs and development of an organization. Each of the following elements below is essential to creating a data strategy plan, regardless of the nature of a company. These components ensure that a data strategy plan helps the business to grow and mature while keeping information safe.

Business requirements: Each data is specific to a particular company and must address its needs to achieve the overall strategic goals and generate revenue. When defining the business requirements for a company, clearly specify a leader, stakeholders, and small and mid-market enterprises (SMEs) in the organization. The leader is an executive that helps gain investment support. Meanwhile, the stakeholders and other SMEs depict the different departments, functions, and processes in the company. There must be specifications of strategic goals that tie the departments together to achieve organizational goals. These two must tie up to reach overall organizational success. The objectives come from different interview processes from the executive level down to department managers. The procedure determines what leaders need to measure, what they want to improve, what answers vital questions, and the KPI for the answers. Essentially, gathering all the necessary information of the business requirements results in gaining knowledge about what a company wants to achieve.Sourcing and gathering data: To source or gather information within the company, take note of the source systems available and the roadblocks to accessing the data. It is also critical to determine whether data has the right level of detail and is within the right frequency to answer the questions effectively. For data that is not available in the company, building a matrix is helpful. In the data matrix, it must list the business questions, the needed data to answer the questions, and the data source systems. The business matrix serves as the basis for all data information coming from the company.Technology infrastructure requirements: The focus of this section is to identify business reasons for business initiatives. Building a flexible and scalable infrastructure shows complexity in its various techniques and approaches. In the technology infrastructure requirements section, the company considers the extent of operating system functions to accomplish analytical needs. Instituting a central data repository backup system is critical. It must also regard the skills and technical infrastructure that supports data warehouses and cloud-based solutions. A gap analysis process must identify deficiencies and establish an estimation or calculation. It must also indicate if third-party information systems can address the details. The company must also specify the standard integration tools to acquire information from data systems to central data repositories and additional information to support business logic usage and a clear provision on data accessibility. These consist of IT reports, self-service reports, printable reports, or user-based reports. It must also identify if the information comes from web embeds or external data sources. The more requirements and needs an organization identifies, the more solutions can support the business in its endeavors. An architecture diagram identifies and represents different data sources, processes to acquire data, and possible landing spots, including data markets, data lakes, and data warehouses. It also gives information about data governance and information security. Turning data into insights: The data strategy plan must indicate necessary recommendations about the analytical processes to gain relevant insights and data visualizations. Different companies rely solely on Excel and email applications that cannot promote data interactions. Data visualization tools seek to make data easier to understand and interpret. When using data visualization, consider quickly identifying trends and outliers to prevent confusion when presenting the information. The interactive dashboard must establish the context of metrics to anticipate user trends, investigations, and diagnoses. It must also indicate the data accessible to individuals to encourage company-wide adoption and standardize definitions and metrics. The data strategy plan must also provide information to audiences accordingly.People and processes: This section of the plan delves into the individuals in the organization and methods of governing, sharing, and creating data. It is critical to acknowledge the skillset of users to understand their strengths and weaknesses, knowing when and how to give them support. The evaluation must also cover employee assessments and incentive plans, helping leverage employee motivations. In terms of organizational processes, companies face their share of roadblocks that hinder the use of data properly for decision-making. In this sense, business processes must undergo a re-evaluation to include data analysis. Documentation of processes and reports is essential. Recognition processes also help with building internal momentum and encourage positive behavior in employees with factual data.Data governance: Data governance allows organizations to share data and relevant information, starting analytics practices. The data governance program ensures that calculations present in the organization come from the input across the enterprise. It guarantees that data accessibility is available to the right people, along with a concrete definition of data lineage. Data governance focuses more on organizational functions rather than tools. It takes advantage of leadership practices, navigating through difficult conversations. Develop a data dictionary that serves as a business document that lists end-user measures and dimensions. Data roadmap: The data strategy roadmap consists of all the processes and activities that make a workable plan. After identifying all business needs, it is necessary to prioritize the activities to accomplish business goals. For each recommendation to bridge gaps, classify the feasibility and expected business value it provides. The data strategy plan must prioritize action plans that require an easy implementation to achieve quick achievements. The roadmap must also contain staff availability, budgeting plans and processes, and competitor projects. Along with the roadmap, there must be a timeline that includes significant milestones of the strategy.

How To Create a Data Strategy Plan

Every stage of creating a data strategy plan must prioritize company goals. It must also discuss the intended data use, whether it is for advertising purposes, content personalization, or any other purposes. It helps develop strategies to drive processes. Below is a helpful guide that helps organizations create a data strategy plan to accomplish organizational goals.

  • 1. Create a Proposal and Accumulate Buy-Ins

    The first step in creating a data strategy plan is to develop a proposal and earn buy-in from stakeholders in the company. Target high-level executives to receive approval and resources to implement the plan. It is also advantageous to buy in stakeholders from different levels and departments to get their participation. Present a strategy that highlights the benefits the company receives to acquire buy-in from company leaders. You can also present information on competitors and how they manipulate data to their advantage while showing clear examples and data to back up these claims. Get all necessary stakeholders involved in the strategy to ensure maximum participation towards a successful data strategy. Be open to revising the proposal to gain more buy-ins.

  • 2. Build Data Management Teams and Assign Their Respective Roles

    After the proposal agreement, establish data management teams. The teams must consist of senior-level managers and department heads that have a background in data storage and management, including technological and organizational capabilities, opportunities, and limitations. Each team must have a variety of individuals that have different viewpoints. Conduct employee assessments to find the right individuals, and incorporate the help of external sources if there are missing aspects that you wish to address in data management. It is the responsibility of the teams to allocate resources, establish and improve policy and procedures, and deal with data issues. Aside from constructing teams, members must have data governance roles. It determines responsibilities for complying with standard operating procedures, deploying technologies, providing updates, and many more. These responsibilities ensure the accomplishment of tasks and the promotion of work ownership.

  • 3. Identify Data Collection Procedures and Sources, Setting Goals for Collection and Distribution

    The next step of the data strategy plan is to determine data sources and collection methods. Remember that data collection depends on the organizational goals. An organization must incorporate a SMART goal-setting process, establishing short-term and long-term goals, including overarching objectives. Elaborate on the role of data collection and distribution and its benefits to determine a suitable vision statement for data use in the coming years.

  • 4. Create a Data Strategy Roadmap

    After setting concrete goals, outline a comprehensive plan to achieve them. A roadmap comes from the collection of these plans. The programs must specify the individuals, processes, technologies, project costs, and period to accomplish each. These must also be flexible enough to make revisions and adjustments as necessary. As these plans move forward, the organization must provide evaluation processes to determine their effectiveness. The data strategy roadmap outlines the plan to accomplish short-term goals to achieve the company vision.

  • 5. Data Storage and Organization Plan

    Create policies that detail data storage and organization. These regulations determine the usability of acquired information. When creating storage methods, consider storage capacities and data sharing capabilities. Data organization also impacts the accessibility of the information, allowing different departments to access and share vital data. The primary goal of data storage and organization is its accessibility to different individuals within and outside the company as necessary.

  • 6. Get Approval and Implement the Data Strategy Plan

    After completing the outline and goals of the data strategy, submit it as a business plan and present it to executive management for approval. It must indicate all the strategies the company needs to implement to achieve organizational goals and vision, including capital investments, hiring processes, and organizational structures. Implementation follows after the plan approval. However, the data strategy plan is an ongoing process that must be open to restructuring and revision as needed.


What is an example of data strategy?

An example of a data strategy is an activity that involves customer insights that utilize data analysis and modeling to predict customer, market, and industry trends.

What are the four big data strategies?

The four big data strategies consist of social analytics, decision science, performance management, and data exploration. Social analytics measure non-transactional activities, including social media presence through conversations and reviews. Decision science involves experimentation and analysis of non-transactional data. Performance management consists of understanding information from the company database through existing queries and analysis. Lastly, data exploration focuses on using statistics to gain answers to existing questions.

What is a ‘data first’ strategy?

Data first strategy focuses on closing any gaps that exist in an organization. It seeks to understand strategic and operational questions to inquiries that have no sufficient data at present.

Creating a data strategy plan can help achieve organizational goals and vision. Having a data strategy plan in a company helps to increase relevant volumes of data, improve data quality, data security, and governance, and make effective collaborations throughout the organization. Ensure that the data strategy plan is flexible enough to endure changes and revisions. Develop your data strategy plan by downloading the samples available in the article today!