Designing a data strategy: 5 steps to success

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Contents

  1. What is a data strategy?
  2. What are the types of data strategy?
  3. What are the benefits of a data strategy?
  4. Common data strategy pitfalls
  5. 5 elements of a successful data strategy framework
  6. The next stop on your data strategy roadmap

In the digital age, data is the lifeblood of every business.

Data-driven insights empower leaders to solve inefficiencies and drive increased value through measurable innovation and cost reduction. But building a data-informed culture isn’t easy.

Today, 70% of transformation initiatives fail(opens new window), with each unsuccessful attempt draining resources, impacting morale, and increasing risk. So how can you build a data strategy that drives value and stands the test of time?

In this article, we’ll walk through the foundations of a successful data strategy and share insights into the latest best practices, including practical ways to align your data strategy with your business goals and increase organizational buy-in for a winning approach that takes you far into the future.

What is a data strategy?

A data strategy is a plan or framework that guides the way an organization collects, stores, manages, analyzes, and utilizes data to achieve its goals and objectives. It involves defining the objectives of data usage, identifying the types and sources of data that will be collected, establishing data governance policies, defining data quality standards, and determining the technology infrastructure and tools needed to support the data strategy at a day-to-day level.

What are the types of data strategy?

There are multiple types of data strategy, including defensive data strategies focused on enhancing cybersecurity and data compliance, data integration strategies aimed at eliminating data silos, and data monetization strategies for identifying opportunities to generate revenue or create value from existing data assets.

While each type of data strategy is important, businesses are becoming increasingly focused on implementing a holistic data strategy that encompasses a variety of business goals, supported by cross-functional partnership and collaboration across the organization.

Examples of data strategies will differ based on an organization’s specific business goals. Whatever the objective, the key is to make sure the data strategy and business strategy align.

To implement a successful data strategy, many leading organizations are focusing on three key areas — people, process, and technology.

What are the benefits of a data strategy?

A well-defined data strategy is important for making informed decisions, improving operational efficiency, identifying business opportunities, and gaining a competitive edge in a fast-paced digital era.

To remain competitive, leaders must have a data strategy that helps them face external disruptions, like economic uncertainty and the rise of AI, while meeting the growing internal demand for data-driven decision making.

Here are some of the core benefits of a modern data strategy:

  1. Data capabilities at every level — employees in every department can access data, use data tools and systems, ask the right questions, and collaborate effectively.
  2. Greater speed and efficiency — teams and individuals are empowered to efficiently process and visualize data, reducing time per data task.
  3. Empowered data teams — existing data scientists and analysts have more time to enhance their knowledge and develop advanced skills.
  4. Increased capacity — when business teams are empowered to self-serve, data teams can spend more time supporting strategic initiatives, reducing reliance on external support.
  5. New opportunities to drive business value — employees can use data to identify opportunities to increase productivity, decrease costs, improve the customer experience, and grow new revenue streams.

Common data strategy pitfalls

Despite the many benefits of a data strategy, businesses are finding it difficult to achieve lasting change, with only 24% of companies(opens new window) saying they have successfully created a data-driven culture.

There’s a common temptation for businesses to test out various elements of their data strategy through short-term transformation projects focused on utilizing emerging technology, like machine learning (ML) and artificial intelligence (AI).

However, the emphasis on process and technology often comes at the expense of the people who use these tools and workflows in their day-to-day work. Research suggests 7% is the minimum “tipping point”(opens new window) required to achieve the positive return on investment (ROI), yet most companies engage just 2% of their workforce in transformation efforts.

To achieve the above benefits, your data strategy must include clear steps for engaging your workforce at every level.

5 elements of a successful data strategy framework


From building organization-wide data management practices to fostering data access and cross-functional collaboration, there are many key components of a strong data strategy.

Let’s explore some of the core elements for a data strategy framework that breaks down costly data silos and paves the way for effective use of data across the organization.

1. A unified ‘big picture’ vision

A good data strategy must be relevant to the business — otherwise, it simply won’t last.

To engage a greater percentage of your workforce, start by defining an ambitious future vision that includes every team, function and department.

Your data strategy vision may include:

  1. A clearly defined “Why?” to articulate benefits to both the business, plus tactics for engaging employees across different functions, geographies, and backgrounds.
  2. A strong answer to the question, “What’s in it for me?” that speaks directly to the individual goals and ambitions of every member of the organization.
  3. A defined end state with clear milestones and outcomes to be achieved before you can call a strategic initiative “finished”.

2. Executive buy-in

Successful transformation requires strong alignment across all levels, starting at the top. Transformations are 5x more likely to succeed when senior leaders model the changes they’re asking employees to make.

However, large-scale data strategy success often feels out of reach, even for the organization’s most visionary leaders. Of the 85% of senior leaders who have been involved in at least two major transformations in the last five years, a whopping 67% have experienced at least one underperforming transformation during this time.

Chief Data Officers (CDOs) can’t do it alone. Early problems arise when leaders disagree on the urgency of the data strategy and the proposed solution, or when they weren’t fully bought in from the start.

Here are some ways to increase executive buy-in:


  1. Align your data strategy to the wider business strategy.
  2. Establish clear goals backed by qualitative and quantitative data.
  3. Determine relevant business objectives and key performance indicators (KPIs).
  4. Measure your progress at each stage to maintain buy-in after initial launch.


Make it easy for your executive team to connect the dots between your data strategy and business strategy. Then ask for a firm commitment from the C-Suite.

3. Well-defined data architecture

In today’s digital age, there is plenty of buzz about technology and the various approaches to data architecture. But your tools are only as good as the people who use them. Without clear guidelines and a data-confident workforce to follow them, organizations end up investing in technology that yields little return on investment (ROI).

To improve the ROI on your technology investments, create a well-defined data architecture(opens new window) to underpin your data strategy.

Here are some key areas to consider:

  1. Data storage — including storage formats, backup strategies, archiving plans and any relevant requirements for real-time analytics and operations
  2. Data integration — including guidelines for moving raw data from data warehouses to business intelligence (BI) applications to increase analytics performance
  3. Data access — including guidelines for data collection from various data sources, and steps to streamline data governance without excessive user controls
  4. Data compliance — including strong data security and data privacy practices to protect your organization’s data

By taking the time to create a detailed data architecture, you can alleviate the pressure on your senior data team and use data to support a variety of business use cases across the entire organization.

4. Clear success metrics

If you’re launching a new data strategy, keep in mind that post-launch is a crucial window of opportunity for increasing the pace of activity.

To maintain momentum for your data strategy, it’s important to share regular reports on the value delivered:

  1. Work fast to turn ideas into actionable roadmaps and back them up with key milestones that are less than a few months out.
  2. Establish common goals across teams.
    Define the metrics you’ll use to track your progress at each key stage.
  3. Update your digital transformation roadmap to include quarterly goals.

By aligning your data strategy with your core business processes, you’ll be better positioned to break existing silos and actively identify end-to-end issues and opportunities. With a clear view of what is and isn’t working — and a well-structured system for measuring your success — you and your employees will also be more likely to stay the course.

5. Commitment to skills transformation

When it comes to executing an effective data strategy, you can go much farther as a team. Yet research shows that only 25%(opens new window) of employees believe they have the knowledge and skills required to use data effectively. To identify these issues before they become a roadblock:

  1. Conduct a skills gap analysis to quantify your data skills gap, pinpoint your current strengths, and identify your future data skills needs.
  2. Quantify the cost of skills gaps, including inefficiencies or delays to key strategic projects.
  3. Calculate the financial benefits of closing them, such as new efficiencies and revenue-generating opportunities.

Change isn’t easy, but it starts with a firm commitment to building a culture of learning – giving employees the confidence to access, interpret, and use data insights to drive decision-making. Here are some key actions to consider:

  1. Provide learning opportunities to existing employees via data upskilling and reskilling to create data champions at every level.
  2. Open up alternative hiring routes for entry-level data roles, such as apprenticeships, building a robust hiring pipeline and increasing the capacity of senior data specialists.
  3. Track the business impact of skills programs as employees use their skills to identify new cost-saving and revenue-generating opportunities.

A strong data strategy will consistently reveal new opportunities to make a bigger downstream impact, while driving full-speed ahead toward the greater business strategy.

With a data-confident workforce, there is no limit. As your organizational data capabilities continue to grow, so does the potential to reach even higher.

The next stop on your data strategy roadmap

An effective data strategy empowers you to use your company’s data for the benefit of your customers, your business, and every individual within it.

Get our free data-driven digital transformation playbook, and learn nine essential tactics to increase your strategic success.

Team Multiverse

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