Building a Future-Proof Data Strategy

In today’s world, the main question is not whether we have data but rather how we use it. Many professionals refer to data as the gold mine of the 21st century, emphasizing its potential to drive business growth and innovation.

However, simply collecting vast amounts of data is not enough. Organizations must implement effective data management strategies to ensure data quality, accessibility, usability, and security. Without a well-defined approach, organizations risk being overwhelmed by data or failing to use it effectively.

This blog post outlines key considerations and best practices for developing a data management strategy before diving into the implementation steps.

Defensive and offensive data strategies

Defensive and offensive data strategies represent distinct approaches to leveraging data assets, each with its own objectives and priorities. A defensive data strategy aims to minimize downside risk, reduce costs, and prevent negative outcomes. It focuses on data governance, security, and privacy—for example, ensuring data flows through internal systems while maintaining the “Single Source of Truth” (SSoT) principle.

On the other hand, an offensive data strategy aims to leverage data to enhance competitive advantage, improve decision-making, and optimize business operations—for example, generating customer insights through data analysis and modeling to better understand the customer behavior, identify their preferences and needs, and proactively suggest relevant products or services, or even offer unique pricing based on their profile and needs to a certain extent.

The defensive and offensive blueprints are just the two ends of the spectrum; in most cases, we see a mixture tailored to the organization’s context and compliance needs. Thus, a complex data strategy should incorporate both approaches, with a key focus on finding the right balance.

Assessing goals for data usage and data sources

One of the critical aspects of a successful data strategy is the data itself, which revolves around two main key points: collecting and using it effectively.

However, before we can collect and use data, we must first define its main purpose.

• What do we want to measure?
• What insights are we trying to gain?
• What are the goals with the data and how will it help the organization?

Similarly, a good data strategy should be based on the organization’s objectives and goals, not just the available data.

A primary goal of a data strategy is to enable better, more informed business decisions and measure past decisions’ impact. The pinnacle of data-driven decision-making is data democratization, where all relevant decision-makers/teams are enabled to work with data on a need-to-know basis. By identifying priorities and sourcing relevant data, businesses can gain insights to understand markets, develop products, and target customers effectively. Such insights can be generated on all levels of the organization; that’s why data democratization is often an end goal.

However, data strategies can not only help in decision-making; they also have the potential to optimize operations by improving business processes, enhancing internal efficiency, and implementing automation.

Data strategies might bring the potential to monetize data by integrating it into products or selling it, turning data into a valuable asset that increases company value. This process involves identifying valuable data and may include exploring secondary markets or creating a dedicated business unit.

There are two ways to collect data: by leveraging existing data and data sources or integrating new ones. In any case, the aim should always be to strategically gather the best data to meet your objectives. Collecting all kinds of data without considering its usefulness, just in case it becomes useful one day, is not an effective approach. Quality is more important than quantity when it comes to achieving your goals.

Data sources can include external data, sensors and IoT devices, mobile data, or data measurement by software on a web application.

Causes of failures in a data strategy

Executing a data strategy can fail due to pitfalls in communication, technology, governance, cultural, and other critical areas.

A common pitfall is failing to define, poorly defining, or setting an unachievable data strategy. It should serve as a roadmap with clear objectives and actionable steps to achieve those goals short-, mid-, and long-term. The organization may struggle with identifying its priorities and key business questions. This can lead to wasted resources and a failure to generate meaningful insights.

As mentioned earlier, it's also important to define the needed data and build the data strategy based on the organization’s needs rather than existing data; despite this, the organization may collect irrelevant or unnecessary data.

Poor data quality is often a challenge. Data strategies should include guidelines for ensuring data accuracy, consistency, and completeness with the principles like "SSoT," for example.

A roadmap should also consider the technology and infrastructure implications of data-related decisions for storing, processing, and analyzing data. A technology-related mistake can be bad scaling decisions, which can mean scaling too high for the organization’s goal or budget or under-scaling compared to its needs, including both technology and data personnel. That’s why data frameworks are, in most cases, built upon cloud computing. With a well-defined cloud computing architecture, they can be scaled up or down in parallel with the business needs and the data competency, eliminating the need for significant upfront hardware investments. Apart from technological errors, bad data quality in general can lead to misinformation and therefore misjudged decisions.

The organization’s culture and data know-how are another key factor in a successfully executed data strategy. A poor data culture can foster negative attitudes and misconceptions about data, not to mention unrealistic expectations. Furthermore, a negative data culture can result in a lack of data literacy and skills. If employees do not have the necessary knowledge and skills to interpret and use data, they may be unable to extract meaningful insights or make informed decisions.

The necessity of iteration

A data strategy should not be viewed as a static but rather as a dynamic plan that adapts to changing circumstances and should be revisited and redefined periodically. The frequency with which you review and revise your strategy should be based on how important data is to your business, what sort of data you’re using, and what you’re trying to achieve with data. However, an annual full review is a sensible rule of thumb. These circumstances can include changing business needs, new goals to achieve with data, new insights from data, expanding data applications and data sources, or even changes in the technology landscape.

Summary

• A strong data strategy ensures quality, accessibility, usability, and security beyond just data collection.
• Balance defensive (governance, risk mitigation) and offensive (innovation, insights) approaches.
• Clear goals and data democratization empower better decision-making.
• Avoid pitfalls like unclear objectives, poor data quality and weak infrastructure planning.
• Keep the strategy dynamic, adapting to business and technology changes.

Sources

Marr, B. (2017). How to Profit from a World of Big Data, Analytics and the Internet of Things.

Davenport, L. D. (2017). What’s Your Data Strategy?

Koenders, W. (2023, Jan 3). Offensive vs Defensive Data Strategy: Do You Really Need to Choose? From Medium: https://medium.com/@willemkoenders/offensive-vs-defensive-data-strategy-do-you-really-need-to-choose-c04f0387dbc3

Fredriksson, A. a. (2023). From Strategy to Execution Bridging the Gap between Data Strategy and Data Governance. From Chalmers Open Digital Repository: https://odr.chalmers.se/items/72867423-3801-4223-9369-72841bb76af6