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How to Build a Data Management Strategy to Support Business Growth


Data is seen as one of an organization’s most valuable assets. Here are points to consider when building a data management strategy to help grow business.

As the speed of industry constantly increases, data is increasingly regarded as one of an organization’s most valuable assets. But while running a data-driven business is important for companies of all sizes, there’s no one-size-fits-all strategy for data management.

Companies that deploy data for action without a thoughtful, well-coordinated framework risk fragmenting their efforts—especially if mergers, acquisitions, or one-off alternative-data purchases lead related or duplicative data sets to be housed and managed in silos.

Maintaining a fragmented data operation can lessen the value of insights generated, and minimize companies’ ability to put those insights to use. Data mismanagement not only lessens the competitive advantages delivered by a data-driven strategy, but also leads to unnecessary costs and failures: According to Gartner, organizations believe poor data quality to be responsible for an average of $15 million per year in losses.

With the right strategy, poor quality data doesn’t inform decisions. Establishing an effective strategy starts with a clear understanding of the purpose of data for the company.

Aligning Strategy to Purpose

Companies have varied uses, expectations and goals for data depending on their industry and objectives. Those in regulated markets like finance may face reporting requirements, for example, around how data informs risk assessments or portfolio models. Companies in markets like ecommerce and retail are subject to less oversight, but may be more directly focused on using data in areas like sales and customer acquisition.

While a company’s goals and applications for data rarely remain static over time, they tend to fall within one of two types (at least at the beginning). Research published in the Harvard Business Review (HBR) encourages leaders to look at and define their efforts in terms of data defense and data offense.

  • Defensive strategies use data to minimize downside risk on a day-to-day, operational basis. Common focus areas include regulatory compliance, fraud detection and prevention, and system/user understanding for areas like privacy, security, or audits.
  • Offensive strategies use data to generate value or uncover insights that can drive business growth, customer satisfaction, or revenue. This would include focus areas like marketing, user experience, product development and so on.

Once a growth-centric purpose is identified, business leaders need to establish a framework that makes "offensive" data use possible without elevating day-to-day risks.That starts with conducting data profiling by taking stock of existing pools of data (ideally across business lines) to understand where the data comes from, what it informs, and how it is applied to decisions.

With that understanding, business leaders can then design a data architecture that incorporates existing sources and uses while addressing gaps or problems with the approach.

Designing for Use and Best Practices

Problems are common when companies lack standardized, cohesive models for working with data. Establishing the right data architecture can solve many of them: If designed and deployed effectively, it acts as a “flight plan” for how data is collected, held and used across the organization.

The data architecture establishes rules and policies for adherence by users and teams across data-centric functions. This helps ensure data is appropriately standardized for integration into various systems, and is disseminated in forms that elevate its usefulness once distributed to and consumed by interested parties.

Since data architecture defines how people, processes and technologies work together, its creation should be a team effort.

Input from IT, security, risk, and data science professionals should inform projects spearheaded by leaders such as the Head of Engineering or Chief Data Officer (CDO), for example, so that day-to-day efforts and processes for things like quality reviews, data tagging, and database transfers are incorporated into the broader data management strategy in clear, compliant ways.

For most companies, that broader management strategy is the CDO’s long-term responsibility. (Though, depending on an organization’s size, there may be multiple ‘unit CDOs’ overseeing various functions.) Establishing effective governance, access, and security paradigms tends to be the CDO’s job, too—but it’s an area where many fail to create and apply appropriate constraints: The HBR research notes that more than 70% of employees have access to data they should not.

Over-exposure internally may not sound especially risky, but it can be a huge driver of data leaks and other privacy and security issues. Especially when it comes to the personal information of customers, companies can never be too careful about limiting internal users’ access to raw data.

Security best practices for data management are also continually evolving, so it’s important to stay up-to-date. CDOs and their peers in IT and security should continually review their processes and policies, and use monitoring tools to understand usage and activities across systems.

And while it falls outside the purview of data management, understanding activities across systems can help companies gauge ROI on their data strategies, too.

Making data useful in various applications—ranging from machine learning and AI-powered decision-making to system or user analytics—depends on companies investing in the resources, training, and personnel that are necessary to put a well-managed data strategy to use. Making a business case for those kinds of investments starts with ensuring that interested parties consume data, and use it for impact, from the very start of the new strategy.

Turning Data Into an Asset

Ultimately, data must get to the right people, in the right form, at the right time, in order to be applied in meaningful ways to the decisions that can drive business growth. If a company’s data management strategy doesn’t address all aspects of data collection, standardization and distribution, then data’s potential to drive business benefits may be limited to one-off decisions—which is hardly a recipe for long-term success.

Data can only be an asset if organizations apply it for purpose, working from a defined foundation designed to meet their unique needs. And even then, achieving rapid ROI is rare. Committing to a long-term approach for long-term effectiveness, and investing in ongoing improvement over time, are essential to creating business value and earning returns. 

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