As the topic of digital transformation looms over many companies worldwide, there have been many books, articles, and guides written about how to take the transformation journey. One of the common themes found in digital transformation efforts is the need to use data. Some companies have a working strategy that's evolving. At the same time, some companies are trying to decide what that even means. Companies have to develop a data strategy, at the executive or board level, that lays out the principle model of how data is to be used at a company. The problem is that the topic of data and strategies associated with it can become so complex. Many companies become overwhelmed by the idea of data lakes, data warehouses, data meshes, analytics, machine learning, AI, data ingestion, and all the other buzzwords surrounding data.
Data Offence and Defense
The most simplistic way I have found with my peers to help explain the differences between different strategies is to approach it as offense and defense. Leandro DalleMule and Thomas H. Davenport have written some excellent articles in Harvard Business Review on the topics I'll post in the references below. Essentially, think of your data strategy as a spectrum. On one end, you focus on defense and the other offense. This framework is easy for me to explain when working with groups that are not data practitioners, and it's a way to abstract away the technical complexities and focus on the company's direction.
A defensive strategy with data focuses on security, privacy, regulatory compliance, and integrity. Data is thought of as something that has to be protected and used for its intended purposes. This strategy has a control orientation around data and is commonly found in highly regulated environments like health care. An offensive strategy implies a focus on using the data for competitive advantages or differentiation. In this strategy, data analytics and enrichment are the main focus, with the company's orientation focused on deriving value from the data. Offensive strategies require a more flexible risk posture in the use of data, which means data silos and sources of the truth start to vary depending on the transformations imposed on the data.
There isn't a better strategy. Many times when I explain this to people, they think they have to be on the offensive. In business, yes, we play offense and defense. Data strategy is the same. But your company is better at one and likely focused more on one. Having a balanced approach can sound great but ultimately creates confusion in the decision-making framework at the company. For instance, if you were to announce that the strategy is balanced between offense and defense, the team will try to use that principle to determine where to focus efforts. It will be difficult, and they won't be able to. They don't know if they're supposed to focus more on offense or more on defense. When I worked with Enterprise Architecture and developed principles so that teams can work in more agile fashions, the same concepts appeared. Your security approach, if balanced, doesn't create a direction and a goal for a delivery team. It creates confusion on how to align to the goals of the company. Principles of design or strategy generate an orientation to how leaders should make decisions. Regardless of the company's strategy, it's essential to know which direction the strategy leans from the executive team.
Applying Scaled Thinking
With my experience and background in working with architecture and data, I have found that enterprise-scale is a huge factor in how these strategies play out. For instance, an offensive strategy at a massive company with 50,000 employees versus a company with 500 people is different. My experience comes from media-sized companies and start-ups, so the operations at scale at a company like Walmart change the dynamic of the strategy. I used to laugh when people would say big data because most companies had medium data, for which I have no formal definition. Tools and technologies might not need the framework from google to be effective, or you might not have enough data for building a strategy around analytics. You might need a download of customer data and a spreadsheet if you only have ten customers. That's important to keep in mind as you work with executives or teams on the data strategy for your company.
Innovation and Digital Transformation
There is a caveat to my experiences with strategy. In my opinion, the digital transformation movement requires an offensive approach. Data silos, or data gardens, walled off by departments and teams for protection will limit digital transformation capability. If the organizational principles focus on avoiding data risks over the management of data to empower the business, digital transformation efforts will suffer. That's not implying that a company shouldn't protect privacy, security, and other defensive techniques. If the focal strategy isn't offensive, it will be hard to win the transformation game with backup from the defense. I'm a big believer in making sure the principles and superordinate goals are in place that can allow self-empowered teams to drive forward. Adan Pope and Peter Buonfiglio discuss aspects of this transformational challenge in their book "Respect the Weeds" which is a fantastic read for anyone thinking about digital transformation.