You’ve got a couple of important data initiatives lined up for your department, and so does every other department at your company. Progress is slow, or rather it’s fast, but still slower than it needs to be to compete in a rapidly digitizing economy. How can you speed things up? This Boston Consulting Group article suggests that every unit in the company – the IT side, the business side – come together and define the company’s broad digital goals, so each new initiative will support the development of the data use cases that will emerge in the future.
- For speedy digital maturity, the business and IT sides of your company must come together to define company goals and coordinate data initiatives.
- Identify your data use cases, and prioritize them by which are most feasible and which will get the fastest results.
- Take an honest inventory of your company’s data assets, and update your data and technology infrastructure according to future data use cases.
For speedy digital maturity, the business and IT sides of your company must come together to define company goals and coordinate data initiatives.
Too often, a company hurtles toward digital maturity, never having defined the company’s digital true north. The IT and business departments are both scrambling toward a vague digital future, but on parallel paths. This slows progress and stymies value capture. To create faster digital transformation, sometimes a company has to press pause on existing initiatives, and bring both sides together to define the company’s digital “North Star.”
“One reason for falling short is that companies are often unclear about the value proposition of their data initiatives. Consequently, they take tentative, incremental steps, which slows their progress.”
The process of defining a company’s North Star will typically take up to 90 days. In order to create a cohesive plan, the IT and business units must work together throughout the process. Start with a detailed exploration of the company’s digital aspirations and possible use cases, followed by an honest assessment of the company’s data assets and technology architecture. Together, use cases, data assets and technology architecture are the “digital trinity” of transformation.
Identify your data use cases, and prioritize them by which are most feasible and which will get the fastest results.
To correctly sequence foundational use cases, start by creating a comprehensive map of the company’s capabilities across the value chain. Most companies will be able to identify 10-15 foundational data use cases that are necessary for competing in rapidly changing markets. Consider a clothing e-retailer, personalized recommendations for shoppers might be at the top of the list, along with demand forecasting and real-time order fulfillment. Any clothing e-retailer needs these capabilities to stay competitive.
Prioritize the use cases that match your company’s current capabilities in terms of data and technology architecture, but also keep in mind how implementing one use case might improve data architecture and benefit future use cases. Beyond that, prioritize use cases that will generate value in both the long and the short term. An e-retailer will have internal data about customer types and preferences, allowing it to enhance personalized promotions. As data architecture improves, external data and behavior analytics can become part of the equation.
“Capabilities serve as springboards for use cases.”
Once a company has sequenced and implemented initiatives for its foundational use cases, it can begin to explore initiatives that will differentiate the company from others in the market.
Take an honest inventory of your company’s data assets, and update your data and technology infrastructure according to future data use cases.
Quality data assets are key to digital transformation, but many companies still aren’t aware of which data assets will set them apart from competitors. Every industry has a generic list of data assets. A clothing e-retailer might start by identifying which generic retail data assets are relevant to its unique situation, then organize those assets into data domains. When improving promotion personalization, companies must locate and assess the quality of their customer profile data, stock data, company order data and pricing data. Integrating these data sets may require new analytic functions and a shift in corporate culture.
“This approach to data assets differs from the typical IT environment, requiring senior leaders to make several mental flips.”
Companies often start with a monolithic data architecture that binds data, leaving it chained to front-end capabilities. The solution is to make data architecture modular, perhaps with the use of a data and digital platform. Consider again the e-retailer. The company had to make existing data sets accessible, and create a means for its websites and apps to collect more behavior data. It utilized AI models to track external data. All of this came together on an analytics platform, where the data was transformed into actionable insights. On the face of things, your company’s business and IT priorities may look incompatible, but when you define company-wide digital objectives, you’ll find that they’re in harmony with individual use cases.
About the Author
Marc Schuuring (Amsterdam), Lucas Quarta (Paris), Aziz Sawadogo (Paris) and Canberk Koral (Istanbul) are professionals with the Boston Consulting Group.