From Data to Profits: Ingredients for Successful Data-Driven Business Development

This webinar was recorded as part of the series by the CSL Community of Practice on Monetizing Data and Analytics.

Any profitable digitization strategy starts with a business model.  

  • Business Model Framework
    • Why going digital? What is the business objective? (Objective)
    • Who are the customers? What are their jobs-to-be-done? (Customers)
    • What is the company offering? (Value proposition)
    • How can the company demonstrate the value of the offering to the customers? (Value demonstration)
    • Which capabilities are central to the business model? (Capabilities)

Where/How does data enter the business model?

  • Customers
    • Need data (reports, KPIs) – re-segment customers based on data need
    • Willingness to share data – different customers have different comfort level with sharing data
    • Customer 4.0 – today’s customers expect everything to be connected
  • Value Proposition
    • Businesses are selling data as a product or service
    • Businesses are selling devices that can collect user data and help generate business insights
  • Value Demonstration
    • Demonstrations as data sources – companies collect data while customers are using services/products (click stream data, tracking purchase cycles etc.)
    • Data in demonstrations – customers expect that value should be demonstrated and backed up by data
  • Capability
    • Data in the organization – digitization of processes
    • IT/Data as a core capability – ensuring control and security of data
    • Industry 4.0 (Internet of Things) – everything is connected

Business Example: WiseCon, a better mousetrap

WiseCon, a Danish company, developed an IoT-based equipment for exterminating rats, which offers a more effective and safe alternative to widely used rat poisoning. WiseCon uses traps placed in the sewer, going to the source of the problem. The high-tech traps can detect rats using heat and motion sensors and kill the rodents in their natural habitat. The data from the traps is sent over the internet to a dashboard, which is accessible anywhere in the world. The company can not only report the extermination results but it can also document if there was an activity. WiseCon offers an example of a new disruptive data-driven business model, which moves beyond the superficial symptom treatment to documented prevention of the problem.

Capability Map: 9 critical capabilities to realize profits from data

  • Data-driven foundation
    • Data is the central element of any data-driven growth
    • Analytics are the next step of leveraging this data for profit. 
    • Permissions  encompasses three types of permissions: 1) what companies are allowed, legally, to analyze; 2) what your partners allow you to collect and analyze; 3) social norms, it may be legal to collect certain data, but society may be against it.
  • Data-driven organization
    • Strategy – what is the organization’s capability in developing a digital business growth strategy?
    • Business development – in what way is the organization prepared to analyze, evaluate and implement new digital, data-driven growth ideas?
    • Autonomy –Often times, new digital business solutions come out of autonomous projects, not from a successfully executed strategy. Individuals experiment in small teams, often under the radar, and once the idea is mature enough, it’s scaled up.
  • Data-driven application
    • Optimization is often the first step. Companies use data and analytics to save money by increasing efficiencies, optimizing time and resources. 
    • Penetration – using data capabilities to cross-sell to current customers
    • Recycling  – use existing data in a new way. (ex. recycling, repackaging existing data to create an offer to a new customer segment. In the example of WiseCon, the company expanded from selling the service to individual households to selling it to utility companies and food processing plants.)

Reflections on data-driven success

  • Most of the service transitions are data-driven
  • Most of the disruptions are data-driven
  • Most of the successes start with a customer need, not a technology
  • Most of the data-driven strategies start below the radar as projects and later get accelerated.

Why is data-driven success difficult? The nine capabilities, that are needed to be successful, reside in different functional groups within an organization, which creates barriers for collaboration. A recommendation would be to map out where responsibilities for each of the capability reside and develop a plan how they can work together.

To learn more about The CBS Competitiveness Platform, visit

To download Dr. Thomas Ritter’s book “Alignment Squared: Driving Competitiveness
and Growth through Business Model Excellence visit


Thomas Ritter is a Professor of Market Strategy and Business Development and Academic Director of CBS Competitiveness Platform of Department of Strategic Management and Globalization at Copenhagen Business School.

Thomas Ritter’s work is published in leading journals including International Journal of Research in Marketing, Journal of Business Venturing, and Industrial Marketing Management. He is currently acting as special issue editor for Journal of Product Innovation Management (on open innovation), Long Range Planning (on business models), and Industrial Marketing Management (on business market management). Thomas Ritter is heading a 3-year applied research project entitled “From Big Data to Big Business: Understanding data-driven business development” funded by the Danish Industry Foundation with 7.5 mio DKK.

In recognition of his contributions, Prof. Dr. Thomas Ritter was granted “Dansk Marketingforsknings Pris” (Danish Marketing Research Award), awarded for outstanding research with high business impact in Denmark, Literati Club 2008 Highly Commended Award, awarded for Ritter (2007): A Framework for Analyzing Relationship Governance, Journal of Business and Industrial Marketing, Vol. 22 (3), Literati Club 2003 Highly Commended Award, awarded for Ritter et al. (2002): Measuring Network Competence: Some International Evidence, Journal of Business and Industrial Marketing, Vol. 17 (2) and”Klaus-Tschira-Preis für verständliche Wissenschaft 1999″ (Klaus-Tschira-Award for Comprehensible Science 1999), awarded for outstanding PhD and related publications.

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