Data-Driven Services

Data can be sold as is, turned into data-enabled products and become the foundation for consultancy services. Choosing the right product strategy can be complex and then implementation introduces a range of considerations – how to price correctly for varying customer types, how to brand and sell the product and how to manage the data to meet customer expectations.

At techobscura we have developed a multi-level framework for commercialisation that addresses all of these issues and takes an organisation from the simple to the more ambitious products and services.


Data-driven services are an exceptionally powerful add-on to an existing service portfolio as they provide three otherwise elusive benefits:

1. Immediate value

Data-driven services that are designed around actionable insightwill give customers immediate value.  Actionable insight comes in a number of forms:

  • Problem identification. These are insights where the customer is told exactly what is wrong and, if possible, how to fix it.  
  • Anomaly detection.  These are insights where anomalous behaviour is detected which the customer should investigate as it may mean a serious issue. 
  • Predictive recommendation.  Using predictive analytics, a recommendation can be given to take a specific action based on the likelihood of something happening.

2. Long-term potential

We are still at the very early stages of exploring and unleashing the potential of data, as evidenced by the rate of growth of tools, techniques, datasets, standards and services. While organisations have for a long time used pre-packaged external data sources, the trend is now towards building internal data analytics (business intelligence) capabilities as organisations recognise that expertise is necessary to unlock value of the data they hold. 

As an organisation builds its internal capabilities around data analytics and data-driven service design then it creates long-term potential for new services and new value.  See more in our notes on Business Intelligence and Data Science.

3. Deepened engagement

With the additional value they are getting, customers are already more interested in you as a service provider.  When they know that you are committed to a path of developing new data-driven services then they are even more interested because they also see the long-term potential and if you’re going to be doing some of the heavy lifting for them then they want to be part of that.

Key design considerations

When designing data-driven services, the following key design considerations should be thought through for each service:

1. Strategic goals

The key goal of any data-driven service is either to increase the knowledge of the customer or to change their behaviour.  Behavioural changes include:

  • Better infrastructure hygiene.  Customers do more to reduce the cyber abuse perpetrated or enabled by their customers.
  • Reduced support costs.  Fewer support calls as customers use self-diagnosis and other self-service tools.
  • Better data quality.  Customers get better at keeping their data up to date and get better quality data from their customers.
  • Evidence-based. The more informed a customer is then hopefully the more evidence-based their decisions will be and the more successful the outcome of those decisions.   

2. Actionability

As noted above, the more immediate the action that can be taken then the more instantly recognisable the value of the service is.  A service that requires a customer to combine the data provided with their own data and then analyse it may provide more value at the end, but it will inevitably have a slower take-up and may never fully get off the ground.

3. Basic competitive advantage

The competitive advantage of the service can come from a number of reasons:

  • Unique data that nobody else can provide.
  • Data that customers are keen to use but it is too expensive for them to purchase individually. 
  • Data that is available elsewhere but through an unwieldy interface or hidden behind multiple obstacles.
  • Data that is of a better quality, generally because more work has gone into cleaning and testing it.

4. Aggregation, comparability and gamification

A more sophisticated competitive advantage is possible when a customer’s data is aggregated with that of multiple other customers.  This creates a significant potential for comparative insight and that in turn can be gamified to drive behavioural changes even more.  For example, if a customer is told that they are #1 worst for data quality out of 7000 customers then they have a strong incentive to correct that position.

5. Community platform

Data-driven services are often thought of as one-way services provided by the organisation to their customers.  Another approach is where the service revolves around a community of data contributors who get the benefits of the service and the organisation gets the benefit of their data.  For example, community surveys where only contributors get the final report.  A hybrid approach also exists where customers submit their data to be combined and analysed against an otherwise unavailable dataset to produce actionable insights.

6. Insight into the next level down

An organisation that services intermediate customers who in turn have customers of their own can add some extra insight if they have visibility of that next level of customer. For example, they can:

  • Classify those next level customers looking across their entire intermediate customer base and so help the intermediate customers understand how their makeup of next level customers compares to the overall base.  
  • Spot a problematic next level customer who takes service from a number of intermediate customers.
  • Understand the characteristics of next level customers that make them good or bad customers and help their intermediate customers recognise these.

How we can help

Here are just some of the ways in which we can help you:

  • Develop a data services/commercialisation strategy
  • Design data products
  • Build a customer strategy for data products
  • Specify metrics to measure product usage and engagement
  • Provide an independent review of existing data services/commercialisation