From insight to action: Why data value strategy is more than just a dashboard

When people talk about data and analytics today, they quickly end up discussing technology: data lakes, AI, cloud platforms. But the real bottleneck lies elsewhere – between insight and action.

Many companies are now able to collect data, visualise it and build impressive dashboards. But they find it difficult to consistently derive measures from these insights and track their impact. This is precisely where it is decided whether data becomes a competitive advantage or degenerates into a pretty reporting accessory.

The real problem: data insights without a course of action

Typical project process:

• A use case is identified,
• a model is built,
• a dashboard is implemented – and then surprisingly little happens.

Why? Because three questions are usually only answered half-heartedly:

• Who changes what in the operational system?
  A churn model that identifies customers at risk of leaving does not generate value unless someone aligns campaigns, offers or service processes with it.
• How do we measure success in concrete terms?
  Clicks, views and open rates are easy to measure, but not necessarily relevant to the business. Revenue per customer, renewal rates, service costs per ticket or acceleration of certain process steps are the key figures that count in the end.
• By when do we expect what effect?
  Data and AI initiatives take time. On average, it takes months for investments to pay off. Those who do not define a clear timeline with interim goals quickly lose patience or abandon projects just before they take effect.

Data value strategy: from business case to established measure

A genuine data value strategy does not start with the tool, but with the value chain:

Clearly define business value

• Which specific use cases have priority?
• What financial or strategic impact is expected (costs, revenue, risk, customer experience)?
• How do these use cases fit into the company's overall strategy?

End-to-end insight-to-action thinking

There are several steps between an insight (‘Segment X has a high willingness to switch’) and the actual action:

• Derivation of specific measures (retention campaign, adjusted pricing, process adjustment in service)
• Implementation in operational systems (CRM, marketing automation, shop, call centre software, ERP)
• Adjustment of processes and roles (Who decides? Who implements? Who is responsible for success?)

Explicitly anchor value measurement

This turns ‘we believe this will work’ into a robust control model. This includes:

• Definition of fewer, but more meaningful core KPIs per use case
• Determination of baseline, target values and timeline
• Measurement concepts that reflect not only short-term lift effects, but also ongoing efficiency and quality gains
• Monitoring setups that automatically show whether measures are effective – and when adjustments need to be made

Value measurement is precisely this discipline: the systematic, transparent measurement of the value contribution of data initiatives – not just once, but throughout the entire life cycle of a use case.

Why many companies fail at data value strategy

In practice, recurring patterns can be observed:

• Incorrect metrics: The focus is on what is easily measurable (clicks, impressions, dashboard usage figures), not on what is critical to the business (contribution margins, churn rates, throughput times).
• No adaptation of operational systems: Insights remain in the analytics cosmos because CRM, ERP, shops or service systems are not adapted – or because no one is responsible for translating insights into process logic.
• Lack of end-to-end responsibility: Data teams deliver models and reports, and specialist departments are expected to ‘do something with them’. This diffusion of responsibility means that projects with good results on paper fizzle out in reality.
• Unclear timelines: Expectations (‘it has to pay off in three months’) are not in line with the nature of the use cases and the necessary transformation work.

How data projects become value creation programmes

An effective approach is to organise data not as individual projects, but as a portfolio of value drivers:

Define lighthouse projects with a clear value focus instead of ten parallel initiatives: 2–3 lighthouses that

• deliver quickly visible, measurable value and
• can be easily scaled and transferred to other domains.

Model insight-to-action chains for each lighthouse use case

• Where does the insight come from?
• Where are decisions made?
• In which system is action taken?
• How and when is the impact measured?

Establish value measurement as an integral part of operations

• Recurring reviews of KPIs, not just project status reports
• Clear responsibilities (‘value owner’/business product owner)
• Adjustment of goals, measures and models based on the measured effects

Feed insights back into strategy and roadmap: successful use cases are scaled, failed ones deliberately provide learnings. Data strategy thus becomes a learning system instead of remaining a static paper document.

How adesso supports: value measurement as the missing link

In many projects, we support companies precisely in this critical phase: translating insights into consistent action and measurable, comprehensible value cases.

• Typical contributions:
• Structuring a data value framework
• Establishing value measurement along defined use cases
• Defining targeted KPIs and measurement concepts, including timelines
• AI-driven assessments of value potential and processes
• Designing the necessary adjustments to operational processes and systems so that measures can take effect

This creates a data landscape in which the success of data initiatives is no longer estimated but proven, and in which every new use case is geared towards value creation from the outset.

Next step: moving from reading to action

If you feel that your data architecture looks good ‘on paper’ but is not yet working properly in everyday life, talk to our experts!

Non-binding pre-call: 30 minutes in which we assess the current situation and outline possible next steps.

If you are interested, simply send a short email or contact request with the keyword ‘data ecosystem’ – I will then get back to you with suggestions.