Data Innovation Toolkit

The value is in the combination.

Most companies judge their data one set at a time. Value appears when data is combined. This toolkit helps leadership teams find those combinations, test which ones they can defend, and plan the work to use them.

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Focus carries through every canvas
1
MapWhat data do we hold?
2
CombineWhat can we join?
3
AdvantageWhich can we defend?
4
PlanWhat work must we do?
Data CardsSpot data on Canvas 1. Suggest combinations on Canvas 2.
Canvas 1 marks feed the plan
14
The problem

Companies hold data. Few know what it is worth.

Leaders can buy tools to process data. What they lack is a way to judge it.

11% vs 2%Share of revenue top-performing firms attribute to data, against bottom performers.1
1 in 3Executives who say their data has value they have not yet realised.2
35%Firms that have achieved extensive value from their data product work.3
Business data literacy

Knowing what data the business holds, how it is managed, where its value lies, and whether it gives advantage.

AI can now process data for anyone. Knowing which data matters is the gap.

Sources: 1 MIT CISR · 2 McKinsey · 3 KPMG
The principle

Innovation is new combinations.

  • Innovation has always come from combining existing things in new ways.4
  • Data is not used up. One data set can join many combinations.
  • A combination can be rare even when each data set is common. That is where advantage sits.
  • Generic data helps you keep up. Valuable data helps you pull ahead.
Source: 4 Schumpeter
DataA
+
DataB
=
RevealsWhat neither
shows alone
Where to start

Start from what you have, a problem, or a connection.

What we have

Existing data

What could our existing data do that it does not do now?

A customer problem

A problem to solve

What do we hold that could solve this?

Linking engines

A new connection

What value appears when two parts of the business share data?

WHAT WE DOThe work inside the firm VALUE COREThe offer HOW WE WIN AND SERVEThe work with customers HOW WE EXTENDThe work with partners Shared data

Linking engines: data from one part of the business changes what another part can do.

Four canvases

From data to decision in four steps.

Each canvas hands one output to the next. The focus line carries through all four, so nothing is lost between steps.

1Data Map

What data do we hold, and what can we use?

Place each data set in its engine. Mark every note five ways.

SourceHoldRightsQualityPosition

Output: a marked map and a gaps list.

2Data Combination

What can we do when we join our data?

Join two data sets. Ask what the pair reveals. Link it to value for us and for customers.

Output: combinations linked to value.

3Data Advantage

Which are worth building, and can we defend them?

Test each combination. Is it valuable, rare and hard to copy? Are we organised to use it?

Output: a decision on each. Develop, Build first, Hold or Stop.

4Data Plan

What data work must we do, and when?

The marks from Canvas 1 become actions: get, clear, clean, partner and connect.

Output: a data plan with owners and dates.

The Data Cards

A shared language for your data.

  • 87 cards across 14 categories and four engines.
  • On Canvas 1, the cards show teams data they forgot they hold.
  • On Canvas 2, each card suggests combinations to try.
  • A blank sheet asks people to remember everything. The cards let them spend the time on combinations.
  • Two of these cards drive the worked example below.
What We DoData 16
Operations
Production Yield and Quality
Definition

How we make things and how well, including output, defects and inspection.

Innovation lens

Shows where output is lost to defects and rework, and where inspection could move earlier.

Value CoreData 32
Products and Services
Product and Service Usage
Definition

How customers actually use what we sell, showing adoption, depth, and underused value.

Innovation lens

Shows the gap between what is sold and what is used, where value waits unclaimed.

How We Win and ServeData 74
Customers and Markets
Customer Feedback and Surveys
Definition

What customers tell us directly, through satisfaction scores, surveys and net promoter feedback.

Innovation lens

Shows which complaints repeat, and which the business has never answered.

How We ExtendData 68
Ecosystem
Networks and Relationships
Definition

The relationship and dependency graph across the whole ecosystem.

Innovation lens

Shows where influence and dependency concentrate, and which ties carry the ecosystem.

In practice

One case, four canvases.

A jet engine repair business asks what its inspection data could do.

1Map

Data Map

Start fromWhat we have
FocusOur inspection data. What could it do for blade repair?
Production Yield and QualityI · H · F · N · E
Product and Service UsageX · C · L · R · S
2Combine

Data Combination

CombineProduction Yield and Quality + Product and Service Usage
RevealsHow each engine was flown, and which blade damage can be repaired
Value for usBlade disposal falls from 20% to 5%
Value for customersEngines return faster, with fuel burn up to 0.5% better
3Advantage

Data Advantage

ValuableLess waste and faster repair
RareFew repair shops hold both data sets
Hard to copyIt improves with every repair
OrganisedNeeds flight data terms with airlines
DecisionBuild first
4Plan

Data Plan

Clear · NowAgree flight data terms with airlines
Partner · NowOpen talks with two airline customers
Clean · NextFix gaps in scan records
Connect · NextJoin scan records to repair history

Illustration based on a jet engine blade repair case. Data set names are Data Cards. Marks and plan actions are examples.

The result

A short list, a decision, and a plan.

Know your dataWhat you hold, what you can use, and what only you hold.
A short listCombinations, each linked to value.
A decisionDevelop, Build first, Hold or Stop, for each one.
A data planThe data work, with owners and dates.
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