The Datentreiber Method: Data Strategy Design

Using the data strategy design method you achieve your goals securely and you find the right way faster. Data strategy design helps you and your team to collaborate efficiently and effective to develop unique as well as the most promising strategies for utilizing your enterprise data.

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What is Data Strategy Design?

To holistically advise its clients regarding individual data strategies, Datentreiber has developed its own strategy method which has been used, proven successful as well as continuously refined in numerous client projects: the data strategy design method is based on Design Thinking. It extends the original method’s user-centric and value-oriented mindset with Data Thinking, a perspective which is, coming from the users’ needs and available enterprise data, identifying, concretizing and evaluating data-driven solutions and business models. The Datentreiber method provides a collection of free to use tools – the Data Strategy Designkit – as well as a library representing typical design patterns – the Data Strategy Designguide.

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What is a Data Strategy?

A data strategy consists of three essential parts:

  1. Short-term: an overview of the company’s data landscape including the exploration of external data sources as well as the qualitative evaluation of the data.
  2. Mid-term: concrete use cases having a positive cost-benefit ratio as well as functional, technical and analytical concepts for an imminent implementation.
  3. Long-term: a roadmap for a gradual increase of the company’s analytical maturity by implementing further data-driven applications.

The data strategy is aiming at a transformation of the company towards data-driven business models and processes in order to get a leading position in the digital race.

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What Is the Datentreiber Designkit?

Working on a data strategy requires an interdisciplinary team of experts. This means for example experts coming from the marketing or production department, IT specialists, data analysts or data scientists as well as further people which are for instance working as managing director or in the legal department in their day to day job. Datentreiber has developed the Data Strategy Designkit to efficiently structure this process: it consists of a collection of visual collaboration tools (called “canvas”) which are used to jointly work on a data strategy in a creative and interactive way. The tools are available for free based on a Creative Commons License. They can be used as (printed) poster as well as digitally.

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What Are the Datentreiber Canvas?

The Datentreiber Designkit consists of various canvas posters. Data strategy designers can use these as an ensemble or one by one to clarify different questions related to a data strategy within their team. Because of their structure, the canvas help to raise the critical questions and to find the right answers. The canvas are divided into three areas:

Data

Data Landscape

Analyze

Analytic Maturity

Prioritize

Data Strategy

Conceptualize

Data Management

Concretize

Strategy

Value Chain

Analyze

Growth Horizons

Prioritize

Business Model / Case

Conceptualize

Strategy Pyramid

Concretize

Design

Stakeholder Analysis

Analyze

Value Curve

Prioritize

Analytics Use Case

Conceptualize

Customer Touchpoints

Concretize

Furthermore the Priority Matrix is also available representing a generic tool for prioritization. The Data Strategy Designkit canvas provides an overview of all canvas as well as a representation of the connections between the different canvas. For planning and visualizing data strategy workshops the Data Strategy Designguide canvas can be used.

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What Is the Datentreiber Designguide?

Companies can use the Datentreiber canvas in different combinations and ways depending on the company’s specific situation and objectives. Datentreiber has developed design patterns for typical starting points or areas of applications. These design patterns are summarized and classified in the Data Strategy Designguide. The following picture shows an exemplary template process for a data driven marketing strategy:

 

1
Focus on the critical area of application.

2
Identify use cases with potential benefit.

3
Sort use cases according to analytics maturity level.

4
Prioritize uses cases according to cost & benefit.
5
Identify decision-makers and users.

6
Understand desires and objectives of the users.

7
Conceptualize analytical solutions: data, tools etc.

8
Explore data sources & identify data providers.
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What Is the Best Starting Point?

All beginnings are…easy. To keep this promise we are offering various content regarding data strategy design:

Social Media:

Partner:

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