What is a Data Strategy?
A company’s data strategy answers questions around
- how a company will use data to generate value – typically with data-driven insights and business processes, and data-enabled business models (=data monetization),
- how a company manages data to generate value, i.e. collecting, storing, processing and distributing data (= data foundation)
The data strategy defines the capabilities and needs to evolve reflecting the organization's current state of data maturity.
Context of a Data Strategy
A data strategy is typically part of a broader strategic framework also including corporate, digital, functional, divisional and IT strategies.
- A data strategy is linked to and derived from the corporate business (and digitalization) strategy
- An enterprise-wide data strategy provides the frame for functional, divisional and regional data strategies
- A data strategy has also mutual dependencies to the IT strategy
Why Is Data Strategy Important? What Are the Benefits of a Data Strategy?
Data Strategy is a "Must Have". Five Reasons Why You Need a Data Strategy:
A data strategy is, therefore, more sought after than ever before.
- The role of data is changing – from a supporting input to a strategic (i.e. business-critical) resource, which enables insights and new, data-based business models
- Dispersed data initiatives and data use cases require coordination. Further, key enablers can only be provided centrally and not from a single initiative - aligns activities with strategic priorities
- Transparency about data is missing – data is typically siloed in functions and spread across a fragmented system landscape
- Regulators and law makers increasingly issue requirements on data. Furthermore, customers and business partners have even stricter expectations
- There is an increasing need to design end-to-end processes, which require supporting data (flows).
How to Build a Data Strategy
Data Strategy Canvas
To help companies create their own data strategy, the member companies and researchers of the CC CDQ have developed a blueprint: the "Data Strategy Canvas". It is a visual design tool, defining the core elements and guiding questions to be addressed.
Here you can download the Data Strategy Canvas, print it as a poster and use it in workshops with business experts, data managers, data scientists and other stakeholders to discuss and define the key elements of your data strategy.
The "Data Strategy Canvas" Consists of the Following Outlines the Key Elements of a Data Strategy:
- The strategic layer defines the Need for Action, Vision, Mission & Scope and Business Value
- Need for action defines the motivation for a data strategy. Where do we stand? Why do we have to change?
- Vision defines the aspiration for data. What is data's future role?
- Mission and scope set the boundaries and defines the purpose for the data program/initiative and (federated) data organization. What is the purpose and scope of our data initiatives and organization?
- Business value explains how data contributes to business success. What is the value contribution of data to the business?
- The operational layer includes data "use cases" and capabilities
- Key capabilities define the capabilities that are needed to achieve the vision and realize data use cases. Which organizational capabilities do we intend to build or improve? E.g. people, roles and responsibilities; processes and methods; performance and metrics. Which technical capabilities do we intend to build or improve? E.g. Data life-cycle; Data architecture; Data applications
- The implementation layer covers the Code of Conduct and Transformation
- Code of conduct describes the future mindset and culture related to data, both internally (employees) and externally (customers and partners). What are the values and guiding principles for data?
- Transformation defines the implementation and execution roadmap for the data strategy. What is the roadmap to implement the data strategy? How will the data strategy be executed?
Where Do European Companies Stand With Their Data Strategies?
To assess the current status of data strategies, we conducted in depth interviews with 18 European companies leading to the following insights:
- Current status:
- 6 of 18 the participating companies are currently developing a data strategy
- 7 companies have one in place since 2 years
- 5 companies have data strategies in place that are between 3 and 10 years old
- Most data strategies have an enterprise-wide scope
- 14 of 18 companies have an enterprise-wide scope
- 3 companies focus on certain functions or division and their data only
- 1 company focuses on a specific country
- Mature companies consider all types of data (structured & unstructured, internal & external) and define the data scope (either as data domains, or opportunity-driven) by use cases and lighthouse projects
- Data strategies of mature companies consider the data foundation and data monetization
- The data strategy:
- secures investments for important groundwork
- evangelizes about data and creates a data culture
Several examples of successful data strategies can be found in the CC CDQ knowledge base. These include (amongst) others:
- PMI's data strategy supporting the company's digital transformation
- Schaeffler's data management journey; and
- Deutsche Telekom's data initiative
Successful data strategy cases demonstrate the following success factors:
- Involvement of business, data and IT - with corporate development and enterprise architecture playing an increasingly active role as facilitators
- Data strategy sponsors and owners at executive board level
- Regular strategy reviews and update, e.g.
- integration of data aspects in strategic planning cycles (every 1-3 years)
- tracking not only of activities, but also KPIs and the financial value contribution from data initiatives
- several data strategy iterations, starting with master data or BI in the 2010s
- A balanced data strategy development approach combining a top-down perspective (deriving objectives and requirements on data from corporate strategies) and a bottom-up perspective (starting from data use cases or even from identifying the pain points in (decentral) approaches)
- Focus on communication to raise awareness and change the mindset towards data
Do You Have Questions About Data Strategy?
Do you need support or advice in developing your company-wide data strategy? Our data management experts are ready to help you and answer your questions.