What is a Data Strategy?
Definition of a Data Strategy
In the past, data was often seen as only one aspect of a technology project and was not treated as a corporate asset. Today many managers and data experts ask for a data strategy as a basis for data-related activities, but they often lack a clear understanding of what a data strategy is and should comprise. The few existing definitions from literature emphasize that data strategies are closely connected to the business strategy and define a coherent approach for the management of data assets:
A data strategy is [...] "a central, integrated concept that articulates how data will enable and inspire business strategy." (MIT CISR Data Board 2018)
"A data strategy should include business plans to use information to competitive advantage and support enterprise goals. Data strategy must come from an understanding of the data needs inherent in the business strategy: what data the organization needs, how it will get the data, how it will manage it and ensure its reliability over time, and how it will utilize it." (DAMA 2017)
"A coherent strategy for organizing, governing, analyzing, and deploying an organization’s information assets that can be applied across industries and levels of data maturity." (DalleMule und Davenport, 2018)
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
Webinar Recording: Data Strategy
A data strategy is a must-have for data-driven companies. In their joint webinar from August 13, Tobias Pentek and Prof. Dr. Christine Legner from the Competence Center Corporate Data Quality (CC CDQ) provide valuable insights from a study and introduce the CDQ Data Strategy Canvas as a tool to design a sustainable data strategy.
Length: 42 minutes
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. Adopting a data strategy helps to optimize technology investments and lower costs.
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" 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 (and technology) 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?
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.
References and Further Readings on Data Strategy
Article: What's Your Data Strategy? (Harvard Business Review)
Publication: PMI's Journey Towards a Data-Driven Enterprise
Schaeffler: Data Management in all Data Areas
Article: An Outcome-Driven Enterprise Data Strategy: Tools And Technology (Digitalist Magazine)
Telekom: The Deutsche Telekom Chief Data Office Start-Up Program
Data Strategy (Case Study)
Data is at the core of PMI’s business transformation. The CC CDQ case study report outlines how PMI established an Enterprise Analytics & Data (EAD) function based on two major pillars (Data Governance and Data Science). Data strategy pursuing offense and defense
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