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
The data strategy defines the capabilities and needs to evolve reflecting the organization's current state of data maturity.
A data strategy is typically part of a broader strategic framework also including corporate, digital, functional, divisional and IT strategies.
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.
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)
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 strategic layer defines the Need for Action, Vision, Mission & Scope and Business Value
The operational layer includes data "use cases" and capabilities
The implementation layer covers the Code of Conduct and Transformation.
Several examples of successful data strategies can be found in the CC CDQ knowledge base. These include (amongst) others:
Successful data strategy cases demonstrate the following success factors:
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.