Data is the gold of our time. 90 percent of all medium and large companies will pursue a data-driven business model by 2023, Gartner analysts estimate. To successfully mine the data gold, companies should integrate a data strategy into their digital transformation.
We explain the advantages that companies with a data strategy can benefit from, how they can develop such a strategy step by step and what is important when implementing it.
Definition: What Is a Data Strategy?
A data strategy is a structured plan for the value-adding use of corporate data. It is derived from the corporate strategy and is aligned with the strategies of the IT and business departments. The data strategy defines company-wide goals, key figures and measures to generate knowledge from data. It must result from the cooperation of management, IT and specialist departments.
5 Benefits of a Holistic Data Strategy for Businesses
No one would dispute that the digitization of corporate processes and business models is necessary to remain competitive in the future. But digitization alone does not guarantee a leap in sales. How strategically corporate data is used and the quality of the data - these factors increasingly determine the market position.
Until now, corporate data management has mostly resembled a patchwork quilt. Individual IT projects on data quality are launched or solutions are developed when an acute need for data arises. In this way, individual departments or individuals benefit, but companies are far from having a company-wide data strategy. However, this has various advantages.
If data is collected and maintained according to a company-wide strategy, unwanted data redundancies are eliminated. It can improve the clarity of the IT infrastructure and thus simplify maintenance. In this way, unnecessary costs for server capacities are also saved.
Centrally defined processes and technology in data management help to clearly identify process costs in the event of data breakdowns and to optimize processes for the future. Our project experience shows that errors in order processing can be reduced by up to 25 percent and returns by up to 40 percent. Companies can realize significant cost reductions; depending on the size of the company, the savings potential is several million euros.
A data strategy ensures that the departments only collect data that is relevant to the overall business objectives and that no data waste is accumulated that causes unnecessary costs.
Furthermore, by coordinating individual data initiatives, the entire company can learn from new experiences. Expensive mistakes are reduced and the learning curve of innovation rises steeply.
When data management is based on a company-wide data strategy, regular ad-hoc IT deployments that develop isolated solutions are eliminated. Employees don't have to resort to workarounds, but have convenient access to all important data and can make decisions faster and complete tasks quickly.
When data is used across departments, companies maximize the efficiency benefits. Because many data projects only show their effect when they are integrated in a strategic context.
The legal requirements for data protection and data security are constantly changing. Among other things, they change with technological progress. To ensure legal certainty and compliance despite an increasingly complex data pool, a data strategy is a good way to go.
If, for example, the data protection regulations for certain countries change, as with the introduction of the GDPR, they can be adapted easily and comparatively quickly on the software side via structured data management.
A data strategy helps ensure that companies always have the right information to improve customer service, develop more relevant products, and reduce operating costs. They know which complaints customers have reported recently, which products sell best in which customer segments, and can utilize their machines and equipment more efficiently.
This knowledge advantage is reflected not only in higher customer satisfaction, but also in revenue increases that isolated data projects cannot achieve.
Two Examples of Successful Data Strategies
Developing a data strategy is not a simple project, but a transformation project at C-management level. It involves a significant investment of time and resources. Practical experience shows that the investment is worthwhile. Two examples of how a data strategy can be successfully created:
Data Strategy at Festo SE & Co. KG
Which small adjusting screws make the difference to success? Festo, one of the leading manufacturers of automation technology, knows the answer. In the OnDemand webinar, Mario John, Head of IT Portfolio Innovation and Architecture, and Diethard Frank, IT Manager Big Data & AI, explain how they introduced a holistic data strategy in the company and give tips on what companies should look out for during implementation.
"Early coordination between the business department, IT and the customer is extremely important. Otherwise, developments take place side by side and you have to reckon with high investments in resources, time and money. "
Mario John, Head of IT Portfolio Innovation and Architecture @ Festo
Data Strategy at Bouwmaat
"No gut feelings. No estimates. Just facts. " – The Dutch wholesaler for building materials Bouwmaat has already transformed itself into a customer-centric and data-driven company several years ago. In the on-demand webinar, Michiel Mak, Manager Category Management, Pricing, Purchase & Article Data, provides insights into the company's data strategy. He explains how they have enabled smarter and automated processes and how these lead to an improvement in data quality.
"It only took 10 minutes, then we knew how Corona was going to impact our sales. Simply because we did a forecast for the next month and the next year. That made us very agile. When you have the data, it's super easy."
Michiel Mak, Manager Category Management, Pricing, Purchase & Article Data @ Bouwmaat
Create a Data Strategy - What to Consider?
A data strategy as part of digitization is not an end in itself. To make the investment worthwhile, companies should proceed methodically and define the desired value of the data. In this way, they ensure that data is collected and processed in such a way that it supports the business goals in the best possible way.
Parsionate recommends a three-step approach for strategy development. In this way, companies achieve data management excellence regardless of their starting point.
1. Location determination and initialisation
First, companies should create awareness among the team that there is monetary value in data and what role data plays in the future of the company. If the revenue potential of data is clearly quantified, this can motivate employees to commit to the value-oriented handling of data.
In parallel, an analysis of the status quo should be carried out at the technical and process level: What data is currently being collected and how? Where is there potential for improvement and which benchmarks should be achieved in concrete terms?
2. Strategy development
In the second step, the individual data initiatives of the business units are reviewed and consolidated. The further procedure of business and IT should be coordinated. Fields of action are identified and measures are developed together.
Once the measures have been budgeted and prioritized, companies have a roadmap that they can implement successively - with iterations for correction and optimization if necessary.
3. Monitoring and interim management
In parallel to the implementation, Parsionate uses its expertise to accompany specialists and managers as they grow into their new roles: they must regularly review and adapt the data strategy and justify their strategic decisions. The focus is on knowledge and know-how transfer as well as individual coaching so that managers quickly gain decision-making confidence and strengthen their opinion leadership.
Parsionate's subject matter experts are also available as interim and program managers to ensure the success of the transformation in operational terms.
Typical Mistakes When Developing a Data Strategy
In many companies, executives are well aware of the relevance of data. However, if the success of data initiatives leaves a lot to be desired, there are many reasons for this.
Companies often start a data strategy as an IT project and the business units are brought in too late, even though they are in a much better position to assess what data is needed and how. The result: acceptance of the new processes is low - as is the business output. Sometimes IT and business units pull together, but management lacks awareness of the value of data. Even then, it becomes difficult to achieve significant results.
A third typical pitfall is that employees are not involved in the transformation process; not only do they lack an understanding of the importance of the new processes, they also do not receive sufficient training.
Frequently Asked Questions Around Data Strategy
A data strategy canvas is a visualization tool with which companies can obtain an overview of their previous data utilization and concretize goals for a holistic data strategy. The categories of the data strategy canvas help to identify problems and potentials and to approach next steps in a structured way.
The Data Landscape Canvas follows a similar approach. This tool also serves to document the status quo of data usage in order to derive a data strategy for the company.
A successful data strategy is designed holistically. It starts with an analysis of the status quo, for example with the help of a data landscape or data strategy canvas, and is geared towards measurable goals. To provide data with maximum benefit for the company, the strategy integrates the requirements of management, business units, market, customers, IT and data operations.
Important aspects that should be considered in the data strategy include technologies and processes for data collection and use, standards for data quality and data analysis, and structures for building internal data expertise.
Download Data Strategy Canvas!