Companies in Germany are not yet fully exploiting the potential of their data. This is despite the fact that some of them invest large sums in Big Data solutions every year. They often lack a mature data strategy to fully leverage their own data treasure trove. In this blog post, we explain what actually constitutes a good data strategy and how it is implemented.
This is why every company needs a data strategy
Whether it’s a small family-owned business or a large multinational corporation, if there’s no data strategy in place, the enormous potential of data can only be exploited to a very limited extent. In addition, the data strategy development process provides a good opportunity to define how data will be used, where the priorities lie, and to create a plan for achieving the defined goals. This is the surest way to identify core business data needs and derive a feasible plan for the future.
With the rapid pace of technological development, many companies – especially the smaller ones – feel they cannot keep up. This can then lead to inaction or a strong urge to ignore the trend. Or it can contribute to the company getting completely bogged down in the new opportunities. Neither is a good way to get the most out of data.
What should a data strategy include?
Below we provide a brief summary of the key elements that any data strategy should include.
Organizations should start with their key strategic data use cases or data priorities. Essentially, this means working out why and how data should be used. Data use cases or data priorities must always be linked to the overarching business strategy. In other words: How will you use data to achieve the company’s key strategic goals and solve its most pressing problems? Most organizations start by using data to improve decision making, which in turn leads to data priorities such as the following:
- Understanding and improving employee engagement
- Developing a more personalized customer experience
- Optimizing pricing
Regardless of which use cases are identified, the role of every finance professional in the organization should be to evaluate the business impact and ROI of potential data projects against the organization’s strategic goals. It is also important that the data strategy focuses on an achievable number of use cases. Here, it is helpful to identify one to five use cases, otherwise there is a risk that the data strategy will become confusing and unrealistic.
Then the requirements and challenges for each use case are determined. Once it has been figured out how the data will be used, one can move on to the actual data strategy. Here, there are the following key questions:
Data requirements
What data does the company need and how will it obtain that data?
Data management
How does the company handle issues of data quality, ethics, privacy, ownership, access, and security?
Technology
What are the software and hardware requirements? This includes the technology for collecting, storing, processing, and analyzing and communicating insights from data. Does the organization already have the technology to support the data strategy? If not, the it needs to identify what is needed to meet these requirements.
Skills and capacity
Lack of data skills and capabilities is a major problem for many companies. So how should the gap in these skills be addressed? This may include training staff and hiring new staff, working with external vendors, etc.
Implementation and change management
What challenges must be overcome to successfully implement the data strategy?
These pitfalls must be avoided
When evaluating a data strategy, it’s helpful to be aware of the most common mistakes companies make. These include:
- Starting with an outdated business strategy
The data strategy must support a business strategy that is current and relevant to today’s digital world. - Not linking data use or priorities to strategic business goals and challenges
Too many organizations develop their data strategy based on use cases that are interesting or easy to implement, rather than use cases that lead to achieving their goals. - Only internal, traditional data is considered
Data today exists in many forms and comes from many sources. A good data strategy should consider all ways to access data, including options such as photo and video data, as well as external sources such as social media platforms and Big Data brokers. - The ethical, privacy, and legal issues are downplayed or overlooked
Consumer trust is critical, so it is important that governance is properly addressed.
A good data strategy is essential for modern businesses. Our tips will help you approach the development of your data strategy with confidence.
If you’re currently thinking about developing a data strategy that’s right for your business, we’ll be happy to help. Get in touch with our Big Data experts today.
About the author
Mathias Herrmann
CEO at ALLEHERZEN
Mathias Herrmann is an internet entrepreneur going back to the Internet’s early days with a deep interest in digital and future technologies. For over 20 years, he has been helping companies make the most of their data by forging innovative solutions – without forgetting the people behind the data.
Book a free video call of 30 or 60 minutes directly now:
Comments