The amount of data continues to grow every year: for one-third of the companies recently surveyed by IDC in Germany, the increase is between 31 and 60%. However, the quality of the data is poor, and this is becoming a major challenge. This is because insights need to be gained from the sea of data and used to optimize business processes. Without a high-quality database, however, it will take some time for the longed-for success of data-based business to materialize. The right data strategy is therefore critical to the success of a business. What do companies that succeed in this respect pay attention to?
1. Maintain focus on the business goal
The goal is clear: data quality needs to improve, and with it the possibility to create value. But when data and AI strategies that have been drafted on the drawing board are supposed to be transferred to business operations, things often get stuck in practice. Why is this? There is a lack of business strategy perspective, and implementation is only sporadic in individual projects. Successful companies are those that adopt an integrated approach and focus all their measures on achieving the company’s strategic goal. This creates synergies in implementation and facilitates the creation of a sensibly prioritized data roadmap.
2. Identify use cases
Only when the strategic direction is made clear does it become tangible: which use cases are the data supposed to support? This varies depending on the company and industry: e.g. regulatory processes in the insurance industry, diagnostics in pharmaceutical companies or procurement and production planning in the manufacturing industry. There are many ways to automate processes, relieve staff from having to reconcile data manually, speed up decisions and avoid errors. If you know the specific use case and the optimization goal, you can select the right data and tools and use them to your advantage.
3. Making data and IT infrastructure fit for purpose
What is the best way to incorporate a data strategy into business operations? One approach is lean data pre-processing: all the data relevant to the identified use cases are collected and prepared in such a way that AI components can evaluate them. Another option is to integrate these AI components into the operational processes in a comprehensible and stable manner. This is achieved through coherent operationalization concepts and secure monitoring for oversight. A good operational concept provides timely information in the event of discrepancies and at the same time highlights potential for optimization. Is the IT infrastructure up to all of this? If not, companies should take a pragmatic approach to extensions: cloud technology and open source solutions are flexible and save resources, which are important prerequisites for a future-proof IT landscape.
What are your company’s goals? Get in touch with Dr. Markus Knappitsch and discuss which data strategy fits your requirements: you can reach him here.