First of all, dealing with big data does not depend on whether a company is large or small. Rather, it shows that such companies are pioneers here that already have a business model that relies heavily on data and information or come from industries with strong competition, such as telecommunications and finance.
Much activity can also be observed in trade and industry and all forms of service companies. Age also seems to play a role because younger companies in particular often seem to be more innovative and faster when using data. Especially among the fintech, there are also small companies building a successful business model with big data evaluations.
Process Control And New Sources Of Revenue Thanks To Big Data
The most common goals of big data initiatives are comparable to the goals of digitization initiatives: On the one hand, it is about improved process control thanks to more precise predictions; to develop new sales potential with data-based products. The starting point for a big data project is typically how data and data analysis can contribute to process improvement or to changing the business model.
So it all starts with creativity and innovation. Promoting this and allowing development and testing from the idea to its implementation require extensive support from management. Experience with data management and data analysis is also required. The culture above of innovation must make it possible for ideas to arise quickly and be tried out as quickly as they can be rejected. Furthermore, the organization must be willing to take up and implement innovations.
Preparation For A Big Data Project
Companies should first clarify the framework conditions when starting big data projects, i.e., management support and opportunities to create innovation from data. Then you should start with two topics: Identifying and prioritizing application scenarios (use cases) and taking a closer look at the data.
Which data is not yet sufficiently combined and analyzed? Which data sources, internal or external, could provide additional added value? Does the data even provide an implementation of the intended use cases? The most common challenges in implementing big data initiatives are data protection, data security, and a lack of technical and professional know-how.
But the biggest problem is procrastination too long instead of starting with small use cases – and a lack of speed can become an existential problem in a digital economy. The good news is: the challenges can be solved if you take the subject seriously.
Also Read: Big Data To Help The SDGs