The five barriers on the path to becoming a data-driven company
What barriers do companies face on the path to becoming a data-driven organisation? We looked into this question and asked 70 Swiss companies what they thought. From this, we identified five major underlying challenges.Download "impulZe Data-driven companies"
Insight in brief
- In our survey, 85% of the companies said that they rate the potential of data and artificial intelligence (AI) as high
- We identified five barriers on the path to becoming a data-driven company
- We see three levels of maturity in companies on the way to becoming data driven
There is currently a lot of talk about data-driven organisations. But is it just the next hype, or does this approach offer real added value for the business? We firmly believe that it isn’t just a hype. With their strategic use of data and artificial intelligence, data-driven companies systematically and continuously generate added value. And this is an opportunity that most Swiss companies generally acknowledge. In our survey, 85% of them said that they rate the potential of data and artificial intelligence (AI) as high. At the same time, however, only 10% of the managers surveyed described their companies as data driven. So where are the specific challenges on the path to becoming a data-driven company? We identified five barriers:
1. An inactive data innovation pipeline
Holistic, ongoing planning and implementation of data and AI projects are critical. If data projects are motivated by technology and not driven by the business, the innovation pipeline remains inactive.
2. Proof-of-concepts falling by the wayside
Without a clear concept for operationalisation, many projects never get beyond the proof-of-concept stage. In other words, even technically feasible solutions do not create any added value in the end.
3. Perfectly implemented solutions that don’t get used as planned
Poor integration into existing tools and platforms is the most common reason for the lack of acceptance of new AI-based solutions. Other reasons include sceptical attitudes towards AI in general or users lacking the proper training.
4. Skills in the field of data
The greatest challenge facing most companies is interdisciplinary collaboration during data projects.
5. The data itself
Many companies have the data at their disposal. But the real challenge lies in accessing it and in its quality. In many cases, effective data governance is lacking.