How to defeat the 'data zoo'
Doesn't sound too complex, does it? The reality, however, is often less straightforward. The main culprit? The presence of a 'data zoo' within a lot of companies: an untethered troupe of incompatible tools, frameworks and approaches used to collect and process data.
This situation often results from a disconnect between the underlying 'boring' data platform and the 'hip' data science applications. The fact is that one simply doesn't work without the other. It also explains why companies that have invested in setting up a future-proof data platform also tend to reap higher rewards from their Al experiments, are more successful in upscaling and industrializing those experiments and show significant faster time-to- market and agility.
The funny thing about Al
A prime example of one such company is BekaertDeslee, where past investments in company-wide standardization have enabled the quasi-seamless rollout of a waste-reduction algorithm on multiple production sites. Here, the model is linked via an API, which receives order information from the ERP to calculate the waste production potential (see the figure above).
All of this makes it clear that Al operationalization is nothing like a normal deployment. While all it takes is a laptop and some creativity to start experimenting, reaping Al's true benefits requires a clear business case, a solid and future-proof data platform, and a long-term strategy. The latter might not be as hip as the latest data science tool, but it's a lot more reliable.
At delaware, we have both the technical skills to build your future-proof data platform and the adventurous spirit required to embark on outside-of-the- box data science experiments.