All businesses have to make decisions. Businesses are in fact information machines. Those that can consume, understand and act upon the best information in an optimal way have an inherent advantage over their competitors.
One of the simplest and most effective data science projects that businesses can undertake is to automate parts of the business that consume and act upon data.
The benefit of this type of project is that we are improving the consistency and the accuracy of these decisions, whilst at the same time saving a considerable amount of time.
Because we can provide quantitative measures of performance we can guarantee the consistency of a decision.
And by moving these models into a production environment we can remove the manual effort that was previously required to make the decision.
Processes go hand in hand with Business. On the one hand, processes are necessary to standardise practises. But quickly processes begin to eat away at a business’ agility and significant sums are spent on employing people to act out these processes.
There is another way. Data science can be used to encoded this processes in a model that can be repeated over and over again all without human intervention.
The time savings can be huge. But more importantly this allows your business to pick and choose when and how these data-oriented solutions are applied. This means that you can still be agile, applying bespoke processes as and when they are warranted. But still maintain the stability of a process driven business.
Truly innovative companies are able to build data driven products that provide features so incredible that all competition becomes obsolete.
Interestingly, these products might not be user visible. For example, manufacturers can automate quality assurance to maximise production speed. And the result of this isn’t directly visible to the end user. But the speed at which manufacturing can take place causes competitors to struggle to reduce costs and remain lean.
Many of these types of products are so called “expert systems”. These systems try to do one thing very well, which is well suited to data science and cloud native software. This also means that these types of products are very domain specific. But lessons from other industries can be utilised anywhere.
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