It is one thing to have a nice proof of concept, but to put it into production is a totally different ball game.Understanding the data and the data stream is sometimes very complex, but it is the only way to get sustainable results.Without a solid foundation and repeatable chunks, the modal will not be future-proof
Working with data scientist and algorithm specialist, the first thing to do is dive deep into the data, getting the data to the proper quality level, have the data flow in a model and think about the results that can come out of the data. Knowing the data and making the data ready to be analyzed is of course important, but what will be happing with the result afterward? Who will be using the results? What are their goals and biggest frustrations at the moment? In which business process will it be incorporated? In order to integrate Machine Learning successfully into your business, you need to answer these questions upfront. Do this and your proof of Concept is not the end station, but the beginning of the envisioned advanced way of working.
Take-away 2: Data is always dynamic
After understanding the initial data, it is also important to understand that this data and its data sources will change over time. We always start from helping you build the database that will serve you well for the years to come, teach you the best way to record your variables of interest, and what might be worth recording, even though at this point it does not seem interesting to you. Understanding your sources, your future source and external sources will guide you to a sustainable future-proof solution.
Take-away 3: Design for the future from the start
From the start of a new project make Data Science and Machine Learning part of your corporate strategy. We are past the moment that it is justified to be a sidetrack in your business, only for experimental purpose. We are all aware of the fact that design thinking and design systems makes your business more innovative and creates the competitive advantage. Extend this thought to your data driven projects; use these methodologies to design for the future. Create components that can be re-used and build upon, considering the full stack, from front-end to back-end. That is the only way you can expand the data driven Machine Learning base to the future.