Virtually every business is planning or executing a major digital transformation involving new business models, new technologies, and new processes. There are several essential ingredients for a successful digital transformation. Data is certainly the most important – data is in fact the critical foundation for every successful digital transformation.
And one of the key areas most organizations are investing heavily today is the implementation of AI algorithms to enable them to better predict future sales, better predict machine failures to allow for preventive maintenance, better detect fraud and so on and so forth. So, applying AI and Machine Learning algorithms sounds like the perfect solution.
However, implementing AI algorithms without the use of good quality data is like spinning the wheel of fortune. You never know what you will get and whether you can trust the outcome of your AI algorithm. Therefore, AI needs good quality data for it to be reliable. The more data you feed into AI algorithms, the more precise their predictions will be.
But how do you get good quality data? Organizations are collecting massive volumes of data. But managing these vast volumes of data (and the variety thereof) is no easy task. To get a good understanding of what data lives where, and what its relevance is to the business, it needs to be tagged, verified, and documented. Doing that on a large scale has its own challenges. So, this is an area where AI algorithms can also help by looking for similar data patterns across your infrastructure, by automatically tagging the data based on its location (table, column, file) or based on its actual contents. AI algorithms can really help simplify that process. So not only does AI need good data, to manage the vast amounts of data we need some help of AI as well.
These two angles will be discussed during this session around the importance of AI in modern business.