How to Break Data Silos to Drive Enterprise-Wide AI
Not many people miss having to manually sort files, label papers, or search for lost forms in huge filing cabinets. That’s because all these tasks have become way easier, faster, and more enjoyable since they’ve become digitized – computers and the internet have revolutionized the way businesses approach organization and task management.
Similar to how computers and the internet made monotonous tasks faster and easier in every department, AI will transform work in every industry in the 21st century. Machine learning will automate away the most time-consuming and repetitive tasks across a company, along with offering predictions that will allow businesses to make better decisions ahead of time.
Introducing these revolutionary processes takes time and specialized knowledge. However, the way this is happening right now is more expensive and less effective than it truly has to be.
There’s an important business disconnect that is holding back the expected ROI for machine learning models. In most companies, the people who can manage large volumes of data and encode it in machine learning models are sitting in IT department silos with people who share their technical skillset. They are away from the action – they have only a vague idea of where the application will interact with its end user, whether that’s a customer, supplier, or employee. They are one step removed from the business, and less intimately acquainted with the most important business inputs and outcomes. The models they make reflect this, often collecting data for a variable that is not as predictive as another.
Additionally, much of the work data scientists are doing is unnecessarily repetitive. Data preparation takes80%of the average data scientist’s time. They struggle to compute features which are the data attributes that their machine learning models use as input.Importantly, many data scientists throughout a company end up slogging throughthe data to calculate the same features that another data scientist in the company has already found. Not to mention the governance nightmare lurking underneath the messy data lineage that underlies new machine learning models!
All this is changing. A new technology called afeature store is providing a central location for all the data related to the machine learning lifecycle and its business benefits. By freeing up time that used to be dedicated to duplicative data prep and feature engineering, data scientists can get more models up and running with a better return.
A feature store is a central repository that stores features, data lineage, and metadata associated with all the machine learning models in a company.
