The advent of AI tools has been transforming businesses across different fields and applications. But there’s one commonality between all of these diverse fields: before an AI transformation can take place, the data this AI will draw from has to be better organized, collated and made accessible first.
What data transformation is this?
The main transformation that is needed is making your data accessible to, and useable by, any AI tool that you want to get utility from.
Data needs to be organized and sorted. ROT data – information that is redundant, obsolete or trivial – needs to be purged to free up space in storage, reduce costs, and avoid skewing any statistical modeling your automated tools will make. Above all else, dark data needs to be addressed.
What’s dark data?
Dark data refers to information that organizations accumulate but often never use for analytics or decision-making. This category can end up being unreasonably large, in part because the common wisdom has been in recent decades to keep all your data if possible, on the off chance it will become useful down the line.
An organization that has large quantities of dark data ends up data rich, but information poor. The data is only as useful as it is actionable, and this applies even once you begin automating processes.
AI is only as useful as the information you feed it
AI tools have been explored for their added utility since the beginning of the craze, but at the end of the day the majority of the automated processes are heavily leaning on applied statistics.
Before you can begin extracting value from your data, with or without AI tools, step one is to ensure your data management is up to the task of transforming your raw information into actionable intelligence. Before an AI transformation – make sure you’ve undergone your data one.
Your Data in Your hands – With TECH-ARROW
by Matúš Koronthály
Image generated by Canva
