Tesco is the UK supermarket firm who saw record growth until two years ago, when their market share plummeted.
Despite still being the largest supermarket chain in the UK, their failed attempts to join the American market and their market share being cut away by budget supermarkets like Aldi, Lidl and Morrisons.
Adding to these problems are their latest accounting mistakes.
After initially issuing profit warnings in early 2014, it became apparent that they had artificially inflated profits by £250m. Further investigation by Deloitte showed that this number was actually £263m, or 30% of their overall profits from that year.
One of the major problems that has been shown is that they were doing promotional deals with suppliers, which is a common practice, but had been booking returns early and pushing the costs back. 8 executives have been suspended over the practice.
However, does this go beyond this?
As a society we are becoming more reliant on data, could Tesco have been too reliant on their data and analytics?
This is not a criticism of analytics in general, but what is clear from multiple examples is that garbage in creates garbage out. Tesco are almost certainly going to suffer from inaccurate data being input through their models, creating poor results and they could account for these huge discrepancies as predicted sales would have been based of expected promotional sales rather than actual sales.
Analytics is a powerful tool, but like anything from a lawn mower to a formula 1 car, if you put the wrong thing in it, it will break, sometimes with devastating consequences.