The High Cost

Research has shown that bad data is on average costing businesses 30 per cent or more of their revenue. Research firm Gartner has found that the average cost of poor data quality on businesses amounts to anywhere between $9.7 million and $14.2 million annually. At the macro level, bad data has estimated to cost the US more than $3 trillion per year. In other words, bad data is bad for business.

For startups in sectors like Internet services, transportation, and data analytics,. Which have found to have the highest rates of cash burn, losses from poor quality data has an unacceptable additional cost. Average burn rates are already high before we factor in these hidden costs: for pre-seed startups in the US, these are just under $18,000 per month. But that number gets bigger as the company grows: $75,000 for seed round companies, just under $400,000 for Series A, $500,000 for Series B, and $900,000 for later-stage startups.

Today, good quality data is so valuable. And so hard to come by, that startups which own large amounts of transparent, proprietary data of excellent provenance can secure nine-figure valuations on the back of it.

Data economy

The problem really starts and ends with the data economy. Broadly put, the data economy is the production, flow, purchase, and sale of data. Data has created by data producers, such as ride-sharing apps, social media networks, telcos, banks. And a whole range of private enterprises. It has then stored anonymously in data storage centres. And often purchased by a third party who seeks to use the data for their own, separate business purposes. “Click farms” exist precisely because they can launder their false data through the data economy, with their data ending up purchased by legitimate businesses. Who then go on to make decisions often worth hundreds of millions of dollars.