“Data-driven decision making” is a term we’ve all heard by now, but what happens when the data driving business decisions is inaccurate? It’s no secret that the task of data entry is infamously inaccurate and nearly impossible to scale, but how great is the threat that “bad” data—most often the result of human error—poses to an organization?
Bad data is bad for business
Gartner found that the average annual cost of poor data quality to an organization is $15 million. At the macro level, bad data is estimated to cost the US more than $3 trillion per year. Meanwhile, business becomes increasingly data-driven; for example, with business models that depend on data such as artificial intelligence (AI). And, the more an organization grows, whether that growth presents in the form of more customers, employees, suppliers, products or business units, the greater the negative impact of bad data.
Why businesses are outsourcing data entry
The growth and spread of bad data and the associated damage to the bottom line are primary drivers for organizations to outsource data entry tasks. Fueling business processes from the start with accurate (or “clean”) data yields considerable savings, regardless of where or by whom the data is processed. However, outsourcing data entry offers additional cost savings and saves employees from having to perform this tedious, time-consuming task.
Crowdsourcing data entry = limitless scalability
Crowdsourcing is one method of outsourcing data entry. At ScaleHub, we’ve combined the best of human and artificial intelligence for high-volume tasks like data entry. In fact, we’re able to guarantee over 99% accuracy for our customers, as well as exponential gains in efficiency and better data privacy. How? We figured the best way to explain how ScaleHub crowdsourcing solutions handle data entry and deliver these kinds of results is not to tell, but to show. That’s why we created this short video, which illustrates how we’ve been able to simplify data entry, and make it massively scalable.
