Anyone familiar with Big Data knows that an exorbitant amount of money is poured into the industry every year, and Ernst and Young estimates that over 46% of Consumer Goods companies still identify challenges with access to accurate data. While the number representing the money that goes into Big Data may not be all that shocking, there is another number and that number represents the money that companies lose every year as a result of poor data, which for IBM, is $3.1 trillion, according to Harvard Business Review article, “Bad Data Costs the U.S. $3 Trillion Per Year."
This staggering amount of money spent on poor data means that there is a rising opportunity for data quality improvement and that it’s time for company leaders to start looking for it. The fact of the matter is, according to the article author, Thomas C. Redman, Ph.D., “The reason bad data costs so much is that decision makers, managers, knowledge workers, data scientists, and others must accommodate it in their everyday work. And doing so is both time-consuming and expensive.”
"Over 46% of Consumer Goods companies still identify challenges with access to accurate data."
Not only that, but this data is also error prone, and when it comes to deadlines, people are likely to make the corrections on their own. According to Redman, 50% of a knowledge workers time is wasted hunting for data, finding and correcting errors and searching for confirmatory sources for data they don’t trust.” Furthermore, the errors in the data have a trickle-down effect that negatively impacts multiple departments within the company. The result is that multiple people in multiple departments are correcting the errors of others, which detracts from the time they need to do their actual job. For example, Redman estimate that 60% of a data scientist’s time is spend cleaning and organizing data.
So how do we improve upon these inefficiencies, increase the ROI and enable employees more time to analyze the business rather than facing the intensity of manually compiling data? The key is adopting an analytics strategy.
Analytics takes the manually intensive guesswork out of managing your data for maximum return. It provides the ability to accurately automate the cleansing and harmonization of the data. This results in maximizing the time analyzing the data for optimal return by eliminating the 75% investment waste that Redman associates with poor data.
"Analytics takes the manually intensive guesswork
out of managing your data for maximum return."
Many technology projects are designed to improve efficiency or add functionality, but rarely both. However, with advanced analytics capabilities like Trade Promotion Optimization, companies can get both. So consider just how much of your 20% trade promotion investment is lost to unreliable data or bad practices. You might be shocked at how much is unnecessarily spent as a result of misinformation. With the availability of technology that can eliminate the financial and efficiency losses associated with bad data, it’s time to take action in pursuing innovative approaches to improving your data’s health and reaching the full potential of the possible results.
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