Can a $3 Trillion Problem Really be Hidden?

According to a published article, HBR says much of the bad data costs come from the adjustments workers, decision makers, and managers make in their daily work to deal with data they know or believe to be wrong. The costs pile up because no one has time to fix problems at the source. Faced with deadlines, workers adjust the data in front of them well enough to complete their part of a process and send the data along to the next step.

HBR calls these extra steps “The Hidden Data Factory” and point out that these processes create no added value.

Knowledge workers waste 50% of their time meandering in these hidden data factories. They waste time searching for data, correcting information, or seeking confirmation of data they don’t trust.

Firstlogic has published a series of whitepapers describing the cost of bad data, the benefits of cleaning and enhancing stored information, and the tools companies use to carry out these tasks.

Organizations rely on data to run their businesses. Companies base critical decisions on statistics and conclusions based on that data, so it better be correct. As companies add artificial intelligence (AI) to their operations, the need for data quality becomes even more important–in two different ways.

First, historical data is used as a model to teach AI systems. These models drive future decision-making, so the data must be clean and relevant. Otherwise, all future AI actions will be founded on faulty assumptions.

Second, future data fed to the AI systems must also be accurate and complete. Without good, reliable data, AI decisions or recommendations are likely to be flawed.

As the Harvard Business Review article declares, the best approach to data quality is attacking the data at the source. Organizations must strive to eliminate all or most of the hidden data factory tasks that are costing them so much money.

Uncover Your Bad Data
Data quality techniques have been evolving for decades as information changes and databases grow in complexity. At Firstlogic we’ve been evolving our data quality tools for decades as well. Our parsing engines and logical processes have deep roots. They are ready to handle the wide variety of data most organizations face today.

Poor data quality expenses are elusive. Most managers aren’t even aware of extraordinary behind-the-scenes efforts expended by countless employees at every level of their organizations. Even though HBR measures the overall costs in the trillions, individual companies probably aren’t accounting for the squandered time in their ROI calculations.

To find out where your company might be wasting money working with bad data, ask for a no-fee data assessment. We’ll sample your data and tell you where you can improve and what it could cost your organization if you don’t step up your data quality efforts.