Let’s take a look at how businesses can use process automation to leverage high-quality data to gain a competitive advantage and achieve better decision-making outcomes.
Data Challenges
If you think the data problem is just a few misfiled documents, think again. Only 3% of enterprise data meets quality standards . That means most organizations are making decisions based on bad data. Here are five other ways poor data can leak into other areas of your business:
8 out of 10 employees waste time recreating documents if they can't find them quickly
9 out of 10 team members waste a full workday each week sorting through company data
Gartner estimates that 40% of enterprise data is inaccurate or incomplete, leading to missed opportunities and misdirected insights.
9 out of 10 spreadsheets contain errors . A few small typos can sink a ship: NASA lost $80 million when a mistaken hyphen caused a probe to explode on its way to Venus.
Poor data quality costs an organization an average of $12.9 million per year, and that's just for the losses it can trace.
If these eye-opening statistics don’t make you rethink your data strategy, here’s what does. None of these challenges are one-offs; they’re piling up in organizations engineering email list every day. Data doubles every two years , making seemingly innocuous challenges snowball into lasting impacts. What does clean data mean for your organization, and how can you leverage it?
illustration of a rocket
How to spot poor quality data
The typo that brought down NASA’s Mariner 1 could be hiding anywhere in your systems. Data cleansing is the process of rooting it out. You can spot errors, duplicates, and typos by cleaning your data.
Validation: There are few safeguards against poor formatting (e.g., some phone numbers contain a country code and others do not; some birth dates are formatted as DD/MM/YYYY, while others appear as DD/MM/YYYY). Impossible entries can also confound decision making, such as a large number of customers accidentally selecting a birth year of 1900.
Outdated information: Data has a limited lifespan. Experts say data degrades at a rate of 30% per year. A database containing incorrect email addresses, last names, and addresses can cost businesses millions of dollars per year.
Missing data: Details may not be standardized across groups. For example, a florist may launch a new campaign asking new customers to provide their birthdays. But they forget to survey their current customers, leaving a significant gap between the two segments. Identify the missing data and take proactive steps to fill the gaps.