Trusting Your Data: Garbage In, Garbage Out
Dirty data—data that is inaccurate, incomplete or inconsistent
When your database is overrun with dirty data, segmentation is difficult, lead scoring is dicey at best, and reliable attribution is out of reach. Anybody who’s had to deal with dirty data knows how frustrating it can be, but when the numbers are added up, it can be difficult to wrap your head around its impact.
Sometimes, costs sneak up on us. What might seem to be an everyday annoyance has been having staggering cost implications for years.
It is common practice in business to tolerate dirty data to a substantial degree rather than to manage or eliminate it. When this is the case, dirty data proliferates across systems and the discrepancies multiply. In the long term, this undermines the business from within.
Unless additional business processes are implemented to accommodate the possibility of bad data, business decisions are taken under false premise.
WHY IT EXISTS
There are many reasons why dirty data exists. Poor data collection, system migrations, and incompatible tech stacks are a few of the worst offenders. There are tools in many systems to dedupe data but many times, there is not enough time or attention given to the problem to make a difference.
Dirty data can remain hidden for years, which makes it even more difficult to detect and deal with when it is actually found. Unfortunately, 57% of businesses find out about dirty data when it’s reported by customers or prospects—a particularly poor way to track down and solve essential data issues.
The Impact of Dirty Data
The problem with ignoring dirty data and not solving the root problems with how it gets into your system is that it compiles over time. Employees who have been around a while know what to look for. They can work around the dirty data challenges by filtering data. The problem is when these employees leave, their knowledge of the database and what skeletons are in the closet often leave with them.
Dirty data results in wasted resources, lost productivity, failed communication—both internal and external. Productivity is impacted in several important areas.
Dirty data lacks credibility, and that means that end-users who rely on that data spend extra time confirming its accuracy, further reducing speed and productivity. Introducing another manual process leads to more inaccuracies and mounting inconsistencies through growing numbers of dirty records.
Garbage in, garbage out—when you can’t rely on your own data, something needs to be done to increase data accuracy and reliability.
BAD DATA = POOR RESULTS
Inconsistent information across data silos in an organization leads to transactional risks such as inaccurate or even fraudulent transactions.
Your data fidelity also affects the effectiveness of any third-party systems you use. This is where the garbage in garbage out applies. Flawed data coming into any system will produce erroneous results regardless of the quality of the analytics.
Common symptoms include:
— Cumulative increases in costs and a drain on the bottom line;
— Rising costs due to down time to reconcile data;
— Risk of using inaccurate data to inform policy and decisions;
— Diversion of resources from mission-critical areas;
— Erosion of trust and credibility with stakeholders;
— Delays in new system deployment;
— Inability to comply with industry and quality standards;
— Increasing focus on internal issues allows competitors to gain ground;
— Demoralized teams: if staff are hampered by poor data, they will lose
impetus;
— Unproductive, frustrating environment will drive talent elsewhere;
— Reduced ability to respond will affect customer service and slow growth;
— Poor performance will jeopardize reputation and damage the brand.
CLEANING YOUR DATA
Depending on what system you are on, cleaning your database can be quite the undertaking. Start slow, determine your most significant pain points, and pick a place to start.
HERE ARE THE COMMON TYPES OF DIRTY DATA
- Incomplete data
- Duplicate data
- Incorrect data
- Inaccurate data
- Inconsistent data