Nearly everyone is gathering larger piles of data these days, harnessing their customers' tweets, posts and likes to go along with the information they keep on file at the cash register and in their internal databases. When this content - both unstructured and structured - is reconfigured in the correct ways and analyzed by experts, users can spot surprising correlations that are considered valuable business insights.
This information might inform auto companies about which color will be the most successful for their next model of hybrid car, or which regional market will sell out during the third quarter. With these insights in their back pockets, decision-makers can act preemptively and produce a greater quantity of vehicles featuring the year's hottest color and a greater supply of automobiles for that particular region in advance. However, this success is contingent on data quality. If data users are filling their systems with incorrect, inaccurate or incomplete content, analysts may discern that car buyers love aquamarine when they truly adore cerulean, or that the Southeast will be the hot market for hybrids although it's actually the Southwest.
To improve data quality, users can implement the following simple strategies:
- Keep it simple when possible
In a recent post for his blog Liliendahl on Data Quality, Henrik Liliendahl Sorensen explains that it's best practice to take a simple approach to information, especially content that's posted on the internet. That's because the posts can become convoluted as they are reiterated by users and re-posters.
Similarly, the message contained in a certain set of data can be changed over time, depending on how it's handled from the point of origin to the final destination.
- Use the right tools
Carpenters wouldn't begin a project without planers and hammers, writers wouldn't begin a novel without paper to write on, but many companies begin big data strategies without data quality tools and address management systems. If businesses want their analytics to come out right, they must first arm their data scientists, entry personnel and analysts with the right equipment.
- Admit you're wrong and move on
At some point, most companies discover that issues have crept into their databases, according to The Obsessive Compulsive Data Quality blog. This is nothing to be ashamed of as long as firms are willing to admit to their mistakes and take the steps to introduce improvements.