A Day in the Life of Your Data
As a life-long marketing professional, my job is to put the most positive spin on products without sounding, well, too salesy, regardless of my personal opinion. In my long career, sometimes this has been difficult. I am very lucky to now be weaving that magic about a product that actually does what it is said to do. But let’s look at the other side of the coin. Bad data.
How many of us have bad data in our organization? I would think you would be hard pressed to state that all your data is squeaky clean. Sometimes glaring errors are easy to spot. For example, a client who appears in the database five times, with the same last name spelled just a little differently. Some are not so obvious.
The reader and assimilator of the data can take a couple of different approaches. Confirmation bias is where people search for, and notice, information that aligns with their views, while ignoring material that does not. Neutralizing bias is potentially taking the step of depending on someone who has a neutral stance to fact-check the material. Being totally transparent, I have a tendency to lean more towards the confirmation bias. In fact, if everyone were honest, so would they. We like information to align with our thinking.
There is also data that has been manipulated so it highlights certain conclusions. This is most often done in scientific areas, such as research studies. Other people investigate the findings and later realize they were chasing a dog without a leash, or a doctored statistic.
We also need to evaluate the worthiness of data. In other words, whether a company or person is stating something for personal or corporate gain, such as getting a promotion or increasing profits. Limitations on how data is managed can also cause inaccuracy. If decision makers place too much emphasis on flawed data, mistakes arise and conclusions are tainted.
Over 84 percent of CEOs are concerned about the quality of their data. Using bad data is also a waste of time – for the people entering it, analyzing it, and using it. It can impact reputation, brand, and even sales. From the perspective of artificial intelligence (AI) and training sets, the outcome can be catastrophic.
Well, that’s a day in the life of your data. You can celebrate it many ways. The second Monday in February is Clean up Your Computer Day, January 28 is Clean Up Your Data Day, and Open Data Day is March 3. Many celebrations.
Remember this when engaged in text analytics or data mining, knowledge management, collaboration, decision making, or just plain old enterprise search – the data is more than likely faulty. Of course, our software generates metadata from the content within context, as it’s a bit hard to judge some types of data.