Organisations across the globe rely on big data to make critical business decisions and take part in digital transformation innovations, but basic data management is still tripping them up. Big data sounds simple, but it’s only as useful as your processes and management systems are. At its best, big data is analysed to reveal trends and forecast the future. Historically, the phrase is used when your data set grows so large traditional tools can’t process it.
Basic data management strategies can be the difference between useful analysis of your big data and finding nothing of value from the information. It’s easy for humans to make mistakes when entering information—but as an IT decision-maker, you can control this better. According to Experian, 94 percent of business leaders believe their customer or sales databases are riddled with incorrect information. However, it’s clear nobody really knows how to fix it. In the end, it all comes down to the process.
Source your data reliably
Unreliable sources are the easiest places for you to get incorrect information—and so, you should evaluate all sources before trusting their data. You’re probably pulling numbers from an internal resource, but external ones (like public data about your state or social media feeds) can prove completely untrustworthy or formatted wrong.
It might be impossible to get rid of these data sources but regular, manual monitoring of their accuracy can help.
Automate anywhere and everywhere
You should also automate your data entry process as much as possible to avoid human error resulting from manual entry. When you’re manually entering data all day, it’s easy to make mistakes. Automated systems can make this process easier. You can auto-complete addresses using the Google API or automatically validate someone’s phone number.
Typos happen, but automated processing eliminates them. The smallest mistakes are the ones that build up over time, as they’re hard to detect once inside your data set—it’s impossible to pick out any incorrect phone numbers when you’re literally dealing with thousands of them.
Make sure your organisation has processes in place to automatically verify and purge data over time. Information changes, and if an invalid entry wasn’t detected when initially entered into the system, it’s possible to catch it later using automated checks. It’s important to identify key business metrics and run integrity checks on a regular basis to make sure something isn’t going wrong.
Install a warning system
The famous Knight Capital story is a chilling example: A stock trading firm lost US$10 million (S$13 million) a minute because its system did the opposite of what it should have and sold all the stocks it accidentally bought—all due to a computer glitch. This software bug could have been caught with an early warning system in place.
With correctly defined parameters and behaviours, your early warning system can notify you before something goes wrong or if your data is leading to decisions that are the opposite of what you expect. It’s important to reduce the time gap between data error detection and reaction, especially if you’re moving rapidly or dealing with automated processes.
Basic data management is one of the hardest problems in technology to solve, given that humans are the biggest sources of error. Unlike machines, humans have short attention spans and aren’t good at picking up on and correcting mistakes. This is exactly why you should start leveraging technology to fix problems and make it easier to get right in the first place.
If you’re working with big data, now’s the time to ensure your processes are right—before it’s too late (“too late” meaning you’ve wasted time going in data circles). If you’re already leveraging it, ensure you have processes in place to catch erroneous data before it costs you time and money.