Big Data Clean Up Project? Here’s Four Tips On How To Get Going
Some people are just crazy. You know who I’m talking about. Maybe your neighbor who is still smiling after their 22 mile morning run. My fellow Oregonians who refuse to use umbrellas in the rain (this is real). Folks who ruin a wonderful cup of coffee by using cream and sugar (only slightly joking). Then there’s another level of crazy: those who love big data clean up projects. This is a rare breed of person but I’m proud to say we employ a few of them at Plutus!
If you’re like most people, big data enhancement and clean up projects are not only frustrating, time consuming, and dreadful, but can generally be so overwhelming that it's hard to even know where to begin. If you’re staring at the clean data peak at the top of the mountain here’s some advice on how to get started:
What Data Do We ‘Actually’ Need?
We’re certainly in the age of “big data'' and for some folks, big data means capturing everything. I’ve worked with groups who track preferred airline loyalty numbers for thousands of people and organizations who keep copies of financial data from nearly 100 years ago. Before you and the team dive into the spreadsheets, you should always take a step back and think: what are we trying to accomplish with this data clean up? Answering this crucial question will likely make the project shorter and your database cleaner.
Start Small & Be Strategic
Like any big project whether it's a home renovation, planning for a major event, or that once and a lifetime vacation, you should always break the projects into chunks and start small. Looking at the whole scope of the project isn’t just daunting but can lower morale if the project seems too big to tackle. Start small and be strategic.
How Are We Going to Not Do This Again?
The question speaks for itself. We’re spending time, money, and staff resources to clean and enhance data, but how are we going to not do this again? What was the initial reason why this data wasn’t making it directly into the CRM in the first place? No data project is complete without having a strategy on how to fully or nearly eliminate manual data entry.
Consider Outside Help
Sometimes it's beneficial to bring in an outside advisor or consultant who can challenge assumptions and review your approach before you dive into a big data project. The outsider not only brings a different and unbiased perspective but also can have the authority to challenge certain assumptions that other staff take for granted. This can be an invaluable addition during the “What Data Do We ‘Actually’ Need?” process.
What are your tips for large data clean up projects? Sound off in the comments below!