Clean Up Your Data Act!

Top Tips To Get You Obsessed with Data Cleansing

It’s cleaning day at Love the Idea. I’m sure you’re now imagining a bunch of app gurus cleaning their computer screens and opening the windows! No no no, this is not just any kind of cleaning day, it’s data cleaning day! 

I know those who are ‘Monica’s’ must be feeling a little bit disappointed we won’t be getting out the dishcloths, but we’re here to talk about serious data cleansing; what it is, why it matters and how to do it.

So what is data cleansing? 

Data scrubbing (yes this is real terminology), involves various techniques to eliminate inconsistencies and mistakes in your data. 

Why do I have to spend time cleaning it?  

Do the words ‘data’ followed closely by ‘cleansing’ turn you off? Not us! Data cleansing is crucial to the effectiveness of your analytics, so you can avoid incorrect insights and use data to make confident decisions.

Data scientists often use the phrase ‘Garbage in, garbage out’ (GIGO) when it comes to data cleansing. If you have poor-quality data, you’ll produce poor-quality results. You need your data to be accurate so you can use it for data-driven decision-making but also so you can effectively develop your business, by gaining analytics that show an accurate view of your customers.

You may have come across research saying that 70 per cent of a data scientist’s time is spent on data cleansing. This feels pretty shocking. How about we reframe how data cleansing is perceived? Instead of boring and menial, see data cleansing as data analysis. 

Data cleansing will allow data scientists to know the data like the back of their hand, allowing them to get the most out of it when it comes to producing results.

Now this is worth your pennies, right? This is where a lot of value comes from, so any algorithm you apply to the data performs and gives you valuable results. 

Here’s how to clean your data in 3 easy steps.

1. Amend Inaccuracies and Duplicates

Comb through the data checking for duplicates so you’re not wasting money sending something twice. Do check for typos and deal with any missing data. Nobody wants the same message sent twice to them, with it covered in mistakes.

2. Same Structure Throughout

Keep the formatting the same. The same rules for each cell e.g. the same unit of measurement and avoid consistencies in upper case or lower case.

3. Eliminate Leads That Go Nowhere

Dead leads are never good for business and things move at a fast pace. You can use suppression files to figure this out for you as the leads will be checked against a wider database. 

Just applying these three simple steps will ensure you can effectively use analytics to move your business forward. Data is a precious thing and can be one of your biggest assets. 

Peter Sondergaard, Senior Vice President and Global Head of Research at Gartner, Inc. put it perfectly, “Information is the oil of the 21st century, and analytics is the combustion engine.” We couldn’t agree more.