Is Data Management Sexy?

Not really.  But just like your first time, we all keep trying to do it better and better.

Data Management is a process, it’s an evolution of thought.  Sure, there are basic concepts that fit most scenarios, and of course there are some fundamental principles to follow.  But, each and every application is usually different.  Just like you would for a yearly skin check, it’s important to ensure that your data management is up to date and still valid in respect to how you run your business today.

It’s been said before, but it’s important to keep spreading the word…Data Management isn’t a business luxury, it’s a necessity.  Yet trying to get this message across is often difficult.  I suppose it’s due to the fact that Data Management, means different things for different people.  Just like when a client comes to be me and says, “I want my data cleaned.”  I also answer with “Sure, now what do you mean by cleaning?”  However, Data Management is really the over arching concept behind it all, with then varying disciplines underneath to make up what goes on in a typical day to day Data Management House.  As you’re all pretty much already DataTools customers(and if not, why not?), you have a bit of a head start on the rest when it comes to “Data Management”.  But as usual, there is always more to it, and generally there is someone else out there smarter then you or I.

Perhaps a Data Management checklist would help, something that you could go through periodically and then walk away knowing that you’re doing the best you can to ensure your data is as up to date as possible.  Over the course of the coming weeks we’ll go through the items that I think are important.  They will be ranked in order of importance, so let’s get started;

Point 1

We’ll start with the basics, and a fundamental before anything else is the normalisation of data.  This means that we need to have the data in a consistent format i.e. 0737871333 or +6138781333.  Ways to ensure that this is done, can in part be through the GUI of the TWINS III interface.  Other ways are to run update queries or “find and replace” on certain columns in your database.  And while we’re at it, a database is anything from an Excel Spreadsheet through to a SQL DB.  Data normalisation is also about ensuring that your data is contained within both the correct columns and that it is consistently placed there.  This may seem like a strange notation and something that just sounds like common sense, but if you have a GUI with four address fields, does your entire team know which address elements go where?

If you can ensure that you data is “normalised”, then the next steps are not only easier, but the results you achieve will be more accurate, more comprehensive and more conclusive then they otherwise would be.

What to do if your database isn’t normalised?  Well, don’t fret, but do something today!  From today, start to set out some rules for how you want to have data inserted into your database.  If you do this, then you essentially have a “Point In Time” from which all data is the way you want it to be.  Once this is done, you’ve got the foundation of your rule set from which you create the business rules to fix your older data.  Now I know I’ve just made this sound very easy, and for all intensive purposes it is.  And the simplest way to do this, is to drop the old data out, fix it, and then put it back in again.  While you’re at it, you’ll want to do a number of other things to it as well, and that’s fine, but just remember the first step.  Normalise your data first, before anything else!