By David Binkley, Senior Technical Account Manager, Harte-Hanks Trillium Software
I’m sure many of you have seen, or at least heard about, those popular television shows that all focus around people buying old dilapidated houses; quickly fixing them up and selling them for huge profits. With help in part from the name of a certain A&E show, this renovation-to-sale process got tagged with the name “flipping.”
While some people end up being fairly successful in their efforts, many others end up being caught in a quagmire of unforeseen problems and costly renovation budget expense line items. While all of that may make for good (OK, entertaining) TV, in the real world, those of us who have ever done renovation projects know how painful they can be when things don’t go as planned.
But why am I talking about flipping houses in a blog devoted to data quality? Well, there are a whole lot of similarities between house flipping and the impact of data quality in today’s business world.
For example, in flipping, the premise is to find an inexpensive home to renovate. How many times in the past half-decade have you been asked to find some inexpensive way to improve sales, lower costs or increase profitability? Have you ever thought of some of your existing data stores and applications from the point of “how dilapidated is the data” and how much could we profit from “renovating” it? I’ll bet you that many of your business users can identify with these issues.
Well let’s say that we’ve found an old house that needs work…uh, I mean an existing dilapidated data store. Just consider it the house we want to potentially flip.
Ok, we’ve found our potential house for flipping but first we need to determine if the house has any major flaws that will either increase the renovation costs or prevent us from completing the renovation. In flipping, you get an expert to perform a home inspection (there’s a whole other show highlighting the pitfalls of those people who fail to get it inspected) and research any applicable local building codes. Those of us in data quality are experienced enough to use data quality tools to perform the analysis necessary to determine if the renovation is possible, if the data is truly dilapidated and if we can really get any benefits from it. I definitely would not be shy about bringing in an expert to review an area where I was unsure of myself.
Then you buy the house – or as we say in the business world “get buy-in from management.”
The next main premise of flipping is, because holding costs lower your profits, you need to quickly perform the renovation. Again, that’s where tools come in – we need to quickly identify and renovate the areas of the “house” where we will get the most return for our money.
Flipping houses also shows us the true value of having contractors experienced in renovations who can quickly and efficiently renovate the property. Time and time again on those shows we see the false economy of trying to cut corners and shoddy workmanship that was performed on the original house and how it ends up costing us when we try to renovate. I don’t think that it’s necessary to give any examples from the data quality side as we’ve all seen those scenarios before. You need experts in there.
If you are experienced in flipping you will know (or will have identified) where you can get the most bang for the buck. Be it a new kitchen, landscaping or wood floors in home flipping – or something like customer, account, or other information.
Finally, after usually more work than expected (tell me where I’m wrong here), the flip is completed. Competent flippers move on to the next house and the others you never see on the show again. If we’ve done our jobs professionally and correctly in a data flip, management lets us find another “house” to renovate and perhaps you’ve just discovered a new data strategy.
But we must warn the unskilled and uninformed data flippers out there that there’s a huge difference between house flipping and the business world – in house flipping the property gets sold and the flipper is done with it. If the flip was done poorly, the paint peels, the roof leaks and people get sued. In data flipping, you will likely be living with the results for a long time and you are just not putting a coat of paint on the data that will peel off six months later.
With the economy the way it is and the home flipping phenomena being a contributor to its downfall, we must always keep in mind that there are no shortcuts for quality work and creating ongoing and sustainable efficiencies and profits. Flipping can be profitable short term, but we’re all in it for the long run.
See you all the next time you decide to flip your data.
