So you run a business. Whether you are a blue chip corporation, a Small to Medium Enterprise (SME) or a mom-and-pop corner store – you’ll always depend on data to deliver success. What some entrepreneurs and executives don’t realize though is that the quality of that data often determines how clear the path to your success is.

Even within large multi-national corporations, where data management budgets run in the millions of dollars, multiple islands of data are maintained – separate from the mammoth corporate data hubs.  The reason: It’s just “practical” that way!  Periodically however, those landscaper do eventually run transformation processes and pass the data on to the mother ship – the central data warehouse, where all of that data is consolidated.

Corporate headquarter staff then use those statistics to make important decisions impacting the very success of the company. The challenging question however is: How reliable is all of this data that’s being assimilated centrally? Take the example of statistics based on disparate data sources. One regional operation uses an Access database to maintain inventory. They use a code “WIDG-2010” to refer to “Widgets – 20 cm X 10 cm”.  Another, relatively smaller region uses Excel spreadsheets and  “20 centimetre Widgets” to identify the same item.

At first glance, this seems to be a no-brainer. Just add the two quantities together, and you’ll get a consolidated picture of how many Widgets of this specific type there are across the corporation. But the issue gets even more challenging when there are hundreds of such unique codes used, across dozens of individual business units, to identify thousands of items. Add to that the complexity of dealing with colour, type of material, effective dates, source of manufacture through the coding system…and this can quickly turn into a data management nightmare!

When Corporate HQ finally does receive input data feeds from these diverse streams, it becomes a huge challenge for them to summarize the data into a single cohesive database that can be relied upon to make business critical decisions.  What ends up happening is that corporate planners believe they sold (or have in stock) 1,000,000 units of one item, 700,000 of another, and 300,000 of another, when in fact they sold (or stock) 2,000,000 units of the same item – except that each of those sales were recorded using three different codes to identify the same item!

Had there been some data cleansing tool used to match, clean and consolidate the data first, before using it to make decisions, consider what it could do for business success:

  • Knowing you sold 2,000,000 units of a particular item instead of 300,000 might give you an indication of that item’s commercial success!
  • Realizing that you buy 2,000,000 units of a particular item (instead of just 300,000) might prompt you to evaluate leveraging bulk quantity discounts from the OEM!
  • A product that sells 2,000,000 units might need higher advertisement, promotion and sales budgets as opposed to one that only sells 300,000. Is this company losing a great sales/marketing opportunity?

If you suspect that you might be in this kind of a situation yourself, there is good news. Today, there are intelligent tools that can be brought to bear to tackle these and many other similar issues. WinPure has been helping businesses get more out of their data through its suite of highly effective data cleansing and data management tools. The sure path to clear business success is in using clean data!

By Mike Hughes | February 17th, 2014 | Posted in Data Cleansing

About Mike Hughes

Mike has over 4 years full time experience blog wand content writing. He enjoys and can write on a wide range of topics all data management related.

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