Poor data quality, software problems, and the resulting sense of despair are often self-inflicted.  Fortunately, the fix is easier to implement than many people realize.

On a typical data and analytics project, somewhere between 60% to 80% of our effort is spent addressing a client’s data quality problems.  The data quality problems we find are caused largely by software and applications that were incorrectly selected, poorly implemented, and inconsistently used.  For example:

  • A retail company had converted to a new point-of-sale (POS) system, only to realize it didn’t meet their needs.  In less than a year, the incorrectly selected POS system was ripped out and the company reverted back to the previous POS.
  • A hospitality company had selected what appeared to be a good reporting technology.  Unfortunately, the IT department was not adequately staffed to support this tool so it was dumped on the business unit to learn SQL and implement in order to  use it.  Poorly implemented technologies were quickly scaled back to fewer locations and a smaller group of users, resulting in the inability to see and communicate the organization’s performance.
  • Another retail company had not managed data quality within their system, resulting in a product list where any given product shows up dozens of times with slightly different spellings, an employee name exists in the product name, or any other number of variations that seemed convenient at the time.  Inconsistently used applications quickly create unmanageable data quality problems.

For these companies, and many like them, the damage has been done.  They have limped along with static, antiquated systems for years.  They dread the thought of upgrading or converting to a new system.  They can’t fathom the effort to manually go through their systems to clean up all the historical data quality issues.  And, even if they did all these things, their worst nightmare is they would find themselves on a new system with equally bad data quality because no one has the time to manage it.

There is another option and that is an application designed and built specifically to:

  • Create great data quality by cleaning and standardizing your data
  • Further improve data quality by transforming your data into decision-quality information
  • Provide your data quality by making it easily available to support your business decisions

In larger organizations, these are often custom-developed applications in the form of data warehouses, dashboards, and the like.  For smaller companies, software-as-a service (SaaS) options like The iBLeague are available.  However, in order to keep this from being another disappointing software implementation, three major areas need to be addressed, planned for, and utilized:

  1. Data – Data drives your business, or at least it should.  You need to ensure you have adequate resources, expertise, time, and budget to climb this mountain.  Most software companies want nothing to do with your data, figuring out your data is your problem.  We love this part and our data experts do the dirty work for you!
  2. Software – Typically your options are custom application development, living with a purchased application as-is, or spending significant time and money to customize packaged software.  We advocate a different approach, providing you the benefits of a personalized application without the headaches, expense, and time of custom development or large-scale customizations.
  3. Coaching and Care – With the increase in SaaS, software companies have been improving their customer care in order to ensure their client’s renewal.  We believe in providing the coaching, tools, information, resources, and services you can’t find anywhere else to ensure the application continues to meet your changing business needs.

So, stop making more problems for your organization. Many data quality problems can be corrected, and it’s likely easier and quicker to implement the fixes than it was to originally implement the system that is causing your data quality problems.

Question: How is the data quality in your operational systems holding your business back?  Leave a comment below.