Clean Up, Clean Up, Everybody Everywhere!

Although the floor of my room may not be a good example of this, I really do enjoy being organized. I feel so much better when I have a schedule and have things put into to-do lists. Organization not only falls into the categories of cleaning up our rooms and making schedules, but also into the category of cleaning up our data.  The cleaning and maintenance of data is a productive and profitable habit to have.

Michael Della Penna of ClickZ Marketing News & Expert Advice claims that a few reasons we should keep data clean are as follows:

• “Dirty Data” costs US businesses over $600 billion dollars annually.
• 46% of survey respondents cite data quality as a barrier for adopting BI/analytics products.
• Poor data or the lack of visibility into data quality is cited as the number one reason for overrunning project costs.
• Data quality best practices boost revenue by 66%.
• If the median Fortune 1000 company were to increase the usability of its data by 10%, company revenue would be expected to increase by $2.01 billion dollars.

There are several suggestions for what businesses should do about the problem of dirty data. One of the suggestions is “Conduct a Data Collection Audit”, which is basically the idea that businesses should only collect data that is relevant and to know how that data will be used. Another suggestion is “Address Dirty or Neglected Data”. This includes inputting, validating, and cleaning up phone numbers and email addresses, as well as reengaging phone numbers and email addresses that haven’t been addressed in a while. Taking time to “Focus on Preferences and Privacy Management” is also important because it is a customer-lead and controlled world; it is essential that customers are aware that the businesses want to know their preferences. Studies show that consumers are more comfortable sharing personal information when it will enhance their experience. Another suggestion to clean up dirty data is to “Break Down Data Silos”. This refers to the breaking down of data silos (mostly in the form of social media programs) by connecting and streamlining all of their efforts into a centralized data-mart. This collection of data could be used to improve program performance. Lastly, Penna suggests to “Invest in Interaction Management”. This idea refers to marketing the data in a way that the customer will enjoy. The data must have value to the customer, depending on both behavior and preferences of the individual.

When considering these suggestions in simple terms, it doesn’t seem like it would take all that much to make large data sets more attractive. I know that it is not as easy as it seems, but if businesses take one step at a time to reach the goal of “Clean Data”, it will be productive. After all, you can’t make your bed and clean your closet out at the same time, can you?

Source:

http://www.clickz.com/clickz/column/2258186/spring-cleanup-bad-data-not-big-data-needs-your-attention