Businesses rely upon their data in order to make decisions. But, if the data that is being analyzed is flawed or not up to standards for use, analytics will not be helpful for making business decisions. For example, if you have huge sets of customer data that are riddled with flaws, have replicated information on certain customers and do not have all of the pieces of data about each client that you really need, your analytics will suffer and thus your decisions that emanate from those analytics will also be flawed.
Before we look at management of data, we need to define what quality data is. According to Profisee, data quality relates to two issues:
- Will the data help us perform whatever task we have assigned it?
- Does the data provide a representation of what is going on in the real world?
Let’s look at the first factor. Information Week explains that data must be gathered in such a manner that it allows us to gain something. For example, we may need to gather data in order to comply with a governmental regulation. The quality of such data would relate to whether or not we can meet the compliance standard. If we would like to use the data in order to improve customer satisfaction, then we must have the proper data that will allow us to analyze it for those purposes. Such data must be complete, clean and may not contain duplicates or inaccurate information.
The Role of Data Quality Management (DQM)-
Obviously, the problem for even small businesses is that one doesn’t just need to gather data for a sole purpose. Most all businesses need to store transactional data, data on customers and data on products or services. As mentioned above, there may be regulatory data that needs to be stored as well. What often happens is that data is compartmentalized for use by different departments. Data stored in one department is not in a form and does not have all of the information that another department needs. No one is responsible for cleaning and inspecting data for flaws. This leads to the goals of the data gathering not being met and analytics that are performed with the data being flawed.
How to Improve DQM –
Audit data: Determining what information is really necessary and which is not. Cleanse the system of that which is no longer needed and stop gathering that information any longer. Examine places where data may not be accurate and beef up the procedure to ensure its accuracy.
Standardize data and create constraints when gathering: All of the data needs to be gathered in a standardized fashion. If you want to spell out “Street,” then do so. Otherwise, all of the data must be gathered with the abbreviation. The reason for this is to ensure the data is clean and is not gibberish.
Constraints force customers, for example, to input data in a required manner in order to allow for the acquisition of clean data and avoid occasions when they intentionally input garbage in order to rush through the system.
Purchase automated data cleaning system: This helps to make the process of data quality a quick and time-saving process that can be accomplished by your organization on a regular basis.
Regularly update data: Forbes states that business to business data decays at a 70 percent rate and is best updated when the customer is interacting with your business.
Remove data silos: Everyone must be on the same page in all departments about the data inputting constraints and rules and be on guard so that they update data, while looking for dirty data.
According to the Harvard Business Review, bad data costs the U.S. economy $3.1 trillion yearly. Implementing common sense steps to audit, standardize, clean and update data becomes a regular part of doing business, and it saves time, money and customers in the short and long run.