Data is a term that is used frequently in the business world, but what exactly is data, and why is it important? Today’s business world is fueled by data, data analysis, and data management. As qualitative or quantitative information collected for observation and analysis, companies are learning every day about the benefits of analyzing and tracking data. Data is a resource that can help any organization:
- Improve processes
- Identify gaps
- Track the allocation of resources
- Identify waste
- So much more
Not all data is accurate or relevant to your business, however, so your company needs to be adept at identifying data inaccuracies, relevant data, and irrelevant data. Missing data, duplicate data, irrelevant data, and inaccurate data are just a few examples of some of the data analysis pitfalls your organization can run into. Identifying these errors is key to maintaining quality, useful, relevant data.
One of the most important ways to improve the quality and tracking of your company’s data is to determine the goal of analyzing the data. Whether it’s to track the effectiveness of a marketing campaign or to identify waste and inefficiencies, the most important thing to know right from the beginning is what you are looking to see within the data. This will help identify what data is relevant to your analysis, and what data can be ignored.
Another way to improve your data quality is to ensure uniformity within your data sets. This includes standardizing the way the data is entered, formatted, punctuated, and spelled across records. This will help identify and prevent duplicates as well as ensure the correct data is being captured and analyzed.
I experienced the impact and severity of un-uniform data in a previous job I held. I used to work at a retail store that had a customer loyalty program. Every time a customer would make a purchase, the company would track the purchase by having the cashier search for the customer’s account on the computer and log the purchase under their account. They would identify the correct account by matching the customer’s name with the associated record in the computer’s customer record log. If the customer’s name could not be found, a new account would be created.
A recurring problem the company had was they did not establish a uniform procedure for the entry and retrieval of data. For example, if a customer’s name was Joseph Jameson, it could be entered in any number of ways (Joe Jameson, J. Jameson, Joey Jameson, Joseph Jamison, etc.). The lack of standardization of data entry and validation led to many customers having multiple duplicates or inaccurate accounts with different names. This meant the sales data was often incorrect, the data would not reflect the true sales of the store, and the customer did not receive their proper loyalty rewards. These types of mistakes can be avoided by standardizing your data and ensuring you have a uniform data entry and retrieval process.
Data and data analysis are essential to any business. netlogx can help your business ensure and maintain the data quality that’s paramount to your business’s success. Contact us to learn more.