In the organizations which support big volumes of data it is of vital importance to ensure the data quality in order to support the decision making process in the business. If the volume of data is big and there are integrations with other businesses and external sources of data, the task becomes big and it becomes crucial to stop the garbage-in and help in garbage-out activities.

Before the rise of the inexpensive computer data storage, massive mainframe computers were used to maintain name and address data for delivery services. This was so that mail could be properly routed to its destination. The mainframes used business rules to correct common misspellings and typographical errors in name and address data, as well as to track customers who had moved, died, gone to prison, married, divorced, or experienced other life-changing events. This technology saved large companies millions of dollars in comparison to manual correction of customer data. Large companies saved on postage, as bills and direct marketing materials made their way to the intended customer more accurately. Initially sold as a service, data quality moved inside the walls of corporations, as low-cost and powerful server technology became available.

Consider the situation if the quality of the data in your organization is not good and there are duplicates and bad volumes of data about the customer information or related to sales. What will you do? Your decision making process will have a low quality because of the incorrect information provided to you and the repetitive data or missing fields in data may cause insufficient information to help you out. So what to do? Follow these steps to improve data quality in your organization

Understand what the need of data and its context is

It is very important to understand why data is collected and the purpose behind it. It takes resources to collect it and therefore collect only the data which is relevant not the garbage. If there is no justification on using the collected data why to collect it?

Create and maintain a data dictionary

It is a tool to document your data. This data document includes all data elements, definitions, validation rules and every piece of information regarding a data. This ensures high quality of data in the organization if a proper data dictionary is maintained for all types of data in the organization.

Take steps to improve data quality

Data cleansing is a very important process to improve data quality in your organization. The data becomes of low quality because of the inflow of information and data from external sources. These sources may be third party tools, other system integrations, social networking sites and others. Due to improper feeding of data due to lack of information is also a big factor for the decrease in quality of data in the organization. Therefore, proper steps have to follow in order to cleanse the data in the organization. You can take the help of data cleansing software which can easily help in fixing the data quality you use and are very convenient to use with a faster action.

Be cautious about the missing data

Always ensure that there is no missing data in your organization. Ask the people responsible to feed the data in your database to be vigilant that there is no piece of missing data. A missing data brings the quality down and you will not be able to make better decisions for your business if the information you have is incomplete.

Author Bio:

Simon Hopes is a renowned author and social media enthusiasm. When the situation becomes like this it is very important to improve data quality so that you are able to handle the decision making process in a better and efficient way.