Data standardization is an important process. It converts the data into a common form. It makes the data suitable for research and comparison. People can collaborate on the data for further use. Before loading it into a central repository, data standardization requires transformation and reformatting.
After data standardization, users can analyze it consistently. It is the primary outcome and purpose. We know that different systems store data in different formats. When you use an automation tool to pull the data together or do it manually, the data structure may not match. However, to analyze the data effectively, you need to standardize the same.
When you standardize the data, there could be two ways.
• You pull data from external systems and map the records to an output schema. It is called Simple Mapping with External Sources.
• You pull data from internal systems and create a single and trustworthy data set. It is a unified source, and called Simple Mapping with Internal Sources.
You can take the example of a library where different bookshelves are arranged in different order. One shelf categorizes the books by genre. Another shelf organizes by color. On another shelf, the books are ordered in the name of their authors. It is very difficult, or sometimes, impossible to locate a book.
It is an example of data arranged without any standardization. It creates nothing but chaos and confusion. Though the data is there, it is of no use. And thus, has no value.
Without standardization, data can create the following problems:
• Application may fail or work inefficiency
• There could be a duplication of records
• Poor marketing attribution
• More human errors
• More efforts needed
• Inaccurate market segmentation
• Poor lead scoring
• Inaccurate market segmentation
These problems will result in a loss of revenue and opportunity costs.
• You create a seamless flow of usable data
• You achieve correct market segmentation and lead scoring
• With improved analytics, you get benefited
• Data personalization and the possibility of sending tailor-made messages to the audience.
• It is possible to share data between Artificial Intelligence and Business Intelligence systems.
The process of Data Standardization can be made easy and systematic by following these four steps.
#1 Ensure data sanity
The first thing is that you should ensure that the data is correct and clean. Also, it is complete and formatted. Make sure you get it validated before performing any action on it. Thus, the accuracy and integrity of the information are ensured. Also, you prevent bad data from entering the system. For example, before loading the data in a CRM system, you should clean it.
#2 Find out the correct data entry point
A second important step is to know the data entry points. What data is being gathered? How is it being gathered? Are there any data verification barriers? Once you know the data entry points, it becomes easy to follow the right data normalization method.
#3 Translation of the data into a standardized list
It is a critical step to find out the data that requires a normalization process. When you translate data into a standardized list, it empowers the user to take the right action. For example, normalization of job title, address, and location.
#4 Creation of normalization matrix
To map, unclean data to new standard data values, you need a normalization matrix. You should begin with a value significant to the business.
For example, you choose the job title. Now you need to identify various job levels for various job title values. Then in the next step, you can refine the job title to job level interpretation.
When the data is cleansed, standardized, and consistent, it brings many benefits to an organization. Data standardization may happen on a field basis. By using an enterprise-level tool, it is possible to integrate data across various systems. It is possible to examine the source data and get detailed information about the data structure, integrity, and quality.
When the data s collected in free format, there are many errors. There are spelling errors, duplicate records, use of abbreviations, and so on. Standardized data is free from all these errors. You can improve productivity by providing clean records. For example, your sales officers need not waste time contacting obsolete or inaccurate customers. Also, your customer acquisition quality will also be improved. The marketing process gets immense benefit from accurate, standardized, and cleansed data. Your business can expect a better return on investment. Standardized data brings better decision-making as well.
Therefore, this critical process has to be followed by every organization. Follow it and make it an integral aspect of business processes. Your decision making gets a great thrust when the data is standardized.