Data Standardization
Peace and tranquility to earth • Roudoudou • 1998
• Data
FR, fr, FRA, Frce, … France !
Addresses, Names, Country Names, Phone Numbers, Company Names Emails … Do you always have well-formated data? No matter what my machine learning, data engineering or data analyst projects are, there are always different formats for the same type.
The effects on your organization are:
- Multi-platform/application inefficiency or even failure;
- Poor lead scoring, segmentation, and routing;
- Increased manual processes
- Duplication of records
- Poor marketing attribution. Data standardization makes the data into a single usable format from different sources to enable users to process and analyze it.
The benefits are:
- Seamless and efficient flow of valuable data through your sales and marketing applications and platforms
- Improved segmentation, lead scoring and routing
- Effective personalization
- Improved analytics
- Streamlined data flow to BI and AI tools.
How to do it? Quite simply.
- Identify required data formats
- Consult business teams to identify format require to execute their processes.
- List all connected systems, note data formats for different field values to have some examples.
- Investigate incoming data formats
- Note the most commun formats in input and the desired format
- Learn how data impacting to prioritize actions
- Consider the impact of non-formatted data on a loss of income, a disruption of business processes, causes of non-useful data, a lower user productivity, a lack of user adoption?
- Put your strategy into action
- Maintain standardization
- Organizations evolve, data too. The standardization methods should continue to do the job.