Everyone is familiar with the saying ‘Garbage in, garbage out’. Using dirty data, not quality data affects the quality of your analytics followed by wrong business decisions, poor strategy and in the end failure. Logical and easy understandable succession of actions that can be avoided by having clean and quality data.
Quality data is an aspiration and high expectation for any analyst or data scientist. What are the main standards of quality data?
- Accuracy is a must for any data. It means that records should be as accurate as possible errors associated with incorrect input, misspellings, wrong values and numbers, etc. For example, reliable data does not support the same customer address associated with different zip codes. It is impossible to have 100% accurate data however, you can measure the quality of your data based on the percentage of data that have been updated due to inaccurate data when validating data against third party providers or when changed internally by sales or customer service.
- Completeness of data represents a challenge of having no missing info in your dataset. If there is a field that corresponds to a job title it should be filled with the same name in every table across your dataset. Depending on data type this data can be filled in using third party data providers or by flagging records to future inputted data.
- Consistency is another characteristic of quality data. It indicates that data is standardized and normalized so you can easily use the group by function which is so critical when analyzing any type of data. If you have a lot of fields that are ‘text’ format and there is a way to group them, you may be able to hire a data cleansing company specializing in standardization of data to get your data into a consistent form.
- Current and updated records represent a value in regards to decision making. Outdated data leads to distortion of facts and numbers, inaccurate and sometimes completely wrong insights. Most systems today come with date stamps, so make sure your filters are set to only look at relevant information or update your data on an on-going basis to not have an aged database!
- Correct Format – Every entry has its own format and quality data is the data where the same format are applicable to the same sort of records. There are multiple formats that can be exploited but their consistency is crucial for further data analysis and interpretation. For example, imagine that date format is YYYY/MM/DD is not consistent and changed to YYYY/DD/MM in some tables. Any report that includes time frames would be twisted and prone to erroneous message.
- Duplicate records have no place in quality data. Any duplicates should be identified and dealt with. Sometimes duplicate records are purged. In some cases duplicate data needs to be merged in order to have a unique and complete record. Duplicates add to poor, pointless reporting, increase database size that increases the storage cost and waste the budgets. To de-dupe your data, hire a professional de-duping company to help clean up historical data and implement a data governance program for the future.
When we say ‘quality data’ the word ‘quality’ is not just a description of data. Data is affected by many changes that have an impact on data quality. Regular data cleansing is one of the ways of keeping your data accurate, complete, free of duplicates, consistent, up to date and easy to work with. Cleansed quality data is pivotal in precise business decision making, marketing strategies refining, customer loyalty, revenue growing, workforce productivity and overall organization health. In order to measure your data quality, make sure you have data quality dashboard set-up and monitored!