Marketers, analysts, CRM specialists and other professionals working with data know that clean data is the core issue and data cleansing is no joke. It is a serious base point and one of the biggest challenges. Everyone would like to work with the cleansed data and avoid dealing with errors.
Sometimes errors are not noticed because the data may change over many years or discrepancies are the outcome of human data entry, or it can be the result of not having enough time to deal with bad data as you are busy putting out fires . Unfortunately these errors are costly and sooner or later data quality is compromised and data cleansing becomes a real challenge. During data cleansing it is vital to understand what errors not to make to improve data quality.
Here are common mistakes you would like to avoid during data cleansing:
- Looking at a subset of data and not the whole picture can create a mess. For example, you decided to clean only prospects and have ignored the customers that are duplicated. The result would be the sales team headache calling the same number multiple times wasting time, money and downgrading your company image. Each field of your data requires equal attention and follow up data cleansing.
- Cleaning data but NOT implementing new rules to stop the bad data from coming into the system. Let’s say you washed the floors (cleansed data) and did not ask people to remove dirty shoes prior to entering your house (the rule to implement and comply with). The floors will be dirty in no time. The same can be applied to your cleansed data. Implement the rules including, for example, strict data format, spell check, set-up automatic de-duping rules, set up naming practice. Establish and apply the rules to maintain the cleanest data possible.
- Not analyzing the system prior to data cleaning. Try to understand the root cause of bad dirty data. Imagine yourself being a doctor finding the cause of disease prior to treatment. Define where the errors come from: human error during data entry, not set up spell check, lack of pre set formats, duplicates, etc. Once you establish where bad data comes from you would be able to control and maintain clean data.
- Not backing up your data prior to data cleansing. How many times in the office you heard your colleague’s or your own frustration when the Excel has crushed and the file had not been saved and as a result hours of work had been lost. Be proactive and always create your data backup prior to data clean up. This procedure will save time and help ensure a smooth data cleansing process.
- Not reviewing data prior to updating your data post data cleaning. It is crucial to track the changes that have been made and share the findings with the sales team. Let’s say you started the deduplication process and forgot to discuss the duplicates removal with your sales team. The outcome will be painful and costly. The determined and removed duplicates will reappear in the already cleansed data and you would need to start the data clean up all over again. Assess the data prior to implementation in your database.
If you are struggling to clean your data or do not have time to do it yourself, why not have a team of professionals handle it in no time. StrategicDB is a full data cleansing service company that handles all data cleaning needs from data appending, standardization to de-duping. We work with both small start-ups and large enterprise corporations, therefore no data cleansing project is too small or complex for us to handle. Contact Us Today.