You business relies on data from marketing to billing. However, data collection that comes from multiple sources is not always as clean and trustworthy as it should be. Manual input, forms submissions, servers’ updates, data migrations, new system implementations are just a few operations that have an impact on data. Also data is a live ‘substance’ that experiences constant changes with time. Needless to say that data dominates all kinds of analytics, decision making, marketing strategies, new product launches, customer communication and loyalty, etc. Too much is at stake when we talk about data. It does not tolerate discrepancies and inaccuracies, i.e quality data means only one thing, data cleanliness. Cleanliness does not appear on its own. It requires ongoing data cleansing actions to remain clean, i.e. accurate, functional, applicable and effective.

There are many ways in which you can do data cleansing. However, here are the 6 best practices for data cleansing that is recommended for any data type or business:

  1. Data Quality Reporting is truly applicable to data cleansing process since you need to establish a data clean up routine plan of actions, key performance indicators (KPIs) that will help you monitor your data quality. Data cleansing metrics will allow you to not only identify the very errors but see where inaccuracies are coming from. Either it is manual input or wrong formatting, or missing fields, etc. you need to know the root cause of the failures.
  2. Data quality monitoring. It definitely helps to catch the errors at the entry levels. It can be achieved by establishing and implementing a set of rules. Also it is vital not only create the rules but discuss them with the teams involved. You can easily do so by flagging any incomplete records, records that are inputted with the wrong format or simply by doing manual check ups on an ongoing basis.
  3. Data Deduplication. Duplicate accounts litter the data and lead to many failures: CRM initiatives are squandered, marketing campaigns lose money, maintenance cost rises, inaccurate metrics affect reporting followed by wrong business decisions. Customer loyalty and company reputation are at stake. Deduping is a significant part of data cleansing process that identifies and purge or merge duplicate records based on the set up rules.
  4. Data validation. Quality data is the data that is credible and reliable. Even if data looks neat it should be verified and updated. At some point, reputable third party providers are exploited to achieve the top notch accuracy. It is recommended to verify or validate your data at least every 2 years and even more frequently for fast paced industries.
  5. Field Standardization. When data is clean it should be transformed into an established, i.e. standard format in order to be easily utilized. It is key for BI tools and overall analytics. If you are collecting data in a non standard way i.e. text field, but it can be changed to a drop down, it will help not only classify data for reporting and segmentation purposes but also save time in data inputting.
  6. Data appending. Clean data needs to be complete. Missing info should be verified and added. Complete data is precious since it allows to deliver more detailed and accurate analysis and extract important takeaways. Today there are many credible third party sources that can help fill in the gaps. Alternatively, you can flag incomplete records for additional data collection next time you talk to your prospect or customer.

Data cleansing performed by an experienced team of data cleansing professionals can save your organization time and money. Quality, cleansed data allows to

  • eliminate inaccuracies and upgrade reporting;
  • refine marketing and CRM initiatives by polished customer segmentation and well targeted campaigns;
  • decrease storage spending by eliminating duplicate records;
  • improve customer service and overall workforce productivity by deduping;
  • enhance customer communication and loyalty by having complete, valid and updated records;
  • drive sales;
  • raise ROI and revenue;
  • and make accurate decision making.

If you are looking to cleanse your data, feel free to contact StrategicDB a data cleansing company at