Data is the lifeline of any business. But gone are the days when one could manage with simple data in tabulated spreadsheets. Today, organizations invest lots of money, efforts, and resources on management of the database. It helps them maintain effective contact with the customers. In today’s highly competitive world, companies cannot expect to generate business with just collection of a huge database with the customer names and their contact details. To ensure that the database helps the business grow, entities should regularly cleanse and update the data.
With increased dependency on data, the amount of unstructured data has also increased. For organizations with a large amount of data, it is really hard to maintain the database with just the manual efforts. Chances of errors, dupes and format issues increase manifolds when the task is done manually. Further, for the organizations where data is sourced from multiple vendors, the probabilities of errors like typos, bad grammar, use of unwanted content, use of slant etc. are more. A small mistake in the spelling can create a whole myriad of problems and can result in wastage of hours on manual cleansing.
The good thing is that with the help of advanced data cleansing tools, you can easily avoid these problems. With regular data cleansing process, you can find out the areas with errors and requirement of more attention. By ensuring regular updates and cleansing of the data, you can produce meaningful actionable insights from the database.
Planning is the first step when starting with any process. In data cleaning process, planning includes identifying the set of data that plays a vital role in making the marketing efforts more effective and beneficial for the organization. When analyzing the database, you should be more concerned about the high priority data with unique features for your business. You should start small with the fields like job title, role, email address, profits, industry etc.
Planning is the right time to decide specific data validation rules to standardize the existing data and automate the process for the future data. Standardizing the address is an example of creating rules to cleanse the data. To your surprise, IT team members can be helpful in creating such rules.
- Data Auditing
Once you are ready with your plans to clean data, the next step in the process is data auditing. You should audit the customer database through statistical and database methods to find out anomalies and errors. The information collected through data audit helps in understanding the characteristics and location of the irregularities that are the root cause of the problem. It also helps you create a list of things missing from the data, information that can be thrown out and the gaps between the data.
During this step, you also need to find out the resources to manually cleanse the exceptions in the database. When deciding manual intervention, keep in the mind that it is directly correlated to the number of acceptable levels of your data quality.
- Make a data cleaning process
Plan out a consistent data cleansing system, which every person involved in data maintenance process needs to follow in your business. The same process should be followed consistently to correct errors or to complete missing data fields. For instance, as per the process, the data entry team needs to conduct a Google search or contact the customer directly to find the missing information.
Once you are ready with the clearly defined process, you should cleanse the flow of new data from the point it enters into the system. Data standardization can be done by creating scripts or workflows. Further, you can run the scripts in real-time or in batches based on the amount of data you have. This process is equally applicable to new as well as previously keyed-in data.
- Add missing data
Data cleansing process is not just limited to finding and deleting the inaccurate data from the database. The process is also used to incorporate the missing information wherever required.
No matter how effective your data cleansing tools are, you will always have a set of information that is missing or not filled properly. For instance some emails, phone numbers, company size etc. need manual efforts to make it error free. For this type of information, you need to find out a right way. To get a hold of the missing information, you need to check the source of the information like from third-party append sites or the Google. Based on the source, you need to reach out to them again to append the missing data.
Data cleansing is not a one-time process, which you perform once and forget. To ensure the quality of the customer database, you need to set up a periodic review. It will not just help you keep your data clean but also help you find the issues before they create a major problem for the business. Your team should monitor the data both as a whole and in individual units.
Moreover, you should also keep a track of the bounce rate as well as response rate. Regular tracking helps you create a list of emails with high bounce rate or the ones from where you are not getting any reply for a long time. Using the list you can find out the reason like an error in the email address or the person whom you are addressing has left the organization or their email address has changed.
You need to remember that data cleaning is a regular process, which you can start small initially. Gradually, you can start making incremental changes by repeating the process several times through data cleaning tools. While following the process just make sure that you complete the circle. Skipping any step during the cleaning process can create a major problem. However, with time you can bring changes in the process based on your priorities and review reports. When running data cleaning tools, you need to ensure that these work smoothly and with accuracy. Consistent and accurate data cleaning process is the only way to get the cleanest and effective database.