From empty data fields to data duplication and invalid addresses, there are so many ways you can end up with contaminated data. Having looked at possible causes and methods of cleaning data, it is important for an expert in your capacity to put measures in place to prevent data contamination in the future. The challenges you experienced in cleaning data could easily be avoided, especially if the data collection processes are within your control.
Looking back to the losses your business suffers in dealing with contaminated data and the resource wastage in terms of time, you can take significant measures to reduce inefficiencies, which will eventually have an impact on your customers and their level of satisfaction.
One of the most important steps today is to invest in the appropriate CRM programs to help in data handling. Having data in one place makes it easier to verify the credibility and integrity of data within your database. The following are some simple methods you can employ in your organization to prevent data contamination, and ensure you are using quality data for decision-making:
● Proper configurations
Irrespective of the data handling programs you use, one of the most important things is to make sure you configure applications properly. Your company could be using CRM programs or simple Excel sheets. Whichever the case, it is important to configure your programs properly. Start with the critical information. Make sure the entries are accurate and complete. One of the challenges of incomplete data is that there is always the possibility that someone could complete them with inaccurate data to make them presentable, when this is not the real picture.
Data integrity is just as important, so make sure you have the appropriate data privileges in place for anyone who has to access critical information. Set the correct range for your data entries. This way, anyone keying in data will be unable to enter incorrect data not within the appropriate range. Where possible, set your system up such that you can receive notifications whenever someone enters the wrong range, or is struggling, so that you can follow up later on and ensure you captured the correct data.
● Proper training
Human error is one of a data analyst’s worst nightmares when trying to prevent data contamination. Other than innocent mistakes, many errors from human entry are usually about context. It is important that you train everyone handling data on how to go about it. This is a good way to improve accuracy and data integrity from the foundation - data entry.
Your team must also understand the challenges you experience when using contaminated data, and more importantly why they need to be keen at data entry. If you are using CRM programs, make sure they understand different functionality levels so they know the type of data they should enter.
Another issue is how to find the data they need. When under duress, most people key in random or inaccurate data to get some work done or bypass some restrictions. By training them on how to search for specific data, it is easier to avoid unnecessary challenges with erroneous entries. This is usually a problem when you have new members joining your team. Ensure you train them accordingly, and encourage them to ask for help whenever they are unsure of anything.
● Entry formats
The data format is equally important as the desired level of accuracy. Think about this from a logical perspective. If someone sends you a text message written in all capital letters, you will probably disregard it or be offended by the tone of the message. However, if the same message is sent with proper formatting, your response is more positive.
The same applies to data entry. Try and make sure that everyone who participates in data handling is careful enough to enter data using the correct format. Ensure the formats are easy to understand, and remind the team to update data they come across if they realize it is not in the correct format. Such
changes will go a long way in making your work easier during analysis.
● Empower data handlers
Beyond training your team, you also need to make sure they are empowered and aware of their roles in data handling. One of the best ways of doing this is to assign someone the data advocacy role. A data advocate is someone whose role is to ensure and champion consistency in data handling. Such a person will essentially be your data administrator. Their role is usually important, especially when implementing new systems. They come up with a plan to ensure data is cleaned and organized. One of their deliverables should include proper data collection procedures to help you improve the results obtained from using the data in question.
● Overcoming data duplication
Data duplication happens in so many organizations because the same data is processed at different levels. Duplication might eventually see you discard important and accurate data accidentally, affecting any results derived from the said data. For example, ensure your team searches for specific items before they create new ones. Provide an in-depth search process that increases the search results and reduces the possibility of data duplication. For example, beyond looking for a customer’s name, the entry should also include contact information.
Provide as many relevant fields that can be searched into, thereby increasing the possibility of arresting and avoiding duplicates. You can find data for a customer named COVRI Solutions PVT LTD in different databases labeled as COVRI SOLUTIONS P LTD or COVRI Solutions PVT LT. The moment you come across such duplicates, the last thing you want to do is to eliminate them from the database. Instead, investigate further to ascertain the similarities and differences between the entries.
Consult, verify, and update the correct entry accordingly. Alternatively, you can escalate such issues to your data advocate for further action. At the same time, put measures in place that scans your database to warn users whenever they are about to create a duplicate entry.
● Data filtration
Perhaps one of the best solutions would be cleaning data before it gets into your database. A good way of doing this would be creating clear outlines on the correct data format to use. With such procedures in place, you have an easier time handling data. If all the conditions are met, you will probably handle data cleaning at the entry point instead of once the data is in your database, making
your work easier.
Create filters to determine the right data to collect and the data that can be updated later. It doesn’t make sense to collect a lot of information to give you the illusion of a complete and elaborate database, when in a real sense very little of what you have is relevant to your cause.
The misinformation that arises from inaccurate data can be avoided if you take the right precautionary measures in data handling. Data security is also important, especially if you are using data sources where lots of other users have access. Restrict access to data where possible, and make sure you create different access privileges for all users.
Looking back to the losses your business suffers in dealing with contaminated data and the resource wastage in terms of time, you can take significant measures to reduce inefficiencies, which will eventually have an impact on your customers and their level of satisfaction.
One of the most important steps today is to invest in the appropriate CRM programs to help in data handling. Having data in one place makes it easier to verify the credibility and integrity of data within your database. The following are some simple methods you can employ in your organization to prevent data contamination, and ensure you are using quality data for decision-making:
● Proper configurations
Irrespective of the data handling programs you use, one of the most important things is to make sure you configure applications properly. Your company could be using CRM programs or simple Excel sheets. Whichever the case, it is important to configure your programs properly. Start with the critical information. Make sure the entries are accurate and complete. One of the challenges of incomplete data is that there is always the possibility that someone could complete them with inaccurate data to make them presentable, when this is not the real picture.
Data integrity is just as important, so make sure you have the appropriate data privileges in place for anyone who has to access critical information. Set the correct range for your data entries. This way, anyone keying in data will be unable to enter incorrect data not within the appropriate range. Where possible, set your system up such that you can receive notifications whenever someone enters the wrong range, or is struggling, so that you can follow up later on and ensure you captured the correct data.
● Proper training
Human error is one of a data analyst’s worst nightmares when trying to prevent data contamination. Other than innocent mistakes, many errors from human entry are usually about context. It is important that you train everyone handling data on how to go about it. This is a good way to improve accuracy and data integrity from the foundation - data entry.
Your team must also understand the challenges you experience when using contaminated data, and more importantly why they need to be keen at data entry. If you are using CRM programs, make sure they understand different functionality levels so they know the type of data they should enter.
Another issue is how to find the data they need. When under duress, most people key in random or inaccurate data to get some work done or bypass some restrictions. By training them on how to search for specific data, it is easier to avoid unnecessary challenges with erroneous entries. This is usually a problem when you have new members joining your team. Ensure you train them accordingly, and encourage them to ask for help whenever they are unsure of anything.
● Entry formats
The data format is equally important as the desired level of accuracy. Think about this from a logical perspective. If someone sends you a text message written in all capital letters, you will probably disregard it or be offended by the tone of the message. However, if the same message is sent with proper formatting, your response is more positive.
The same applies to data entry. Try and make sure that everyone who participates in data handling is careful enough to enter data using the correct format. Ensure the formats are easy to understand, and remind the team to update data they come across if they realize it is not in the correct format. Such
changes will go a long way in making your work easier during analysis.
● Empower data handlers
Beyond training your team, you also need to make sure they are empowered and aware of their roles in data handling. One of the best ways of doing this is to assign someone the data advocacy role. A data advocate is someone whose role is to ensure and champion consistency in data handling. Such a person will essentially be your data administrator. Their role is usually important, especially when implementing new systems. They come up with a plan to ensure data is cleaned and organized. One of their deliverables should include proper data collection procedures to help you improve the results obtained from using the data in question.
● Overcoming data duplication
Data duplication happens in so many organizations because the same data is processed at different levels. Duplication might eventually see you discard important and accurate data accidentally, affecting any results derived from the said data. For example, ensure your team searches for specific items before they create new ones. Provide an in-depth search process that increases the search results and reduces the possibility of data duplication. For example, beyond looking for a customer’s name, the entry should also include contact information.
Provide as many relevant fields that can be searched into, thereby increasing the possibility of arresting and avoiding duplicates. You can find data for a customer named COVRI Solutions PVT LTD in different databases labeled as COVRI SOLUTIONS P LTD or COVRI Solutions PVT LT. The moment you come across such duplicates, the last thing you want to do is to eliminate them from the database. Instead, investigate further to ascertain the similarities and differences between the entries.
Consult, verify, and update the correct entry accordingly. Alternatively, you can escalate such issues to your data advocate for further action. At the same time, put measures in place that scans your database to warn users whenever they are about to create a duplicate entry.
● Data filtration
Perhaps one of the best solutions would be cleaning data before it gets into your database. A good way of doing this would be creating clear outlines on the correct data format to use. With such procedures in place, you have an easier time handling data. If all the conditions are met, you will probably handle data cleaning at the entry point instead of once the data is in your database, making
your work easier.
Create filters to determine the right data to collect and the data that can be updated later. It doesn’t make sense to collect a lot of information to give you the illusion of a complete and elaborate database, when in a real sense very little of what you have is relevant to your cause.
The misinformation that arises from inaccurate data can be avoided if you take the right precautionary measures in data handling. Data security is also important, especially if you are using data sources where lots of other users have access. Restrict access to data where possible, and make sure you create different access privileges for all users.
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