When it comes to managing and retaining your existing customer base, the quality of your data is essential. The more accurate and consistent your CRM data, the easier it will be for you to connect with your customers. Please take a look at the following best practice data rules in order to be able to perform even more promising analytics at the management level. Moreover, this level of data integrity will also help you increase user confidence when using CRM. If you are curious as to how you can achieve such high levels of data integrity, we would like to offer you a number of useful tips in what follows below.
Taking Preventative Action
If you want to increase both the reliability and quality of your CRM data, taking preventative action is key. Not only do you need to make sure that you are only entering the most accurate data into your system, but you should ensure that you are not missing out on any important details when you are setting up your CRM. This will greatly reduce the risk of you running into problems with bad data at a later point in time. Instead of having to spend a lot of time cleaning up bad data, you should simply add the most accurate and well-defined data from the very start. Taking the following steps will help set you up for success in the long run:
- Provide data access on a need to know basis. Only giving a select group of users access to a specific set of data might help you in avoiding bad data in the long run.
- Your CRM should come with a duplicate detection feature. Definitely put this feature to good use in order to prevent duplicate data entry. Of course, you can define the specific rules that are going to govern duplicate data detection.
- Before any data becomes part of the database, specific CRM data validation rules apply. Make sure that you define the type of data that is required for each field. While some fields might require a numeric value, a date might be required for other fields, for example.
- You can also set up a method for forcing a specific piece of data to be entered before the new information can become part of the database. This should help with the overall integrity of the record that is being entered into the system.
While taking these preventative steps is one of the most important measures in maintaining the integrity of your data, certain rules for editing existing data apply in order to insure a high level of data quality. This step is also referred to as CRM data management.
CRM Data Management
While new information is being entered into the CRM system, you are given the option to alter or edit already existing data. However, you should keep in mind that you can also decrease the quality of your data by making incorrect edits. In order to make sure that you are not going to have to clean up bad data at a later point in time, you should set up rules as to who will be allowed to edit already existing data under which circumstances and at what point in time. It is best to keep the following guidelines for CRM data management in mind:
- Only grant data editing permission to those users with a need for updated data in a particular area. By restricting the level of access and putting strict rules for data editing in place, you can help avoid errors.
- Data auditing at the field level can be useful for some organizations. If you are making use of the auditing feature, this means that you can change data that has been incorrectly edited back to the original data. Nevertheless, auditing should only be used sparingly. Take all of the required preventative steps in order to minimize the amount of auditing that is required.
- Moreover, you should define the rules that apply to the altering of data. This is important to ensure data integrity. You might consider putting an approval process in place, for example.
As you can see, data change management plays a key role in maintaining data integrity. If you pay attention to these guidelines, you might be able to identity and avoid poor sources of data early on. Once bad data has been identified, you should take the required corrective action in order to address this problem. Of course, we also provide you with some best practices for how to go about repairing bad data.
Repairing Bad Data
Data maintenance is required whenever bad data has been discovered. However, you should not only replace bad data by more accurate data, but also put all of the preventative steps in place that are necessary to prevent bad data from being entered into the system in the first place. Learning from your mistakes is key in order to avoid more bad data in the future. You might want to consider the following recommendations during this process:
- Thanks to the duplicate detection feature, you can merge records that contain duplicate data. The end user entering the new set of data will thus be faced with the decision of either continuing with the creation of a new record or abandoning his record in favor of the already existing record. This is important so that duplicate record creation can be avoided at all costs.
- You should perform a data cleanup process on a regular basis. Even if you have all of the right checks in place, this step is important in order to maintain data integrity.
- Periodic backups are key. This will ensure that you are safe even if major problems come up. Make sure that your backup makes a complete recall of data possible.
As you can see, all of these processes go hand in hand in order to ensure a high level of overall data integrity from start to finish. Make sure that you regularly schedule each of these steps in order to make the best possible use of your data.
Learn more about how to ensure a high level of data quality and how to use data for customer retention, customer acquisition and for in-depth analysis. Take a look at the many functions Microsoft Dynamics CRM has to offer in this area.