Contrary to what everyone believes, data quality management is not an easy job. The software administrators who are dealing with big data management may already be knowing it. In fact, data quality assurance can be a very overwhelming and complicated affair in a real-time scenario. When dealing with customer handling, sales leads, training new users, updating or repairing technology, or attempting to make any beneficial improvement, etc., one needs to handle a huge amount of information with optimum quality.
As we mentioned above, this job can be highly overwhelming, but there are different ways to handle this task. There are many data quality management tools available out there for auditing your existing data and ensuring the quality of the data coming into your system. One can make use of these tools like KennerPIM.com to handle your data quality management job much easier. In this article, we will discuss the top data management practices on the Salesforce platform.
For those individuals who need to handle huge volumes of data and ensure the best data quality, it is essential to have various tools. If you are cleaning and clearing high quantities of data by ensuring quality, ETL tools can effectively extract, transform, and load data. These tools can handle data-related tasks much quickly and automatically with minimal human involvement.
ETL tools usually come in the form of various retail software which needed to be installed. Once installed and running, these tools can run the below three different tasks effectively.
- Firstly, it can evaluate the spreadsheets or other data platforms’ content to clear all the mapping fields in it and limit, if not eradicate, the amount of duplicate data.
- Next, it can transform any invalid information in the database into the needed format.
- Finally, it can also load new information required by creating correct accounts and spreadsheets for effective use.
Validation rules are the most effective data management practices on Salesforce. It is constructed basically as Salesforce algorithms. Data management validation rules are highly useful for individuals who are working independently to manage smaller or bigger data stores like big data management projects. Valuation rules function by creating and editing any records automatically once if they meet the different criteria as your custom defines. This can specifically work on data that meets the listed criteria. It will also help to locate specific data within larger pools of information much easily and quickly. For more information, contact FLOSUM.
Documenting and orienting
Another practice which any data management agent can effectively utilize is the documentation and teaching tools. This basically means teaching the individuals who handle the data and document the same. It can be completed by including some help text in the Salesforce programs by showing the uses what type of inputs are required and what tasks to be completed. For staff, this can be made discrete so that it will not overtake the full page but still be highly comprehensive and useful.
To make this practice successful, it is recommended that at least a single person in the business for the particular department has to be trained on this tool’s operation. This is a very popular option as it can remove the administrators’ responsibility and place it on the users. Now, let us discuss some data management best practices on Salesforce.
Salesforce data management best practices
Naming conventions could be the most important initial step in Salesforce data management. There are various strategies and methods you can adopt to name your records, but you should maintain it consistently and ensure that it sheds some light on the content of the records it holds.
When it comes to data management in organizations, records and data’s supplication may create bigger problems. This is more so if you are getting your data from different sources. The duplicates will not only create an inaccurate view of the database, but it can also result in wastage of effort by the sales or marketing teams. Making things even worse, duplicate orders or opportunity records may lead to miscalculating the revenue sales or projections.
In order to avoid any duplicates, you need to train the entire team for using Salesforce data improvement Wizard and select the matching fields to identify any duplicates like the order number, phone number, emails, and other unique criteria. Even though you put in all the effort, there is still a chance that a duplicate may get into the database once in a while. When it happens, you may use the built-in duplicate management tool of Salesforce, which can check your records. Using this tool, you can also easily select which fields you have to save and merge the duplicate records to the original.
Making sure that your data is up to date
As with any other applications or platforms dealing with data, the quality of data and integrity can be degraded overtime on Salesforce too. Even the most functional databases may become useless when the data is out of date. So, you need to add on products and tools in order to clean the database by scrubbing it from time to time and enriching the entries.
Many organizations, especially those who mail or ship products regularly to their customers, must consider implementing third-party validation for addresses, phone numbers, and other demographic information. You can add on a simple tool to provide time and cost-saving with relatively little effort at the front end. You can also easily find some plugins to make use of these features on AppExchange.
You may also consider the quality management practices like implementing a scoring model using unique identifiers like external ID, making use of the Salesforce connect tool to back up your data from time to time, as well as ensuring a proper practice in place for recovery of data. Anything related to adding, updating, or changing database management or security practices should be well informed.