Enhance Your Data Quality with Salesforce
Every small or large business generates a lot of data, some of which becomes irrelevant and outdated over time. This data, if not sorted and managed properly, can affect the quality and efficiency of the entire system. Data quality is the yeast that makes business up a larger portion. Without it, you have no chance of becoming successful and are left with a meager amount of revenue.
Simply put, data quality is significant. You look around a little bit more and learn that:
One day of work per week, or 20 percent, of productivity, can be stalled as a result of inaccurate or incomplete data.
Inaccurate data causes the average business to lose 12% of its revenue.
Due to low-quality data, 40% of all business initiatives fall short of their intended goals.
Poor data frequently correlates with:
Incomplete or incorrect insights
Time and resources wasted
Slow information retrieval
Inadequate client service
Damage to reputation
Decreased rep adoption
Good data changes the world. It turns out that reliable data enables your business to:
Recognize and pursue potential new clients
Identify opportunities for cross-selling and upselling
Obtain account knowledge
Fast information retrieval
Increase customer trust
Reps should adopt more ideas
Better plan and align territories
Score and route lead more quickly
The secret to good data is to follow data quality rules. A plain language statement that defines some characteristics of the data you're using is all that makes up a data quality rule.
However, it's likely that you will need to establish regulations unique to your business and the way you want your systems to display data. Let's examine the procedures while keeping in mind that everyone is responsible for data, so involve the users.
Profile: We must profile the data in order to ascertain the quality in order to determine what it is and what it should be in accordance with the established data quality rules.
Cleanse: Check to see if any information is duplicated, inaccurate, bad, missing, or irrelevant. Create naming conventions and data standards, enforce them with picklists and validation rules, and train all users on them.
Merge & Match: Make your golden record, match and merge duplicates (as necessary), and take into account the principles of master data management.
Monitor: Create procedures and checks to evaluate the ongoing quality of your data.
Let's take a broad look at the features that our ecosystem and the Salesforce platform can provide to assist you in establishing and maintaining your data quality. The AppExchange should be your first stop if you can't accomplish what you need to with the features that come pre-installed.
Advanced Validation and Data Cleaning
While validation rules can help you verify the format of a postcode or financial information, they cannot vouch for the accuracy of the data. For this, we go to AppExchange and use products from Salesforce partners that, among other things, perform address, email, and bank verification.
These solutions typically operate in a similar manner, utilizing an app installed in your organization to perform searches of the pertinent data in their cloud-based solutions. The majority of these solutions also enable bulk verification, which is excellent for your purge phase.
Monitoring and Reporting
Dashboards for Data Quality Analysis. With the help of the predefined dashboards offered by this app, you can assess the accuracy and completeness of the data by using custom fields on a variety of common objects. These dashboards can be used for ongoing monitoring as well as data profiling to determine the scope of any issues with data quality.
When a field's data is dependent on other data or record statuses, validation rules can be used to simply make sure it contains the required value. Consider these examples as you create your validation rules. Consider adding an additional check to the rule that permits you to use a custom setting to get around validation requirements in specific situations, such as when dealing with large amounts of large datasets.
Use of Data
You can use the Salesforce Optimizer to better understand uniformity and deliverability. You can identify which fields are empty by looking at the field usage in the data list section.
Salesforce includes duplicate management out of the box and can send you a quick alert when a record has been created twice or stop a duplicate record from being entered.
There are two components to duplicate management: A fuzzy match or an exact match can be used in the matching rule, which specifies the object and fields to check for duplications. Second, duplicate rules make use of the corresponding guidelines to decide what should occur in the event of a match, such as an article, notify, or enable.
Check out the AppExchange for some of our partner duplicate management products if you require more functionality.
Duplicate records might be required depending on your business process, so make sure you can manage this and identify the records in your workflows.
The magic wand of your Salesforce implementation is made up of workflow rules. To save time throughout your organization, you can automate routine internal processes and procedures using workflow rules. You configure workflow rules to direct leads to the closest representative. You assign service requests in the same way.
You know that some records have a ton of fields that aren't being used by your reps. You take them out of the reps' page layout. In order to give different types of reps and managers across the company the fields they need when they need them, you actually create personalized page layouts for them. While you're at it, place the most crucial, mandatory fields at the top.
To conclude, it is crucial to promote a data-centric culture because of this. Salesforce is fortunately here to assist. We are Salesforce registered partners here to help you enhance your data quality to its finest. Visit our website for more information and get in touch with us in case of any further queries.