Скачать или смотреть Defining Data Quality in Salesforce

Are you failing to harness the potential of Salesforce because you are plagued with data quality issues? Data is the driving force of our business, and the recent explosion of data collection brings interesting challenges as we try to adhere to data quality standards. When we don't get ahead of data quality concerns, poor data can infect every area of our business. Let's look at what data quality is, including the characteristics and dimensions of data quality. Before we do, take a second to subscribe to our channel, Salesforce Explorer. We’re constantly exploring the edges of Salesforce so please join us.

Data quality is about helping you meet your business objectives with the customer data you have. When you run reports and sit down to analyze your business, does the data provide an accurate view of the current state of your company? If you can't trust your data, you’re forced to make decisions in the dark. So, what does poor data quality look like in real life?

• Its missed opportunities to cross or up-sell products
• Your executive team is drawing inaccurate conclusions based on flawed reporting
• Customers get understandably angry when their items are delayed as they get shipped to the wrong place
• For those operating in tightly regulated industries, this could amount to your organization getting hit with large fines

As Salesforce administrators, we should see ourselves as the protectors of our organization's Salesforce data. Low quality data taints our environment, causing users to mistrust what they see and seek solutions outside of Salesforce so they can do their jobs. We should see that as a failure on our part. We should always look to increase the trust of our users and stakeholders. Adopting data governance policies to ensure high data quality is one of the best steps we can take.

When we talk about data quality there are a set of dimensions we look at to evaluate the quality of our data.

The first is completeness. Is the data we've captured comprehensive? If we are missing customer street addresses or zip codes, our data lacks completeness. These gaps will result in product deliveries being delayed or not delivered at all. This erodes customer satisfaction.

Next is timeliness. Timeliness asks when our data was last updated? If our team consistently makes daily updates against our client sales records, our data has a much higher quality than if our updates are input via a spreadsheet by the sales team weekly. Stale data will impact its accuracy.

Validity is our next dimension. Does our data follow our business rules and the format we've defined? Say we've standardized dates in our organization to follow the month/day/year format (12/31/2023). We'd have data quality problems if half the sales team entered dates as day/month/year (31/12/2023).

Consistency is when we expect the data stored in two separate places to match. Say I had an organization that used both Salesforce and Dynamics Business Central. These systems have areas where their data overlaps, and it’s the responsibility of integration jobs to keep them in sync. In that scenario, if I make an update to Salesforce, but that update doesn't push to Dynamics Business Center, our data suffers from consistency problems.

Accuracy asks how well our data reflects reality. If I work for a financial institution that is evaluating whether to give a new customer a loan, I need to make sure the details provided by that person are accurate. When we fail to make those comparisons and details don’t match facts, our company may make a poor decision to extend credit and potentially take on bad debt.

Our final dimension is uniqueness. Essentially, do I have duplicate data in my dataset? Are Ray M. Anderson and Raymond M. Anderson the same person? We have to define how we are defining a unique person (i.e. what data fields come together to define uniqueness)? This can also happen when importing data from other systems into Salesforce without a unique key or without proper data integrity checks.

When we monitor these various dimensions of data quality, it helps us highlight areas we can improve our data quality, what data is fit for use, and what the data health looks like across the organization. It’s also important to educate our users about the benefits of data quality so they can be an ally in this fight and not our adversaries.

If you are struggling with data quality concerns and need an expert to help advise your team, consider Improving. We have a bunch of brilliant Salesforce data consultants who can help you identify, plan, and implement measures to improve the data quality of your Salesforce org. We'd love to talk with you. Please reach out to us at https://improving.com.

Watch part 2 in our series on data quality in Salesforce:    • 6 Tips & Tools to Improve Data Quality in ...  

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