How to improve data quality in Salesforce? Data hygiene best practices
Picture this: you are at a gas station to refuel your car. You have the choice between top-of-the-line premium fuel versus a poor-quality adulterated alternative. Which one would you pick? The former, of course. After all, the fuel that feeds the engine impacts the performance, mileage, and even the health of your vehicle.
The same analogy applies to data as well.
Given that data is the fuel for businesses of the 21st-century, you can no longer afford to use substandard data. The role of high-quality data gains greater significance when it finds use in customer-facing activities, say, marketing.
Bad Salesforce data can undo all the efforts that you put into such activities. In this article, we will talk about the importance of data quality, what one means when we say bad Salesforce data, and how to maintain Salesforce data hygiene.
What is “Bad” Salesforce Data? And How Can it Affect You?
Before you go about eliminating poor-quality data, one needs to have a firm grasp of what constitutes “bad” Salesforce data.
Bad or dirty data in Salesforce is any form of error that gets injected into the database. Broadly speaking, bad Salesforce data include:
- Inaccurate data
- Inconsistent or inappropriate data
- Duplicate (or confetti) data
- Outdated or irrelevant data
- Incomplete or partial data
- Incorrect data
- Missing data
- Disparate or siloed data
- Business Rules Violation or non-conforming data.
Now, coming to the more pressing question - what is the impact of this bad Salesforce data?
Poor data will lead to poor outcomes - this fact can be deduced intuitively. That being said, here’s a detailed look at how it can affect you:
- Poor quality data can cost businesses as much as USD 700 billion per annum or 30% of their average revenue.
- It renders as much as 20% of business records useless, with incomplete data (90%), outdated information (74%), and duplicate entries (25%) being the top reasons behind it. And while this data is unusable, it also occupies a chunk of your resources!
- Poor data births overly optimistic or pessimistic sales forecasts that can result in frustration for your sales and marketing teams. At the same time, it can result in missed opportunities due to poor prioritizing of sales-ready leads.
- Incomplete data that compels research on the part of a sales rep consumes about 21% of their time - and these numbers are for just one form of bad data! Almost 64.8% of salespersons waste their time on non-revenue generating activities, which leaves a massive dent in productivity.
- When your Salesforce operates on bad data, it generates misleading and inaccurate reports that can result in bad business decisions or poorer user adoption rates.
- Since Salesforce data influences customer service, it can hamper customer experiences and earn you a bad reputation. Further, the overall failure in delivering to expectations damages the brand perception.
How to Perform a Salesforce Data Cleanse?
Given the gravity of the situation, you may be itching to get rid of bad data in your Salesforce. And to do so, you will have to follow the steps given below:
Perform a Data Audit
First things first, you will have to dip into your existing data reserves to gain an overview of the scale and magnitude of the data quality problem. As a starting point, you can initially dig into various Salesforce list views to quickly filter for missing or inaccurate data (Salesforce kanban views are less appropriate here). Once you are done scrutinizing the databases, expand further to analyze data sources and related systems that depend on Salesforce data. At this stage, you would have successfully mapped out the complete data network for your business - right from the source to the sink. Plus, the insights generated in this exercise will act as a realistic baseline for the data cleansing activities.
Round-Up the Necessary Tools
Naturally, going through high volumes of data manually can be a daunting task. And here’s where you should seek the help of data cleansing tools. These intelligent tools can identify discrepancies, validate data, update records, and flag business rule deviation, etc., by analyzing Salesforce data at a granular level. Tools like Weflow ensure that your team spends more time selling and less time maintaining Salesforce data!
Scrub Out Poor Data
By this point, you would have identified bad data sectors. Now is all about booting it out of your Salesforce database to standardize it. You will have to start by removing all the irrelevant and duplicate data. Then, fix any structural errors to implement standardization. You will also have to filter the unwanted outliers and determine their validity. And finally, you will have to find a fix for missing data - whether you drop this information, input the missing detail, or alter it.
Once again, the aforementioned tools can come in handy to go assiduously go through the database and take appropriate action. Whether it is deduping data or eliminating it entirely - tools like Weflow employ AI/ML to learn from every iteration and make smarter QA decisions.
What is Salesforce Data Hygiene? And How Can You Maintain It?
Cleaning up Salesforce data is not a one-time activity. It is, in fact, a constant and cyclic process. What we have discussed so far revolves around reactive data cleansing, while Salesforce data hygiene covers the proactive aspect of it.
Salesforce data hygiene relates to maintaining data sanctity using the following measures:
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#2 Follow a Data Cleansing Routine
According to Salesforce, about 70% of data goes bad every year. And so, data cleansing should be a recurring activity to mitigate data decomposition. Depending on your business requirements, you can schedule data cleansing annually or weekly.
#3 Implement Data Validation
Data validation addresses the “garbage in, garbage out” problem by building safeguards at data entry points. Whether it is the format for phone numbers or validity of email service provider - data validation filters in clean data only.
#4 Enforce Data Quality Standards
In addition to data validation, businesses can also design schemas with data quality in mind. They can ensure data quality by defining required fields, granting drop-down options or auto-populated fields, programming dependencies and workflow rules, imposing restrictions, etc.
#5 Educate and Train Teams
All your efforts towards maintaining data fidelity will be in vain if you fail to bring all the stakeholders on the same page. After all, it is your staff that is responsible for maintaining data cleanliness. And so, make it a priority to educate, train, and inform them of their role in the larger goal of maintaining data quality.
Whenever data quality is in question, remember the 1-10-100 principle. According to this ideology, the relative cost of fixing a problem arising out of bad data compounds and increases with time.
As such, the best way to address it is by focusing on data quality right from the data collection stages. Thereafter, you can bolster this goal by setting in place specialized resources to dedicatedly maintain data quality. You can improve your Salesforce data hygiene using Weflow. Book a demo to know how we make it happen.