Data CleansingDedupe Salesforce

How Duplicates Degrade Your Salesforce Database and What to Do About It  

By March 10, 2022March 25th, 2022No Comments

Salesforce is a very powerful tool for managing all stages of the sales process, but its effectiveness is contingent on the quality of data you put in. If your data is riddled with duplicates and other data quality issues, you are not getting all of the deep data insights offered by Salesforce, thereby diminishing the overall return on your Salesforce investment. However, a lower ROI is just one of the many issues caused by duplicates. In this article, we will take a look at some of the biggest issues that can be attributed to duplicate data and how such dupes drain your company’s valuable resources.  

Duplicates Cause All Kinds of Data Quality Issues 

Duplicate data leads to all kinds of data quality issues such as inconsistent data, which might provide a false sense of confidence in a result. For example, you might think that you have a certain number of leads, but this information is skewed by duplicate data so your decisions may be based on inaccurate information. Additional data quality issues caused by duplicates include:  

  • Data inconsistency – Data inconsistency is often compounded by duplicate data because this problem occurs when the same data exists in different formats in multiple tables. 
  • Curation time – This includes all of the time that needs to be spent manually checking that all of the information in the record is correct. If your sales reps have been let down by the data in Salesforce before, they will spend additional time verifying customer data moving forward.  
  • Propagated impacts – Even after you have detected and removed duplicates, there are still some lingering effects. For example, your Salesforce admins will need to investigate how a duplicate made its way into Salesforce and create a new rule to prevent this from happening in the future. This process would need to be repeated every time a new duplicate is discovered if you are using a rule-based deduplication tool. DataGroomr leverages machine learning to eliminate rule creation and any other complex setup processes.  
     

Detecting database records that are “fuzzy” duplicates, but not exact duplicates, is an important task. Databases may contain duplicate records concerning the same real-world entity because of data entry errors, unstandardized abbreviations, or differences in the detailed schemas of records from multiple databases – such as what happens in data warehousing where records from multiple data sources are integrated into a single source of information – among other reasons.  

Lack of a Single Customer View

Your sales professionals trust the data in Salesforce when reaching out to leads and prospects and they trust that the information in one record is complete and accurate. The problem that many companies are dealing with is that they have customer data scattered over several duplicate records. This means that your salespeople need to search through all of the duplicates and piece together all of the customer information to get a single customer view. Not only is this problematic from a sales perspective, but imagine how much wasted time all of this causes. After all, providing a single customer view is one of the top features offered by Salesforce, so there’s no need to do all of this manually.  

This is a much bigger problem than you think because research shows that 42% of sales reps feel they don’t have enough information before making a call and working with patchy, inaccurate data further compounds their ability to do their job well. By eliminating duplicate data and maintaining a high level of data quality, your sales reps will be able to get through their call list faster and make better use of their time by connecting with quality leads and prospects.  

Duplicates Cause Your Data to Work Against You

Duplicate data can lower productivity, but it also drains capital from your company. A great example of this is the study that was done by the Children’s Medical Center Dallas, which engaged an outside firm to help clean up their duplicate data. The initial duplicate rate was 22% and the firm managed to reduce it all the way down to 0.2%. The data revealed that, on average, a duplicate medical record costs the organization more than $96. While the exact costs of duplicates will vary from one company to another, keep the following general rule in mind: it costs $1 to identify a duplicate record, $10 to remove it, and $100 per record if you do nothing about them.  

There are many different ways duplicate records can cost you money. One example is the increased mail costs of having to send brochures or other marketing materials to the same customer over and over again, not to mention the aggravation this causes which can undermine their confidence in your company. The longer you wait to get your Salesforce duplicate management issues under control, the more the costs will add up.  
 

How Can You Improve Your Data Quality?

The first step on your path towards increasing data quality is to understand the state of your data. You can use DataGroomr to get an understanding of how widespread your duplicate issues are. DataGroomr performs a data quality assessment once you connect it to your Salesforce. If you require deeper analysis into your data quality issues, there are many other tools out there that allow you to do things like automate anomaly detection, manage metadata, create an authoritative view of business-critical data from disparate, duplicate and conflicting sources, and many other things. If you would like more information on the best tools to perform a data quality assessment, we invite you to read the third installment of our five-part series on conducting a data quality assessment titled Choosing the Right Data Quality Tools.  

As soon as this process is complete, you can move on to data quality profiling, which involves examining data from an existing source and summarizing information about the data. It helps identify corrective actions to be taken and provides valuable insights that can be presented to the business to drive ideation on improvement plans. Data profiling can be helpful in identifying which data quality issues must be fixed at the source, and which can be fixed later.  

It is also a good idea to create a data quality dashboard that will provide a comprehensive snapshot of data quality to all stakeholders, including data from the past to identify trends and patterns that can help design future process improvements. It can be used to compare the performance over time of data that is critical for key business processes. This enables the organization to make the right business decisions to achieve the desired business objectives based on trusted quality data.  

Take a Comprehensive Approach to Data Quality

As data increasingly becomes a core part of every business operation, the quality of the data that is gathered, stored, and consumed during business processes will determine the success of doing business today and tomorrow. There are many apps on the AppExchange that can help you to achieve the type of success you are looking for, but they will not help you solve fundamentally flawed processes. Therefore, improving data quality takes a balanced approach that encompasses people, processes, and technology as well as a good portion of top-level management involvement. 

 
DataGroomr is easy to check out with a free trial. Get started today and clean your database just like that!   

Steven Pogrebivsky

About Steven Pogrebivsky

Steve Pogrebivsky has founded multiple successful startups and is an expert in data and content management systems with over 25 years of experience. Previously, he co-founded and was the CEO of MetaVis Technologies, which built tools for Microsoft Office 365, Salesforce and other cloud-based information systems. MetaVis was acquired by Metalogix in 2015. Before MetaVis, Steve founded several other technology companies, including Stelex Corporation which provided compliance and technical solutions to FDA-regulated organizations.