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Data Management

4 Steps for Year-Round Data Deduplication

By January 26, 2023No Comments

This time of year, people are starting to actualize their new year’s resolutions to get things right that didn’t go so well last year. When it comes to your organization’s data, you shouldn’t be thinking any differently! Clean data is one of the most useful – and potentially lucrative – resources for a business. Data can provide powerful insights into consumer behavior and help improve analytics, strategies, and goals for virtually every department, from marketing to customer support. Conversely, dirty data creates many challenges for teams and results in millions of lost revenue for the company. Following are four steps you can start taking today to improve your data quality.  

1) Leverage Automated Data Deduplication

Duplicate data causes a lot of chaos inside your organization, making it difficult to analyze information accurately, causing confusion and bottlenecks, and creating wasted space within your CRM. In addition to stifling business processes, duplicate data is literally draining money from your budget. Poor data quality costs organizations approximately $12.9 million every year, according to a report from Gartner. In addition, duplicate data makes your teams less productive since they often need to deduplicate records manually, which causes a lot of wasted time. Also, if your sales reps have been burned by duplicate data once before, they will start double-checking records before they speak with prospects. Not only does this waste their time, but it degrades the perceived integrity of the overall database.  

Salesforce organizations have access to many data deduplication apps on the AppExchange. However, DataGroomr is the only one that employs machine learning to dedupe data and requires no setup. Rules and filters are not needed. A user simply connects to Salesforce and can being deduplicating immediately. 

2) Implement Daily Data Maintenance Procedures

Incorrect and incomplete data is a continual problem for most companies. In fact, an HBR study involving 75 executives reveals that only 3% found that they had accurate data within the acceptable range of 97 or more correct records out of 100. Companies often struggle with maintaining data accuracy because their focus is only on gathering more data rather than cleaning and maintaining current data. And each import generates additional duplicates. 

Incorrect and incomplete data can lead to inaccurate forecasts, missed opportunities, and low customer satisfaction levels. To fix these issues, you will need to fix the underlying problems that cause them in the first place. This includes things like poor data entry practices, lack of data accessibility controls and no data maintenance procedures. Start by conducting a data quality audit to get an understanding of how serious your problems are and measure the estimated impact.  

3) Centralize Data Management to Eliminate Silos

A large enterprise often silos data within different departments or physical locations. When this happens, it’s not easy to obtain a comprehensive view of your business or efficiently locate and use all the data you possess. Data silos, operating independently with their own rule sets, are also prone to data quality issues. Centralize your data to make it more usable and to ensure that all data is subject to standardized data quality management processes and requirements.  

Data silos arise from company culture, where each department naturally considers itself as a separate business unit, distinct from other teams, which carries over to data. Instituting data centralization via a shared data repository provides team members with a complete and accurate view of organizational data, thereby restoring database integrity.  

4) Conduct Regular Data Quality Checks

Significant data quality issues usually start off as relatively small problems that were either overlooked or left untreated. As a result, data issues easily snowball into large-scale issues that can no longer be ignored. Regular data quality monitoring can help to forestall such costly issues. Automation empowers companies to conduct regular checks for data uniqueness, completeness, and integrity. The frequency of these checks will vary from one organization to another. Daily monitoring is also enabled by automation and ensures ongoing data hygiene.  

As a general rule, it is a good idea to check data attributes at a minimum of a weekly basis. Staying on top of the health of your data ensures that the information your teams are using is reliable.  

Trust DataGroomr to Help You Clean Your Data

The beginning of a new year is a perfect time to implement regular data monitoring and to undertake a regimen of ongoing data hygiene. When you connect DataGroomr to your Salesforce org, you will get an instant data quality assessment which will provide you with a better understanding of the health of your data. You will then be able to start deduping your data right away without having to waste time creating matching rules. You can also use DataGroomr for data enrichment purposes to help you address any issues you may have with incomplete or inaccurate data.  

Team productivity will benefit greatly from a systematic approach to data hygiene. DataGroomr’s data professionals also offer a real-time data cleansing service that lets your team get back to business. Start with a free, one-hour consultation. Our engineers schedule time to gain insight into your data challenges. We will review the current state of your Salesforce org and the goals you wish to achieve and seamlessly get you on the road to consistently clean data. 

Try DataGroomr for yourself with our free 14-day trial.  

Il'ya Dudkin

Ilya Dudkin is a Business Development Manager at Softwarium. He is a frequent contributor to popular Salesforce outlets such as SalesforceBen, Force Talks, SFDC Panther and many others as well.