There are a number of huge benefits of big data in the world of marketing. A growing number of marketers are using big data to better understand their customers, find the most effective advertising mediums and optimize their creatives.
Unfortunately, there are some challenges that companies using big data for their marketing strategies must contend with. One of them is trying to avoid data duplication.
Data duplication in data-driven marketing can create several problems. First of all, it increases storage costs. It can also cause inefficiencies in data processing and analysis, as duplicated data can skew analytics results, leading to inaccurate insights and poor decision-making. Another issue is that it can result in poor customer experience; for example, customers might receive duplicate marketing communications, which can be annoying and diminish brand reputation. Additionally, managing and cleaning duplicated data consumes valuable time and resources that could be better spent on other strategic activities. Ultimately, data duplication undermines the overall effectiveness and efficiency of data-driven marketing efforts.
Data duplication can be especially frustrating for data-driven marketers using Salesforce. Thousands of companies depend on Salesforce for information about their customers, and prospects. Teams make critical segmentation and outreach decisions based on the data every hours. However, the decisions are only as good as the data they are based on. If the data quality is poor, the resulting decisions may not achieve the desired results. That’s why Salesforce duplicate management tools are increasingly important to marketers, sales leaders and operators alike.
What Makes Quality Data?
A data record should be accurate and complete. The values in data fields should be valid and traceable to a reliable source. If information is collected from multiple systems, the data should be in a consistent and uniform format. Quality data has the following characteristics.
- Accuracy. While 100% accuracy is ideal, achieving that goal is time-consuming when data comes from multiple sources. For example, sending the same data through the cleaning process multiple times may eventually deliver 100% accuracy; however, the data may be outdated or irrelevant by the time 100% accuracy is reached. Establishing criteria for determining acceptable accuracy sets a guideline that ensures usable data without compromising timely delivery.
- Completeness. Usable data is complete data. Pulling up a customer record that includes historical information on the number and types of contact is more valuable than a record with a name and primary contact information. Combining historical data with purchasing history shows the number of types of contact needed before a purchase is achieved.
- Consistency. When data comes from multiple sources, the information needs to be consistent. For example, a state field should use abbreviations or complete spelling to ensure the data fits in the Salesforce field.
- Traceability. Data traceability is essential when determining the authenticity of information. Where did the value come from? Is the data source reliable? If there are duplicates, which source takes precedence?
- Uniformity. Records should share the same field categories. A field containing numbers should be identified as numeric to ensure that mathematical functions can be performed.
- Validity. Sanity checks determine whether the values make sense. Telephone numbers have different lengths based on the country. Clean data ensures that the values make sense, given the corresponding address.
Quality data means clean data. Before it is considered clean, the above attributes must be addressed. If they are not, the result is dirty data that leads to questionable results.
What Makes Data Dirty?
Dirty data is faulty data. It may contain inaccurate or outdated information, be corrupted, or contain duplicates. Dirty data may have inconsistent or missing information.
- Duplication. Multiple entries with the same or similar information can create havoc in Salesforce. Sales may access one record, while customer service uses another. As a result, a single source of truth regarding a customer is lost.
- Corruption. Data can become unusable as it is moved from one source to another. Records can become misaligned or even lost. Fields can be merged incorrectly, leaving the data unusable.
- Incomplete. Quality data is complete. Although incomplete data records can still be used, the accuracy of the existing information may be questionable. Was the incomplete record abandoned? Did the customer fail to provide the information?
Using faulty information to make business decisions can produce unexpected or unwanted results.
How Does Duplication Happen in Salesforce?
Salesforce serves as the focal point for customer information. It allows companies to share information across an enterprise. Sales, marketing, and customer service may enter or retrieve shared data. The capabilities encourage collaboration and provide comprehensive information for decision-making.
With multiple entry points, dirty data can accumulate, reducing the effectiveness of the stored information. Three ways Salesforce data can become dirty are:
- Data Entry Errors. Unintentional data entry errors may create duplicate entries. For example, people may spell as street name differently, creating duplicate records for the same entry.
- Data Integration Errors. Importing and merging data from another source can lead to integration errors unless checks are performed before placing them into Salesforce.
- Data scrapping extracts data from another application’s output. It is used to capture data from websites for storage in Salesforce. However, the scraped data may have missing, incorrect, or duplicate values.
How to Clean Salesforce Data
No matter how careful a company is, data will always need to be cleaned. Establishing a data governance policy makes identifying and cleaning dirty data easier. Additionally, as your organization begins to explore and realize the power of clean data, it’s equally as important to meet SOC 2 compliance standards for to preserve data integrity, and safeguard customer data. Accordingly, when searching for a platform to test and partner with, make sure to to fully understand their compliance guidelines, and select a specialist in the deduplication industry that meet SOC 2 compliance for data security. Creating a data-cleaning process such as the following ensures that all information meets the same standards.
- Examine Data. The first step is to look at the data and determine what entries are duplicates. Accuracy is essential when looking for duplicates to avoid having possible duplicates get through.
- Remove Duplicates. Remove duplicate records, ensuring the final record is as complete as possible.
- Remove Irrelevant Data. Not every data field in duplicate records is pertinent to Salesforce. Removing extraneous information reduces record size and data volume for faster and more accurate processing.
- Standardize Data. Setting data standards allows Salesforce to use all its data to provide and deliver reports for decision-makers.
- Validate Data. Ensuring the Salesforce information is accurate means validating the data through sanity checks.
Erroneous data produces erroneous results. Salesforce users need a cleansing tool that removes duplicate records and inaccurate data.
Why Use Artificial Intelligence (AI) to Clean Salesforce Data?
AI algorithms can process more data in less time than traditional data processing programs. They reduce the time people spend reviewing questionable data using comparison models rather than rule-based processing. AI can contextualize the results to extract features that are automatically applied to improve accuracy. Large language models (LLMs) help identify similarities and differences at a granular level for better matching. An impressive example of robust Salesforce duplicate management tools is DataGroomr, which combines AI technology and data governance best practices to capture more duplicates and cleanse data fields for more accurate information.
- Catches more duplicate data
- Cleans data fields
- Incorporates AI
- Applies best practices
If your Salesforce data has duplicate information that complicates reporting and frustrates employees, initiate a test with a deduplication and data quality solution that eliminates duplicates, while leveraging machine learning techniques to reduce more duplicates from populating your date as you move ahead. The investment to verify records, and normalize your data enables companies of all sizes to schedule and run operations autonomously and efficiently.