TL;DR: Data quality is vital in Salesforce for reliable analytics. Issues like incomplete data and duplicates affect productivity. Strategies to mitigate these challenges include removing duplicate data, validation rules and regular cleansing.
As we’ve discussed the previous articles, data quality is important. To recap what data quality is, we can say; ‘’data quality refers to the overall accuracy, completeness, consistency, and reliability of data. It is a measure of the fitness of data for its intended purpose. High-quality data is crucial for making informed decisions, conducting analysis, and achieving reliable results.’’ If you want to catch up on those articles, you can find them all in the navigation panel.
When it comes to Salesforce, data quality is also an important aspect of your business and shouldn’t be ignored. In fact, it’s crucial to business. High-quality data ensures that sales reports, forecasts, and analytics generated from Salesforce are accurate and reliable. Sales teams heavily rely on Salesforce for managing leads, opportunities, contacts, and accounts. High data quality also ensures that this customer information is accurate and up-to-date, enabling businesses to gain meaningful insights into customer behavior, preferences, and needs.
There are many more reasons that the quality of data in your Salesforce organization is critical, and we've covered that. Now, to master your Salesforce data, we’ll go over some proven principles to yield results.
To master Salesforce data and ensure high-quality data management, several strategies can be employed:
*We'll cover these in more detail later in the article.
Bad data comes into existence through various mechanisms imbedded in the overall way we capture and store data. These issues in data quality take several forms, and to begin dealing with the issue it’s best to identify exactly what they are. We can break these data quality issues down into categories which help us determine the reason they are happening and begin to picture ways in which we can address some of these problems.
Poor data affects decision making processes. No matter if it’s Salesforce, driving your car, reading the nutrition label on foods or looking at the price of hotels for your next holiday – you are hoping that the data you receive is accurate. Imagine you wanted to book a hotel for an upcoming long weekend, and on the hotel’s website the cost of each night was listed ‘about $150’. That wouldn’t make you feel very confident in making a booking, right? What does ‘about’ mean? You want to know exactly what you’re going to pay, so you can plan it alongside your budget. Business decisions are no different. The more accurate your data, the better equipped you are to take action. How else can poor poor data impact your business?
It's time to talk about the ways to combat the negative effects of poor data using 4 essential principles. When you put these principles into place, you will see that mastering your Salesforce data objectives can be reached through quantifiable, objective steps.
Although ensuring data quality in Salesforce might seem like a challenge, when you take this structured approach to address each of the fundamental pitfalls one-by-one, you can quickly get going on your path to a future of data happiness. Let's get into them;
Data validation rules can be used to enforce data quality standards in Salesforce. By applying data validation rules, you can define criteria that must be met before data can be saved, ensuring that only accurate and valid information is entered. This way, you stop bad data in tracks. For instance, when entering a date into a form, a certain format should be met otherwise it will not be accepted. You could also apply a validation rule to an address, phone number or any other field you deem important.
Regularly performing data cleansing and deduplication tasks is crucial for maintaining data quality in Salesforce. When it comes to data hygiene, duplicate management is like brushing your teeth and one of the most essential actions in the prevention of poor data. The management of duplicate data can be achieved through manual review and cleanup. Of course, this is time consuming, and therefore many organizations will seek out automated data management solutions like Plauti Duplicate Check that can identify and resolve data issues more efficiently. We’ll talk about Duplicate Check later.
Good data hygiene comes from routine and ingrained practices, and for the most tangible results, those routines and practices need to be defined through Data Governance Policies. Data governance policies define guidelines, standards, and procedures for managing and maintaining your Salesforce data quality. By implementing data governance policies specific to Salesforce, you can establish clear rules and responsibilities for data entry, data updates, and data access. These policies can include guidelines on data formats, naming conventions, data ownership, and data security.
One of the key factors influencing data quality in Salesforce is the behavior of users entering the data. Providing comprehensive training and education to users on data entry best practices seeds a culture of data quality awareness. This can involve teaching them how to enter data accurately, emphasizing the importance of data quality, and demonstrating the impact of poor data quality on business processes and decision-making. Regular training sessions, user documentation, and ongoing support can help users understand the significance of maintaining high data quality standards.
We’ve mentioned that data quality routines are crucial aspects when it comes to maintaining clean and reliable data within Salesforce. Fortunately, there are several tools and technologies available to help in this endeavor, both third-party and native Salesforce solutions. Let’s discuss them in general.
As we’re talking about Salesforce data, most people will start off by looking at what is possible in Salesforce itself. Salesforce indeed has functionality to manage data quality, such as its native solution called Duplicate Management. However, when it comes to larger, more complex sets of data, Salesforce's limitations hinder effective duplicate management. For instance, Duplicate Management only alerts about manual duplicates, it lacks compatibility with large data volumes, has limited matching algorithms, merges a maximum of three records at a time, lacks cross-object matching support, as well as numerous other limitations. Overall, it simply falls short in solving the problem of duplicate management significantly. And this brings us to our next point -3rd party solutions.
The need for data management in Salesforce is huge, and since the native abilities of Salesforce are lackluster, there is a strong demand for tools and solutions to help organizations achieve this goal. As Salesforce solutions fall short in this area, 3rd party tools are sought-after applications for the complexities of data management.
One such solution is Plauti Duplicate Check. Duplicate Check is a data cleansing tool specifically designed for Salesforce. It offers robust features for identifying and managing duplicate records. For instance, it can apply advanced matching algorithms to scan and compare data across various fields to detect potential duplicates. Duplicate Check also provides flexibility in defining matching rules, allowing organizations to finely tune the duplicate identification process according to their own thresholds.
Plauti Duplicate Check offers robust automation options, such as automated merging and deduplication capabilities, simplifying the process of resolving duplicates efficiently. As data quality is about routine, the ability to automate routines is a key step to strengthen any approach to data hygiene.
Automation is another critical aspect of managing data quality and the power of automation is already highly leveraged. Of course, this trend is further amplified by the rise in artificial intelligence, especially since ChatGPT exploded onto the scene.
Organizations can set up scheduled jobs or workflows to regularly clean, deduplicate and standardize their data. This can involve processes like validating and updating contact information, removing unnecessary or outdated records, and normalizing data formats. By automating these tasks, data quality can be maintained consistently over time without the need for manually orchestrating these tasks.
As we reach the conclusion of this guide, let's cover 4 best practices for data quality improvement that you can take forward with you. Taking these steps might improve your overall approach to data quality, regardless of which solution you are using for achieving your goals.
In conclusion, data quality is crucial in Salesforce for accurate reporting, reliable analytics, and gaining meaningful insights into customer behavior. Common data quality issues include incomplete or missing data, duplicates and inconsistencies, incorrect or outdated information, and lack of standardized data formats.
Poor data quality can lead to decreased productivity, inaccurate reporting, negative customer experiences, as well as flawed decision-making. To ensure data quality, organizations should adopt strategies and technologies that allow for automation, data validation, regular data cleansing and deduplication. They should also work on implementing data governance policies, and training users on data entry best practices.
Tools and technologies, including native Salesforce solutions and third-party solutions like Plauti Duplicate Check, can aid in data quality management and data deduplication. By following best practices and implementing a data quality improvement roadmap, organizations can maintain high-quality data in Salesforce.