Mastering Salesforce Data Quality

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.

Taking Control of Salesforce Data Quality

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.

Four Proven Strategies to Improve Salesforce Data Quality

To master Salesforce data and ensure high-quality data management, several strategies can be employed:

• Data Validation: Implement data validation rules to enforce standards and criteria for data entry.
• Regular Data Cleansing and Deduplication: Perform routine data cleansing and deduplication tasks to eliminate duplicate records and maintain data hygiene.
• Implementing Data Governance Policies: Establish data governance policies specific to Salesforce to define guidelines, standards, and procedures for managing and maintaining data quality.
• Training and Educating Users: Provide comprehensive training and education to users on data entry best practices to promote a culture of data quality awareness.

*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.

Common Data Quality Issues in Salesforce

• Incomplete or missing data: Incomplete or missing data occurs when required fields are left empty or when certain information is not entered in a web form. It’s not a huge surprise that people are reluctant to leave all of their personal information on a website they don’t fully trust. After all, how can you trust 100% what happens to your information once left in the hands of website form. The consequence is that this reluctancy to divulge personal details leads to incomplete customer profiles, which leads to inaccurate reporting, and hindered decision-making. For example, if a customer's contact information is missing, it becomes challenging to reach out to them effectively.
• Duplicates and inconsistencies: Duplicates are often the biggest headache for companies who are dealing with data cleansing. Duplicate are multiple records that represent the same entity, such as duplicate accounts, leads, or contacts. Let’s imagine a customer is already in your system, but you hosted an event after which they left their details in a form. The next day, it’s entered in the system again and voila - you have another record of the same person.
  
  And, perhaps, while filling out the form, the customer left another email address than the one you had stored in your records. What do you do? Is this the same person? Should you discard the old email address? This is how inconsistencies starting creeping into your dataset. Inconsistencies can also refer to other variations in data formatting, such as different spellings, abbreviations, or inconsistent use of capitalization.
• Incorrect or outdated information: Over time, data can become outdated or incorrect. In fact, this happens faster than we think. For instance, someone might change their job, their phone number, their address. This happens all the time. Of course, the customer will call you the following day to make sure you have their latest information, right? If you’re reading this far, I guess you already know that is rather unlikely.
• Lack of standardized data formats: Data in Salesforce often comes from various sources and if there is no standardized format, it can result in data quality issues. For instance, inconsistent date formats or varying units of measurement can cause confusion. On a more personal level, this happens to me whenever someone from America tells me the temperature and I have to convert Fahrenheit to Celsius -something I still have not fully mastered. And while this is likely to be the case for the foreseeable future, there are some places where formatting can really make a difference. For example, standardizing date formats helps ensure we don’t send a customer a birthday voucher on the 1st December instead of the 12th of January. As they say -the devil is in the details!

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?

Impact of Poor Data Quality in Salesforce

• Decreased productivity and efficiency: One tragic result of poor data is wasted time and effort. As any business knows, lost time spent on fruitless efforts is painful. A majority of businesses have felt this in one way or another. For instance, most sales representatives have at some time or another struggled to find accurate and up-to-date customer information, leading to delays in responding to customer inquiries or addressing their needs. This can lead to a frantic back and forth between reps to confirm something that isn’t clear. Sound familiar?
• Inaccurate reporting and analytics: The role of data driven analysts is on the rise, and most analysts know that reliable and accurate data is essential for generating meaningful reports with insightful knowledge. Tardy data quality can introduce errors, inconsistencies, and duplications, resulting in inaccurate reports and misleading analysis. The evil twin of a misleading analysis is flawed decision-making, as stakeholders will potentially make strategic decisions bad on something that is partially true, or even worse – completely false.
• Negative customer experiences: A successful business will cherish every customer and personalization is becoming more important to stand out in a world where many online customers are treated as numbers in an excel sheet. Just like a physical shop, it is more expected for online shops to add a personal touch to their loyal customers. The last thing any business wants is to leave a sour taste in the mouth of their customer. Sadly, poor data quality can directly affect customer experiences in negative way. Let’s say you have inaccurate or outdated customer information. That can result in failed communications, such as sending a promotion on a product that customer already bought last week. Inconsistencies in customer data can lead to fragmented interactions and a lack of personalization, which negatively impacts customer satisfaction and loyalty. With personalization, you can also enhance your ability to meet a customers real needs. Did they buy a pair of shoes last week? Well, maybe you can offer them a promotion on some new socks!?
• Impacted decision-making processes: As we pointed out, the evil twin of a poor analysis is poor decision making. While a poor analysis can often be reworked, poor decision making is harder to undo. Decision-making relies heavily on accurate and reliable data. When data quality is poor, decision-makers may base their strategies and plans on flawed information. The biggest problem with poor data quality is that many companies might go unaware of its scope and overall impact until a bad decision forces them to realize something is wrong.

4 Strategies for Ensuring Data Quality in Salesforce

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;

1. Data Validation

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.

2. Regular Data Cleansing and Deduplication

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.

3. Implementing Data Governance Policies

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.

4. Training and Educating Users on Data Entry Best Practices

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.

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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.

Tools and Technologies for Data Quality Management & Data Deduplication

Salesforce Duplicate Management (Native Salesforce Solution)

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.

Third 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 and Artificial Intelligence Solutions

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.

Best Practices for Data Quality Improvement

1. Establishing data quality metrics and benchmarks
   Defining data quality metrics and benchmarks is crucial to measure the accuracy, completeness, consistency, and timeliness of your data. These metrics could include criteria such as data accuracy percentages, completion rates, duplicate record counts, and data currency. By establishing benchmarks, you can compare your data quality over time and identify areas that require improvement.
2. Conducting regular data audits
   Regular data audits are essential to identify and rectify data quality issues. Audits involve examining the data in your Salesforce system to identify errors, inconsistencies, and gaps. During the audit process, you can clean up duplicate records, standardize data formats, validate email addresses, and verify other critical data points.
3. Encouraging user engagement and ownership
   We discussed how data quality is a collective responsibility. Encouraging user engagement and ownership fosters a culture of data quality awareness and accountability. Users should be educated about the importance of data quality and provided with the necessary training and resources to maintain high-quality data. Creating a productive culture about data quality doesn’t mean mentioning the importance of data quality once a year. To foster this awareness continuously, you should establish data governance policies and procedures that clearly outline how data should be handled. If these procedures are followed, they soon become habits, and a culture of data quality forms naturally.
4. Implementing a data quality improvement roadmap
   Many organizations are new to the journey of improving their data quality. If that’s the case, developing a roadmap for data quality improvement is a great place to start. In this roadmap, you should carefully address data quality issues systematically, one-by-one. This roadmap should outline the steps and initiatives required to resolve and data quality issues over time. It may include tasks such as data profiling, data deduplication, data validation. The roadmap should also ensure that data quality improvement is an ongoing process, instead of a once-off event.

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.