Companies struggling to maintain the quality health of their data should not expect the deluge of data to slow down anytime soon. In fact, the International Data Corporation (IDC) is expecting the amount of data around the world to grow by 61% and reach 175 zettabytes, with an equal amount of data residing in both data centers and cloud environments. To place this in its proper context, keep in mind that one zettabyte is equal to one trillion gigabytes. What this data shows is that companies need to start taking a more proactive approach towards data cleansing and the traditional methods you have grown accustomed to may not be up to the job as time goes on.
This problem concerns both technical specialists such as developers, systems administrators, and many others, but also non-technical professionals as well as your sales and marketing teams who often have a lot fewer tools and skills to clean up the data by themselves. This is a big problem a lot of IT managers are grappling with on a daily basis while having to deal with hurdles like budget constraints and constant pressure from management and other internal teams to clean up the data.
The good news is that it's not all doom and gloom. Today we will provide you with some data cleansing processes you can start implementing today to clean up your Salesforce environment. As we said in the very beginning, it is important to be proactive to ensure the integrity of your data regardless of the scale or complexity. Below we will discuss five stages you will need to analyze along your data cleansing journey so you can identify areas of improvement in the overall process.
Don't forget to check out: Import Data With The Info Import Wizard | Salesforce Guide
Assess the Extent of the Problem
You can notice the data quality issue due to declining sales data and also by getting the information straight from the users. If your team is telling you that they constantly have to re-enter data into Salesforce, correct outdated information, or, even worse, they don’t trust the data in Salesforce, that’s when you know that the issues are pretty serious. This is where you have to perform an extensive audit of your data to identify the particular issues that you are having.
As an interesting example of the extent of bad data, let’s examine the following scenario. An insurance company with more than 54,000 employees realized that they had a problem with conflicting data when they were experiencing payment issues from their customers and mismatched vendor information. The issue was that they did not have a standard method of entering payee names so every time they ran a query against the database they had to look through all of the duplicate records for a single account. In this situation, the company had an issue with data standardization which led to another issue of duplicate records. You need to audit the data inside your Salesforce environment to identify your individual issues. Since we touched on data standardization, let’s explore this a little deeper.
Data standardization is taking all of your disparate datasets and converting them into a common data format. Your organization may collect a lot of data from various external sources but when it enters your Salesforce environment, each entry will appear differently causing a lot of confusion. For example, let’s think about the ways people enter account names into Salesforce. They could use words like Incorporated or Company or they could use the abbreviations of these terms, Inc, and Co. Unless you have a validation formula or workflow rule in place they could be using whichever one they want even though the client prefers to use the long variety for email or print communication.
Therefore you need to make sure that you have specific rules in place to standardize data entered manually and the data coming in automatically such as through imports or web-to-lead forms. Even if you have hundreds of thousands or even millions of records, it is still possible to standardize this data to give your sales team an accurate picture of the lead source, industry vertical, geolocation, and a lot of other important information.
Without data validation, you are running the risk of making important business decisions on flawed data. The structure of your data from the previous step will also play a key role in the validation of the data since it can be difficult to use the data files with the various applications that you will be using. Both of these steps are designed to help you prevent the “garbage in = garbage out” scenario. While Salesforce offers validation rules, they only verify that the data a user enters in a given field meet specific criteria before they will be able to save it. However, you still need to append and clean up your mailing lists, verify email addresses, and a lot of other validation tasks. If you have hundreds of thousands of records, it will not be possible to perform this process manually, but there are a lot of third-party apps on the AppExchange that will help you validate your data.
There are a lot of benefits you can get from performing such data validation including cutting your prospecting time in half and a 65% savings on resources since you are not pursuing the wrong type of prospects. Given how frequently people change their email address and the high turnover rate nowadays, it can be difficult and time-consuming for your internal teams to perform such work. Therefore, if you can automate such processes, it would be a win-win for everybody.
Dedupe Your Data
While Salesforce has some built-on duplicate catching functionality, it's not nearly enough to meet the needs of modern companies. The way it works is that you would need to create a matching rule to find already existing records with the criteria you have entered. However, this presents many problems. First of all, there is a limited amount of matching methods, the duplicate jobs are limited to only the standard objects i.e. Leads, Contacts, and Accounts and it cannot process the results automatically. Keep in mind, there are many other limitations as well. This is why a lot of companies install third-party apps that provide even more functionality, but theta is also rule-based, which means that you will have to keep creating more and more rules to account for every possible variation of the duplicate.
This is why it would be better to invest in a tool that uses machine learning to catch the duplicates since the system learns on its own to identify duplicates based on your criteria without explicitly being programmed to do so. Also, since your Salesforce admins will no longer have to keep creating rules, they can focus more attention on helping users develop reports, adding fields, running backups, and a lot of other important day-to-day jobs.
Check out another amazing blog by ildudkin here: How to Properly Implement a Salesforce Deduplication Feature: 5 Tips You Need to Follow
The Effects of Bad Data
Bad data inside your Salesforce will affect more than just the bottom line. It will have a detrimental effect on employee morale, decrease their efficiency, and form an overall negative opinion of your company. Since they are the ones on the frontlines communicating with customers, they will have to sort through all of the information inside Salesforce and double-check it for accuracy. In order to boost efficiency and reinforce employee confidence in the data, you will need to install comprehensive solutions that take care of the standardization and duplicate issues respectively. Remember, the longer you wait to resolve the data issues the more money it will cost you over the long term.