Customer data is the core of every business. It dictates the way that we engage with customers and prospects. You use it to forecast what the future of your business will look like. It plays a role in all facets of your business — marketing, sales, success, support — and ultimately is the defining variable that determines the type of experiences that customers have.
However, the act of simply collecting data is not in and of itself enough to allow you to improve those aspects of your business. Data collection is only useful when you can actively use the data that you collect. To do that, you need data scrubbing.
The problem is that raw data is just as the name implies — raw. It’s full of errors, typos, formatting issues, and other issues that become apparent once you dive into an un-scrubbed dataset.
Raw data simply isn’t ready for “prime time.” To ready it, data scrubbing must play a critical role in dealing with customer data problems.
- What Is Data Scrubbing?
- Data Scrubbing vs. Data Cleaning
- Customer Data Quality Impacts Business Processes
- The 5 Steps for Data Scrubbing
- A Comprehensive Customer Data Scrubbing Tool
What Is Data Scrubbing?
Data scrubbing refers to the process of preparing, processing, and cleaning your customer data in order to ready it for use within your business for marketing campaigns, sales initiatives, or customer support and success.
Data scrubbing refers to repairing, deleting, or normalizing data so that you can use it in those campaigns. The data scrubbing process typically follows a number of simple steps to identify and fix issues within a dataset.
Customer data scrubbing also refers to a number of steps and processes. In the end, the goal is to free your data from common errors that inhibit how it can be used and drives up costs. Some of the common data issues that are remedied in the data scrubbing process include:
- Duplicate data. Duplicate customer records break up the single customer view that is shared by all of your in-house teams. It is important that every customer has a single record so you have the full context to guide your interactions with the customer.
- Inconsistent data. Ensure that all fields follow a consistent format. For instance, there are multiple ways to express a phone number in data. 1234567890 vs. 123-456-7890 vs. (123)456-7890. Having a singular format ensures that you won’t run into issues down the road and that the data can be used seamlessly with other software integrations.
- Redundant data. The data scrubbing process will help you to merge or remove redundant data to improve usability and minimize costs.
- General errors and typos in data. Whenever a human enters data manually, you can be sure that there are going to be some mistakes. Simple things like a first name being all-caps (JANE vs. Jane) break the veil of personalization and hinder your marketing automation campaigns.
These are just a few of the many different common data problems that data scrubbing can help you to remedy.
Data Scrubbing vs. Data Cleaning
This is a question that we often get — “What are the differences between data scrubbing and data cleaning?”
The truth is that the two are often used interchangeably, especially in the context of customer data. More broadly in academic terms, there are more nuanced differences between the two. Data scrubbing in that context involves a number of specialized processes such as decoding, merging, filtering, and translating data.
For our purposes, data scrubbing and data cleaning can be used interchangeably to refer to the same process. They have the same end goal — to clean up your data and ready it for long-term storage and use within your business.
Customer Data Quality Impacts Business Processes
The quality of your customer data ultimately reverberates throughout your business, impacting all teams that rely on the data.
For marketing teams, low-quality data hinders their ability to create believably personalized campaigns. When your marketing teams have no faith in the quality of your customer data, they are likely to avoid injecting it into your messaging. This lowers conversion rates and ultimately harms relationships with customers.
Sales teams rely on accurate customer data to provide a context for the conversations that they have with prospects. If the data is unreliable (or split up between multiple records as is the case with duplicate customer records) it harms their ability to speak directly to the customer and address their biggest concerns. Low-quality data means lower sales.
For customer support teams, low-quality data also hinders their ability to make sure that your customers get the most out of your solutions. Being able to look through a customer record to discern what is important to each individual customer is an important part of providing a better experience. Customer success teams have the same requirements.
IT teams also spend a great deal of their time dealing with data issues as well. It is estimated that 50 percent of IT budgets are spent on data rehabilitation.
Additionally, low-quality, unscrubbed customer data also means that you end up storing more data, inflating your costs and making the data harder to search and utilize. With all of these issues combined, Gartner estimates that businesses miss out on $9.7 million on average due to bad data.
The 5 Steps for Data Scrubbing
Data scrubbing customer data typically involves a set of processes. As companies move through the phases of customer data management they will discover new benefits.
Although each of these steps may be made up of many sub-steps, the standard process of data scrubbing includes:
- 1. Audit and Inspect. To fix issues in your customer data, you have to be able to identify what those issues are. A data audit not only helps you to identify individual data problems, but also clues you into the overall health of your customer data.
- 2. Data Cleaning. The process of actively fixing the issues that you find in your audit. This can include fixing duplicate customer records, fixing formatting issues, standardizing fields, removing redundant data, and fixing individual data errors and issues.
- 3. Verification Of Data Cleanliness. Once you go through the process of data scrubbing, you then have to verify the cleanliness of your customer data. This is a secondary audit step that examines the results of the scrubbing process.
- 4. Report. Report the results, show the progress, and trends. This is important for justifying the resource investment and tying data scrubbing to real-world benefits and revenue gains.
- 5. Create Automated Processes to Limit Data Issues. Identify the reasons why low-quality data was hitting your database in the first place. Do customer input fields need more validation? Do you need to train your internal teams about the importance of data quality and data scrubbing? Do you need an automated process to cleanse data on an ongoing basis?
A Comprehensive Customer Data Scrubbing Tool
Insycle is a comprehensive data scrubbing solution. With Insycle, you can use our pre-built templates or create your own custom templates to fix your company’s specific customer data issues, then schedule those data cleaning templates to run on a daily, weekly, or monthly basis. Insycle delivers full data cleaning automation, cutting down on the time and headaches associated with cleaning your customer data.
With Insycle’s Health Assessment, which is updated daily, your customer data will be audited and analyzed for more than 30 of the most common customer data issues. You’ll have a complete picture of the health of your customer data. You can also load your own customer data scrubbing templates into the Health Assessment to track issues that are specific to your organization. With Insycle’s Health Assessment, you can not only identify issues but with the click of a button, you’ll be directed to the right tool to fix those issues as well.
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