4 Steps for Approaching Spring Data Clean-up Projects

Dirty data is very likely lurking in your databases – incomplete form submissions, inaccurate entries, duplicate records, and/or decaying contacts. Unfortunately, these dirty data have the potential to diminish your marketing efforts and cost you time and lost revenue down the road.

It’s spring and that means Spring Cleaning. There’s never a better time to address and prevent dirty data from piling up than now. In fact, many in the industry say dirty data follows the 1-10-100 Rule. That means it costs $1 to prevent dirty data and get it right the first time. Or it will cost $10 in the future to correct or $100 if the data ultimately fails. Simply put, if you let dirty data linger in your databases, the costs to remedy the problem grow exponentially.  

But how exactly should enterprises approach data-cleaning projects?

First, there is no one-size-fits-all approach, and it depends on the type of data that you’re collecting and how it is used in your organization. But, in all instances, though, there are a few fundamental problems that must be addressed and a few steps you can follow to get started.

1. Audit Your Data Operation: Before getting started, you need to ask some important questions: What data is being collected? Why it’s being collected? And how is it being collected? Not all data is created equal. Some pieces are more important to your business than others, and gathering too much, especially points you will not use, is detrimental to your operation.
Once you know what you need to be collecting, you can examine how it’s being collected. Check for consistency across all customer touch points – both online and off. Ensuring consistency is one of the faster ways to cleaner, more actionable data.

2. Address Your Dirty Data: Updating and improving low-quality data can seem like a daunting task, and that’s why many organizations put it off. This is especially true for businesses with databases with hundreds of thousands of contacts. Cleaning data manually takes your team away from essential tasks.

Fortunately, you have options. Big Data algorithms and database cleaning software have been developed, which can quickly resolve common data errors. For example, by running an algorithm, you can repair data syntax errors – for example, using both New York and NY in addresses – and eliminate redundant entries.

Plus, if you’re experiencing data decay, like a growing number of inactive or dormant accounts, it might be time to re-target these users. Create a re-targeting campaign that incentivizes re-engagement with a special offer, and determine which dormant accounts should be purged.

3. Ask for Customer Feedback: Customers are the key to higher-quality data, because customers who are comfortable with a brand are more likely to share their data. Ultimately, by focusing on customer needs and asking for their feedback, organizations can reduce the instance of inaccurate or missing data from the get-go.

First, as you on board new customers, ask them about their preferences, including:

• Preferred contact method
• Frequency of messaging
• Type of content they prefer
• Products and offers they would like to see

But also, it’s vital to demonstrate the value of having them provide data. Tell users exactly how they will benefit by offering their email, phone number or home address. Research has shown that customers are much more likely to opt in, if they’re aware of how it will improve their experience. So you might offer an incentive upfront, or explain the value of signing up with effective copy-writing.

4. Align Your Data Infrastructure: Finally, aligning and breaking down silos should be the final step in the data-cleaning process. And this problem affects organizations in two ways.

First, dirty data is commonly seen as a problem for IT departments. In fact, surveys have found that IT specialists and data analysts spend a lot of their time cleaning up data. But low-quality data should be addressed across an organization collaboratively. Both the business and IT departments should join to determine the cause and resolution for dirty data. This will ensure consistency during collection, as well as help all understand the importance of cleaner data.

Secondly, large enterprises can struggle to align data from all sources, i.e. social media, CRM, Internal applications and offline data. This first requires collaboration between departments. Plus, data integration tools can help to seamlessly combine data from these various sources and make it more unified and actionable.

Are you planning a spring data cleaning this year? If so, this framework can help guide you along the way, and offers a few key points you must consider.

TEAM CLUTCH understands the impact of data and analytics can have on businesses. We make Big Data and Business Intelligence technology simpler, stress-free and actionable – so you maximize the impact of high-quality data can have on your business.