It’s the new year, and you’ve got some shiny new revenue goals, right? The beginning of the year is a great time to take stock of your resources for growing your revenue.
Data is one of your greatest assets. Almost 67% of businesses rely on CRM data for revenue growth. Yet 94% of B2B businesses believe there are inaccuracies in their databases.
So, it’s likely that you need to do some data cleansing.
It’s estimated that 30% of B2B data is out of date within 12 months. Imagine how much easier it would be to hit your revenue goals if you stopped wasting 30% of your direct marketing budget.
These best practices will help you:
- Detect and eliminate errors and inconsistencies in single data sources or when combining multiple data sources.
- Use tools to automate manual inspection, data collection, and data completion processes.
- Prevent bad data from entering your databases for better long-term data health.
The year is getting older by the second. Here are some data cleansing tips to help you make the most of 2019.
Data Cleansing Tips and Best Practices
Develop a Data Management Plan
Poorly managed data will generate a poor ROI. Even if it’s clean. That’s why having a data management plan is important. Your data management plan is the framework for establishing goals and tracking progress with your data.
Identify KPIs for your data.
Identify your most important data points.
Establish a system for tracking the health of your data.
Find tools for keeping your data healthy.
With a good data management plan, you can avoid mad scrambles to get your data cleaned on a short deadline.
Validate Data at the Point of Entry
This is by far the best way to keep your databases clean. The most common source of bad data is mistakes on entry forms.
You can set up a screening system that batches new data together and verifies it before it’s loaded into your databases. Or you can use an API that validates data as it’s entered into form fields.
This won’t fix bad data that’s already in your databases. But it makes data management far easier in the long run.
Standardize Data Fields
Create standard operating procedures for the format of each type of data. It’s much harder to spot and remove duplicates, analyze, and use your data if every piece of information is entered in several different formats.
Additionally, require field separation. Data fields can be merged if you need.
But it’s much trickier to separate contact data into different first name, last name, address, phone number, and other fields if it’s entered as a single, nebulous field.
You can standardize data at the point of entry by creating formatting rules on your entry fields. But you also need to communicate these standards to your team so they can maintain the standards when they’re manually manipulating data.
Clean Data Based on Urgency
Since data goes out of date quickly, you need to cleanse the data and then use it as soon as possible.
If you clean a database, then let that data sit for a year, you’ll just need to clean it again before you use it.
So set priorities for your data and cleanse the hottest data first. It’s tempting to update your oldest data right away. But it’s best to cleanse data as close to the point of action as possible.
Leads that haven’t been contacted in 12 months can wait a few more weeks while you verify the data and reach out to your most relevant prospects.
Append Missing Data
Even with good data collection, it’s still possible that you end up with incomplete data entries.
Make all form fields mandatory to prevent incomplete entries from getting into your database.
But sometimes you need information that you can’t ask people for. Like their SMS email address or line type and carrier. Maybe you need to know the location of each contact for GDPR and CASL compliance.
You can complete each entry with customer outreach. But this is inefficient and time intensive. It can also create a bad customer experience and signal incompetence to customers.
Take Advantage of Spreadsheet Software
Tools like Microsoft Excel and Google Sheets have basic tools that make it easier to manage your data and help you prepare your data for processing.
Both Excel and Google Sheets have ‘Remove Duplicates’ tools. Cutting duplicate entries makes data processing faster and saves you money, if you’re paying per entry.
There are also functions that help you remove spaces and other weird characters from your data:
These functions require a bit of coding to use. However, they make it faster and easier to tidy up your data so your systems can use it efficiently.
Follow these best practices, and you’ll get the ROI you need from your data to hit your revenue goals quarter after quarter.
If you want to start cleaning your data right now or need tools to round out your data management strategy, check out the Searchbug business services.
Searchbug provides data cleansing, bulk data append tools, people finding services, and APIs that help businesses keep their databases clean and automate data management processes.