Data is the engine that drives your sales funnel. Make sure you’re getting what you need and handling it properly.
In today’s rapidly changing market, the intensity of competition makes it difficult for companies to grow or even survive.
Accurate, valid, standardized, and clean data is essential for organizations to gain actionable insights that drive decision-making and promote the human connections that drive sales. Clean data also allows businesses to deliver a superior customer experience, increase their competitive advantage, and grow more profitable.
As the old adage says, Garbage in, garbage out. Raw data must be cleansed and processed before it’s truly usable and useful, so it’s important to develop a data cleansing strategy before adding anything to your database. It’s also important to ensure your data suppliers are playing their own part in providing the best possible data.
One study of audience segments from the Harvard Business Review found that just 42.5% of male gender segments were accurate and that age tiers were incorrect in 77% of evaluated cases. The research found that data quality varies wildly in the marketplace.
Developing a data-cleansing strategy
Unfortunately, marketers pay a lot for consumer data. When it’s not certain if the data is high quality, these investments see only minimal returns. It’s particularly expensive to buy detailed profiles of certain audiences.
Poor data quality costs an estimated $3.1 trillion each year for the U.S. economy, according to IBM research. These high costs trickle down to marketers in the form of negative brand reputation effects, poor decision-making, and wasted time and money spent on marketing campaigns undermined by bad data.
The objective of your data cleansing strategy is to rectify any data that is incorrect, inaccurate, incomplete, improperly formatted, duplicated, corrupted, or irrelevant to your objectives.
After the initial step of defining your goals and objectives, follow these steps to develop a strategy that is backed with rule-based best practices:
- Look at your current processes:
- How do you currently clean your data?
- What validation methods do you use?
- What are the most common errors?
- How do you test and monitor your data quality?
- Who is accountable for data quality?
- Create uniform standards at the point of entry: It is much easier to clean data that starts off good quality than trying to clean up garbage data. Create a data entry standards document, share it with everyone in your organization, and be sure to update new employee training and re-train current employees.
- Use models to develop a complete set of rules that include instructions for:
- Segmentation
- Data audits
- Data filtering
- Corrections that would enrich or delete data
- How to improve data sources
To avoid re-polluting an existing database, make these rules essential for each existing source as well as new data.
Now that you’ve figured out who and what, and set standards, it’s time to discuss how to implement your strategy.
8 steps to cleansing your data
There’s also a lot of bad data in ID graphs on the market. This data is inaccurate, incomplete, out of date, or inaccessible. BDEX has found that 45% of device graph data is bad. And when this bad data is placed in the marketplace, it infiltrates the entire ecosystem.
Developing an overall strategy is essential, and understanding why you need clean data is important, but how do you actually cleanse your data? Here are eight steps, based on industry best practices, that every new data set should go through:
- Remove duplicate data: Data is a duplicate if you find it repeated in a dataset, meaning it occurs more than once. This can happen when data is combined from more than one source, a customer makes more than one submission, or there’s an error in data entry.
- Remove irrelevant observations: This problem is usually created when data is generated via scraping from another source. You want to eliminate data that doesn’t address the issue you’re trying to solve. For example, if you are building a model to set pricing for rental housing, you don’t need to know how many people live in each house.
- Repair the data structure: Look for typos, grammatical errors, and other categorical data, including headings that are too long.
- Filter for outliers: These outliers are the data points that significantly differ from the other observations in your data set, yet they might be the same type. As an example, a certain data point might be numerical, like the other point, but may turn out to be a number in the thousands when the other numbers in the range are from 1-10.
Outliers, while unwanted, can give you additional insight, so be careful when removing them. - Deal with missing data: Missing values in data can happen in several ways. Handle missing values by creating a missing category for categorical data and flag missing numerical data and fill it with zero this means the algorithm will know there are missing values.
- Validate, validate, validate: Once your data is clean, it must be validated for accuracy. Invest in data tools that clean your data in real-time; some even use AI or machine learning that greatly increases accuracy.
- Analyze your data: Use a reliable third-party source to append your data. They can capture information directly from third-party vendors, clean the data, and then compile it to provide more information you can use for business intelligence and analytics.
- Communicate to keep things clean: Once you’ve created a strategy and a best-practice process, it’s important that everyone on your data team knows and implements these practices to keep your data clean. After all, it will help them and you develop and improve your customer segmentation so you can send more targeted information both to them and your prospects.
Choose the right data supplier
Of course, not all errors are intentional. Human error happens frequently, and these errors, however small, contribute to poor data quality. Errors could be anything from a transposed phone number to a missing character in an email address.
When a piece of contact information is inaccurate, it’s impossible to successfully reach the people marketers are trying to target. Their messages either go to the wrong person, or they go to no one if an email address is defunct or nonexistent.
You’ve set up your systems, and you’re ready to buy data from a reliable supplier. But how do you know that the supplier is reliable and can provide the cleanest possible data for you to start with? Make sure your vendor does the following:
- Weeds out invalid data
- Provides accurate data using a modern approach that combines accuracy with broad coverage
- Matches and scrubs data using machine learning to produce the highest possible match-rate
- Offers a diverse set of signals for wide-ranging attributes
- Provides data that integrates seamlessly with your CRM
- Has the ability to track buyer intent
- Offers post-sales support that’s committed to results
- Has a solution that can be quickly implemented
- Understands your industry
Quality data is no accident. It takes knowledge, care, and attention to produce the best data for the best results.
When a piece of contact information is inaccurate, it’s impossible to successfully reach the people marketers are trying to target. Their messages either go to the wrong person, or they go to no one if an email address is defunct or nonexistent.
BDEX-when quality matters
The tools at BDEX help you get the quality data you need, right when you need it, which means increased ROI for your data investments.
We have over 6 billion unique IDs over 5,500 data categories, more than 800 million mobile ID-to-email matches, and over 1 trillion data signals. With BDEX, you’re empowered to create your own custom audience. So, all you must do to get your messages in front of the right people at the right time is to build your ideal customer to target and download the data.
BDEX data is always clear, high-quality, and current, which isn’t easy to find in the ever-expanding world of big data.
Make real human connections with BDEX. Contact the team today to start transforming the quality and accuracy of your data.
