Marketer’s Guide to 7 Best Practices for Enhanced Data Cleansing

High quality data is the key to making smarter decisions, but maintaining data quality requires careful planning, vigilance, and above all, automated workflows. Perhaps that is why more than half of all businesses (54%) say data quality is their biggest marketing data management challenge. What’s more, a survey of B2B companies found that 94 percent suspect they have inaccurate data in their database. It’s no wonder that Forbes recently reported that poor quality data usurps as much as 12 percent of annual revenue from businesses. Imagine all the things you could do if you were able to recover 12 percent of your budget and spend it on something that delivers results, instead of seeing it carted off by dirty data. If you’re tired of poor data quality negatively impacting marketing’s performance, it’s time to implement our best practices for enhanced data cleansing.

5 Elements of Quality Data

In order for data to truly be clean, it must be thoroughly analyzed and continuously monitored in real-time to ensure all five elements of quality data are present and accounted for. High quality data is:

  1. Accurate – Data records feature data values that are correct.
  2. Complete – The data required for each data set is present.
  3. Consistent – Data values are consistent across all data sets.
  4. Uniform – The data strictly adheres to established standards for all data sets.
  5. Valid – The data is usable (formatted correctly) and reliable (current).

Best Practice #1: Develop a Data Cleansing Strategy

In order to prep your data to uphold all five elements of quality data, you will need to develop a data cleansing strategy. Essentially, this strategy is a simple written plan or outline that chronicles how you plan to keep your data clean, complete and ready for use. The ultimate goal of all data cleansing strategies is to maximize data quality. High quality data empowers the decision-makers in your organization to act on more accurate insights, make well-informed decisions (as opposed to misinformed ones), and maximize efficiency, opportunities and profitability.

Best Practice #2: Account for All Data Storehouses, Entry Points and Exits

You need to know where all data that flows into and out of the marketing department is coming from, as well as how it is stored and shared. Ideally, every department that uses data in your company needs to take this approach, but in the likelihood other departments are not on-board with your mission to enhance data quality, focus on what you can change and make plans to campaign for sales, customer service, and other departments to get with the program as opportunities arise.

Once you have accounted for data origins, housing and journeys, across all marketing technology and data storehouses within your department, you should be able to see where data issues are occurring, such as siloed data, and create a plan to correct these issues. This practice is labor intensive in the beginning; however, if you add a marketing ops dashboard to your martech stack, this will help you by centralizing your view and access to connected data sources.  

Best Practice #3: Connect Data Sources

It’s tough for data to deliver results when it’s disconnected from all the other tools, systems and platforms in your marketing technology stack. Make it your priority to see that your data is well-connected. Single out the siloes and outliers in your martech stack and introduce them to one another through intelligent integrations. Nowadays, you don’t need IT to connect your various platforms and systems and enable them to share data in smarter ways. You can secure the services of a company, like Sureshot, that offers a hybrid tech approach, wherein tech experts review your integrations and design and implement new ones that do exactly what you want them to do.

Best Practice #4: Set Data Quality Standards

Ad Age contributor, Chris Comstock, a Chief Product Officer for Claravine, says “Data standards are an organization’s unique data language—a blueprint for defining and managing common formats for data across all regions, teams, campaigns and use cases.” In short, setting data quality standards is about defining how you want your data to appear in each field, column, and parameter. Once you have defined how data in your records should be recorded, then you can set up an automated workflow to ensure in-house and incoming datasets comply with those standards. Comstock recommends the following steps when setting data standards:

  1. Identify stakeholders who own channels and information domains.
  2. Determine which data points to include, define metadata and add hierarchy and relationships.
  3. Create a data document that defines a common data language across data sources.
  4. Create a standard approach to naming conventions within campaigns, content, product listings, etc.
  5. Develop processes for updating data standards and communicate changes to all teams.

Best Practice #5: Automate the Process of Cleaning Data

Your mission in cleaning data is to heighten the performance of your marketing campaigns and other endeavors by finding and fixing data that is:

  • Inaccurate – Outdated data will often contain inaccuracies, such as a previous title, email address or mobile number.
  • Incomplete — Data that has missing values within the dataset.
  • Invalid Data that does not adhere to established standards is typically unusable.   
  • Duplicate — Data that has two or more identical records.

Most companies use software, like a data orchestration tool,  to automate the process of finding and fixing the aforementioned issues. Automation makes it possible to mine large volumes of data (i.e. data lakes, warehouses, etc.), clean the data, and prepare it for use by performing critical data quality functions at inhuman speeds. These functions include:

  • Removing duplicate records and entries
  • Removing incomplete entries and values
  • Removing incorrect or outdated data
  • Correcting typos and other data errors
  • Standardizing and merging data across multiple sources
  • Appending missing data

Best Practice #6: Screen Incoming Data In Real-Time

The best way to handle bad data is to never let it enter your systems. This requires you to monitor and catch data errors at the time they are most likely to happen—during human input. To ensure only high-quality data is allowed to enter your gates, make sure all forms and sources for incoming data are pre-screened for cleanliness in real time. In addition, apply data standards to form fields and use autofill and multiple-choice options to ensure the data coming in matches the pre-determined formats of the data you have stored.   

Best Practice #7: Enrich Data Using a Waterfall

Current research suggests that 40 percent of your database is either missing information or contains misinformation. That’s why it’s imperative that you use a waterfall enrichment approach to keep your data in peak performance condition. What is waterfall enrichment? It’s using multiple third-party data sources to continuously enrich all datasets. When you add waterfall enrichment to your data strategy, you empower your marketing team to push beyond previous limits and get a more complete picture of your customer. Broadening your data horizons will inevitably open up new streams of revenue by uncovering segments and customer characteristics that impact everything from lead generation and sales to repeat business, customer loyalty, and more. Your data management solution should provide a multi-vendor data enrichment strategy (enrichment waterfall) that ensures the best data provider for each field is used.