The Importance of Clean and Standardized Data

The Importance of Clean and Standardized Data

When it comes to concept data cleanliness, most businesses know that the end goal is a constant challenge. We often hear sentiments from clients like this:

“We need cleaner data.” “Our data is dirty.” “It’s a data issue.” “Can we trust these metrics?” “Is this data accurate enough to allow us to report up the chain?”

While most people know data is important, the word “data” is often spoken of in ambiguous statements like the above. It often comes without a practical understanding of why it’s important to have clean and standardized data. Flushing out these ambiguities is necessary in order to advocate within your organization for creation of processes and programs that facilitate smart data practices to ensure clean and standardized data.

One of the biggest challenges for mid-size or large organizations is connecting the expertise of campaign marketers with the expertise of IT or data teams. Far too often marketing has a great idea for a campaign and will rely on the IT/Data team for campaign support, only to find out that they don’t have good enough data to push the initiative forward. And similarly, far too often IT/Data teams don’t have the advocacy or resourcing to standardize their data to make marketing’s ingenuity come to life in the market. Organizations then often fall into patterns of launching campaigns that fall short of expectations. Not good!

Striving for clean and standardized data within your organization will ultimately lead to:

  • increased advocacy for smart data practices
  • identification of specific data issues (often times low-hanging fruit is identified, and quick improvements can be made)
  • improved communication between different functional teams

The more you can articulate these things the better off you’ll be.

So, with that, why is it important that data is clean and standardized?

There are two main reasons that will be touched on in this article:

  • Systems are dependent upon it
  • Analysis is dependent upon it

Systems are dependent upon it

While systems are becoming more proficient in their capabilities, particularly with AI, these systems rely upon unified data to make decisions and take actions. When systems have cleaner data to work with, it can result in identifying leads to send to your CRM, sending an email to particular contacts, linking contacts to particular accounts, displaying first name or other dynamic information in an email, or scoring a contact based upon profile information. Consistency in spelling, formatting and the values used are also critical. Almost always, clean and standardized data will directly contribute to how successfully the system is able to do its intended action.

(For the purpose of this article we’ll take a look at this in particular reference to Marketing Automation Platforms, particularly Oracle Eloqua. However, these concepts apply across all Marketing Automation Platforms and other tools in your marketing tech stack as well.)

Here are a few examples in Oracle Eloqua:

  • Targeting / Segmentation
    • Example: Try targeting contacts with an “Industry” of “Education” when the “Industry” field contains numerous spellings, abbreviations or formats of “Education”. While there are work arounds at times, like using a “contains” function, it becomes increasingly difficult to correctly target those contacts. In this simple example that work around is relatively easy, but now imagine trying to segment using 10+ criteria with numerous fields that have this same issue. A lot of times it becomes impossible. In this case, setting up processes to ensure that the “Industry” field only contains one value for “Education” would allow for much easier segmentation.
  • Personalization
    • In today’s world, personalized experiences are expected by consumers. To meet these demands, personalization often only becomes possible for businesses if resource-heavy, manual processes are put into place. With cleaner data, this manual strain of personalized marketing efforts is greatly reduced. The companies that have mastered personalization are the ones who have mastered data to allow automation to do its work.
  • Lead Scoring
    • Data cleanliness and standardization is particularly important to accurately evaluate contacts within Lead Scoring. Your Marketing Automation system bases its scoring on specific values on the contact record. If there are not standardized values for the system to look for, but rather hundreds of different values, it can be almost impossible if field values remain unstandardized. For example, one common field that is often used in scoring is “Job Title”. Because this field is usually a free text field on forms, there are all sorts of variations. Create a contact washing machine to standardize this data and write it into a new “Standardized Job Function” field. This is a great way to make this field scorable in your Lead Scoring models.
  • Lead flow and routing to sales
    • Lead routing workflows have many decision steps that rely on data being correct. Example: Many US based companies exclude leads from being passed to their CRM that are not from the United States. To accomplish this, they create a decision rule that removes contacts that do not have a country value equal to “US”. If some contacts have a country value of “United States” or “USA”, because technically their value does not equal “US”, those contacts would end up getting excluded, which ultimately results in a loss of valuable leads passing to sales. Use a picklist and contact washing machine to ensure you keep your country values standardized.

Analysis is dependent upon it

Systems and reporting engines are also reliant upon your data being standardized. Meta data that is linked to contacts, leads, opportunities, campaigns, emails or any other object is what allows for more meaningful reporting. Meta data are the different variables in your data, allowing you to see results in different ways. If this data is unstandardized, analysis becomes increasingly difficult. Let’s take a look at a couple examples below:

Example 1: Take a simple pivot table. Imagine you are looking to see these statistics broken down by a particular sub segment in your business. You find a field called “Sub Segment” and drag it onto your pivot table. You think you’re about to get what you need, but alas, instead of 9 rows to view the data by, one for each sub segment in your business, 60 rows show up. As you scroll through the values you realize that some of the values are old from a few years ago, and many are different variations of one of the 9 sub segments that you were expecting.

You need to define and standardize your sub segment values! There may be workarounds, but as previously mentioned, when analyzing across large sets of data this becomes increasingly complex. While a manual rework of the data may be feasible in the short term, doing this over and over again becomes unscalable. This is particularly true when you have many team members looking for this information on a regular basis who may not have the necessary access to your BI tool, Excel skills, or context necessary to manipulate the report for their needs.

Example 2: If you are trying to report on how a campaign performed based upon “Company Size”, but have unstandardized values, it can be difficult to make conclusions from the data.

Let’s take three different contacts – one with a company size of “1-50”, another of “1-20”, and a third with “1-100”. In this example assume that it is critical to evaluate performance by Company Sizes of “1-30”, “31-70”, and “71-100”. How do you group these ranges systematically? It’s easy enough manually to go through three contacts and sort in a way you need, but when this data is inconsistent within large data sets, it becomes unfeasible to create reports.

If you expand these issues across large data sets with multiple fields, it very quickly becomes a complex web of disparate data that doesn’t allow you to report in a way you, or your leadership, need to.


At the end of the day, lack of standardized data dramatically hinders an organization’s ability to launch smart effective campaigns, while also making it increasingly difficult to seamlessly operate between divisions. We’ve outlined how data standardization can help create more accurate marketing efforts, drive efficiencies for the business, and better articulate campaign success and failures.

Are you facing similar challenges? We’re here to help! Do you have success stories of how you standardized some of your data? Let us know by commenting below or get in contact with us!

By | 2019-03-14T14:29:10+00:00 March 14th, 2019|Data, Oracle Marketing Cloud|0 Comments

About the Author:

Kyle joined Relationship One in 2018 and loves the intersection between marketing and technology. He enjoys problem solving and believes the more challenging a problem is simply means the more satisfying it is to solve it. When not working, Kyle likes exploring the Minneapolis restaurant and brewery scene, along with tormenting himself by being a Minnesota sports fan. At least we have the Lynx!

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