Data Harmonization

Does your cross-channel ad campaign data need to be harmonized?

Data Harmonization

For digital marketers — and anyone who deals with marketing analytics — data harmonization is important. Data harmonization is the act of bringing together data from multiple platforms and/or sources and ensuring that they can be compared in a meaningful way.

Data harmonization benefits are clear:

  • Data from multiple sources can be compared apples-to-apples, ensuring validity. Otherwise, data sets that appear to be the same may actually show very different pictures.
  • When automated, errors are reduced and the process is accelerated. By avoiding manual entry, fewer human touch points and use of spreadsheets are avoided – two major sources of mistakes.

It’s very common that data must be brought in from different sources. In science, “meta studies” often bring in data from multiple experiments to form a thesis — all this data has to be harmonized, or normalized, to be useful. In marketing, data has to be pulled in from potentially dozens of channels. Without harmonization, the data can be misleading at best, useless at worst.

But data harmonization isn’t simple. It requires a deep understanding of all platforms and when done manually, it requires a major time commitment. These challenges can be avoided by using a software solution intended for harmonization. Lumenad provides an advertising intelligence solution that includes data harmonization and normalization, ensuring that marketers get the information they need — from where they need it.

Data harmonization is steadily becoming more necessary, because marketers are using more platforms than ever. The fragmentation of marketing data can be a chore and many organizations find themselves trying to manually consolidate their information. But with the right automated platform (and the right marketing staff), it’s easy enough to bring in and analyze all your data at once.

Lumenad Data Harmonization Interface

Data Harmonization Techniques

What are some data harmonization techniques? Most companies do understand the need to harmonize their data. They create a data harmonization process. But not all data harmonization techniques are going to be made equal.

  • Spreadsheets and pivot tables. Marketers might bring all their data into Excel and try to compare them in this way. But because there may not be a rigorous structure for data harmonization, they may not be comparing the appropriate attributes. While Excel is an excellent spreadsheet program, it isn’t the best program for raw data analysis.
  • Google Data Studio. Google Data Studio makes it possible to bring in large volumes of data and compare them in a visually interesting way. But much like a spreadsheet, it’s up to the marketer to find the appropriate ways to compare and consolidate the data. If the data isn’t consolidated correctly, it will yield unpredictable and incorrect results.
  • Extract, Transform, and Load Tools. While these tools pull data in and aggregate it, they won’t transform it into a true apples-to-apples format. You’ll need an engineer or a data scientist to do that work.

Companies frequently try to perform data normalization in-house, without methods that they might worry are disruptive. But this only leads to data having to be manually entered and consolidated — a time-consuming process that can lead to error.

Having a complete data intelligence tool makes more sense. An ad intelligence tool makes it possible to achieve greater data harmonization much faster, without the need for the data to be manually normalized.

Data Harmonization Example

First, let’s look at a data standardization example from real life. Food science is continually changing. Someone may be running a meta study on whether eggs are good for your health. They look at 12 different studies. But the problem is that each study defines “health” in a different way. So, how can they really draw comparisons?

The key is data normalization and data harmonization. Here, the scientists would need to find some sort of standard for “health.”

Now, let’s take another data harmonization example. You have three platforms: Facebook, Instagram, and Twitter. You want to compare key demographic information. However (as an example), Facebook lists 15 to 25 year olds, Instagram lists 15 to 20 year olds, and Twitter lists 16 to 30 year olds. In other words, the demographic information does not match up.

If you have a proliferation of 15 year olds, then Facebook and Instagram are going to look wildly more popular. But Twitter might look just as popular if it also listed 15 year olds within its core demographic. The data has to be normalized to reflect accurate age demographics or it’s going to be completely incorrect.

But what’s the answer? The answer is to work with a platform that understands the differences between data and can normalize it. Once the data is normalized, it will be accurate — you just need to make sure you’re drawing similar comparisons. And it has to be truly normalized, not just blended.

Looking through data harmonization examples and case studies can make it much easier to understand not only how harmonization is achieved but why it’s important. In the above example, it immediately becomes clear how harmful not having aligned data can be, even if the differences initially appear to be slight.

And that’s over only a single variable. In reality, the data being drawn from these platforms often numbers in the hundreds. When you consider that dozens of core metrics could be off, you find that the entirety of the campaign data could be adversely impacted.

Data Harmonization Best Practices

Now that we understand basic data harmonization methods, we need to understand data harmonization best practices.

The best data harmonization occurs automatically. Data should be imported, exported, and transformed without any human element (beyond the humans who may need to initially train the system). The more automated the process is, the less likely there are to be errors introduced into the data.

The best data harmonization is consistent. Data must always be consistently transformed. So, data from each platform should be treated the same way and given the same guidelines. Consistency is what ensures that the data is as accurate as possible, because otherwise different methods could be used for processing data, which would effectively not lead to data harmonization.

Further, data harmonization should be transparent. It should always be clear where the data is coming from, how it’s being processed, and how it originated. Though the data does need to be transformed, it shouldn’t entirely override the rest of the data. It’s important that you always be able to go back and look at the data that was there.

Together, this forms the basis of creating consistent, accurate, measurable data. Without the above best practices, it’s very hard to ensure that the data remains trustworthy.

The best data normalization tools are going to follow the above best practices. Having a tool means that you don’t need to concern yourself with the intricacies of data harmonization. You only need to know that data transformation is occurring and that the transformation is making your data easier to consume.

Data Harmonization Tools

Of course, some companies do attempt to complete their own data harmonization through the use of spreadsheets or algorithms. But data harmonization software is the easier way.

Lumenad uses a proven data transformation framework, created using industry best practices and cross-discipline expertise, to automatically import and standardize data. The platform is automated, consistent, and transparent — all hallmarks of software solutions that follow data harmonization best practices.

But other data harmonization tools could include spreadsheet utilities (like Google Sheets and Microsoft Excel), Supermetrics, Google Data Studio or others. You would bring in your data by downloading reports or via APIs. All the data would need to be consolidated, but it would actually be first collected from each service.

Without data harmonization tools, companies are going to find their marketers and analysts spending a significant amount of time sifting through data. And that data may not be very useful, because inaccuracies can be introduced. If platforms change the way they collect data, marketers and analysts will need to entirely relearn how they are harmonizing the information. With a platform, everything can be handled directly.

It’s critical that marketers start to consider new processes for data harmonization. The marketing world is fragmenting and marketers are increasingly finding themselves adding more and more data sources that must be aggregated and standardized.

Data harmonization is also going to become progressively more important for marketers interested in improving their reach. At a certain point, it will be almost impossible for marketers to keep up with all their different marketing venues without the assistance of a data harmonization solution. Lumenad provides more than just data harmonization, however. Lumenad is a data management, transformation, and reporting platform that empowers marketers with the data and analysis they need to succeed.