Data Harmonization: Benefits And Pitfalls for Business

Promoting your brand across different regions, markets, industries, and target audiences generates tons of different marketing metrics and requires data harmonization. Without consistent marketing data monitoring, you can’t optimize your campaigns to achieve your desired results. However, consolidating cross-channel metrics may lead to data discrepancies. Different marketing channels assign different names to the same metrics, which makes it difficult to create a holistic picture of your performance. Furthermore, fragmented data types slow down marketing analysis and add routine manual operations for analysts.

To enhance your marketing capabilities and accelerate growth, you have to align your data. That’s where data harmonization comes in. In this post, you’ll learn what data harmonization is, how this process works, and the pitfalls to watch out for.

What is Data Harmonization?

Data harmonization is the process of unifying disparate data fields, formats, dimensions, and columns into an aligned dataset. Different marketing channels often use different terms and words for the same metrics. That’s one of the main reasons why data discrepancies appear.

Moreover, experienced marketing teams utilize various CRM systems, DSP software, and other tools that assign different names to your marketing campaigns, creatives, and other data. For example, it’s very common for the same metrics in Google Analytics and CRM systems not to match.

To solve this problem, you must first identify the names of similar metrics across your different channels. Then, you need to come up with a common data field for these metrics and create a custom data view that lines up the required data. That way, it’ll be much easier for your marketing analysts to work with the gathered insights and arrange all data in your marketing dashboards.

In the majority of cases, companies implement ETL solutions to harmonize data. ETL (Extract, Transform, Load) systems automate manual data processing and ensure data integrity. The transform module is responsible for data harmonization. For example, Improvado’s MCDM (Marketing Common Data Model) algorithm allows analysts to align data based on any parameter. The MCDM module harmonizes data based on age, gender, devices, traffic type, and many other metrics. It also handles CRM datasets that have custom data fields and require unification.

How Does Data Harmonization Work?

Clearly, raw, unharmonized data isn’t suitable for marketing analysis. It can ruin the overview of your marketing efforts and cause confusion among analysts. Raw data often contains irrelevant data clusters, incorrect values, and duplicates. That’s why it’s crucial to understand how data harmonization works to standardize your data in the right way.

Data Mapping

Before delving into actual data harmonization, marketing analysts must study their data sources first. Marketing data can be streamlined from various sources, like:

Social ad platforms
Demand-side platforms
CRM systems
Web analytics software
Paid search ads platforms

It’s essential to understand from which exact sources you want to extract data. Without this knowledge, you won’t be able to create data harmonization patterns and align your insights.

When your list of marketing sources is complete, you must then take an in-depth look at the data structures these sources contain. Analyze rows and columns, data labels, the types of information these structures contain, and the relationships between data. That way, you’ll understand the connection between data from different sources and what transformations it requires.

Data Transformation

During the second phase of data harmonization, you carry out all transformations that were planned during the data mapping process. There are several ways you can transform data.

The first one is manual transformation. In this scenario, your database developers or data analysts with coding experience will create SQL queries that extract and modify raw data from your marketing channels. This approach wastes your developers’ time and prevents them from getting the required information in time. Besides, manual operations aren’t scalable. If you need to add a data source or change something in your data harmonization query, your developers will have to create new algorithms from scratch.

A more efficient approach is to create an on-premise ETL solution. That way, you can automatically process marketing data on your own servers. Broad customization capabilities are the main benefit of on-premise ETL systems. However, this solution takes a lot of time, resources, and competent professionals to develop. If you’re aiming for an on-premise solution, get ready to hire a team of experienced data engineers to maintain your new system.

Finally, the most relevant solution is to implement a third-party ETL solution. ETL systems like Improvado or Supermetrics integrate into your existing data infrastructure and harmonize your raw data right away. This type of solution doesn’t require you to have a team of developers to create the system. The vendor takes care of the integration and performance of the system for you, so you only need a team of experienced marketing analysts to make right decisions based on the analysis-ready data.

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Data Transformation Types

To fully understand the concept of data harmonization, we have to consider all of the operations that take place during the data harmonization process. The majority of these operations are performed automatically by ETL systems. However, if you’re taking a manual approach, you’ll have to create SQL queries and waste resources for each of them.

Data Aggregation

With data aggregation, marketing analysts can search, collect, unify, and present data in marketing reports. This process helps you to get the full picture of any data column and compare it with other columns. For example, let’s say you want to identify the average age of the male audience and compare it to the average age of the female audience. You can aggregate your data table based on age and find the average values.

Later on, you can push this data to your business intelligence tools to get visualized insights and accelerate the decision-making process.

Data Cleansing

Raw marketing is most often unstructured and contains useless data rows. Data cleansing removes out-of-date, imprecise, or incomplete data structures to make the final insights more accurate and clear. The process includes scanning data for syntax errors, deleting fragmented data, correcting typos, and more. Besides, data cleansing also removes duplicates that might appear as a result of merging two datasets from different marketing channels.

Data Filtering

Data filtering is a data harmonization technique that refines datasets and helps users get only the data they need by eliminating duplicate and irrelevant information. The most practical and simple way to use data filtering is to let users select the rows, columns, and fields they want to see in the dataset. If users don’t want to see the channel where the prospect came from, they can simply exclude it from the data view.

Data Integration

Data integration is a vital data harmonization process that merges different data types (for example, datasets from Google Analytics and Facebook Ads) into a single structure. Unifying large, disparate datasets into a single analysis-ready structure is the primary goal of the whole ETL process.

Data Validation

To avoid data discrepancies and inaccuracies, ETL developers create automated rules and algorithms that verify data integrity and notify users when they encounter data issues. For instance, the data validation notification might be triggered when the algorithm comes across an undefined data field. The program marks this field so users can investigate the problem later.

Data Splitting

Sometimes users need to split a single data column into multiple columns. Data splitting might be useful for creating “training” datasets or simply dividing up a data structure that has gathered a large amount of data over a long period of time.

Data Harmonization Pitfalls for Businesses

Even though data harmonization seems like a direct path to improved marketing analytics and optimized campaigns, in reality, it’s harder than it seems. Let’s find out what’s stopping some companies from implementing ETL solutions and why they don’t use their data to its full capacity.

Hard to Find the Right Talent

If you’re going for an on-premise ETL solution, you’ll need to hire data engineers and developers with an expertise in this niche. Today, the market is filled with interesting job openings with large paychecks. That’s why the hiring process for big data specialists has become far more difficult.

If you’re experiencing hiring issues, it may be more beneficial to reach out to third-party vendors. They’ll quickly and efficiently integrate the ETL system into your existing data infrastructure for you. Besides, if you have limited resources or your marketing analysts don’t have experience with a particular ETL system, some vendors will offer their help with all adjustments and configurations of the ETL system. For example, Improvado offers ETL personnel training under our professional services package. We also provide decentralized marketing analysts who can configure dashboards, create effective reports, and take care of all metrics.

Time-Consuming Data Preparation

The least favorite and most time-consuming job for data analysts is data preparation. According to Forbes, data scientists spend around 80% of their time on cleaning and organizing data. Even though modern ETL systems automate routine processes and take the burden off data analysts, it still takes time to complete that initial data preparation. Especially if you have accumulated a large database of marketing insights.

ETL companies take over data preparations, enabling in-house data analysts to focus on higher priority tasks. At Improvado, we offer reusable data preparation templates that can automatically unify and harmonize your data from any marketing source. Furthermore, we provide our users with extensive documentation and a data dictionary that explains the different metrics, fields, properties, and more.

Improvado’s data dictionary

Data Harmonization is a Tough Process Without the Right Tools

As we’ve mentioned above, before completing your data harmonization, you have to map your data and clearly understand how your data sources will interact with your data infrastructure. This isn’t an easy task, but it becomes even more difficult if you don’t have decent experience in this niche and don’t know what tools to use. Tools like IBM Infosphere, Improvado, and Informatica PowerCenter will help you create the correct data infrastructure and establish a seamless data pipeline.

How Improvado Can Help?

Data harmonization often becomes a stumbling block for companies striving for new heights in marketing analysis. Manual data processing is prone to human error and data discrepancies inevitably arise. To get the most out of your data, you have to find the right data harmonization tool.

Improvado is a full-cycle ETL marketing solution that helps businesses automate routine processes and make informed decisions faster. With our platform, you can choose from 200+ marketing data sources and integrate any of them into your data infrastructure. You don’t need to handle complex SQL queries or maintain in-house ETL developers. We’ll connect all data sources and arrange a data pipeline on our own. Now, you can concentrate on growth instead of data preparation.