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In the modern world of marketing, understanding and harnessing the power of your business data is essential for delivering business success. 

However, many businesses aren’t using their data to its fullest potential, often still working with disparate data sources and struggling to draw meaningful comparisons between them. 

As part of your data management strategy, data transformation can unlock the true potential of your raw data to help you generate more meaningful insights and analysis. 

Want to know how to achieve excellence with data transformation? 

In this guide, we’re going to delve into the specifics of data transformation a little more, plus a few other data management concepts, explaining how you can leverage them together to drive smarter optimization decisions and business growth. 

What is data transformation?

Before we explain how data transformation can be used to enhance your business data, it’s probably worth giving an overview of what data transformation is.

Explained simply, data transformation is the process of converting data from one format or structure to another.

In the context of marketing data integration, the data transformation process is important for taking data from each of your different data sources and standardizing it before it’s loaded into your centralized data destination.

This outcome is a single source of truth where you can accurately and consistently compare the performance of different channels for more informed analysis.

Beyond ensuring consistency in data formats and structures, data transformation can also unlock fresh insights by creating new dimensions and metrics from existing data.

By transforming and enriching data, you can explore new angles and perspectives in your data, leading to richer, more nuanced insights for improved marketing performance. 

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The importance of data transformation in marketing

If you’re involved in the day-to-day management of marketing data, you’ll most likely be familiar with the challenge of having to refer to multiple platforms to try and get an understanding of which channel is performing best. Metrics are often reported differently between platforms, making it harder than it needs to be. 

Or you might share the common frustration that the social media team measures performance one way, while the paid search team reviews alternative metrics, and the email marketing team works in its own silo with completely unique KPIs. There’s no consistency and limited collaboration across teams. 

Sound familiar? 

Data transformation, as part of your data integration process, can help solve both of these challenges. 

By consolidating data from your various marketing sources into a central data destination, marketers gain access to a single source of truth, empowering them to make quicker, more accurate, data-driven decisions.

Data transformation also ensures consistency in data analysis, as all fields are standardized.  This can enhance cross-team collaboration and help teams make reliable comparisons to aid in more accurate decision-making and strategic planning.



Discrete Vs. continuous data: What’s the difference?

Before you jump in and start planning your data transformation process, it’s important first to understand the different types of data you’re dealing with.

An important distinction for marketing teams is between discrete and continuous data.

Discrete data represents countable values. These are finite, specific figures that you can count in whole numbers, such as website visits, social media likes, or email campaign click-throughs. 

Continuous data, on the other hand, includes values that can be measured on a continuous scale, like time spent on a website. You wouldn’t want to round this to the nearest minute! Continuous data can take any value within a range, offering a high degree of precision and variability, and is important in situations where detailed measurements are required. 

The distinction between these two data types has practical implications in marketing analytics, as it influences how you choose to analyze and visualize data. 

It’s important to choose a data integration solution capable of consolidating all your important marketing data, whether structured or unstructured, discrete or continuous. 



Why do you need an efficient data transformation tool?

For businesses that manage marketing data across multiple channels, manual data integration and data transformation simply isn’t feasible. 

The time and complexity involved in manually combining and standardizing diverse datasets make it an impractical approach for businesses, slowing down access to consolidated data insights. A manual data integration process also puts your data quality at risk as it’s prone to manual errors - even the smallest mistake in data entry can have a significant impact on the accuracy of decision-making. 

Automated data transformation is the only viable solution for businesses seeking efficiency and accuracy in their data management. 

Working with the right data integration platform can automatically bring together data from multiple channels, eliminating the inefficiencies and inaccuracies of manual data handling. It can also seamlessly transform each of your disparate data sets into a standardized format, enriching the data to suit specific business needs.

Most leading platforms have functionality that can enforce schemas, combine data streams, and create calculated metrics to further enhance the precision and value of your data.



The best data aggregation tools in the market

Having established the importance of automated data integration platforms in effective and efficient data management, you might be considering how to choose the right tool for your business.

In addition to integrating and transforming your data, it’s also important to make sure that your chosen platform can effectively aggregate all your data, summarizing key metrics for faster, more efficient decision-making. 

We’ve put together some of the key factors that your business should consider when looking for a platform to integrate, transform, and aggregate your data. 

Connectivity

It’s important to ensure that your chosen platform can connect to all of your important data sources. Look for a solution with a wide library of out-of-the-box data connectors and the ability to create custom connectors for unique sources​​.


Scalability

Another factor to consider is how well the platform can adapt and scale with your expanding marketing activities. It must be able to support your business in the immediate term but also in the future​​.

Speed

Ask about the data fetch frequency of the platform, which refers to how often data can be pulled from each of your different sources.  Some of the market-leading platforms are capable of near real-time data integration, which is crucial for making timely decisions and staying ahead in the market​​.

Cost

Consider both immediate and long-term costs, and be aware of potential hidden costs in pay-as-you-go pricing models. For a lot of businesses, the stability of fixed monthly costs can be beneficial.



What is a data maturity model and how can it drive business efficiency

Large multinational businesses like Google and Amazon are often used as prime examples of the success that can be achieved when you truly embrace the power of data and have a mature data strategy.

However, achieving this level of sophistication in your data management isn’t something that happens overnight, and it’s something that remains a challenge for many organizations.

Becoming data mature as a business is best seen as a journey rather than a destination, with businesses typically going through a number of stages. 

 

Manual chaos

This stage is characterized by data still being held in silos and inconsistencies in data analysis and management. 

Increased dashboard usage

Businesses in this stage are beginning to consolidate data, but the process lacks robust data governance.

Single source of truth

At this stage, businesses have succeeded in implementing an effective single source of truth and have a robust data governance strategy. 

Predictive capabilities

Data-driven decision-making becomes integral to the organization at this stage. 

Strategic revenue driver

At this final stage, data is used extensively across the business for strategic planning and decision-making to drive revenue growth. 

Want to find out more about how to become data mature as a business? 
We’ve got a full post that covers the investment in technology, the upskilling and culture change needed, and the buy-in required by leadership. 


What is data enrichment?

We’ve already briefly touched on the concept of data enrichment when we talked about data transformation. 

If you’re thinking, “What’s the difference between data transformation and data enrichment?” you’re not on your own. 

These phrases are often used interchangeably when talking about the automated improvement of your data as it passes from your multiple different platforms into your single source of truth.

You can view data transformation as the process of ensuring the format and structure of your data are consistent before it’s loaded into your centralized data destination. Data enrichment on the other hand is adding more details to your existing data or improving its accuracy, ensuring it’s optimally relevant to your business needs. 

With this distinction, processes like currency conversion, language translation, and blending data from different sources to create new records would be defined as data enrichment. 



How to enrich data with an efficient and user-friendly tool

When you choose to embrace data enrichment, you move beyond standardization and unlock the true value of your data by making sure it’s tailored to your unique business needs. 

Leading data integration platforms come packaged with a variety of different data enrichments to help achieve this goal. 

For example, Adverity has functionality that helps ensure your naming conventions are adhered to and allows you to turn text fields into filterable dimensions for more advanced insight. 

The platform also allows you to join data streams, which has a wide range of different applications. For example, imagine having a November 2023 campaign that ran across multiple platforms and had “BFCM-23” in each campaign name. You could quickly and easily join all the costs, clicks, sales, and revenue for this cross-channel activity just by joining data on this unique identifier. 

Another powerful feature is the out-of-the-box currency conversion and language translation, which can help to ensure consistency and ease of data analysis. 

And if you have a team that’s able to complete some basic coding, Adverity allows you to create your own custom data enrichments, giving you complete flexibility over your data. 



Choose among the best data enrichment tools

When you’re choosing the right data integration tool for your business, it’s important to choose a solution that has the data enrichment functionality to meet your specific business needs. 

Adverity is a market-leading data integration platform tailored to the specific needs of marketing teams. We’ve worked with businesses of all sizes to improve the efficiency and effectiveness of data integration, and help them unlock the true potential of their data. 

In your search for the perfect data enrichment solution for your business, you might consider:

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A great solution for smaller businesses that need a ‘no frills’ approach to data integration. 

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Designed for marketing and data analysis professionals, this ETL tool connects to over 600 data sources. It offers real-time data fetching and advanced data transformation capabilities.

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An entry-level data integration platform that has more than 100 different data connectors. 

 

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A popular data integration platform that’s ideal for data teams that have knowledge of SQL and don’t need extensive support. 



What is data mapping and how to do it

Data mapping is another critical part of effective data integration.

The data mapping process is typically one of the first steps you go through when you’re integrating your business data. 

It involves identifying which fields from each of your data sources should be ‘mapped’ to the corresponding field in your centralized data destination and ensuring the structure and format align with an agreed-upon data schema.

If you’re currently managing your marketing activity across multiple platforms, you might have come across a situation where some platforms refer to website traffic as “landing page visits,” others as “clicks,” and others as “visits.” 

This isn’t helpful for achieving consistency in your analysis or decision-making. 

Data mapping helps avoid confusion and inconsistency in data analysis by sending each of these unique labels to an agreed “Website Visits” field in your data.

To approach data mapping effectively, it’s important to follow a robust process:

Identify your source data

Define data relationships

Create the transformation rules

Test and implement

Review and maintain



Automated data mapping and governance: All you should know

By investing in the right automated data integration platform, you can not only improve your data mapping process but also significantly enhance your data governance. 

Some of the data governance benefits of automated data mapping and data integration include: 

  • Improved data accuracy 
  • More efficient and consistent data integration processes
  • Scalability and flexibility
  • Compliance with data privacy regulations
  • Data access controls for risk mitigation
  • Enhanced data accessibility to enable data democratization


Rely on the best data mapping tools

To ensure you’re able to use automated data mapping to enhance your data governance, it’s important to select the right tools for your business. 

Your chosen solution needs to connect to all your important data sources in order to map them effectively. 

To improve data democratization across your business, it’s also important to make sure the platform has an easy-to-use and intuitive mapping interface and a data dictionary that helps everyone across the business understand how data is mapped.

Another functionality you should look for is the flexibility in data transformations and enrichments to ensure you can get your business data just the way you want it - and also check the data fetch frequency so you’re confident teams across the business are working from the freshest data possible. 



Map data and take your data management strategy to the next level with Adverity

Throughout this guide, we've explored the essential aspects of effective data management in today’s marketing landscape, from understanding the power of data transformation to the importance of data mapping and enrichment. 

We’ve discussed the importance of choosing the right tools for your specific business needs, the critical role of data maturity in becoming a data-driven organization, and the transformative impact of automated data integration on your data governance strategy. 

If you’re looking to find out more about how a market-leading data integration platform can help you map your data and take your data management strategy to the next level, book a demo with Adverity today.