In today's data-driven landscape, marketing teams are often juggling an overwhelming number of data sources, with more than 50% saying they’re managing data from 14 or more different sources.
This situation can present significant challenges, especially when it comes to accurately measuring performance across different channels.
Having data located in multiple different sources can lead to inefficiencies and inconsistencies in data analysis, with different teams across a business interpreting and making decisions from data in different ways.
The key to overcoming these challenges lies in data integration.
By integrating data effectively, businesses can achieve a single source of truth, and create a centralized destination for business data that can be reliably and consistently used for decision-making across the entire organization.
However, to realize the full potential of data integration, it’s essential to work from a strong foundation, which begins with accurate data mapping.
In this article, we’re going to delve deeper into the importance of data mapping within data integration and run through the key steps to implement data mapping efficiently and effectively.
Explained simply, data mapping is the process of standardizing and consolidating your disparate data sources into a consistent, coherent, and usable format.
It involves matching different fields from each of your data sources and ensuring they align with an agreed-upon data schema before being loaded into your centralized data destination.
Consider a common scenario in marketing, where website visits are tracked across various platforms, but they are referred to differently. Some platforms might label them as “landing page visits,” others as “clicks,” and others simply as “visits.”
This variation in terminology can create confusion and inconsistency in data analysis.
With effective data mapping, these different metrics can be unified under a single, standardized field like “Site Visitors” in your centralized database.
Consistency and confidence in data are critical for making accurate, informed decisions and enabling data democratization.
Data mapping ensures that everyone in the business is on the same page, essentially comparing apples with apples when evaluating campaign performance across different channels.
However, it’s important to implement the right approach (and the right tools) to the data mapping process to prevent any errors from entering your centralized data destination.
The repercussions of errors in data mapping can be significant and lead to inaccurate insights and decision-making.
To illustrate this point, consider a marketing scenario where metrics for a particular channel (Channel 03) are incorrectly mapped, with “Site Visitor” and “Impressions” metrics being loaded into the incorrect fields.
|
Impressions |
Site Visitors |
Sales |
Conversion Rate |
|
|
Channel 01 |
400000 |
1200 |
37 |
3.12% |
|
Channel 02 |
450000 |
1350 |
37 |
2.73% |
|
Channel 03 |
1800 |
600000 |
63 |
0.01% |
|
Channel 04 |
350000 |
1050 |
29 |
2.76% |
If a marketing team was making optimization decisions based on conversion rate alone, the incorrectly mapped data for Channel 03 would lead to erroneous decision-making about the performance of this channel.
And even if the marketing team identified the error ahead of making campaign adjustments, their confidence in the accuracy of the centralized data would be negatively impacted, which could influence their likelihood of trusting it for future optimizations.
This demonstrates the importance of investing in time and technology to ensure data mapping is implemented effectively.
It’s the foundation of effective data integration and critical for the accuracy and reliability of your data-driven decisions.
Having established the importance of following the right process for effective data mapping, let’s take a look at the steps that your business should take to ensure the accuracy of data in your centralized data destination.
The initial step in the data mapping process is the identification of the source data you want to map.
This involves a careful and comprehensive review of all the data you intend to map from each of the various channels and platforms, gaining a clear understanding of which data you want to centralize and its current format and structure.
By thoroughly understanding your source data, you lay the groundwork for effective and efficient data mapping and gain insight into how source data may need to be transformed.
The next step in the data mapping process is to define the relationships between different data fields across your various data sources and agree on how they should be named and structured in your single source of truth.
Let’s go back to our previous example, using “Clicks,” “Visits,” and “Landing Page Visits” to describe website traffic.
In this step, you would identify that each of these labels actually refers to the same thing and that in your centralized database, you were going to name it “Website Visits.”
You could then set up a relationship between each of these fields in the source data and the “Website Visits” field in your data destination.
This step is crucial for effective data mapping, as it helps ensure that similar data from different sources are correctly mapped in your centralized database.
Additionally, by using consistent naming conventions (with definitions held in a centralized data dictionary), you can eliminate confusion and ensure that everyone in the organization understands what each data field represents.
With a clear understanding of the relationships between data fields across your sources and how they are going to be standardized, the next step in the data mapping process is to establish the transformation rules.
Data transformation is essentially a set of instructions that dictates how data from each source will be converted and formatted before being loaded into your target data destination.
These rules can range from simple transformations, such as renaming fields, to more complex transformations, like combining multiple fields or converting data types.
Each data integration platform has its unique way of handling these transformation rules.
In Adverity’s data integration platform, the data mapping process and transformation rules are managed through the Data Dictionary. This feature offers a clear and intuitive way to map different fields from your various data sources to the correct field in your data destination and apply any necessary transformations.
By having all your data mapping and transformation rules in one place it makes it easier for all teams across your business to understand and manage the data mapping process, which plays a vital role in enhancing data accuracy and transparency as part of your data governance strategy.
Once the transformation rules are in place, the next step in the data mapping process is testing and implementation.
It’s important to thoroughly check that your data mapping is working as planned and that the data is correctly being transformed according to your predefined rules before your centralized data is used for business decision-making.
In addition to checking the accuracy of your data, the testing phase is also a chance to evaluate the performance of your data mapping process, looking at the speed of data fetches and transformations to ensure your business can work off the freshest data available.
Having the right tools can help simplify this process, for example, Adverity’s Activity Monitor can provide granular insight into the performance of each of your data fetches and any transformation tasks.
Once you’re happy that all your data is making its way into your centralized data destination as intended, your initial data mapping process is complete.
The data mapping process doesn't end with implementation.
Ongoing reviews and maintenance are important as part of your data governance strategy to ensure that your data mapping continues to deliver a single source of truth that your business can trust.
Leading data integration tools can often help automate the review and monitoring process, automatically detecting anomalies or errors in your data and preventing the upload of data that doesn’t meet agreed naming conventions.
While data mapping can theoretically be completed manually, the reality for the majority of businesses is that there’s so much data being managed across so many different channels that manual processing is impractical.
For most businesses, automation in data mapping is necessary for efficient and effective data management for a number of reasons.
One of the most significant benefits of automation in data mapping is the reduction of human error.
Manual data mapping is prone to mistakes due to its tedious and repetitive nature. Errors like mistyping data and incorrectly mapping data fields can significantly impact the accuracy and quality of your data.
Automated data mapping is a core part of many data integration platforms, with most solutions having dedicated tools to ensure consistent application of mapping and transformation rules across each of your data sources.
Automated data mapping tools are also capable of processing large datasets much faster than manual data processing.
For businesses that want to make decisions on the freshest possible data, choosing a data integration platform with frequent data fetches is important.
Leading data integration platforms like Adverity can fetch and process data as often as every 15 minutes.
This near-real-time capability means that businesses can access and act on insights much more quickly, making them more responsive and agile in their decision-making.
Another key advantage of automation in data mapping is the ease of calculating new metrics from consolidated data.
Automated tools can effortlessly combine, calculate, and transform data points to create new, insightful metrics that would be complex and time-consuming to calculate manually.
This capability significantly enhances the power of data analysis, unlocking new insights and enabling a deeper understanding of your data.
For any business looking to become more data-driven in their decision-making, effective data mapping is essential.
Data mapping is the foundation on which successful data integration is built, ensuring that all your different data sources are brought together efficiently and accurately.
In the modern marketing landscape, where businesses are managing an ever-increasing volume of data from a variety of channels, manual data mapping is no longer a viable option.
The need for efficiency, accuracy, and speed of insights requires automation.
Adverity is a leading data integration platform that excels in streamlining and automating the data mapping process, making it more efficient, transparent, and user-friendly.
Experience the full capabilities of Adverity by booking a demo today.