Why Data Mapping is Critical to Your Company’s Success with Analytics
Using data analytics to make key business decisions is often what separates outdated, laggardly management approaches with today’s best-in-breed high-growth companies. But collecting and integrating data from various source files into “a single source of truth” can be a major nightmare — that is, if you do not have the right strategy and tools.
The information you use for your company’s analytics or business intelligence activity is likely to come from a diverse pool of sources, which are often hosted on distinct platforms, in any number of formats, and each with its own set of metrics and structure. If you don’t know how to handle getting all of these signals to fit together cohesively, you are not alone. There are many ways to go about this type of problem solving, which is why it is so important to find the data mapping process that works best for you.
At its core, data mapping allows you to transform and unify your collected information into a formatted, integrated repository suitable for analytical and operational tasks.
This process sets the groundwork for implementing your business intelligence strategies effectively, managing your data, and getting the best insights to make smarter and more strategic decisions.
In this guide, we will dive into several other reasons why data mapping is critical to the success of your company.
What Is Data Mapping Really About?
If you’re new to data mapping, it’s essentially the process of defining how data sets relate to one another and aligning all the information from different databases for data management and effective analysis.
Imagine having three databases categorized into fields and columns with different organizational strategies for each one. To gain a holistic view of all this information, you’ll need to merge the three databases into a data warehouse, or a similar solution, that lets you search for data like it was a single database.
Through a data map and using data mapping tools, you can create instructions to merge information from a single or multiple data sets into one table configuration or schema that you can query and extract data from – and have a clear view of where the data intersects.
Your data mapping can get more complicated depending on the hierarchy of your data, the size of datasets, differences in source data and target structures, and more.
Successful Data Integration
Both the source and target data storage need to have the same data model to ensure successful data integration. This means that for you to gain a unified view by consolidating information from different sources, all data must share or have the same schema.
However, this is rarely the case from the get-go, and you will have to spend time and energy on adapting all your data so you can integrate it all successfully.
Data mapping tools can help eliminate this complication by bridging the variations in the data source and destination schemas, so you will not have to adapt source and data repositories to the same data model manually.
With that said, data mapping allows you to integrate information from different data points seamlessly – which also helps in the implementation of your business intelligence strategies to gain valuable business insights.
Efficient Data Migration
Moving your data from one database to another is a complex and time-consuming process, since it involves the selection, preparation, extraction and transformation of information before you can aggregate it to target fields.
While the process includes many other steps, the lack of proper mapping can lead to incomplete data migration – and even failure of the project. With valid and accurate data mapping, however, you can transfer source data to new target repositories more efficiently and with lower probabilities for errors and corrupted data.
In a nutshell, data mapping ensures that your target location “understands” what the information is and where to place it when you transfer – which allows for accurate results, keeping your data intact. You can also use code-free mapping solutions to help automate the process, which can save you time and energy while ensuring you migrate data to the destination successfully.
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Simplified Data Warehousing
Building a logical data model and defining how your data will be structured is crucial to setting up proper data warehousing.
Through data mapping, you can create a link between the attributes or tables of your source and target data models. You can start mapping in your data warehouse by gathering all the necessary information and understanding the source data. After accomplishing that and creating a data mapping document, you can then build transformation rules and create mapping workflows easily.
Many stages of the data warehouse life cycle require data mapping to help you save processor overhead. Additionally, data mapping ensures that the information that comes into the warehouse gets to its intended destination.
This streamlines the process of pooling your data into a single source for analysis and other tasks, running a report or query from one location, and accessing transformed, migrated and integrated information. After all, the faster and easier you can access valuable data, the more insights you can get to improve your strategies and make strategic decisions for the growth of your business.
Streamlined Data Transformation
Raw data comes from a variety of sources and in different formats, sizes and structures. You’ll need to cleanse your data, eliminate duplicates or nulls, collect and enrich information, and more to break them into digestible data and draw actionable company insights via business intelligence.
The first step in transforming your data is to map them, to help you establish a framework that identifies the required changes to your information before loading them to the target database.
Without data mapping, you’d
have to go through thousands, if not, millions of data points to check how to
treat each piece of information for it to become usable before storage – which
is a huge time-suck.
Data mapping tools can streamline the process of building a structure that helps you transform your data efficiently.
Seamless Electronic Data Interchange (EDI)
Data mapping is a vital aspect of converting electronic data interchange files into different formats, such as JSON, Excel and XML. The data mapping process prepares your existing business apps to receive and generate EDI communications for seamless data exchange.
Through intuitive data mapping tools, you can extract data from various sources and use built-in functions and transformations to map information to EDI formats without you having to write a single line of code.
Data mapping software can also aid you in specifying the relationship between every single data element in an EDI message – including its corresponding application field and its use by each trading partner.
Your company may be collecting massive volumes of information every second, but if you’re not mapping your data efficiently, you’ll miss out on establishing a process that lets you manage and make sense of the firehose.
Through data mapping, you can lay the foundation of your business intelligence strategies and gain valuable insights to make better decisions that can lead to the success and growth of your company.
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