Data Mapping Software Trends
Data mapping software gives you the ability to connect data across multiple databases, which can help reduce redundancy and streamline business intelligence and analytics. Usually known as “mapping,” this process helps combine and standardize data across multiple databases, pipelines, and formats.
When working with data, mapping is only one of several important processes for standardizing data formats. In many systems, data must also be converted and translated before becoming usable. Without the right software and tools, however, these processes can require large amounts of code and substantial effort.
With data mapping software, the mapping process is effectively streamlined and automated—all without the code. Data mapping is quickly becoming an essential process for many data-driven processes, especially as data pipelines grow in size and variety. With most enterprises receiving data from many different sources, data mapping is often the only way to connect different data elements.
While data mapping software is often used directly by database managers and other IT roles, it’s true use is in business intelligence, analytics, and any other data-driven role. As more enterprises and departments become increasingly data-driven, however, data mapping software will likely become a necessary tool for anyone working with data.
Why use data mapping software?
Data mapping software allows you to connect data across multiple databases, allowing you to maintain consistency for future use. This capability is becoming especially useful as data sources become more varied while analytical processes still require more consistent data formats.
Your data pipelines are growing
Data has become one of the most valuable resources for modern business. As more businesses integrate technology into their products and services, customer data is more abundant and varied than ever before. With this data, businesses of all types can perform business intelligence and analytics, unlocking insights that may have previously gone unnoticed.
In most cases, this data “flows” into businesses through data “pipelines,” a general term for any system which moves data from one place to another. While some pipelines are completely internal, connecting databases to analytics applications, others are external, connecting raw data to an enterprise’s data storage solutions.
One thing remains consistent across implementations, however: Data pipelines are getting bigger. As more businesses use more data from more sources, businesses have more data than ever before—especially with so many actively seeking data from every possible corner of their business operations and customer interactions.
Oftentimes, this data comes in through a number of different pipelines from an equal (if not greater) number of data sources. With so much data “flowing in” at any one time, it can be difficult to maintain consistency across databases and other data storage solutions—especially if some pipelines result in the same data being stored in multiple locations.
While data mapping software won’t help structure your data pipelines, they’ll help to organize what comes out (data). As mentioned, this capability will be essential as more businesses (including yours!) adopt more data sources and pipelines; without using data mapping software, your databases, pools, and lakes could quickly become filled with inconsistencies and redundancies.
Improve consistency and reduce redundancy
In the previous section, we saw how the sheer size and number of data sources and pipelines can cause inconsistencies and redundancies in data. With so much data across potentially many storage solutions, it’s easy to run into redundancies and inconsistencies. Such problems can cause inefficiencies for analytics applications, which could potentially be tasked with processing the same data multiple times.
For example, consider company databases storing customer information. Depending on the company’s data sources and pipelines, two different databases could both hold the same customer information.
Let’s take a look at a particular customer profile—we’ll call him “Terry” for the sake of example. Terry’s customer data could include his email, birthday, home address, purchase history, and so on. Terry’s customer data includes anything about him that’s relevant to the business.
In most cases, the business storing Terry’s customer data probably got each component from different places. For example, Terry’s email could’ve come from when he signed up for the company’s mailing list, and his purchase history and home address could’ve come from his order history. In any case, however, information such as customer data is often sourced from multiple places.
So, where does this data end up? While some businesses may have more centralized data storage solutions, many do not—and that’s perfectly fine, if not preferable in some cases. However, this means that Terry’s email might be stored in one database, his purchase history in another, and his name and birthday in yet another. This “separation” of data is simply the result of how the business chose to gather Terry’s data over time.
The business can now use data mapping software in many possible ways depending on how exactly they want to work with Terry’s data. In the most common scenario, they would use data mapping software to “link” Terry’s data between databases, effectively creating a map between his email, his purchase history, his personal information, and so on.
Through this map, business intelligence and analytics don’t need to worry about gathering everything from different databases. Instead, the customer profile “Terry” is a single entity that, while comprised of data across multiple areas, can be treated as a single entity. The same, of course, extends to other customers in the company’s databases.
In another scenario, the company might have produced redundancies in their customer data—something which data mapping software can also help with.
For example, suppose that the company gathered Terry’s name, email, and birthday when he signed up for their mailing list. Then suppose the company gathered the same information for one of Terry’s purchases. As a result, many instances of Terry’s name, email, and other information are stored across multiple databases, creating a redundancy.
Without data mapping software, analytics software working with Terry’s data could have to process his name, birthday, and other information more than once. While this redundancy isn’t a big issue on a small scale, consider the possibility of hundreds – if not thousands – of other customers apart from Terry. In such a case, the redundancy could have massive impacts on processing time and efficiency.
In this scenario, the business would use data mapping software to identify and fix the redundancies. Now, even though multiple databases might contain duplicate data, they can be combined under a singular entity. Plus, in many implementations, the raw data remains intact in the case of requirements changing.
Streamline analytical processes
In the previous section, we outlined how data mapping software helps connect data across databases and other storage solutions. In doing so, the company in our example was able to create more holistic views of their data (in this case, a combined customer profile) and reduce redundancies.
But why is this process important? The answer, as we briefly saw, was to help streamline analytical processes. Indeed, using data mapping software is an essential part of preparing data for analysis, especially in cases where data comes from multiple sources (which, frankly, is true in most cases).
Without data mapping or other forms of data preparations, analytics, and business intelligence software could easily become lost in the sea of data. Even with the right pipelines, some scenarios could leave analytics software processing the same data multiple times, which could have drastic effects on efficiency and effectiveness.
Consider the case of Terry’s customer profile again. Suppose analytics or business intelligence software was set to analyze his data, but the company didn’t prepare it with data mapping software first. Here, instead of working with a single, homogenized “Terry” profile, the software would instead grab different sets of Terry’s data from different databases. If multiple databases included Terry’s name, for example, then the software would have to process Terry’s name multiple times. While not the “end of the world” when only processing one customer profile, it’s not hard to see where doing this would become inefficient in the case of hundreds or thousands of customer profiles.
Data mapping software is a crucial tool for streamlining analytical processes. Working in conjunction with other processes such as data translation and conversion, data mapping software can effectively prepare your data for processing—now matter how “disconnected” it might be.
Improve your insights and make the most of your data
We’ve already seen how unmapped data can slow down analytics and data management, but how does it affect the results? If processing power were suddenly unlimited or unimportant, would data mapping (or any type of data prep) be necessary if analytics delivered the same results in either case?
Not necessarily. While some analytics and business intelligence applications could make sense of unprepared data, it would still be a remarkable challenge—and any insights that came from this data would likely be inaccurate or downright useless. So, even if processing time somehow wasn’t a factor, thorough data prep would still be a very necessary step for getting the right insights.
As a result, data mapping software often plays a crucial role in data prep. While good data prep can help improve processing times (by avoiding redundancies, etc.), perhaps its greater value is in delivering better insights. With data properly mapped and formatted, your analytics will be in a better position to “understand” what it’s working with. In exchange, you’ll be able to extract more valuable insights from your data.
Keep everything organized and scalable
Depending on your data sources and data pipelines, your data storage may be decentralized; in other words, instead of relying on a single database or data pool, your data may be stored across several databases or data pools throughout your enterprise.
In many cases, keeping your data decentralized is good practice. This case is especially true if certain “groups” of data fill highly specific purposes in your analytics and business intelligence (or any other data-driven applications).
However, sometimes data needs to be “linked” together without actually being moved between servers. One such example is the “Terry” customer profile from earlier, where Terry’s email and personal information were stored in different databases. While each database might fulfill a specific purpose, some analytical processes might need the “full picture” of Terry— which is where data mapping comes in.
With data mapping, you can keep information decentralized yet still connected. With this capability, your data storage solutions can remain flexible and scalable, which aren’t always qualities found in more centralized storage solutions. Data mapping can also help you organize your data, which is often necessary in cases of decentralized data storage.
Automate the mapping process
As we discussed earlier, data mapping is an essential part of the data prep process. While data mapping is almost always a technical job, data mapping software can help automate most of the work.
With or without software, data mapping almost always requires some form of manual configuration—even if most of the process is automated. As a result, data mapping is a technical process requiring some amount of skill and knowledge, particularly in the areas of coding and database management.
These technical skills are especially important in the case of manual and semi-automated data mapping. In both of these cases, technicians usually have to manually connect data sources while custom-coding solutions to document and map their data. While semi-automated data mapping can help streamline some of these tasks, both methods require coding knowledge and some technical expertise.
While manual and semi-automated data mapping may afford the most customization and flexibility, they’re often impractical for small or understaffed teams. By contrast, fully-automated data mapping handles almost all of these processes, allowing even those with limited technical knowledge to perform robust data mapping.
Further, by automating most of the “grunt work” associated with data mapping, data prep becomes more flexible, scalable, and easier to schedule compared to manual or semi-automated prep. When customization is required, some data mapping software packages allow for custom-coded solutions.
Who uses data mapping software?
While data mapping software is mostly used to augment data prep for analytics and business intelligence, almost anyone in a “data-facing” role can use it. Here are just a few examples.
Data mapping software is becoming an increasingly familiar tool for many in database management, especially as the role becomes more closely tied to analytics and business intelligence.
As data pipelines grow and data sources become more numerous, database managers are finding themselves on the “front lines” of most initiatives. Now, instead of only occasionally onboarding data, database managers are tasked with a near-constant “flow” of data from a myriad of diverse sources.
To keep up with the flow, database managers must now adopt every tool at their disposal to keep their data holistic and organized. While a big part of this task is maintaining storage solutions (i.e. databases and data pools), another part is quickly becoming making sure that data is ready for analysis—in other words, data prep.
Data mapping software can help database management augment a large part of the data prep process, especially where mapping solutions become semi- or fully-automated. Of course, since any mapping effort requires some level of skill beyond using software, many database managers are now using mapping software in conjunction with data scientists and data management.
Data Scientists and Management
Ultimately, it comes down to data scientists (and similar roles) to determine how exactly raw data is going to be converted and used. While database managers help organize their storage solutions, data scientists often work closely alongside them to help perform data prep and management.
This dynamic between database management and data scientists is essential of database managers aren’t as knowledgeable about data science and analytics. In other words, in areas where database management is mostly an IT role, data scientists are necessary for guiding data prep.
Here, data scientists and managers will directly use data mapping software for data prep. With the extra technical skills that often come with data-related roles, most are capable of using semi-automated solutions to ensure maximum customization and compatibility. By contrast, fully-automated data mapping solutions are often only compatible with certain technologies and software.
Ultimately, it’s the goal of data scientists and managers to prep data for future use. While many data scientists are responsible for analytics, they may not have as big of a hand in other areas, such as certain applications of business intelligence. In these cases, some aspects of data prep are shifted to other analytical roles.
Business Intelligence and Analytics
As business intelligence and analytics are crucial data-driven applications, they have a vested interest in proper data mapping and other forms of data prep. As we discussed earlier, proper data prep is essential for not only maintaining the efficiency of data-driven applications but also for ensuring that their results are meaningful and insightful.
In many cases, business intelligence and analytics roles work closely with data managers and scientists to determine how best to prep data for a particular task. By using data mapping software, data mapping can be done regardless of (most) technical abilities.
Other Data-Driven Roles
Almost anybody interacting with data might have to utilize data mapping for some purpose or another. With data mapping software, data-facing roles and departments no longer need to hard-code data maps or deal with extensive documentation.
Regardless of technical ability, data mapping software can help offload much of the “grunt work” associated with data prep and mapping, giving everyone more time to focus on what’s important.
Data mapping software packages can have widely different features depending on how automated its solutions and tools are. However, most should still share a few key features.
Data mapping software should feature intuitive, visual mapping interfaces. Most data mapping software comes equipped with visual maps showing connections between data sets and databases. With many supporting drag-and-drop mapping and web GUIs, visual maps are ideal for non-technical users.
Data mapping software should offer both semi- and fully-automated solutions. Fully-automated data mapping can be a great solution in many cases, but some cases require more customizable solutions. As a result, make sure your mapping software gives you the ability to custom-code certain mapping functions when needed.
Data mapping software should be able to execute code and database functions from inside the map. Mapping software should be more than a visual tool—you should also be able to perform mapping functions and database calls from inside the software itself. Check to make sure your software is compatible with your languages of choice, such as Java and SQL.
Data mapping software should be able to handle large files efficiently. With today’s massive data sets (i.e. larger than 1 MB), your mapping software should be able to take on some hefty loads.
Data mapping software should be compatible with your technologies and data formats. Data can come in a variety of formats—some of which you might actively switch between. As a result, your mapping software should be capable of supporting most major data formats such as XML, CSV, Database, and so on.
Data mapping software should feature robust testing and validation capabilities. Your mapping software should be capable of running tests and troubleshooting with test data, as well as validating for business rules and syntax. In either case, make sure that your mapping software can report errors.
Full-automated data mapping software usually only integrates with certain applications. This point is less of a feature and more of a warning. If you choose a fully automated solution, make sure it’s compatible with your existing formats and applications.
Q: What is data mapping?
A: Data mapping is the practice of “linking” data elements between multiple data models/storage solutions. This practice helps organize data and is essential for preparing data for analytics.
Q: What is data mapping software?
A: Data mapping software presents visual data maps while automating certain mapping processes. For example, when someone using mapping software drags-and-drops a link between elements, the mapping software executes the mapping code and database calls and then displays the link on a visual map.
Q: What is a data mapping template?
A: Data mapping templates define how certain data elements should be mapped. In some respects, a data mapping template is a sort of “map for mapping.”
Q: Do data mapping software support data mapping templates?
A: Yes—and mostly because data mapping software is essentially a self-executing data mapping template! Data mapping software functions do virtually all the work of a mapping template, just with the added benefit of executing the actual mapping.
Data mapping software gives you the ability to perform data mapping without the need for manual coding, database calls, or mapping templates. Mapping software is useful for both technical and non-technical users alike, featuring intuitive, drag-and-drop interfaces capable of performing most mapping processes internally.