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What Is Data Mapping?

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What Is Data Mapping?

A high-level but informative discussion of the main concepts behind data mapping, how it can benefit your team, and the challenges you may face.

· Big Data Zone ·
Free Resource

It’s no secret that collecting data from websites, mobile devices, customers, vendors, or other sources is key to surviving today’s super competitive business environment. These data points are essential to creating a comprehensive and detailed view of your business, with the goal of uncovering hidden opportunities to gain a competitive advantage.

But data is only valuable if you have a way to make sense of it, and aggregating information from disparate, variable sources can lead to a number of challenges. For example, what appears as one field in one system needs to appear correctly in another field in another system, if you’re going to get the most accuracy and usefulness out of your own data.

Adequate data mapping is the preemptive solution to a potentially damaging problem.

Data Mapping Defined

Data mapping is a necessary component of the larger processes of data migration and data integration. It’s a mechanism that matches fields from data sources (system A) to the target fields in a data warehouse or other storage repository (system B). Fields can be names, phone numbers, emails, URLs, financial amounts, or any number of other inputs you need to create and capture for querying and reporting purposes.

At a time when organizations have vastly more data sources, types, and formats to work with than ever before, it’s especially important to address data mapping as part of your overall data strategy.

Benefits of Data Mapping

Data mapping is essentially a way to surface and prevent issues ahead of time before they create bigger problems later. For example, two critical data sources may provide information in different formats. And those formats — either one or both — may be incompatible with the way a data destination is configured, increasing the danger of data getting lost, duplicated, or simply incomplete. Any of those outcomes will have a negative impact during the data analysis phase.

But data mapping neutralizes the potential for data errors and mismatches, aids in the data standardization process, and makes intended data destinations clearer and easier to understand.

Quality data achieved through a data mapping process enables effective data analysis. And effective data analysis allows your business to make smart decisions with the speed and confidence needed in today’s market.

Challenges With Data Mapping

Here are a few of the major challenges that can arise with data mapping:

  • Inaccuracy. Any process undertaken by humans can turn into a liability since the potential for errors and misinformed decisions is so high. Inaccurate, duplicate, or otherwise decayed data has little use to the various teams in your organization as it can provide false insights that take the company further from its goals, not closer.
  • Time-wasting. In-house teams already have enough responsibility on their plates. Tasking them with mapping data means time spent double-checking and re-working scripts and schemas to approach a high level of accuracy and certainty. And if fields are mapped incorrectly, it can result in significant data loss and even more re-work.
  • Changes. Rarely can you "set it and forget it" with a data map. Changes can occur at any time — to standards, reporting requirements, software processes, and systems — which makes any prior data map obsolete.

Methods for Data Mapping

As with other aspects of data strategy and operations, organizations are faced with a choice when it comes to data mapping: manually do it themselves, or engage third-party services to do it for them. Data mapping tools and processes run the gamut from on-site to in the cloud. Here are the most common methods for data mapping:

  • On-premise. Data processes that happen on site can feel more secure, accessible, and controlled. But unless you need extremely fast access to your own data, on-premise data mapping is often too unwieldy and cost prohibitive in the long term due to the purchase and upkeep of hardware, software, and other equipment.

  • Open source. On the other hand, open source data mapping tools can be quite cost effective. Using the latest code bases, these tools are both reliable and efficient. But they still require a level of knowledge and hand-coding to be able to use effectively.

  • Cloud-based. When it comes to meeting the needs of today’s organizations, cloud-based data mapping tools fit the bill since they are built to be fast, flexible, and scalable. These tools can easily adapt to changing schemas without slowing down or losing information and are generally backed up with expert setup and support.

Topics:
big data ,data mapping ,data management ,data migration ,data integration

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