Unlock the Need and Major Challenges of Data Mapping

By Staff Reporter - 18 Nov '19 03:10AM

(Photo : Unlock the Need and Major Challenges of Data Mapping)

Digital transformation has changed the business arena in its entirety. It has laid a major impact on business trends as well as on data.

The size and variety of data have amplified marginally in the last few years. In order to deal with this enormous data, organizations need to use systematic methods to deal with this humongous data for delivering actionable insights that can help in making improved business decisions. Data mapping has a major role to play here.

Data Mapping and its Need

Data mapping mechanisms allow organizations foster relationships between different data models by mapping data sources to target data fields. With the capability to provide easy access to data, data mapping allows companies improve decision making and enhance business efficiency. Data mapping also plays a key role in identifying emerging trends and take actions accordingly.

Data mapping tools use data from various sources and extract value by merging and transforming it into a digestible format. It can assist in 4 areas.

Data Transformation: Data is normally available in many formats. To use data effectively, it is important to use a data mapping tool to transform data in a specific format. This allows enterprises analyze information and extract valuable insights.

Data Warehousing: During integration, the data mapping tool is used to make connections between data sources and the warehouse's target sources including tables and schemas. For assistance, you can watch many data mapping tutorials to learn how to build a logical data model and define ways of how data will be stored in a data warehouse.

Data Migration: The process of transferring data from one target database or repository to another is called data migration. Data mapping can be used to migrate data to the target destination with minimal errors.

Data Integration: Organizations rely on a plethora of target as well as source repositories that share the same data model type to allow successful integration. But, having access to repositories with similar schemas is difficult. Data mapping tool serves as a boon in such situations as it allows companies bridge the differences between data source schemas and destination schemas.

Data Mapping Challenges

Data mapping is a challenging task, and at times companies find it difficult to finish their data mapping projects. One must be wary of these challenges in advance to avoid problems later. Here are some of the major challenges.

Time-consuming: To map different data sources to target data fields, one must have access to all the information residing in the business ecosystem. The work that goes into this is immense. To begin with, companies need to employ efficient methods to collect information. Automated data mapping mechanisms can help organizations simplify this task, making it more detailed and secure. It involves a more efficient way of document collection that eliminates the reduced risk of inadvertent noncompliance or underproduction.

Outdated: Data mapping patterns need to be constantly updated, evaluated, and verified for quality. If this approach isn't followed religiously, data maps will become obsolete before it provides any real value to the company.

Data mappings can be updated by employing a defined, automated procedure or an integration solution. Integration platforms allow require updates and changes data maps as new data sources are added, as data sources change, or as requirements at the destination change.

Poor information available: One of the most common mistakes of companies is that they ignore important information while building the data maps, thus making them less useful.

Essentially, before data mapping initiative starts off, business teams must gather all information from stakeholders. For example, organizations need to make sure that the data map includes litigation risk profile, retention schedules, and accessibility constraints of respective data sources. In the meantime, selective teams need to take a closer look at the sources to check whether they comprise sensitive information that needs to be protected.

Lack of precision and expertise: Data mapping's accuracy depends upon how comprehensive it is. With so much information, organizations find it difficult to create data mappings. Modern data integration platforms with low-coding approach allow business users easily create AI-assisted data mappings in an accurate fashion.

Though data mapping is a useful technique, the challenges posed by it should not be ignored. Find a robust data integration platform embedded with an AI-powered data mapper to make your job easier.