The wave of digital transformation has disrupted business settings to the core. The change triggered has left a huge impact on business trends as well as data.
In the last few years, the size and variety of data have amplified by leaps and bounds. In order to handle this humongous, highly complex data, organizations can depend on systematic methods to leverage this data for delivering actionable insights that can enable improved decision-making. Data mapping has a major role to play here.
Data Mapping and Its Purpose
Data mapping empower enterprises foster relationships between multiple data models by mapping data sources to target data fields. With the capability to proffer easier data access, data mapping tools enable business users improve decision-making and enhance business efficiency. Additionally, data mapping also plays a fundamental role in recognizing emerging trends and taking actionable steps accordingly.
Data mapping solutions use data from different sources and extract value by combining and transforming it into a digestible format. They can assist organizations in four primary areas.
Data transformation:Normally, data is available in different formats XML, HTML, JSON, etc.Now, to usedata effectively, it is crucial for organizations to use a data mapping tool to transform data in a specified format. Data mapping is actually the first step of data transformation and it helps users establish relationships between different data models. This allows organizations analyze information and extract mission-critical insights.
Data warehousing:During the course of the data integration process, data mapping solutions help to make connections between data sources and target sources of the data warehouse.
Data migration:Data migration is the process of transferring data from one database or repository to another.Data mapping supports migration by mapping source fields to destination fields with minimalerrors.
Data integration:A lot of organizations bank on a multitude of target and source repositories that share the same type of data model to enable successful integration. But, gaining access to all such repositories with similar schemas isn’t easy. Data mapping solutions prove extremely beneficial in such situations as it helps business users bridge the differences between data source schemas as well as destination schemas.
Troubles in Data Mapping
No matter how easy it may sound, data mapping is a challenging task. And for many organizations, finishing their data mapping projects is difficult. It’s important to become wary of the data mapping troubles ahead of time to evade problems later. Here are some of the most common challenges.
Data mapping needs time:To harness the true potential of data,organizations need to map multiple data sources to target data fields. In doing so, they must have access to all the information residing in the business ecosystem. The amount of effort that goes into this is immense. For starters, organizations need to leverage many methods to collect information. Data mapping coupled with transformative technologies such as AI can help organizations streamline this task, making it detailed and secure at minimal effort. Users can make use of machine learning algorithms to create intelligent mappings without complexity or additional effort. The accurate data mappings can be used to drive data transformations and ultimately data integrations.
Data mapping needs constant attention:For better results, data mapping patterns need to be updated, evaluated, and verified constantly.In case this is not done on a regular basis, chances are high that data maps will turn obsoletebefore it provides any real value to the company.
To update data mappings, users can employ modern data integration solutions. Organizations can use a self-service-powered data integration platform to empower even non-technical users handle data mappings and update them without any external support. It allows users make updates and changes as new data sources are added or data sources change or requirements at the target database change.
Data mapping needs all information:One of the most common blunders organizations can make is that they ignore essential information while building the data maps. This makes companies less useful and productive.
Essentially, before starting the data mapping process, companies must have all the information from the stakeholders. For example, users must make sure that the data map encompasses litigation risk profile, accessibility constraints, retention schedules, and more. During that time, teams need to go through sources to check whether they have all the information protected or not.
Data mapping needs expertise and precision:One needs to carry out data mapping comprehensively. With a lot of information, it’s apparent that users would find that creating data mappings is not easy. Modern self-service integration solutions that use a low-code approach can help users create accurate, AI-powered data mappings with ease and precision. With features like pre-built application connectors, shared templates, user-friendly dashboard, etc., these platforms allow users map, transform, and integrate data without complexity or delays.
It’s clear that data mapping is extremely useful for business. However, challenges posed by it should not be overlooked. See how Adeptia can make your job simpler to “step out of the usual” approach of data mapping.