Big data has undoubtedly transformed the nature of business. However, many data strategies fail to provide reliable benefits, because they have difficulty maintaining data quality. An estimated 30% of data becomes inaccurate every year, which undercuts business strategies that depend on it.
One of the most important ways to resolve these concerns is through data mapping. More companies are using data mapping as part of their data strategies in 2022.
What is Data Mapping?
Data mapping plays a pivotal role in enabling organizations to facilitate insights delivery and decision-making. When an enterprise fails to create an optimized data mapping strategy, data transformation logic, as well as filtration errors, become evident which could lead to poor data quality and slow insights delivery, ultimately impacting decision-making and revenue generation. To overcome that, companies must maintain data integrity over the data mapping routine.
The big data revolution has caused a huge disruption in the business environment. It has transformed the way business customers think, feel, expect, and function. For any enterprise or business ecosystem to meet or exceed the emerging needs and expectations and improve ease of doing business, it’s essential to leverage the true potential of complex customer data and transform it into valuable insights. But that’s easier than done. Only 26% of companies have successfully developed a data-driven culture, even though most businesses recognize the benefits big data provides.
Organizations, in current times of disruption, gather information from disparate data points, and they don’t need to speak the same language and format. With an impactful data mapping strategy, business users can effectively integrate disparate sources and use the information to make decisions.
But the question is, what is data mapping?
Data mapping is known as the process of creating relationships between a plethora of data sources and models. It enables business users to map different data fields between data sources and data models. When users can do that, they can integrate data in minutes – that again helps users extract actionable information and make decisions in the business. Let’s dive into detail and find out how data mapping tools enable companies to grow and innovate at speed and scale.
What Should You Know About Data Mapping?
Data mapping is a process of mapping the source data with the target database. The target database is typically a relational database. The target database could be a CSV document. In multiple cases, data mapping templates help organizations match data fields from one database to another.
To understand it in detail, let’s take an example. Employee details such as name, phone fields, email, etc. are mapped to other fields that are relevant in a delimited file, particularly the target destination.
More often than not, users find it difficult to map source to target. It mainly is based on the hierarchy of customer data being mapped, along with the disparity between multiple data structures of the source and target. Multiple applications, whether on-premise or cloud, use metadata to describe data fields along with attributes that include data and semantic rules that determine how data is stored within the target database. In the process, business users can savor data movement controls to create seamless data flow without encountering delays or losses.
How Is Data Mapping Done?
Data mapping is done in the following ways:
Manual Data Mapping:When legacy solutions are used, IT must write long hours of code and execute lengthy EDI mapping routines to map source data to the target. Not only is that time-consuming but also error-prone. Even a single error can distort the quality of insights delivery which, in turn, impacts decision-making and value generation.
Schema Mapping:Schema mapping leverages a semi-automated strategy that enables users to create data mappings between the source and target schemas. After creating the relation, connections can be monitored by a schema data mapping solution.
Automated, AI-Enabled Data Mapping:It leverages AI, ML-enabled technologies to create intelligent data mappings – at speed and scale. Even non-technical business users can use this technology to make predictions to quickly map customer data in minutes instead of months.
Use AI-Enabled Data Mapping to Speed Up Insights Delivery and Decision-Making
Data mapping solutions powered by AI and ML enable business users to bridge differences in the schemas of sources and destinations in a target repository. Consequently, data mapping enables business users to build integrations faster and extract actionable insights. Additionally, data mapping break down data silos and garner actionable information.
In simple words, data mapping is the first step of data integration. Once users map their data streams, they can integrate them into a data lake or target database. Real-time insights can then be extracted from that database to make decisions in business.
Modern data mapping tools with AI and ML can help users map customer data from source to target fields – securely and easily. By empowering business users to collect insights and use the information to make decisions, companies can deliver delightful CXs and generate revenue.
