Imagine getting lost in a maze without a map. That’s the same thing that happens to a business without a data mapping solution. Data mapping is the process of linking data from one information system to different data from another information system. By establishing a connection between disparate datasets, companies can simplify their decision-making process, minimizing disparate potential issues that lead to lower sales and profits. Moreover, it helps managers and other stakeholders have access to vast amounts of actionable information, and they can comprehend data without confusion to drive their businesses forward.
However, data mapping is not an easy task to accomplish. In fact, many businesses find it to be a huge behemoth of a project. According to Bruce Phillips, Vice President, Information Security Manager at Fidelity National Financial, data mapping is not for the faint heart. Lots of detailing go into this, which makes the process tedious and resource-intensive. Plus, it’s expensive; especially if you don't get it right the first time. And once it is created, users must maintain it. They must commit to adding resources as well as staff to manage data itself. Its accuracy and speed also matter. If data mapping is not done correctly and at the speed of business, you are likely to negatively impact other data-driven tasks such as data transformations and data integrations. Ultimately, decision-making takes a hit.
Let us find out what possible data mapping techniques can help companies minimize risks and costs while handling data and how transformative technologies such as artificial intelligence can prove a game-changer in such situations.
Data mapping is a crucial segment of data management. As already explained, it’s a mechanism that matches fields from data sources to the target fields in a data warehouse or repository. An organization can use a variety of techniques to transform data from different information systems into one visual representation. These techniques include:
Data-Driven Mapping: In this technique, data is evaluated from two different sources simultaneously with the support of heuristics and statistics. The analysis is done to elicit complex mapping patterns between two data sets. Data-driven mapping is one of the most common techniques as it discovers transformations between the two data types.
Semantic Mapping: This particular type of mapping is quite similar to the auto-connect feature that is used in graphics mapping. The manner that the metadata registry can’t be used to look up for data element synonyms is the difference. Semantic mapping finds out exact matches between data columns.
Transformation Logic: This technique is used to create applications that are responsible for data mapping.
Hand-Coded Mapping: In this type of mapping mechanism, graphical mapping tools and procedural code are used.
Though these techniques are immensely useful, they are not accurate and fast most of the times. In which case, the entire purpose of the process gets defeated.
Artificial-Intelligence-Powered Data Mapping: Artificial intelligence can verily solve this problem. It can be used to map data faster and better. Users can employ machine learning algorithms to “learn” from your existing data maps to predict and suggest data mapping rules automatically. As decisions can be taken from previously gathered information, the possibility of errors in the data including duplicities, missing values, reduce. Ergo, artificial-intelligence-powered mapping not only accurately maps assorted data sources to the target fields but also maintains data integrity to streamline decision-making and completely change the way you do business.