Data Mapping

What is data mapping

Data is being created faster than ever before, with 90% of all data created in the last two years. Most companies use a growing amount of data, coming from diverse sources in a variety of formats. To help companies drive insights from this huge amount of changing data, companies often need to create a plan or map that organizes the data to ensure it is readable, queryable, and actionable. 

Data mapping is a preliminary step to establish a cohesive data model for a data-driven organization. The data map provides instructions that align multiple data sets into a single configuration. A map is stored and analyzed in a data warehouse and in a single database. The data is easily digested by business executives across the organization by using a BI tool to create data visualizations like charts, graphs, and dashboards. 

As companies grow, data configurations become more elaborate and the sources more variable. Organizations with complicated data structures may benefit from a graphical mapping tool, which makes the process significantly easier by automatically coding the data field connections, according to a map created by the user. Some graphical mapping tools offer drag-and-drop capabilities, web user interfaces, and the auto-generation of a PDF mapping document. 

Data mapping tools are commonly included in BI and analytics platforms. Be sure to choose a platform that includes a tool that adequately fulfills your organizational needs for customization and development. This will ensure that you get the most comprehensive, precise, and valuable results from your BI and analytics.

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Why data mapping is important

An effective data map will show the connections and relationships between data sources, so any potential problems will become obvious when you look at the map. This can prevent issues during data integration or migration projects. Data mapping also makes data analysis more accurate by reducing the risk of oversights, errors, and repetition. Other benefits include enhancing reporting capabilities and increasing data security, governance, and compliance. 

Data mapping can boost competitive advantage and accelerate time to insights by improving data collaboration between internal and external stakeholders. By delivering data insights in real-time, data mapping can increase the efficiency of your business operations and streamline your processes. 

Speak the same data language

Part of the data mapping process includes categorizing your data and setting standards for naming and identifying it — similar to how a style guide standardizes copy and content. These data descriptions are kept in data dictionaries, which allows developers to manipulate datasets that may have been compiled by previous employees or other departments. This collection of data elements includes names, definitions, and attributes that make the data mapping process easier and more effective.

Steps to an effective data map

Data mapping can organize data to be relevant to many different tasks and use cases. The operations generally fall into three categories: data transformation, integration, and migration. Data transformation is the cleaning and aggregation process that prepares data for extraction and loading.

Data integration introduces data from a new system and connects it to existing data models, with the paths created by an accurate data map. Any type of transfer of information from one model to another is referred to as data migration and can be set up to occur on a regular basis, automatically at preferred intervals (quarterly, monthly, etc.). 

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Data Mapping Examples

Examples of data mapping might include a path that is set up to feed the results of a web form or online retail shopping experience into a data warehouse. This process can bring together not just simple results like text and data, but also information from third-party systems that might be set up and categorized differently. 

An effective data map can also be used to prevent cyber-attacks and data breaches. This exercise can serve as a perfect opportunity for an organization to identify where their key data assets are stored, processed, and transmitted. Then the organization can determine its protection requirements based on the sensitivity, confidentiality, and availability of its data. 

A BI and analytics platform like Sisense can provide a complete solution to a company’s data preparation needs — without the need for separate data mapping. The architecture is designed to pull in data from all different sources, merge the data, and allow users to manually reconcile differences in the way this information is categorized and presented. With this tool, you can perform ELT (extract, load, and transform) functions and create relationships between data using a visual environment.

In conclusion

Data mapping is one of the most powerful tools in the developer toolkit to help shape data into a presentable form. Once data has been adequately mapped, it’s easy to filter out exactly what you want to show. These insights can be plugged into visualizations, and inserted into presentations, reports, and company content. Data mapping creates conditions that make it possible.

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