Visualization is critical in digital analytics. After all, visual representations of data can, for example, tell you at a glance how well your marketing efforts are doing.
The ongoing evolution in technology is requiring companies to constantly examine how different types of data need to be adjusted before being blended and imported into a reporting database. Fortunately, database makers are becoming more adept at developing systems that blend front-end and client-end activity. As a result, we have better tools that do not tax marketers’ skill sets.
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New Tool for MongoDB Visualizations
For example, MongoDB has introduced MongoDB Charts, a tool for creating visualizations of data in MongoDB databases. I learned about MongoDB Charts in September at MongoDB.local Chicago, a one-day event featuring a series of sessions covering the latest MongoDB features.
Currently in beta, MongoDB Charts is designed to enable users to create charts, graphs and dashboards and then share them with collaborators. The system’s main advantage is that it is designed to handle nested and hierarchical data, which should make it unnecessary for users to have to flatten subdocuments and arrays into tabular structures before creating a graph.
Flattening data means storing data in one or a few tables containing all of the information. Sometimes flattening is necessary, depending on the task or tool. But sometimes flattening is done without a structure, removing a logic that could make data access easier for repeated processes, like updating a graph.
By avoiding flattening, analysts using MongoDB Charts can save time by avoiding a low-value data preparation step.
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Visualization Tools Ease Self-Service Analytics
MongoDB is among a number of database systems that offer auxiliary visualization tools and backend processes to make self-service analytics easier. In 2016, I wrote about a product called Neo4j, a graph database that reveals associations between data and the metadata that describes it.
Online data sets are becoming better at data visualization options as well. For example, Data.world just launched the ability to create query templates to allow users to see how the schema of a data set works in SQL and Sparkl before implementation.
None of those products will replace current visualization tools such as Google Data Studio and Microsoft Power BI. In fact, MongoDB claims that its tool will complement Tableau, another visualization product.
Data visualization tools like MongoDB Charts can support analysts’ DataOps initiatives. DataOps are methodologies for designing and maintaining a data architecture. But maintaining the right data among various data types adds complexity, making it necessary to take additional technical steps to view insights. Those steps waste time, increasing the time it takes to just prepare data sets for use. The challenge is especially difficult if there is no way to identify whether other analysts have encountered similar struggles.
Tools like MongoDB Charts will make analytics easier. And the analysts who use them will be able to ask better questions, usually because they will have better visualizations to see how data types can be managed for regular data pulls.
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