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3 Trends in Embedded Analytics

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Data visualization is everywhere. Whether you check your online bank account, monitor your workouts, discover the energy consumption of your house, check your pipeline in your CRM system or view remaining vacation days on your HR application, visualizations are part of the large majority of web applications.

When data visualizations are embedded well, they appear to be seamlessly integrated into the application. But they may, in fact, be managed and generated by a dedicated visualization server.

In a previous post I presented four different categories of users that interact with applications: Content Viewers, Data Discoverers, Content Creators and Query Experts. In this post I’ll present three major trends affecting these user categories.

Interactive Content Viewing

Today, the large majority of applications present static content in a “take it or leave it” approach. But with the trend of Interactive Content Viewing, visualization content will increasingly respond to the viewer’s commands. Content Viewers will be able to personalize the way information is displayed, without querying the data, using easy, web-based, secure tools.  This  trend affects millions of users.

Embedded Data Discovery

Today’s data discovery tools are good-looking and powerful, but they tend to be standalone applications. The next trend, Embedded Data Discovery, provides Data Discoverers with prepackaged data and the appropriate toolset, seamlessly embedded and secured within an application. However, even with embedding, data discovery will remain confined to a relatively modest subset of business users in my opinion.

Data Driven Applications

This is by far the most interesting and far-reaching trend. Getting the pieces of the big data puzzle together is challenging: You need data scientists, data architects, ETL expertise, storage and technology in order to access unstructured data. To expand the population of users who can access big data, the challenge is to bring together Query Experts and Content Viewers to create easy-to-use applications that are accessible to both business and casual users. We call these Data Driven Applications. Under this trend, branding and user experience are at least as important as having the right machine learning algorithms.

The key to success is to think about your end users. What will big data mean when they look at the screen of a PC, tablet or smartphone? They may see some fancy networking graphs showing interconnected social media activity, but that’s of marginal usefulness. The large majority of visual impacts will come from alerts, recommendations and relatively straightforward graphs. It’s all about the data and a new generation of applications that will deliver value from that data.

Which brings us to the topic for my next blog post: Beyond the marketing buzz, can we exactly define a data driven application? Subscribe (using the link at left) and you’ll get notifications when future posts are available.


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Published at DZone with permission of Michael Singer, DZone MVB. See the original article here.

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