Emerging Architectures for Real-Time Analytics in Applications

There’s been a proliferation of applications that are all about using data to derive actionable insights. Companies like Command Alkon digitize construction logistics by providing a platform for suppliers, transportation providers and contractors to monitor and coordinate the shipment of construction materials. And, fintech startup Matter uses natural language processing to evaluate the sustainability of investments. In the last five years, the number of these data applications has grown by 73%, representing more than 20% of SaaS applications (data from Crunchbase). 

This wave of data applications incorporates predictive features, to automate decision-making, as well as drill-down features so users can reach their own conclusions. For example, security analytics applications notify users to potential threats but security teams still want to dig into the data to determine the risk to the organization. In many scenarios, including in security analytics applications, there’s a symbiotic relationship between predictive capabilities and embedded search and analytics.

The challenge in delivering real-time analytics in applications is different than surfacing analytics to a team of analysts. For one, users don’t want to wait seconds for in-app analytics to load. They want the same snappy, responsive experience across the application. User engagement wanes when users have to wait for queries to load. They run fewer queries, log in less frequently and don’t take the desired actions in the product. So, slow analytics ends up reducing the value of the application to the organization.

The other big change is that users want to take immediate action on the freshest data. Industries like security, logistics, and advertising need to access the latest data to make operational decisions; data delays can result in security breaches, suboptimal routes and inefficient bidding. These aren’t just periodic, strategic questions that are being asked. Many applications are using analytics for operational decision-making and that requires the latest data.

Getting data applications to meet user expectations requires a new type of data architecture: a data architecture

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