Delivering intelligent applications that build confidence
There’s no denying that we live in a world of fast data, and many already recognize the value in applying analytics to these quick and potentially fleeting data streams. But what separates a good analytical system to capture data stream insights from a great one? It comes down to three things: time to value, application intelligence and insight confidence.
Time to value
A short time to value for newly implemented data stream analytics solutions is vitally important. The extra time added could mean the difference between gaining a competitive advantage or barely missing an opportunity. One of the best ways to boost time to value for your streaming analytics solution is to reduce the learning curve for application developers. Make sure the option you choose uses their pre-existing skills, such as knowledge of Java, so applications can be up and running in the least amount of time.
As anyone who’s ordered an inferior pizza can tell you, delivery speed doesn’t matter when what you receive is subpar. In the context of data stream analytics, this means the intelligence of applications is critical. Applications are more intelligent when they can rely on more data sources and use sophisticated analytics on data streams. One way to make this a reality is through integrating with open source technology. Find a solution that enables data streams to be captured efficiently and used alongside data at rest within the organization, such as Hadoop. The solution should also be able to integrate Spark and complement it with event-driven, low-latency apps. Furthermore, it should provide a broad range of machine learning options such as Spark MLib to increase analytic sophistication. With these three elements are in place—integration with Hadoop, integration with Spark and broad machine learning—a streaming analytics solution can generate superb application intelligence and much-needed insights.
Even a solution that can deliver intelligent applications quickly means little without an important third component. Decision-makers must be confident in the insights produced and actually put them into practice. Too often, gut reactions are used instead of data-based insights, revealing a lack of trust. However, this can be countered by strong data lineage and data governance procedures. Look for solutions that make data ingestion easy through data lineage and flexible schemas. Also, be sure to note options that have automatic schema discovery and mapping (integration with a governance specific solution can help here). Data lineage and governance will help decision-makers feel more comfortable with the source of these insights and the precautions taken to make sure they are usable. And when decision-makers are more confident, they can base their actions on a more solid data foundation.
Better time to value, application intelligence and insight confidence pushes a good streaming analytics solution to the next level. If you believe faster delivery of confidence-building, intelligent applications would help your business gain a competitive edge, check out the recently released Streams V4.1. This new version was crafted to address the three points above. Get more information on streaming analytics today, such as customer stories, elements to try for yourself and other materials that highlight the benefits of Streams.