The business advantages of embedding analytics into applications
Imagine, if you will, that you’re a product manager recently tasked with adding some whiz-bang visual analytics to your software, web site, or mobile app. Your first instinct may be to build your own solution as part of your existing product. However, you have to ask yourself if you have the team resources to accomplish the chore, or if you would rather have your team work to keep your core product fast and functional.
Luckily, there is another option, one that leverages embedded analytics. With that in mind, let’s take a deeper dive into the world of embedded analytics and determine why you might choose it, who is doing it, and how to evaluate available off-the-shelf solutions.
Organizations are clamoring to unlock the value inside the massive volumes of data they are collecting. Data-driven employees make more informed decisions that help companies beat the competition. Employees need applications that help them make sense of all the data available for decision-making. Then they need to explore and analyze the data so they can make the best decisions.
The hunger for data and analytics is changing the expectations that workers have for software. Business users expect customizable dashboards and reports as part of every application they use. More importantly, they expect to be able to quickly and easily explore and interact with data sets. Combine that with the growing complexity, size, and variation in data sets, and software developers are facing increasingly demanding challenges in helping their users and customers gain the insights they need.
We live in a time where the speed and availability of multitudes of data is unprecedented. Every data set can be tapped, combined with others, and analyzed to yield business insights. The challenge of quickly providing powerful insight is compounded by the rapidly developing modern data stores that have been built to handle the dramatic increase in volume, velocity and variety of data — billions of rows, fast streaming data, and often unstructured textual and document data. There are NoSQL databases like Cassandra, HBase, and MongoDB; data processing frameworks like Hadoop and Spark; query engines like Impala and Hive; stream processing tools like Storm and Kafka; and text indexing frameworks like ElasticSearch and Solr. Cloud providers have entered the fray with scalable and inexpensive datastores such as Amazon’s Redshift, Google’s BigQuery and Spanner, and Microsoft’s HDInsight.
As you can see, building your own visual analytics within your product could quickly strain architectural and developer resources, and their knowledge. You may end up in the not-so-rare scenario where you’re trying to hit a moving target as business users discover new data sources and want them included in their analyses. You may also find your homegrown or legacy analytics solution is locking you into using traditional data stores that don’t meet today’s requirements. Building analytics from scratch might be doable, but how can you budget and plan when requirements are constantly evolving?
Embedded analytics is the use of reporting and analytic capabilities (including visualizations) from packaged software running within business applications.
Posted on 7wData.be.