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Three Notable Trends from the 2017 Gartner Magic Quadrant for Business Intelligence and Analytics Platforms

Gartner recently released the 2017 Magic Quadrant for Business Intelligence and Analytics Platforms*. For an overview and methods of evaluation in this year’s MQ, you can access the summary or download the full report from Gartner’s website.

At Search Technologies, we've seen some recent developments in our customers’ analytics projects that also emerged in this year’s Gartner Business Intelligence and Analytics MQ. In this blog, we'll be sharing the top three notable trends. Let’s start with some background of what led to “the rise of analytics.”

As more business use cases rely on advanced, real-time analytics to gain competitive advantages, modern business intelligence and analytics platforms have increasingly become business-driven as opposed to traditional IT-led reporting.

At the Gartner Business Intelligence & Analytics Summit last year, Joao Tapadinhas, Gartner’s Research Director and an author of this MQ, illustrated the demand for better analytics in his “three tiers of needs” model:

 


analytics-stages-of-needs.png


In his discussion, Mr. Tapadinhas stressed that the need for an advanced Analytics Workbench is on the rise – when data is growing exponentially, the next viable step is to derive insight from it, using the appropriate business intelligence and analytics platform. Businesses that can leverage their data not just for information discovery but also for analytics and collaboration will create a competitive edge. And three noteworthy trends emerged. 

1.  The Convergence of Search and Analytics

There has been a movement towards making use of search engines’ scalability upon which to build visualization and analytics dashboards. 

This is evident through the addition of ThoughtSpot – a Palo Alto search-based BI platform provider - in this year’s MQ. Although the company is still a niche player and provides a smaller range of functionalities, ThoughtSpot's main differentiator is a search-based interface for visual exploration. It’s also worth noting that several of ThoughtSpot founders were from Google, which evolved from simply a search engine giant into an intelligent digital assistant in content exploration (think Google Now, Google Home, Google Trends, etc.)

2.  Cloud and Open Source Go Mainstream

cloud-machine-learning.jpgMany of the platforms in the MQ offer the ability to store, process, and analyze huge amounts of unstructured and structured data within a cloud-based environment.    

For instance, Microsoft, the leader in the MQ, leverages its Azure cloud platform to deliver an end-to-end BI solution: 

  • Azure Search – a cloud-based search service (check out our engineers' demo for searching Wikipedia with Azure Search)
  • Azure Machine Learning - a cloud-based analytics component of the Cortana Intelligence Suite 
  • Azure HDInsight - a cloud-based Hadoop offering that provides optimized open source analytics clusters for Spark, Hive, MapReduce, HBase, Storm, Kafka, and R Server. 
  • Stream Analytics – real-time analytics in the cloud 

In this example, we also observed the rise of open source big data analytics tools, such as Spark, HBase, etc. Gartner expects that “smart, governed, Hadoop/Spark-, search- and visual-based data discovery capabilities will converge into a single set of next-generation data discovery capabilities as components of modern BI and analytics platforms.”

Similarly, there are multiple use cases where Spark and complementary open source technologies like search engines and big data processing tools are used to build modern, real-time BI and analytics applications for our customers. Some examples include:

  • Log analytics 
  • Sentiment analysis
  • Recommendation engines
  • Fraud detection
  • Threat detection
  • Bioinformatics / genomics studies

3.  Natural Language Processing and Voice Search

cognitive-search.jpgAccording to a survey among smartphone users, 60% of users want more natural and relevant voice answers as opposed to a list of search results. For businesses that seek fully mobile and speech-enabled interfaces, the question/answer system is required functionality. It can understand questions spoken through speech recognition APIs and can then provide natural language answers which can be read to the user. This links mobile and speech-enabled users to corporate systems in a very powerful way.

Among the MQ’s leaders, Microsoft provides natural language capabilities with Cortana. Tableau recently partnered up with Automated Insights, Narrative Science, and Yseop, to add natural language power to its platform. Qlik, through its partnerships with Narrative Science and Yseop, brought natural language extensions to data visualization in Qlik Sense.

At Search Technologies, our Natural Language Processing Toolkit, with text processing, language processing, entity extraction, and advanced pattern/relationship recognition, is leveraged for a multitude of search and analytics use cases, including the development of intelligent question/answering systems

Businesses, from large to small, have been able to make use of their data in more practical ways using full-featured BI and analytics platforms as well as custom-built technology stacks. We expect the momentum will continue in these directions as the demand for complex analytics has become an integral part of the big data revolution.


* The Gartner 2017 Magic Quadrant for Business Intelligence and Analytics Platforms, authored by Rita L. Sallam, Cindi Howson, Carlie J. Idoine, Thomas W. Oestreich, James Laurence Richardson, Joao Tapadinhas, was published in February 2017.  

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