Natural Language Question Answering and Business Intelligence
From Watson To Siri, Users Want To Ask Questions As Opposed to Submitting Queries
Without true self-service business intelligence (BI) solutions that harness the power of natural language search, IT departments are going to drown in requests for more and more analytics reports from just about every corner of their organizations.
I’ve touched on this in a previous post (“Keep Data Close at Hand: Real-Time Business Analytics Requires Better Access to Information”), but I wanted to take a deeper dive and bring in a few more voices to the conversation. While some of the best research and analysis concerning Natural Language Question Answering (NLQA) comes from Jamie Popkin, a managing vice president at Gartner, I’ve found some great research and analysis on BI coming from Boris Evelson of Forrester, too.
In a well-read blog from earlier this year, Evelson laid out a stark future for enterprises not thinking about BI (the bolding is mine):
“The repercussions of not handling BI change well are especially painful and may include lost revenue, lower staff morale and productivity, continued proliferation of shadow IT BI applications, and unwanted employee departures... Firms that fail to prepare employees for enterprise BI change early enough or well enough will be left behind.”
That does not sound like a group with which anyone wants to be associated.
The team behind entellitrak understands the power of self-service BI. The creation of entellitrak’s Natural Language Analytics module was based on a growing demand from our existing customers as well as the realization that in today’s competitive world enterprises need real-time analytics tools in order to be as proactive as possible.
The Natural Language Analytics module, which builds on entellitrak’s Data-First™ approach to case management, puts self-service business intelligence in the hands of front-line workers and management so they can make informed, real-time decisions. (Download a Data Sheet for more details.)
- Put the business into business intelligence.
- Be agile, and aim to deliver self-service.
- Establish a solid foundation for your data as well your BI initiative.
- Select the most appropriate tool set.
- Seek external help if needed.
- Make change management and training an integral part of any BI initiative.
These are all great practices. I especially think all organizations need to keep the first two top of mind when thinking about BI and analytics tools. Putting and keeping core business functions in mind when selecting a BI or analytics tool is imperative. There is very little point to a BI solution that collects and analyzes data that has almost nothing to do with your day-to-day operations. That doesn’t benefit your team in the short term or the long term.
Making sure that an analytics tool is agile and self-serving comes full circle back to the role Natural Language Question Answering should play in BI and analytics tools.
Jumping back to Gartner’s Popkin, there are clear advantages to implementing a BI solution that leverages NLQA because it provides enhanced information retrieval value by:
- Giving answers not documents,
- Using natural human language,
- Providing answers that lead directly to actions, and
- Learning and deriving inferences from user interactions.
Most of Popkin’s research surrounds the biggest of the big tech companies: Google, Apple and IBM. However, embedded Natural Analytics tools that are not tied to search engines, iPhones or Super Computers called Watson, can be used for real-time operational intelligence, too.
Case Management and Business Process Management (BPM) systems capture and store a wealth of powerful data. While these systems empower the front-line worker to collect and manage the data, management often finds itself at the mercy of the IT department to get the information they need to make real-time decisions.
For more on the advantages of Natural Language Analytics, you should sign up for a free webinar scheduled for Dec. 5.
Dan Woods, CTO of CITO Research and a contributor to Forbes.com, will join MicroPact to discuss how Natural Language Analytics can be used to help your organization make the most of the information you are already collecting.
A final thought on the role Natural Language Analytics is going to play in enterprise case management and BPM comes from Popkin. He is quick to point out that the “consumerization” of Natural Language Search is a major driver of the technology and that, like everything from the Internet to the iPhone, means it’s going to change how organizations think and work.
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