Table of Contents
This report analyzes an innovative approach to enterprise search that parallels one of the highest values attainable by harnessing Big Data: not analyzing part of the data and summarizing the rest, as is done in traditional BI platforms, but analyzing all of the data and presenting only that which is most relevant to the user. In this instance, artificial intelligence adds an interesting twist that has the potential to revolutionize the most important aspect of Big Data: how companies use it.
Enterprise search is one of the areas2 of Stratecast’s Data Management Model.3 Equipping organizations with functionality in all areas is vital to get companies beyond industry buzzwords, to take a holistic, integrated approach to Big Data, analytics, and business intelligence (BI). From knowledge management and baselining, to master data management (MDM), to tasks and administration, each component fulfills essential data management functions.
Stratecast believes that one area of the model, Enterprise Search, provides one of the most important functions a Big Data implementation can deliver. Everything else that occurs throughout the Big Data ecosystem is, frankly, useless if users cannot run a search and quickly get the exact information they need. A company should:
• Clearly define what it wants its Big Data system to achieve, and the metrics it wants to measure most closely
• Establish a high-performance Big Data architecture
• Integrate all data sources, with each other and with the existing IT infrastructure, and operate a data warehouse
• Make the data system collaborative and user-friendly for business users
• Ensure system security at all access points—or, as data and IT teams refer to them, handoffs
• Make sure the system is both customizable and well-managed
Yet, all is lost if users cannot, via any authorized access point, get the same sub-second search results from the Big Data system that they are accustomed to getting from a simple Web search. They will lose confidence in the system and use other means, including simple Web searches on their smartphones, to find what they wrongly believe is a workaround. The company will have wasted a great deal of time and money on a system that gathers dust instead of gathering happy users. That hinders the credibility of the project’s sponsor, and jeopardizes the ability to secure support and funding for the next Big Data effort.
The beauty of enterprise search, as part of a Big Data system, is that instead of pulling search results from a single source, the Web, users are accessing all data that is relevant to their jobs, from internal and external sources. This report analyzes an innovative approach to enterprise search that parallels one of the highest values attainable by harnessing Big Data: not analyzing part of the data and summarizing the rest, as is done in traditional BI platforms, but analyzing all of the data and presenting only that which is most relevant to the user. In this instance, artificial intelligence adds an interesting twist that has the potential to revolutionize the most important aspect of Big Data: how companies use it.
The Problem with Search
The reader may feel safe in dismissing the headline of this section as a falsehood: mankind is living in an age when our ability to search for the information we need, no matter where it may exist, anywhere on the planet, is the best it has ever been. Users today literally have the world at their fingertips. So, what could possibly be wrong with search today?
What is wrong with search is what will be wrong with it soon, as users who demanded, and received, sub-second search results turn their sights toward a more customized search experience—one that makes them truly feel as if they are no longer obtaining search results for the masses. Users are starting to demand increased access to the kind of expert and specialized knowledge that currently resides inside corporations and in online forums. Needed, instead, will be specialized search services capable of being tailored to an individual user’s preferences and behaviors. Users are coming to expect some analysis, or at least responses, which address what the user means, not what they said (or typed into the search engine). So, users may now expect search to be more of a query; e.g., “Tell me what I need to know about XYZ.”
With mobile search growing, these services must also be built to meet the special characteristics of mobile as a medium—much as Web sites are designed to display and function in either Mobile or Desktop mode, depending on whether the device accessing the site is a smartphone or a computer. In most cases, a subject-specific search app is more likely to generate the customized content a consumer is looking for than the global one-size-fits-all search engine. Proof points of that evolution include:
• The explosion of mobile apps addressing vertical markets and specific, targeted user groups with common characteristics, needs, and interests.
• The largest global one-size-fits-all search engine itself, Google, which has been targeting certain verticals to provide better quality results tailored to user groups (or the same users but in different scenarios). One of those verticals is the travel and transportation industry; and Google moved to address that vertical through its 2010 acquisition of ITA Software. In the wake of the acquisition, Google has released its Flight Search service and OnTheFly app.
A Better Way: Enterprise Search
Stratecast has identified a substantial number of data sources that enterprises must contend with; and even more if a company is a communications service provider (CSP).4 Stratecast organizes these sources into eight broad categories, as shown in Figure 1; and enterprise search opens a window into them all.
Few technologies exist that cannot be improved upon; in the case of enterprise search, blending its attributes with those of a complementary technology is yielding beneficial results.
Artificial Intelligence Expands Enterprise Search: Expertmaker
A system based on artificial intelligence (AI) “learns” the way the human brain learns. AI builds human comprehension into a service that behaves with human-like insights, and can take intelligent actions such as reasoning, gaining knowledge, planning, learning, communication, perception, and the ability to move and manipulate objects.
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