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Posted by on Jul 16, 2014

I’ll take Cognitive Analytics for $1000, Alex

I’ll take Cognitive Analytics for $1000, Alex

Jeopardy! Power Players WeekOne of my fondest memories from my childhood is my family’s nightly ritual of gathering around the TV to watch Jeopardy! with Alex Trebek. I’m still a big fan of the show and when, in 2011, IBM”s Watson took on two Jeopardy champions I was captivated. Having worked on some early efforts to use Natural Language Parsing (NLP) and Latent Semantic Analysis (LSA), it was great to see how the technologies had advanced to allow querying of large sets of unstructured data using plain language queries.

Watson is just one, impressive, example of the growing field of cognitive computing and cognitive analytics.  Cognitive analytics refers to process of bringing together machine learning, natural language processing, and artificial intelligence to analyze large quantities on unstructured data in ways similar to those used by the human brain.

According to Deloitte’s Tech Trends 2014, “cognitive analytics relies on technology systems to generate hypotheses, drawing from a wide variety of potentially relevant information and connections,” and the emerging technology will be a growth area for many organizations in 2015.

In honor my favorite game show, I thought I’d provide a Jeopardy style list of ways Federal managers may, in the not too distant future, be able to use cognitive analytics to improve organization performance.

Answer: Natural language search agents.

Question: What tools can government agencies use to improve customer service in a resource constrained environment?

Apple’s Siri is arguably the most familiar version of an artificial intelligence-based natural language query engine, but corporations have been introducing more rudimentary versions of automated agents to support customer service for nearly a decade.

With enhanced language understanding, the introduction of machine learning that can improve the recommendations coming from search agents, and more access to information storage and processing power, opportunities are increasing to automate customer service functions. Cognitive analytic techniques will enable systems to interpret and connect disparate pieces of information to provide better answers and more resources in response to customer inquiries.

Automating elements of customer service processes (for both internal and external customers) helps support the agencies drive to maintain service levels with decreased resources. The key will be implementing the type of technology users are becoming accustomed to (e.g. Siri), while still allowing for easy access to customer service agents before users become frustrated with the technology experience.

Answer: Social media monitoring and sentiment analysis.

Question: How can a federal manager use cognitive analytics to understand trends in employee engagement and brand management?

In an increasingly connected world it is important for organizations to maintain awareness of how they are perceived on social media. Using analytic tools, agencies can monitor, aggregate and analyze trends in messaging on internal and external social media networks to understand how employees and the public view the organization. And emerging technologies for sentiment analysis offer a glimpse into the positive or negative views being communicated about the organization.

Answer: Better data aggregation, improved used of unstructured data, and faster data processing.

Question: How can cognitive analytics enable data driven decision making at my organization.

A recent survey by MarkLogic and GovLoop (here) suggests that many government agencies are struggling to realize the benefits of big data and advanced analytics for their organization. The merging of advanced technologies in the field of cognitive analytics will offer agencies a more streamlined, less technically challenging path to exploiting the volume of (structured and un-structured) data held across the enterprise to improve organizational performance.

By connecting previously disparate and often unstructured data sources to make predictions about organization performance and, ultimately, learning to improve those predictions over time, cognitive analytic systems can provide a leap ahead in an organization’s ability to make data driven decisions.

 

The rapid rise of big data, analytics, and the focus on data driven decision making has presented numerous challenges to Federal agencies.  Whether due to lagging technology infrastructure or critical skills gaps, many agencies have yet to realize the benefits of advanced analytic techniques. Exploring cognitive analytics may offer a near term solution to the analytics challenges facing Federal organizations.

Do you see a need for improved analytic capabilities in your organization? What challenges are you facing in improving your data driven decision making capabilities?

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