IBM recently announced, “Ushering in a new era of cognitive computing systems, IBM today unveiled the IBM Watson Engagement Advisor, a technology breakthrough that allows brands to crunch big data in record time to transform the way they engage clients in key functions such as customer service, marketing and sales. Now businesses can better serve consumers with a cognitive computing assistant that learns, adapts and understands a company’s data quickly and easily, enabling users to have IBM Watson at work quickly, while increasing its knowledge and value over time.”

The article continues, “Two years after its triumph on Jeopardy!, the IBM Watson Engagement Advisor is a first of a kind system designed to help customer-facing personnel assist consumers with deeper insights more quickly than previously possible. Delivered through cloud-delivered services and online chat sessions, IBM Watson will empower a brand’s customer service agents to provide fast, data-driven answers, or sit directly in the hands of consumers via mobile device. In one simple click, the solution’s Ask Watson feature will quickly help address customers’ questions, offer feedback to guide their purchase decisions, and troubleshoot their problems.”

Read more here, or watch a video about the announcement here.

photo credit: IBM

Haowen Chan and Robin Morris of GigaOM recently wrote, “Expectations surrounding the future of  big data range from the just huge to absolutely enormous – a reflection perhaps of both its real inherent potential and all the massive hype. Certainly though there is no dispute that companies can reap big benefits from exploring patterns found in the data they already generate and collect. Further, depending on the algorithms used, machine learning can even serve as a real world crystal ball: There are countless examples, but the story about Target’s ability to predict pregnancies by analyzing customer consumption patterns, or how well known mathematician Nate Silver predicted the winner in all 50 states during last November’s presidential election are two poignant examples.”

They continue, “But the fact remains that big data can only ever be as good as the machine learning that is used to provide insight, and even the most sophisticated machine learning techniques aren’t omniscient – the old adage “garbage in, garbage out” sums up this dilemma perfectly. Businesses planning to invest in big data science, with the hopes of reaping the potential wealth of insights available, must at all costs avoid introducing bias into the process – or risk jeopardizing everything. Data bias comes in many forms. It can come from poorly defined business domain objectives. Or, it can come from opting to gather data that are easy to collect rather than data that are most informative. Data scientists can also receive data that have been biased by incorrect assumptions by the domain experts. (And as a footnote, the recent example of the austerity economics Excel scandal shows how a minute data error can have cascading and devastating effects.)”

Read more here.

Mare Lucas of Wired recently wrote, “Close behind ‘Big Data’ as one of the most utilized enterprise technology terms today is ‘Data Scientist.’ Many postulate that the explosion in Big Data will usher in an insatiable demand for data scientists able to slice and dice data to guide more informed decision making within the organization. Others go a step further, bemoaning that a chronic data scientist shortage will hold back the full potential of Big Data.”

Lucas continues, “Concern is unsurprising. For years, the BI and data analytics conversation was framed around how to aggregate massive volumes of data and then unleash the data scientists to find the value. Today, despite the information deluge, enterprise decision makers are often unable to access the data in a useful way. The tools are designed for those who speak the language of algorithms and statistical analysis. It’s simply too hard for the everyday user to ‘ask’ the data any questions – from the routine to the insightful. The end result? The speed of big data moves at a slower pace … and the power is locked in the hands of the few.”

Lucas goes on, “Data scientists represent important cogs in Big Data’s future. But a strategy built around throwing more people at data challenges is a near-term fix that overlooks the need to consumerize the Big Data experience. What if any user in an organization could ask a question in natural language and the have their analytics tool look across data sets, answer the question in the most relevant and meaningful visualization, and deliver a social learning layer where the users continually train the tool to their needs? What if data were as accessible to salespeople on smartphones as they are to data specialists analyzing information on multiple dashboards?”

Read more here.

In an effort to leverage the knowledge of several of the top minds in the Data Management industry, DATAVERSITY™ has been conducting a series of interviews on some of the most relevant topics in the field today. Recently, we interviewed Peter Lawrence, a Solutions Architect at TopQuadrant.

Peter will be part of a panel discussion at the Enterprise Data World 2013 Conference in San Diego, CA from April 28-May 2, 2013. The panel will include Peter, Chris Moran, and Lee Feigenbaum; it is titled “Integrating Semantic Technology with Enterprise Information Management.”

The Speaker Spotlight Column (and its parallel venture the Sponsor Spotlight Column) is an ongoing project that focuses on highlighting several of the central issues represented at the many Data Management conferences produced by DATAVERSITY.

The primary emphasis of the interview was to question Peter Lawrence on his work and history within the industry, with particular importance on his discussion at the upcoming conference:

DATAVERSITY (DV): Please tell us a little about yourself and your history in the industry e.g role at company (as opposed to job title), past experience and how you got started in the data profession?

Peter Lawrence (PL): My background and experience is in Process Manufacturing Information systems applied to Oil and Gas, Refining and Petrochemicals where the only contact anyone has with the ‘product’ is via data. Throughout my career, I have passionately and innovatively applied automation, software, and information technology to create transformational solutions to the data management challenges within the Process Industries.

DV: What’s the focus of the work do you currently do within your organization?

PL: Over my career I had developed and deployed (successfully) increasingly complex enterprise data integration platforms. However 7 years ago I had an epiphany in which I realized the complexity was the enemy of sustainability. I switched technological horses to the semantic database world, creating the Intuition Executive for Matrikon Honeywell that semantically federated real time process plant measurements, and plant configuration, applied inferencing to deduce more knowledge, used workflow to automate procedures, and presented semantically-driven schematics and graphics via a SharePoint portal.  I am now evangelizing semantic solution from within leading supplier TopQuadrant.

DV: What is the biggest change going on in your particular area of the industry at this time?

PL: Semantic data management is emerging from its early adoption status to solve the problem of search, but not limited to text search, but merging both entity search (Google’s things) to enterprise structured data search.

DV: How does such a change affect your job?

PL: Bridges, both technological, and political, need to be built between the domains of text search, text mining and enterprise data integration.

DV: How has your job, and/or the work you’re doing at your organization, changed in the past 12 months?  How do you expect it to change in the next 1-2 years?

PL: Currently an evangelist of semantic solutions to early adopters, but will be dealing more with the early-majority adopters of such solutions.

DV: More broadly speaking, what do you believe is the most significant change happening in Enterprise Data at this time?

PL: The years of SQL-for-everything are over, and at the same time the hidden value of the huge volume of enterprise data is being recognized.

DV: How is Big Data going to affect your job (in your organization) in future?

PL: Since we are a vendor of solutions this is not affecting us directly, but our customers will increasingly be using BigData which means more data, which more data access problems.

DV: What is something noteworthy about yourself that you would like to tell the conference attendees and our readers that they may not know?

PL: In my career I have flown throughout the world to mess with databases, so it is quite natural that my hobbies are flying and playing with database technology.

If you are interested in attending Peter’s panel discussion at EDW2013, please see the conference schedule at: http://edw2013.dataversity.net/agenda.cfm?confid=72&scheduleDay=PRINT

The panel is on Tuesday, April 30, at 3:20pm.

About Enterprise Data World:

Enterprise Data World is the business world’s most comprehensive educational event about data and information management. Over five days, EDW presents a diverse schedule of programming that addresses every level of proficiency, including keynotes, workshops, tutorials, case studies, and discussions.

Andy Plesser of Beet.TV reports, “The New York Times R&D Labs has created a prototype search engine that provides Times article results with embedded links to sources outside of the paper, says Michael Zimbalist, VP for Research and Operations in this conversation with Forrester Principal Analyst Joanna O’Connell. As an example, he says the search engine allows users to input the name of a college and then see Times articles containing alumnae of the college regardless of whether of or not the article mentions explicitly that the subject attended the college or University.”

Plesser goes on, “Zimbalist frames the initiative as part of the linked data movement, also known as the semantic web, as a way to surface up content which had not been previously linked. He explains this and the other implications of ‘big data’ for the media industry in this segment from the Beet.TV Big Data Summit at NBC News presented by RAMP.”

Watch the video here.

photo credit: Beet.TV

According to an article out of BizTech2.com, Gartner has identified a number of top tech trends impacting business in 2013. The list includes Big Data, Semantic Technologies, and NoSQL. Under the category of Big Data, the article states, “Gartner defines big data as high-volume, high-velocity and high-variety information assets that demand cost-effective, innovative forms of information processing for enhanced insight and decision making. Big Data warrants innovative processing solutions for a variety of new and existing data, to provide real business benefits, but processing large volumes or wide varieties of data, remains merely a technological solution, unless it is tied to business goals and objectives. New forms of processing are not necessarily required, nor are new forms of processing always the least expensive solution (least expensive and cost-effective are two different things). The technical ability to process more varieties of data in larger volumes is not the payoff. The most important aspects of big data are the benefits that can be realised by an organisation.”

On semantic technology, the article continues, “Semantic technologies extract meaning from data, ranging from quantitative data and text, to video, voice and images. Many of these techniques have existed for years and are based on advanced statistics, data mining, machine learning and knowledge management. One reason they are garnering more interest is the renewed business requirement for monetising information as a strategic asset. Even more pressing is the technical need. Increasing volumes, variety and velocity — big data — in IM and business operations, requires semantic technology that makes sense out of data for humans, or automates decisions.”

Read more here.

photo credit: Gartner

Jeff Bertolucci of Information Week reports, “The W3C envisions the Semantic Web as an extension rather than a replacement of the current Web — a framework that extends Web principles from documents to data. Many semantic specifications for the Web are already in place, and the W3C continues to develop more specs to standardize the technology… In a phone interview with InformationWeek, Cambridge Semantics chief technical officer CTO Sean Martin summed up the Semantic Web in a nutshell: ‘In essence, all you’re doing is tagging data and giving it a description of what it is’.”

Bertolucci continues: “‘If you can put more information in — more metadata with the data — then the software can interrogate the data to find out what the data is, and what it’s capable of,’ added Martin, who believes the rise of big data could help spur the adoption of Semantic Web technologies. Here’s why: ‘First of all, a lot of the big data efforts are still very crude,’ said Martin, referring to Hadoop and related technologies. ‘The tools are relatively immature, and you’ve got specialized people using them.’ And while the Hadoop Distributed File System (HDFS) offers many benefits, including excellent redundancy capabilities for big data operations, it has its shortcomings.”

Read more here.

photo credit: Chris P. Jobling

Derrick Harris of GigaOM reports, “When it comes to using data to determine how to build a team or manage a game, the National Football League appears years behind its professional sports brethren such as Major League Baseball and the National Basketball Association. But perhaps the increasing popularity of machine learning can change that by helping NFL teams make more sense of their very complex datasets. Delving deep into the world of computer science might sound like overkill, but professional football is big business in America, and an analytic edge off the field might be just as important as athletic or strategic edges on the field. Heck, it might help create them.”

Harris continues, “The New York Times highlights the current state of statistical reliance among NFL teams in an article on Sunday. The  NYT’s Judy Battista reports that teams are finally beginning to hire statisticians and take statistical analysis seriously in limited areas — but there’s always a disclaimer. Football is such a variable-rich and complex game, her sources claim, that the human eye and human intuition will always be best at assessing certain things.”

Read more here.

photo credit: NFL

Andre Bourque of Technorati recently interviewed Blekko CTO Greg Lindahl. Bourque begins, “Years ago the federal government launched a project where they had millions of documents that needed to be assessed and then declassified. It was much too large a job for human curation, so a method needed to be designed and implemented that would assure one hundred percent accuracy when determining if in fact the document should be declassified. The gist of the design was to use Google type documentation database handling in conjunction with plagiarism matching algorithms that were weighted for classified data. The objective was to differentiate classified, and declassified content. In the end, it was determined that it was always necessary to involve a human element to ensure accuracy. Years later, search engine provider Blekko is proving the same theory: necessary human element involvement ensures accuracy.”

He goes on, “I had the great fortune to mingle with the who’s-who of CTOs and CIOs at the NoSQL Now! conference last month in San Jose. I met with Blekko CTO Greg Lindahl at dinner one night and he explained it to me: In the world of search, it’s not just about the data, it’s how you enable people to access it. Using the same coarse granularity Google uses, Blekko retrieves pertinent search data based on user query data. Once the query results are received, they are sorted and presented to the user in a familiar text format. Lindahl explains how the company’s search is based on three distinguishing elements: (1) Algorithm inclusive of user input. (2) Customizable search engine settings. (3) Transparency. Collectively, these elements are what set Blekko apart from the company’s larger, search engine rivals.”

Read more here.

photo credit: Blekko

Ron Powell of BeyeNetwork recently interviewed SAIC SVP Jonathan Litchman regarding the intersection of Big Data and linguistic technology. Litchman commented, “Big data, as you know, is a term used for lots of different things. When I think about big data, it depends on how big you want to get. If you think about the vast amounts of data that people need to be able to handle in only one language, you have tremendous big data issues; but if you understand that the most effective use of big data is to be more inclusive and make that big data more global, then you have a situation in which your data increases exponentially with the inclusion of multiple languages within that dataset.”

He continues, “[SAIC's] Omnifluent products help people who want to do analytics and mining on big data be able to do so without having to confront the barriers that different languages pose. Whether it’s multilingual search, translation summarization, or automatic alignment of a transcript with video or audio, big data has to expand beyond single language capability in order to be able to understand what’s useful within that big data. There are several features of the product that I think are special. The first is that the translation technology that underlies the Omnifluent platform is really a true hybrid machine translation capability. It’s a combination of machine translation that includes rules-based and statistical engines, each of these engines working together as one within a single decision engine.”

Read more here.