Business

Internet of Things Impacts Big Data

depiction of internet of things objectsAccording to Mike Kavis of Forbes, “Companies are jumping on the Internet of Things (IoT) bandwagon and for good reasons. McKinsey Global Institute reports that the IoT business will deliver $6.2 trillion of revenue by 2025. Many people wonder if companies are ready for this explosion of data generated for IoT? As with any new technology, security is always the first point of resistance. I agree that IoT brings a wave of new security concerns but the bigger concern is how woefully prepared most data centers are for the massive amount of data coming from all of the “things” in the near future.”

Kavis went on to write that, “Some companies are still hanging on to the belief that they can manage their own data centers better than the various cloud providers out there. This state of denial should all but go away when the influx of petabyte scale data becomes a reality for enterprises. Enterprises are going to have to ask themselves, “Do we want to be in the infrastructure business?” because that is what it will take to provide the appropriate amount of bandwidth, disk storage, and compute power to keep up with the demand for data ingestion, storage, and real-time analytics that will serve the business needs. If there ever was a use case for the cloud, the IoT and Big Data is it. Processing all of the data from the IoT is an exercise in big data that boils down to three major steps: data ingestion (harvesting data), data storage, and analytics.”

 

To read a different perspective on these challenges and how Semantic Web technologies play a role in them, read Irene Polikoff’s recent guest post, “RDF is Critical to a Successful Internet of Things.

 

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Big Data Challenges In Banking And Securities

Photo courtesy: Johan Hansson, https://www.flickr.com/photos/plastanka/

Photo courtesy: Johan Hansson, https://www.flickr.com/photos/plastanka/

A new report from the Securities Technology Analysis Center (STAC), Big Data Cases in Banking and Securities, looks to understand big data challenges specific to banking by studying 16 projects at 10 of the top global investment and retail banks.

According to the report, about half the cases involved e petabyte or more or data. That includes both natural language text and highly structured formats that themselves presented a great deal of variety (such as different departments using the same field for a different purpose or for the same purpose but using a different vocabulary) and therefore a challenge for integration in some cases. The analytic complexity of the workloads studied, the Intel-sponsored report notes, covered everything from basic transformations at the low end to machine learning at the high-end.

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Text Analytics Company Linguamatics Boosts Enterprise Search with Semantic Enrichment

Linguamatics Logo CAMBRIDGE, England and BOSTON, June 19, 2014 /PRNewswire/ — Today, Linguamatics launches I2E Semantic Enrichment to provide increased return on investment in enterprise search systems and radically improve speed to insight.   I2E Semantic Enrichment is used within an existing enterprise search deployment to enrich the current data, make it more discoverable and provide more relevant search results. The software scans millions of documents to identify and mark-up semantic entities such as genes, drugs, diseases, organizations, authors and other relevant concepts and relationships. Enterprise search engines consume this enriched metadata to provide a faster, more effective search for users. Read more

Extracting Value from Big Data Requires Machine Learning

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James Kobielus of InfoWorld recently wrote, “Machine-generated log data is the dark matter of the big data cosmos. It is generated at every layer, node, and component within distributed information technology ecosystems, including smartphones and Internet-of-things endpoints… Clearly, automation is key to finding insights within log data, especially as it all scales into big data territory. Automation can ensure that data collection, analytical processing, and rule- and event-driven responses to what the data reveals are executed as rapidly as the data flows. Key enablers for scalable log-analysis automation include machine-data integration middleware, business rules management systems, semantic analysis, stream computing platforms, and machine-learning algorithms.? Read more

Yahoo7 Upgrades to Bigdata Open Source Graph Database

bigdataPenny Wolf of IT News in Australia reports, “Yahoo7 is rolling out a semantic publishing platform across the web sites that promote Channel 7’s local free to air programs, building its new content systems atop an open source graph database. Craig Penfold, CTO Yahoo7 was inspired by a similar project undertaken by the BBC, which initially launched semantic web publishing for the channel’s 2010 World Cup website to increase viewer engagement. After conducting research and discussions with BBC and Yahoo, the Yahoo7 team chose to change the datastore from a standard SQL database to a graph database.” Read more

The Skills Gap: How to Close the Gap in Your Career

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Greg Satell of Forbes recently wrote, “In the late 90’s McKinsey declared the war for talent and argued that, in a knowledge economy, having the right people is even more important than having the right strategy or technology.  Recruiting and retaining the ‘best and the brightest’ quickly became a corporate mantra. Yet today, the firm is more concerned with the skills gap.  In data science, for example it estimates a shortfall of 140,000 to 190,000 data scientists and 1.5 million managers who have the skills needed to use the insights to drive decisions. But even that understates the problem. With technology accelerating change in the marketplace and automation replacing highly skilled workers with robots, the decision to invest in any particular set of skills is far from a forgone conclusion and platitudes about ‘investing in our people’ will no longer suffice.  We need to start thinking seriously about viable strategies for managing the skills gap.” Read more

Ersatz Labs Hopes to Answer Your Questions with Deep Learning

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Jordan Novet of Venture Beat recently wrote, “A startup called Ersatz Labs wants to help lots of companies intelligently answer lots of questions after reviewing lots of data, just as big tech companies like Google and Netflix do. Toward that end, today Ersatz is launching a cloud service for deep learning, as well as a hardware-software package to run inside companies’ existing facilities. While deep learning services are often geared toward specific uses, like text processing and image recognition, Ersatz makes deep learning available for any type of use.” Read more

The Data Behind the Internet of Things

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Nancy Gohring of Computerworld recently wrote, “The market for connected devices like fitness wearables, smart watches and smart glasses, not to mention remote sensing devices that track the health of equipment, is expected to soar in the coming years. By 2020, Gartner expects, 26 billion units will make up the Internet of Things, and that excludes PCs, tablets and smartphones. With so many sensors collecting data about equipment status, environmental conditions and human activities, companies are growing rich with information. The question becomes: What to do with it all? How to process it most effectively and use it in the smartest way possible?” Read more

Where The Money Is: Data Science

datascigrafWhat’s a data scientist worth? Give me a second to identify the relevant data sources, build the machine learning algorithm and create a visualization.

So much for a new take on an old joke. The real answer is about six figures, information I recently came across in a report released earlier this spring: Burtch Works Executive Recruiting survey, Salaries of Data Scientists. The median base salary of data scientist managers is $160,000, it says, while individual contributors average about $120,000. The information comes from 171 data scientists for whom the recruiting firm has complete and current information. Whether a data scientist is at a lower or higher job level, across the board he or she is doing financially better than other Big Data professionals, the report shows.

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Sentiment Mining for Real Time Insights on Twitter

 

syKalev Leetaru of Wired recently wrote, “For its flagship new reality show Opposite Worlds the Syfy channel wanted to let the audience ‘remote control’ the show via social media. I worked with Syfy to create what ultimately became its real-time ‘Twitter Popularity Index.’ The Index combines the intensity of conversation around each character, the number of unique discussants, and the emotion of that discussion using a new sentiment engine powered by over 1.6 million words, phrases and common misspellings and colloquial expressions. Using our Index, Opposite Worlds records across the board in Twitter engagement for a cable television series.” Read more

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