University Partners up with Leidos to Investigate How to Cut Costs in Healthcare with Big Data Usage
October 22, 2015
The article on News360 titled Gulu Gambhir: Leidos Virginia Tech to Research Big Data Usage for Healthcare Field explains the partnership based on researching the possible reduction in healthcare costs through big data. Obviously, healthcare costs in this country have gotten out of control, and perhaps that is more clear to students who grew up watching the cost of single pain pill grow larger and larger without regulation. The article doesn’t go into detail on how the application of big data from electronic health records might ease costs, but Leidos CTO Gulu Gambhir sounds optimistic.
“The company said Thursday the team will utilize technical data from healthcare providers to develop methods that address the sector’s challenges in terms of cost and the quality of care. Gulu Gambhir, chief technology officer and a senior vice president at Leidos, said the company entered the partnership to gain knowledge for its commercial and federal healthcare business.”
The partnership also affords excellent opportunities for Virginia Tech students to gain real-world, hands-on knowledge of data research, hopefully while innovating the healthcare industry. Leidos has supplied funding to the university’s Center for Business Intelligence and Analytics as well as a fellowship program for grad students studying advanced information systems related to healthcare research.
Chelsea Kerwin, October 22, 2015
Sponsored by ArnoldIT.com, publisher of the CyberOSINT monograph
Algorithmic Bias and the Unintentional Discrimination in the Results
October 21, 2015
The article titled When Big Data Becomes Bad Data on Tech In America discusses the legal ramifications of relying on algorithms for companies. The “disparate impact” theory has been used in the courtroom for some time to ensure that discriminatory policies be struck down whether they were created with the intention to discriminate or not. Algorithmic bias occurs all the time, and according to the spirit of the law, it discriminates although unintentionally. The article states,
“It’s troubling enough when Flickr’s auto-tagging of online photos label pictures of black men as “animal” or “ape,” or when researchers determine that Google search results for black-sounding names are more likely to be accompanied by ads about criminal activity than search results for white-sounding names. But what about when big data is used to determine a person’s credit score, ability to get hired, or even the length of a prison sentence?”
The article also reminds us that data can often be a reflection of “historical or institutional discrimination.” The only thing that matters is whether the results are biased. This is where the question of human bias becomes irrelevant. There are legal scholars and researchers arguing on behalf of ethical machine learning design that roots out algorithmic bias. Stronger regulations and better oversight of the algorithms themselves might be the only way to prevent time in court.
Chelsea Kerwin, October 21, 2015
Sponsored by ArnoldIT.com, publisher of the CyberOSINT monograph
Business Intelligence and Data Science: There Is a Difference
October 6, 2015
An article at the SmartDataCollective, “The Difference Between Business Intelligence and Real Data Science,” aims to help companies avoid a common pitfall. Writer Brigg Patton explains:
“To gain a competitive business advantage, companies have started combining and transforming data, which forms part of the real data science. At the same time, they are also carrying out Business Intelligence (BI) activities, such as creating charts, reports or graphs and using the data. Although there are great differences between the two sets of activities, they are equally important and complement each other well.
“For executing the BI functions and data science activities, most companies have professionally dedicated BI analysts as well as data scientists. However, it is here that companies often confuse the two without realizing that these two roles require different expertise. It is unfair to expect a BI analyst to be able to make accurate forecasts for the business. It could even spell disaster for any business. By studying the major differences between BI and real data science, you can choose the right candidate for the right tasks in your enterprise.”
So fund both, gentle reader. Patton distinguishes each position’s area of focus, the different ways they use and look at data, and their sources, migration needs, and job processes. If need to hire someone to perform these jobs, check out this handy clarification before you write up those job descriptions.
Cynthia Murrell, October 6, 2015
Sponsored by ArnoldIT.com, publisher of the CyberOSINT monograph
Visual Analytics Makes Anyone a Data Expert
October 5, 2015
Humans are sight-based creatures. When faced with a chunk of text or a series of sequential pictures, they will more likely scan the pictures for information than read. With the big data revolution, one of the hardest problems analytics platforms have dealt with is how to best present data for users to implement. Visual analytics is the key, but one visual analytics is not the same as another. DCInno explains that one data visual company stands out from the rest in the article, “How The Reston Startup Makes Everyone A Big Data Expert.”
Zoomdata likes to think of itself as the one visual data companies that gives its clients a one up over others and it goes about it in layman’s terms.
“Zoomdata has been offering businesses and organizations a way to see data in ways more useful than a spreadsheet since it was founded in 2012. Its software offers real-time and historical explorations of data streams, integrating multiple sources into a cohesive whole. This makes the analytics far more accessible than they are in raw form, and allows a layperson to better understand what the numbers are saying without needing a degree in mathematics or statistics.”
Zoomdata offers a very interactive platform and is described to be the only kind on the market. Their clients range from government agencies, such as the Library of Congress, and private companies. Zoomdata does not want to be pigeonholed as a government analytics startup. Their visual data platform can be used in any industry and by anyone with the goal of visual data analytics for the masses. Zoomdata has grown tremendously, tripled its staff, and raised $22.2 million in fundraising.
Now let us sit back and see how their software is implemented in various industries. I wonder if they could make a visual analytics graphic novel?
Whitney Grace, October 5, 2015
Sponsored by ArnoldIT.com, publisher of the CyberOSINT monograph
Redundant Dark Data
September 21, 2015
Have you heard the one about how dark data hides within an organization’s servers and holds potential business insights? Wait, you did not? Then where have you been for the past three years? Datameer posted an SEO heavy post on its blog called, “Shine Light On Dark Data.” The post features the same redundant song and dance about how dark data retained on server has valuable customer trend and business patterns that can put them bring them out ahead of the competition.
One new fact is presented: IDC reports that 90% of digital data is dark. That is a very interesting fact and spurs information specialists to action to get a big data plan in place, but then we are fed this tired explanation:
“This dark data may come in the form of machine or sensor logs that when analyzed help predict vacated real estate or customer time zones that may help businesses pinpoint when customers in a specific region prefer to engage with brands. While the value of these insights are very significant, setting foot into the world of dark data that is unstructured, untagged and untapped is daunting for both IT and business users.”
The post ends on some less than thorough advice to create an implementation plan. There are other guides on the Internet that better prepare a person to create a big data action guide. The post’s only purpose is to serve as a search engine bumper for Datameer. While Datameer is one of the leading big data software providers, one would think they wouldn’t post a “dark data definition” post this late in the game.
Whitney Grace, September 21, 2015
Sponsored by ArnoldIT.com, publisher of the CyberOSINT monograph
Europol and FireEye Are Fighting Digital Crime
September 15, 2015
The Internet is a hotbed for crime and its perpetrators and Europol is one of the main organizations that fights it head on. One the problems that Europol faces is the lack of communication between law enforcement agencies and private industry. In a landmark agreement that will most likely be followed by others, The Inquirer reports “Europol and FireEye Have Aligned To Fight The International Cyber Menace.”
FireEye and Eurpol have signed a Memorandum of Understanding (MoU) where they will exchange information, so law enforcement agencies and private industry will be able to share information in an effort to fight the growing prevalence of cyber crime. Europol is usually the only organization that disseminates information across law enforcement agencies. FireEye is eager to help open the communication channels.
” ‘The threat landscape is changing every day and organizations need to stay one step ahead of the attackers,’ said Richard Turner, president for EMEA at FireEye. ‘Working with Europol means that, as well as granting early access to FireEye’s threat intelligence, FireEye will be able to respond to requests for assistance around threats or technical indicators of compromise in order to assist Europol in combating the ever increasing threat from cyber criminals.’ ”
The MoU will allow for exchange of information about cyber crime to aid each other in prevention and analyze attach methods. The Inquirer, however, suspects that information will only be shared one way. It does not explain which direction, though. The MoU is going to be a standard between Big Data companies and law enforcement agencies. Law enforcement agencies are notorious for being outdated and understaffed; relying on information and software from private industry will increase cyber crime prevention.
Whitney Grace, September 15, 2015
Sponsored by ArnoldIT.com, publisher of the CyberOSINT monograph
The AI Evolution
September 10, 2015
An article at WT Vox announces, “Google Is Working on a New Type of Algorithm Called ‘Thought Vectors’.” It sounds like a good use for a baseball cap with electrodes, a battery pack, WiFi, and a person who thinks great thoughts. In actuality, it’s a project based on the work of esteemed computer scientist Geoffrey E. Hinton, who has been exploring the idea of neural networks for decades. Hinton is now working with Google to create the sophisticated algorithm of our dreams (or nightmares, depending on one’s perspective).
Existing language processing software has come a very long way; Google Translate, for example, searches dictionaries and previously translated docs to translate phrases. The app usually does a passably good job of giving one the gist of a source document, but results are far from reliably accurate (and are often grammatically comical.) Thought vectors, on the other hand, will allow software to extract meanings, not just correlations, from text.
Continuing to use translation software as the example, reporter Aiden Russell writes:
“The technique works by ascribing each word a set of numbers (or vector) that define its position in a theoretical ‘meaning space’ or cloud. A sentence can be looked at as a path between these words, which can in turn be distilled down to its own set of numbers, or thought vector….
“The key is working out which numbers to assign each word in a language – this is where deep learning comes in. Initially the positions of words within each cloud are ordered at random and the translation algorithm begins training on a dataset of translated sentences. At first the translations it produces are nonsense, but a feedback loop provides an error signal that allows the position of each word to be refined until eventually the positions of words in the cloud captures the way humans use them – effectively a map of their meanings.”
But, won’t all efficient machine learning lead to a killer-robot-ruled dystopia? Hinton bats away that claim as a distraction; he’s actually more concerned about the ways big data is already being (mis)used by intelligence agencies. The man has a point.
Cynthia Murrell, September 10, 2015
Sponsored by ArnoldIT.com, publisher of the CyberOSINT monograph
Datameer Declares a Celebration
September 8, 2015
The big data analytics and visualization company Datameer, Inc. has cause to celebrate, because they have received a huge investment. How happy is Datameer? Datameer’s CEO Stefan Groschupf explains on the company blog in the post, “Time To Celebrate The Next Stage Of Our Journey.”
Datameer received $40 million in a round of financing from ST Telemedia, Top Tier Capital Partners, Next World Capital, Redpoint, Kleiner Perkins Caufield & Byers, Software AG and Citi Ventures. Groschupf details how Datameer was added to the market in 2009 with the vision to democratize analytics. Since 2009, Datameer has helped solve problems across the globe and is even helping make it a better place. He continues he is humbled by the trust the investors and clients place in Datameer, which feeds into the importance of analytics for not only companies, but also anyone who wants supportable truth.
Datameer has big plans for the funding:
“We’ll be focusing on expanding globally, with an eye toward APAC and Latin America as well as additional investment in our existing teams. I’m looking forward to continuing our growth and building a long-term, sustainable company that consistently provides value to our customers. Our vision has been the same since day one – to make big data analytics easy for everyone. Today, I’m happy to say we’re still where we want to be.”
Datameer was one of the early contenders in big data that always managed to outshine and outperform its bigger name competitors. Despite its record growth, Datameer continues to remain true to its open source roots. The company wants to make analytics available to every industry and everyone. What is incredibly impressive is that Datameer has numerous applications for its products from gaming to healthcare, which is usually unheard of. Congratulations to Datameer!
Whitney Grace, September 8, 2015
Sponsored by ArnoldIT.com, publisher of the CyberOSINT monograph
SAS Text Miner Promises Unstructured Insight
July 10, 2015
Big data is tools help organizations analyze more than their old, legacy data. While legacy data does help an organization study how their process have changed, the data is old and does not reflect the immediate, real time trends. SAS offers a product that bridges old data with the new as well as unstructured and structured data.
The SAS Text Miner is built from Teragram technology. It features document theme discovery, a function the finds relations between document collections; automatic Boolean rule generation; high performance text mining that quickly evaluates large document collection; term profiling and trending, evaluates term relevance in a collection and how they are used; multiple language support; visual interrogation of results; easily import text; flexible entity options; and a user friendly interface.
The SAS Text Miner is specifically programmed to discover data relationships data, automate activities, and determine keywords and phrases. The software uses predictive models to analysis data and discover new insights:
“Predictive models use situational knowledge to describe future scenarios. Yet important circumstances and events described in comment fields, notes, reports, inquiries, web commentaries, etc., aren’t captured in structured fields that can be analyzed easily. Now you can add insights gleaned from text-based sources to your predictive models for more powerful predictions.”
Text mining software reveals insights between old and new data, making it one of the basic components of big data.
Whitney Grace, July 10, 2015
Sponsored by ArnoldIT.com, publisher of the CyberOSINT monograph
New Analysis Tool for Hadoop Data from Oracle
June 23, 2015
Oracle offers new ways to analyze Hadoop data, we learn from the brief write-up, “Oracle Zeroes in on Hadoop Data with New Analytics Tool” at PCWorld. Use of the Hadoop open-source distributed file system continues to grow among businesses and other organizations, so it is no surprise to see enterprise software giant Oracle developing such tools. This new software is dubbed Oracle Big Data Spatial and Graph. Writer Katherine Noyes reports:
“Users of Oracle’s database have long had access to spatial and graph analytics tools, which are used to uncover relationships and analyze data sets involving location. Aiming to tackle more diverse data sets and minimize the need for data movement, Oracle created the product to be able to process data natively on Hadoop and in parallel using MapReduce or in-memory structures.
“There are two main components. One is a distributed property graph with more than 35 high-performance, parallel, in-memory analytic functions. The other is a collection of spatial-analysis functions and services to evaluate data based on how near or far something is, whether it falls within a boundary or region, or to process and visualize geospatial data and imagery.”
The write-up notes that such analysis can reveal connections for organizations to capitalize upon, like relationships between customers or assets. The software is, of course, compatible with Oracle’s own Big Data Appliance platform, but can be deployed on other Hadoop and NoSQL systems, as well.
Cynthia Murrell, June 23, 2015
Sponsored by ArnoldIT.com, publisher of the CyberOSINT monograph
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