No more ‘army of one’ with Big Data accountability revolution | #MITIQ

091013-A-7540H-016At this week’s MIT CDOIQ Symposium, held in Cambridge, Massachusetts, you wouldn’t have been faulted for recognizing that two industries that have embraced the Big Data revolution are healthcare and the financial sector. The benefits of employing analytics models to these fields are more and more apparent. You might be surprised, however, to learn of one particular public sector player that is seeking an operational advantage via Big Data: the Department of Defense.

Mort Anvari, Director of Programs and Strategy within the Deputy Assistant Secretary of the Army’s Cost and Economics division, has overseen the creation and implementation of directives aimed at the cost culture and cost management of the US Army.

“Private industry is easier because everyone is cost conscious,” Anvari explains. “In government, particularly during a war, that mission is driving everything. Most of our officers are only concerned with how much money they need and how they spend it.” Anvari admits that his task of directing commanders to accede to a cost culture mindset was a pretty big deal. “We had to look at it from the people’s perspective. That includes convincing leadership that attention to cost doesn’t create a bad image for the country.” The main argument centered on the perception that being cost conscious could appear to be putting our servicemembers unnecessarily into greater harm’s way.

 

Surviving the Culture Clash

Anvari claims an early success with the education of commanders and other officers that paying special attention to cost and soldier safety were not mutually exclusive. “You can be cost conscious. You can do more with your resources. You can be more efficient with it and still care about safety and care about the soldiers.”

With projects budgeted above $10 million, an automatic cost/benefit analysis is enacted. Anvari oversees more than 2,000 Army analysts who perform and validate each of these. Knowing the process, commanders will typically consider an additional course of action to what they submit for review. “Talking to commanders and leadership, we ask ‘What is your information need?’,” he stated. “Based on that, we develop a data need for that organization.”

Similar to challenges faced in the private sector, one of the issues overcome by the Army was convincing certain organizations that possessed data to share that property with other organizations. “Communicating the data need from organization A to organization B, telling organization B you need to provide this data that is not for you, it’s for someone else,” Anvari said, “that was a big culture shock.”

However, in explaining the funding structure for the military like an upside down tree, Anvari was able to bring an understanding that all funding came from the top and spread out to all of the “branches” within the Army. “We call it fund centers. They have all the money. The cost centers are the ones using this money. It could be them or it could be others. It’s truly like a neural network of information.”

Unlike private sector companies, the Army realized they had to streamline their budgeting and allocation process due to the fact they are subjected to strict oversight by the Congress. As all of the funding is taxpayer monies, citizens are also privy to the budgeting process through the use of Freedom of Information Act requests.

As noted above, the cost-benefit analysis process is automatic on projects in excess of $10 million. However, Anvari notes that projects under that threshold, undertaken by commanders of smaller outfits, are pretty well self-monitored because those commanders want to show that they are capable of critically applying the new cost culture analysis in the hopes they will be promoted to heading up larger projects in the future.

“Accountability is hard to swallow,” says Anvari, with regard to the early push back from Army leadership, “no matter how normalized the process is.” Anvari’s work is seeing results, however. “Cost management is on its feet and working,” he concluded.

(Originally published at SiliconANGLE.com)

photo credit: The U.S. Army via photopin cc
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Every company is a media producer, says Ustream founder | #IBMimpact

ON AIR signSiliconANGLE’s live broadcasts of theCUBE are facilitated by Ustream.tv. Joining John Furrier and Paul Gillin at this week’s IBM Impact conference at Las Vegas’ The Venetian Resort and Casino was the CEO and founder of Ustream, Brad Hunstable.  The conversation discussed the importance of media production in the Enterprise, the capability of Ustream to safely and effectively transmit media for organizations and what the future holds for his company.

Ustream is currently the largest HD-capable live streaming option on the market. As Hunstable notes, “We started humbly and now have grown into a large provider for businesses. We built it completely from the ground up.”

Hunstable shared the origin of Ustream, which he created while he was deployed in the military so he would be able to watch his brother’s different band performances.

Watch the interview in its entirety here:

“I think where the growth in video is happening,” stated Hunstable, “is within the enterprise.” Enterprise customers are looking to Ustream because they require a full solution and Hunstable and his company can offer that to them. “The beauty of the Cloud is that you can, regardless of [company] size, quickly try different solutions and change or adopt,” he noted.

The reason Hunstable sees tremendous opportunity in the enterprise is evident. “Nestle produces more hours of content than all of Hollywood combined,” he said. “They viewed over 1 billion hours of video live.” This refers to events like corporate messaging aimed company-wide or at specific divisions among many other uses of media.

“Enterprise is a tremendous opportunity,” he stated. “We are going to continue to provide robust solutions for these companies to suit their specific needs. We come from a consumer background and want to bring that knowledge to the enterprise,” he continued. “The reality is that every company is a media producer. They are creating content in many different ways and that helps them reach their customers on a more personal level.”

While the opportunities for growth are at the enterprise level, Hunstable points out that his full solution is able to be utilized by smaller companies as well. “[Our solution] serves the needs of the entire Enterprise. That means small business should get the same consideration as the enterprise.”

The considerations he refers to have to do with a product that offers security and scale. “They want assurance that your product is safe and that their info can’t be compromised,” said Hunstable. “They also want scale. They want reliability.”

Hunstable cites the simple platform for Ustream’s success. “You can try before you buy. You can start off immediately,” he said. “And if you are a larger company with greater needs, you can talk with one of our sales associates and we can help get you started.”

(Originally published at SiliconANGLE.com)

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Predictive analytics stepping front & center in the business world | #ibmimpact

Crystal Ball BusinessmanThis week, SiliconANGLE’s theCUBE broadcast from both the ServiceNow Knowledge 2014 event at the Moscone Center in San Francisco and the IBM Impact Conference held at Las Vegas’ Venetian Resort and Casino. Helming theCUBE desk for IBM Impact were John Furrier and Paul Gillin. On Day 1, they welcomed the CEO and Principal Consultant for Decision Management Solutions, James Taylor.

Taylor’s biography explains that he is a 20-plus year veteran in the field of Decision Management and is regarded as a leading expert in decisioning technologies. He is also the author of Decision Management Systems: A Practical Guide to Using Business Rules and Predictive Analytics.

At the start of the conversation, Furrier noted how we are in an age that really should be considered one of the most dynamic times for IT. He cites a recognition by the top line of business focusing on utilizing IT for revenue and business growth and no longer employing their IT simply as a means of cost reduction. He attributes this to significant converging trends that are changing the role of IT, like increased speed and agility, unlimited compute in the Cloud and the fact everything is now instrumentable.

Interjecting on this thought, Taylor stated, “I think what has really changed is the acceptance of analytics. When I wrote the book, people were uncertain about it.” He continued, “There were ways to use small data that weren’t predictive analytics. If you don’t process it and turn it into a usable prediction, it’s hard to consume it.” As the landscape has changed, he believes predictive analytics is stepping to the front and center in the business world.

Watch the interview in its entirety here:

Much of that change is being driven by the habits and expectations of the emergence of a more technologically savvy consumer base. Talking about his own 25-year-old son having to purchase auto insurance, Taylor said, “When he gives you his data, he expects a quote. If you say you’ll let him know what the quote is, he’ll just go somewhere else. He wants the answer now.” This expectation requires real time responses even when the proposition is relatively complex and involves assessments of risk. “You have to use analytics to determine how risky he is and you have to answer now.” Taylor claims that capability has to be embedded not only into call center scripts but also into a company’s mobile applications and website where no human-to-human interaction ever occurs. “Because if you don’t, you’re missing the point,” he stated.

Understanding the Principles of Decision Management

Much of the rest of the conversation centered on Decision Management principles Taylor outlined in his book. The first such principle, ‘Begin With The Decision in Mind’ was recounted by Gillin. “That struck me as kind of obvious,” he said. “Isn’t that how you would go about this? Obviously there is a reason why you said that. Do you find that people typically don’t,” he asked.

“The reason for misquoting the late Stephen Covey there is really two fold,” Taylor began. “The first is that when you look at Decision Support Systems, and there’s obviously a long history there, people are often very unclear what decision it is, in fact, somebody’s going to make with the data.” He goes on to state that all sorts of data is put in without any thought to the eventual decision that will need to be made. Companies, he claims, expect the employee, whom they regard as smart and experienced will be able to use the data to arrive at a decision. “What happens when I try to make a decision about what offer to make to a customer who’s on the phone to the call center right now,” he posited. “[The employee] has seven seconds. And they were hired yesterday. And they got three hours of training. They’re not in a position to know what decision they should be making.” He continued, “So, if you don’t know what decision you’re trying to embed the analytics into, you can’t do a good job with the analytics. I put [that principle] in because there was this sense that people were very (sic) lacksadaisical about what the decisions were they were supporting with their Decision Support Systems.”

Another of Taylor’s principles outlined in his book is to ‘Be Predictive and Not Reactive.’ Gillin stated, “I think you’re right. Using data to support decisions reactively is more intuitive.” He then asked, “What is the mind shift that is involved in moving toward predictive analytics?”

“It turns out to be one of those things where it’s very easy to build things that are predictive,” Taylor stated. “But if they don’t change people’s decision making behavior, they don’t help. Talk to anyone who does predictive analytics and they’ll tell you stories of building highly predictive models that didn’t change the business,” he explained. “You have to be clear how it’s going to affect the decision you’re going to make before you can build the predictive.”

One reason Taylor believes there has been resistance thus far is because the whole notion of predictive analytics shifts the operation of business from the realm of absolutes to the realm of probabilities. “If I’m measuring last month’s results,” he said, “I can give you an absolute number. I can tell you exactly what you sold last month.” Projecting demand for the future doesn’t give you that same certainty. “I can give you a probability or a range,” he stated. “You have to start dealing with a little bit of uncertainty. That’s why it’s important to wrap some rules around these predictions.” However, Taylor says we, as humans, operate in probabilities and chance subconsciously every day. Once that is understood, the mind shift to predictive analytics becomes easier to adopt.

Embracing the Opportunities of Unstructured Data

One key to building a robust predictive analytic structure is incorporating multiple data streams, including unstructured data. “There are a slew of startups getting funding around Business Intelligence and data warehousing,” Furrier pointed out. “That market is shifting. But how does loose data affect some of the opportunities,” he asked.

Taylor pointed out that it is a strength to be able to store data before you’ve figured out how you might use it. “That’s a key advantage,” he said. “I did some surveys recently on predictive analytics in the Cloud,” he explained. “We asked about some of these Big Data sources and what we found was that people who are getting value from Big Data and these unusual sources were people who had some experience with more advanced kinds of analytics.” He further explained that the combination of traditional data with data from e-mails, texts and other unstructured data could only serve to produce a more fine-grained model, improving accuracy. “Its ability to refine existing predictions is really strong. Right now, that’s the biggest use case I see in customers,” he stated.

For more on this interview and others broadcast by SiliconANGLE’s theCUBE from both of this week’s events, be certain to visit SiliconANGLE’s YouTube Channel.

(Originally published at SiliconANGLE.com)

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