Data science news

‘Predictive policing’ takes byte out of crime

Crime fighters have long used brains and brawn, but now a new kind of technology known as “predictive policing” promises to make them more efficient. Colleen McCue, pictured on July 10, who is a behavioral scientist at GeoEye, a firm that works on predictive analytics, said studying criminal behavior was not that different from examining other types of behavior like shopping.

A growing number of law enforcement agencies, in the US and elsewhere, have been adopting software tools with predictive analytics, based on algorithms that aim to predict crimes before they happen. The concept sounds like something out of science fiction and the thriller “Minority Report” based on a Philip K. Dick story. Without some of the sci-fi gimmickry, police departments from Santa Cruz, California, to Memphis, Tennessee, and law enforcement agencies from Poland to Britain have adopted these new techniques. The premise is simple: criminals follow patterns, and with software — the same kind that retailers like Wal-Mart and Amazon use to determine consumer purchasing trends — police can determine where the next crime will occur and sometimes prevent it. Colleen McCue, a behavioral scientist at GeoEye, a firm that works with US Homeland Security and local law enforcement on predictive analytics, said studying criminal behavior was not that different from examining other types of behavior like shopping. “People are creatures of habit,” she said.

75 percent of Insurers to Increase Data and Analytics Spend

Analytics increasingly provide competitive differentiation for insurers and are at the heart of the industry’s transformation to a more customer-centric business model, according to “Data and Analytics in Insurance: The Dawn of a New Era,” from Strategy Meets Action.

“Analytics hold great promise for the insurance industry, including the application of  traditional business intelligence approaches, as well as advanced techniques such as predictive models and Big Data,” said Mark Breading, SMA Partner. “The keys to success for insurers are improving data quality and data management, and  creating a corporate culture based on management by analytics.”

E-Commerce Style Big Data Analytics Meet Brick And Mortar Retailers

“In-store analytics now rivals online analytics in its depth, reliability, and usefulness,” wrote CEO Alexei Agratchev in VentureBeat. Stores can see where shoppers go, where they linger, detect whether they are shopping alone or with friends or children, and match shopping to weather. By equipping staff with RFID chips, they can see if sales people are interacting with customers.

Tim Callan, chief marketing officer at RetailNext, said web sites have been using analytics since the mid-1990s. “But people who run brick and mortar stores have not had the technology to optimize their stores. They have relied on crude tools such as walking around the store to see what they think is working well, but they have not been able to optimize the way e-tailers could.”

Business Intelligence (BI) Trends Go Beyond Analytics

The IDC’s latest big data report contains several new observations on the big data market, which the research firm says grew by almost 15 percent in 2011.

But as more machines take over the analytics and visualization, will the need for data scientists be minimized before it even really gets going as a career path?

Herscher ( CEO of FirstRain) thinks not. Noting the changes in BI that shook an industry over a decade ago, she recognizes the ongoing need for a human touch to analytics and doesn’t expect things to change this time around. Echoing much of the advice we’ve heard from proponents of big data like IBM, Herscher thinks that we in fact need more data science, and more students in the areas of math and science. The automation of data analysis and visualization has “made it easier to access and manage data,” Herscher says, “but it also developed a class of companies that build software and apps to manage this data.”

Can big data analytics reduce cyber risk?

The Information Security Forum (ISF) has released a report that recommends proactive, preventative big data analytics for businesses that want to increase business agility, improve information security and reduce cyber risks.

The report claims that the importance of big data analytics has never been greater however few organisations recognise the benefits for information security.

Does IT Really Care About Big Data?

Recent surveys show IT and business unit managers are more worried than eager about big data analytics– but those surveys are probably misleading, according to at least one expert.

A seminal study on big data by McKinsey and Co., for example, found that analysis of big data sets could enhance the productivity and competitiveness of many companies, save more than $300 billion in healthcare alone by increasing the industry’s efficiency, and help retailers increase profit margins by as much as 60%.

Another study, “The Future of Big Data” by the Pew Internet and American Life Project, reiterated the findings. It quoted Microsoft chief strategy officer Craig Mundie and Wal-Mart CIO Rollin Ford predicting that a “data-centered economy” in big data analysis will help both government and corporate organizations avoid big mistakes and waste by pointing out persistent errors in practice or belief.

Gramener lets you see the Big Picture from the Big Data

From http://cloudstory.in/2012/08/gramener-lets-you-see-the-big-picture-from-the-big-data/

Gramener lets you see the Big Picture from the Big Data

“A picture is worth a thousand words”. This seems to be the premise on which Gramener, the new age data visualization company is built. Founded in 2010 with its headquarters in Hyderabad, India, Gramener is one of the first few companies to be based on the big data and the visualization service offerings.

A couple of days ago, I had a chance to talk to Mr. S Anand, Chief Data Scientist at Gramener and the brain behind the powerful visualizations.

Anand and his team offer a unique, unconventional but highly useful service to their customers – translating data into meaningful information. This may sound very mundane and obvious but only till you take a look at some of their demos. For example, imagine how long will it take for you to find out which of the sections of the 18 books of the epic Mahabharatha has a mention of Krishna and which characters of Mahabharata interacted with Krishna more than the other. Gramener has a cool visualization to find this.

This technique can be highly useful for a retail company to perform sentiment analysis of the real time streams coming from various social media channels. When you get lost in the sea of numbers, data visualization plays a significant role in helping you focus on the results that really matter. This will help enterprises quantify results from various investments that have made. According to Anand, despite investing heavily in expensive data warehousing technologies, organizations still struggle to get the results that they really want. Many times CIOs face the challenge of justifying the massive investments they made in the enterprise OLAP platforms since they don’t really deliver the key data points that the CEO and CFO would love to see. This is exactly where Gramener found their niche. They complement existing tools and platforms and simplify the process of getting insights from the historical data that the organizations would have mined over a period of time. Gramener delivers the real results that matter to the decision makers in just a few days which the internal IT team may take months to deliver. Anand says that their competition is not really the traditional enterprise data warehousing companies, but a tool that all of us use which is Microsoft Excel!

When I asked Anand on his views about the latest trend, Big Data, he sounded slightly sceptical. According to him, before going out to grab large datasets, organizations should take a hard look at their existing data where valuable insights are buried. He says that majority of the facts that contribute to the decision making process are not a part of Big Data yet but come from a highly concentrated and smaller datasets that the organizations fail to exploit! I tend to agree with his viewpoint. In my experience of dealing with enterprises, I have rarely come across CXOs making the best use of their OLAP investments.

Gramener has customers coming from various verticals including retail, manufacturing, pharma and utilities and boasts of high profile clients like Airtel, BOSCH, AP Transco, Tamilnadu Education Department.

I strongly encourage you to play with the demo visualizations that Gramener published on their web site to witness the power of their offering.

We thank the organizers of The Fifth Elephant where we first interacted with Anand.

Janakiram MSV, Chief Editor, CloudStory.in

Data science news

Looking at the Big Picture: How Big Data Gets Personal 

Industries from technology to advertising, healthcare to government are abuzz about the ability to draw new insights from their data. As computer processing power has increased and the price of data storage has fallen, huge pools of data can now be captured, analyzed and paired with other sets of data from still more sources.

In the healthcare industry, vast pools of data can be used to develop new drugs, diagnostics or protocols. It can also offer researchers and clinicians the ability to tailor existing treatments to individuals based on the genetic components of their condition.

“We try and leverage very, very large-scale data of very, very deep complexity to probe questions about how biology works and that can be used to help patients,” said Andrew Kasarskis, co-director of Mount Sinai’sInstitute for Genomics and Multi  scale Biology.

Big Data Holds Big Promise for Government 

Scientists in Singapore, are developing software to turn cities into “real-time control systems” that combines all sorts of data feeds like information about rainfall and the   location of taxis so the government can match the demand and the supply of taxis in specific weather conditions, particularly when it rains – a common occurrence in Singapore.

Furthermore, the Centre for Advanced Spatial Analysis (CASA) at University College London is combining data from London’s Oyster cards, used to pay for public transport and Twitter messages. Tube-travel patterns are regular: people who enter the system at one station tend to leave it at a particular other one.Twitter messages reveal a city’s structure and its activity.

Immersive Visualization: The Future of Data Presentation

The goal of data visualization is more than simply conveying information. Spreadsheets and Word documents can perform that same function in tightly organized columns. But visualization allows us to draw lines between data points like a constellation and present the viewer with a fully-formed concept in a way that rows and columns cannot. If we take it a step further, we can even use visualization to create a fully interactive experience.

Call to action: how to make the most of big travel data 

Travel companies can use data analytics to create competitive advantage.

Travel companies are taking a closer look at formal data management strategies in order to derive more value from data assets. Data analytics is an interesting  prospect for the travel sector as so many data streams can be combined.

Predictive sports analytics put in play for Wimbledon, Formula One 

Here’s a safe sporting bet: take any roomful of fans of, say, cricket or baseball, and you can guarantee that there will be at least one person there with an encyclopedic knowledge of the sport’s history, including players’ highest scores, batting averages and strike rates. There’s something about sport that attracts the anoraks. But here’s another sure-fire bet: knowing about past performance is about to become old hat. The smart money now is on using data to predict future sporting outcomes. Sports analytics promises to be the bookmaker’s worst nightmare.

Advanced data visualization – a critical BI component 

As you venture down the ADV road, Forrester recommends paying at least equal (if  not more) attention to ADV best practices as you do to technology. Forrester has identified multiple such practices including screen layouts, data-to-ink ratios,  appropriate use of text and labels, using similar sequencing of objects, using parallel scales, minimizing the use of color, showing causality, and many more.

Read more about ADV research and remember: a picture speaks a thousand words!

Big Data Is The Future Of Marketing

Welcome to the era of performance brand marketing where measurement of social engagement using big data will transform the way brand marketers view the internet.

Big data analytics finally allows marketers to identify, measure, and manage what is positively impacting their brand. Social media activity harvested from the entire open social web with technologies like Hadoop, Cassandra, Mahout and Pig combined with advanced analytic techniques like natural language processing, semantic analysis, machine learning, and cluster analysis can reveal the true consequences of marketing actions online.

These developments enable a whole new world of brand measurement for digital marketers. Unlike most approaches to web analytics that can only attribute directly measurable consumer action, big data analysis of social performance offers campaign data that correlates with impact on brand. For example, in Super Bowl XLVI anlysts used big data to analyze the actual engagement of all the Super Bowl ads during the game. The traditional measure provided by USA Today AdMeter suggested that Coca-Cola had done rather poorly, yet when examined the actual levels of consumer response and engagement Coca-Cola’s was top of the charts.