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Tesco's Dunnhumby ends exclusive JV in U.S. ahead of possible sale - Reuters

31/5/2015

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Dunnhumby, the customer data business which has been put up for sale by British retailer Tesco, said it was free to work with new clients in the United States, in a move which could make it more attractive to potential buyers.

Dunnhumby and its U.S. partner, The Kroger Co., said on Monday they had agreed to change their exclusive joint venture deal, enabling Dunnhumby to work with other retailers and consumer goods companies in North America.

Tesco, Britain's biggest retailer, controls Dunnhumby but is looking to sell all or some of the business, as part of a drive by its new boss Dave Lewis to slash costs and sell assets to mend group finances.

Analysts say Dunnhumby, which gathers and analyses data from almost 1 billion shoppers globally to help companies create customer loyalty and personalization programs, could be valued at up to 2 billion pounds ($3.1 billion).

Kroger said that as part of the deal it would take on a license to use Dunnhumby's technology and retain 500 staff to continue to find customer insights in its customer data. Under the deal, Dunnhumby will no longer have access to Kroger's data.

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How Big Data And The Internet Of Things Improve Public Transport In London

30/5/2015

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Transport for London (TfL) oversees a network of buses, trains, taxis, roads, cycle paths, footpaths and even ferries which are used by millions every day. Running these vast networks. so integral to so many people’s lives in one of the world’s busiest cities, gives TfL access to huge amounts of data. This is collected through ticketing systems as well as sensors attached to vehicles and traffic signals, surveys and focus groups, and of course social media.

Lauren Sager-Weinstein, head of analytics at TfL spoke to me about the two key priorities for collecting and analyzing this data: planning services, and providing information to customers. “London is growing at a phenomenal rate,” she says. “The population is currently 8.6 million and is expected to grow to 10m very quickly. We have to understand how they behave and how to manage their transport needs.”

“Passengers want good services and value for money from us, and they want to see us being innovative and progressive in order to meet those needs.”

Oyster prepaid travel cards were first issued in 2003 and have since been expanded across the network. Passengers effectively “charge” them by converting real money from their bank accounts into “Transport for London money” which are swiped to gain access to buses and trains. This enables a huge amount of data to be collected about precise journeys that are being taken.
Journey mapping

This data is anonymized and used to produce maps showing when and where people are traveling, giving both a far more accurate overall picture, as well as allowing more granular analysis at the level of individual journeys, than was possible before. As a large proportion of London journeys involve more than one method of transport, this level of analysis was not possible in the days when tickets were purchased from different services, in cash, for each individual leg of the journey.

That isn’t to say that integrating state of the art data collection strategies with legacy systems has been easy in a city where the public transport has operated since 1829. For example on London Underground (Tube) journeys passengers are used to “checking out and checking in” – tickets are validated (by automatic barriers) at the start and end of a journey.


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The future of making things with big data

28/5/2015

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By applying data science and analysis to 3D design data, this information can be used to help design software to emulate nature and produce highly optimised designs that achieve exactly what’s required of them in the most efficient and elegant way possible.
For the past couple of decades, nearly everything we use on a daily basis, from cars to mobile phones, has been designed with some kind of CAD (Computer Aided Design) software. Advances in computing power, mobility and cloud technology have made digital design tools more powerful than ever, and have made it easier for anyone to bring their ideas to life. Every finished CAD design is a complex 3D model made up of hundreds, if not thousands, of parts. For design and manufacturing firms with many products, that’s a lot of separate parts and components that need to be designed, catalogued and made.> See also: Why manufacturers need to wake up to the Internet of Things Keeping track of all these parts is a major issue for any type of design or manufacturing business. The overriding issue is that not knowing what components you have available means engineers and product designers have to search for the digital models of the parts they need, and even design them from scratch if they’re not available. This takes time away from the creative aspect of the design process. At the same time, if a component exists but the designer can’t find it, they could end up designing something from scratch which is almost, but not quite, like the original component. This then creates confusion for other designers further down the line. It can also seriously hinder the manufacturing process if a design incorporates a new type of component and an entire factory has to be retooled in order to use it rather than a pre-existing, standard part. It also means that in the event of a product recall situation, it can be much harder to identify which finished product used a certain faulty component, which can have wide reaching implications for a business. We’re reaching a point where the thorny issue of product data management will become a thing of the past though, and will usher in an era where engineers and product designers are free to focus on innovation, rather than nuts and bolts. Fundamentally, there are two major trends that are behind this new future of making things: big data and the cloud. Using advanced big data algorithms, it has become possible to analyse, sort and categorise millions of pieces of design data. But rather than categorising parts according to standard criteria (name, length, width, material etc), it is now possible to take this one step further and categorise parts according to their shape and context of use, and for this algorithm to then look at a digital model of a part and know which category it should be assigned to. For example, a plastic garden chair is different to a wooden kitchen chair, but they essentially share the same characteristics (four legs, seat), and would both belong in the same category if an interior designer was looking to create a 3D model of a kitchen. Using this same categorisation approach it is possible for hundreds of very complex industrial components to be automatically categorised and sorted. The cloud has enabled this big data analysis to be taken to the next level by giving these algorithms access to millions of different CAD models, allowing them to learn about the types of components available, and how they interact with each other.


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Insurers use fraud analytics to stop unnecessary payments

27/5/2015

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Commercial insurance companies lose billions of dollars each year to fraud, but there is a major opportunity to use analytics tools to stop this bleeding.

Every year, insurers dole out billions of dollars to fraudulent claims. But insurance companies are increasingly looking to fraud analytics technologies to reduce the problem.

In the past, stopping fraud was a time-consuming and often fruitless process. Insurers simply paid all claims that seemed even remotely reasonable and then reviewed the details after the fact. If anything stood out as suspicious, investigators could dig deeper, a process known as pay-and-chase. But even if they found evidence of fraud, tracking down offenders and recovering payments so long after the fact was difficult.

But payers are getting smarter. Rick Statchen, manager of informatics in the Special Investigation Unit at Aetna Inc., one of the largest health insurers in the US, said his team uses a combination of improved retrospective reviews and predictive models to identify fraud earlier in the payment process and with greater precision.

Aetna uses a mix of commercially available and home-grown tools. In the area of retrospective analysis, it looks at links between referring physicians and specialists, as well as pharmacies, to detect anomalous activity that might be a sign of illegal collusion intended to drive up claims. It also monitors providers' claims over time to see if there are unusual spikes, which could indicate fraud or abusive coding of procedures.

Analysts use this historical data to build patterns of what fraud looks like. They can then use the patterns to predict the likelihood of individual claims being fraudulent, which enables them to investigate and potentially stop payments before they are issued, a far more efficient and effective method than pay-and-chase.
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Big Data: The Interdisciplinary Vortex

27/5/2015

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Big data analytics requires companies to operate outside departmental boundaries, and sometimes beyond the spheres of their expertise, to find solutions to certain challenges. 
Getting the most from data requires information sharing across departmental boundaries. Even though information silos remain common, CIOs and business leaders in many organizations are cooperating to enable cross-functional data sharing to improve business process efficiencies, lower costs, reduce risks, and identify new opportunities.

Interdepartmental data sharing can take a company only so far, however, as evidenced by the number of companies using (or planning to use) external data. To get to the next level, some organizations are embracing interdisciplinary approaches to big data.

Why Interdisciplinary Problem-Solving May Be OverlookedBreaking down departmental barriers isn't easy. There are the technical challenges of accessing, cleansing, blending, and securing data, as well as very real cultural habits that are difficult to change.Today's businesses are placing greater emphasis on data scientists, business analysts, and data-savvy staff members. Some of them also employ or retain mathematicians and statisticians, although they may not have considered tapping other forms of expertise that could help enable different and perhaps more accurate forms of data analysis and new innovations.

"Thinking of big data as one new research area is a misunderstanding of the entire impact that big data will have," said Dr. Wolfgang Kliemann, associate VP for research at Iowa State University. "You can't help but be interdisciplinary because big data is affecting all kinds of things including agriculture, engineering, and business."

Although interdisciplinary collaboration is mature in many scientific and academic circles, applying non-traditional talent to big data analysis is a stretch for most businesses.But there are exceptions. For example, Ranker, a platform for lists and crowdsourced rankings, employs a chief data scientist who is also a moral psychologist."I think psychology is particularly useful because the interesting data today is generated by people's opinions and behaviors," said Ravi Ivey, chief data scientist at Ranker. "When you're trying to look at the error that's associated with any method of data connection, it usually has something to do with a cognitive bias."



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How a Bank Will Use ‘Big Data’ To Study the U.S. Economy

21/5/2015

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What if banks could tap into their vast network of consumer and business accounts to ferret out signals about where the economy is headed?

J.P. Morgan Chase is doing just that with the launch of a new institute that uses “big data”in analyzing hundreds of thousands of accounts for clues about income and spending patterns.

The institute’s first report, released Wednesday, offers a deep dive into consumer finances and shows that income and spending remains volatile for the broad middle class, not just the poor.

Researchers tracked the spending and income patterns of 100,000 randomly selected individuals from a sample of 2.5 million accounts at the bank over a 27-month period ended last December. Among its findings: While two in five individuals saw their income vary by at least 30% from one month to another, three out of five individuals saw their spending vary by at least 30%.


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The potential dividends from building this data asset aren’t lost on Diana Farrell, the institute’s chief executive. She learned first-hand about the need for real-time information about household financial behavior as a top economic adviser to President Barack Obama in 2009 and 2010.

“I can’t tell you how frightening it was to be in the middle of the debacle of the recession and not have a good understanding of what was happening in the household sector,” she said. “We were just starving for real-time information.”

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Big data to change urban mobility habits across the globe

21/5/2015

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An interactive map of the world’s major transit systems has been launched, using live public data feeds from trains and buses to show how major cities move.

The Travic map is the brainchild of Swiss-German technology firm GeOps and the University of Freiburg, and features over 200 systems from around the globe as colourful dots, which slowly move across the grid.

The interactivity is mostly based on static schedule data from transit authorities, but even so, it incorporates live data where it can, making it possible to watch the world’s public transport live.
Harnessing dataBig data is a megatrend in the transportation industry as major cities worldwide turn to troves of information to better understand how people are moving around the city.

Predictive analytics, the equally popular counterpart, on the other hand, harvests that data to find solutions to transportation problems we couldn’t really ‘see’ until recently.

Imagine having a city-wide picture of transportation operations, much like Travic, but where schedules, car park availability or passenger flux come together.

Phil Blythe, Professor of Intelligent Transport Systems at Newcastle University and IET Fellow, said: “Applications to support public transport, travel and parking have widespread use and offer the possibility to develop smarter and more user-friendly services, which will promote more sustainable transport use in major cities.”

Every day, millions of commuters buy and use tickets, creating massive amounts of data about daily transportation habits.

Xerox introduced an analytics platform this year that filters this anonymous data and presents the information with graphics to help transport and parking operators understand and predict commuter needs.

The Mobility Analytics Platform uses data analytic algorithms and visualisation technology to predict where passengers will alight, but also the impact of running ahead or behind schedule and even the weather.

Adelaide in Australia, which has a 30-year long urban development plan in place, is piloting MAP to improve its public transport services by analysing people flows between different sectors of the city.


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Is your organisation throwing big data down the drain?

21/5/2015

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Companies can avoid wasting their valuable big data by giving their employees the right tools for access.
Big data has undoubtedly been one of the technology world’s best-known buzz phrases for many years. Organisations are finally realising that the intelligence derived from big data can significantly improve their business models and increase profit margins. With increasingly connected workplaces, employees can also now use data in real time to become more effective and efficient in their jobs and make their lives easier.

The right tools

Unfortunately, the volume of data that’s being collected goes to waste if employees are not able to access it when and where they need to. This is especially crucial in certain industries not limited to desk-bound office jobs like retail, manufacturing and healthcare, which require many employees to be constantly mobile across large campuses.

The problem is that today, the term 'mobile technology' in business is often synonymous with the use of consumer-grade smartphones. However, consumer smartphones are not equipped to deal with the stresses and strains of large and complicated workplace environments. 

In many sectors, consumer-grade smartphones simply do not equip employees with the ability to safely access data or analytics on the go. With the proliferation of consumer device use, many companies have resorted to strategies incorporating consumer devices and implementing BYOD strategies in an attempt to give employees efficient access to data. This brings with it a whole host of compliance, security, technology and accessibility issues.

Coping with compliance and security

In the retail, healthcare and manufacturing industries, access to data comes with heavy compliance challenges. In healthcare, for example, staff regularly need to access various aspects of patient data. Mobile use must take into account the confidentiality of protected health information as required by the Data Protection Act (DPA), proving a huge challenge for health organisations.

For retail employees, they must maintain Payment Card Industry (PCI) data security standards compliance if using personal smartphones. And for manufacturing workers, the loss or theft of a device can mean serious loss of intellectual property.

This echoes the wider security issue surrounding devices storing corporate data that are stolen or leave the organisation, and are then able to connect to open or unsecured Wi-Fi networks. The volume of corporate data accessible through the device would be exposed via these unsecure networks. With employees taking their smartphones home with them, this means that sensitive business data is leaving the building.

Indeed, lost or stolen devices are one of the most common problems with the use of mobile devices. Furthermore, with consumer smartphone devices designed to access and share data in the cloud, a side effect is an increasing potential for data to be easily duplicated and moved between applications. This kind of risk has huge implications for all industries.

Device drama

In addition to compliance and security challenges, for organisations willing to let employees select or bring their own devices, the IT department has to deal with a range of systems, products and platforms. IT teams may find maintaining and integrating these devices is a complicated – if not impossible – task.

Additionally, problems like insufficient battery life of consumer devices means they are unsuitable for long working days or use across shifts. Not only would this be an obstacle to data access, but can also lead to costly and potentially dangerous delays due to missed communications.

Similarly, consumer devices lack the durability required in demanding workplace environments. Dropped devices and various forms of impact can easily damage handsets that are not equipped to cope.

Built for a purpose

As such, we’re beginning to see bite back on the BYOD trend – especially as businesses in these industries begin to experience the costs associated with the short lifespan and physical delicacy of consumer devices.

Organisations are realising that there are alternatives to these devices when it comes to giving employees flexible access to big data and the associated analytics – while still remaining compliant and secure. A key contender for industries with demanding workplace environments is purpose-built wireless devices, particularly purpose-built smartphones that bridge the gap between an enterprise device and a consumer-grade one.

Such devices enable employees to truly gain value from the growing volumes of data being accumulated in any location, and at any time. To illustrate this, if you’re a nurse, in order to securely access the growing volume of big data around patient and medical information, you’re going to need a rugged device that you can disinfect with strong chemicals.

And if you work in retail, a device that allows for long battery life that can be shared between shifts will mean you can guarantee quick response to customer requests. In logistics, you’d need a device that can survive drops onto concrete floors and other surfaces, in order to access the data needed to immediately deal with emergency or supply chain issues

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When Big Data Becomes Your Most Valuable Asset

21/5/2015

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Caesars Entertainment, formerly known as Harrah’s – the company which runs the famous Caesar’s Palace Las Vegas and more than 50 other casinos worldwide – established itself as an early leader in Big Data customer service. It has hit more than a spot of bother recently with a messy bankruptcy of its casino operating unit and reportedly facing fines of up to $20 million over money laundering allegations.

The most valuable of the individual assets being fought over by creditors is the data collected over the last 17 years through the company’s Total Rewards loyalty program, which gained Caesar’s a reputation as a pioneer in Big Data-driven marketing and customer service. Total Rewards is estimated to be worth over $1 billion. The program was launched in 1998 by Caesars current CEO Gary Loveman when he was the company’s chief operating officer.  “We use database marketing and decision-science-based analytical tools to widen the gap between us and casino operators who base their customer incentives more on intuition than evidence,” said Loveman way back in 2003 in the Harvard Business Review.

Total Rewards gives away meals, room upgrades, tickets to shows and limo rides to customers who spend money at Caesars’ resorts and gambling tables. Over the years, Caesars extended the scope of its rewards scheme and the depth of its analytics. Customers advance through rewards tiers as they spend more. In return Caesars receive information from the customer on who they are, and how they behave while using the facilities. This meant that offers could be tailored and floor staff could be ready to greet customers by name and direct them to their favorite game. At the top tier, known as seven star, guests receive up to four night’s complimentary stay at Caesars hotels, and even get their air fare comped. With customers this valuable (seven star members spend upwards of $500,000 with the company in a year) no expense is spared in getting them through the door.

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What's the next step for big data and the media?

18/5/2015

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Big data is beginning to have a big impact on the broadcasting industry, increasing customer satisfaction, reducing churn and enabling broadcasters to more effectively monetise their services and content - however, it is also clear that the potential to the industry has not as yet been fulfilled 
Big data has created a lot of buzz in recent years and it is a popular term used to describe the exponential growth and availability of data - large amounts of it at that - to inform business decisions, customer relationships and more.

Broadcasting is no exception- the evolution of big data has expanded and improved the tools and expertise we have available to process and extract value from large and increasingly real-time data sets. The media industry is able to take advantage of these tools, many of which are freely available open-source software which enable those in the industry to improve their services. 

This has a number of benefits in terms of increasing customer satisfaction, reducing churn and enabling broadcasters to more effectively monetise their services and content. In essence, if we have a better view of customer preferences, we are far more able to cater to their needs. These are just two obvious examples.

Customer experience is one area that is particularly benefiting from the effective use of big data. As big data analysis processes are refined further, the benefits to consumers and broadcasters will be amplified.

Viewing data, social media activity and content metadata can now be combined like never before to allow the industry to better understand their audiences and improve their relationship with them.

The upside of this is clear and simple – keeping customers loyal boosts revenues. Improving the customer experience has benefits for both the industry and customers; it is a genuine win-win. 

If big data is all about delivering insights, one particular benefit that it can deliver is being able to improve the quality of human predictions. More specifically, it also allows us to effectively measure predictions over time. This has implications for how the industry monetises content as the successful utilisation of machine learning and predictive analytics will enable better ad and content targeting.

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