Experian is one of 25 data management organizations that will be honored at the 2015 Data Impact Awards in New York on September 29, 2015. The awards celebration, hosted by Cloudera, honors big data success stories and recognizes the impact data-driven technology can have on the organization, business, and society at large. The common link between each of these 25 Big Data success stories is CDH, the world's most popular open source Hadoop-based distribution and key component of the Cloudera Enterprise platform. The event will kick-off the annual Big Data conference, 2015 Strata + Hadoop World and a weeklong series of events as part of Data Week in New York City. Experian is a finalist in the category of “Data-Driven Transformation” for the launch and deployment of the Experian Marketing Suite. During the last two years, Experian made transformational decisions about their marketing portfolio to unify offerings in data, technology and services into a single platform that allows marketers to create rewarding and relevant customer experiences in any channel via any device. This transformation culminated in July 2014 with the launch of the Experian Marketing Suite, a cloud-based marketing platform that leverages Experian’s customer identity and recognition technology, consumer data (the largest in the world), analytics and interaction technology. Hadoop technology was a foundational element of the Experian Marketing Suite and in particular and allowed Experian to realize their vision of creating a platform that would put the data and technology into the hands of the marketers themselves, linking disparate and disconnected customer data from any difference sources at scale and then leverage that cross-channel intelligence in real-time.  “Nobody is doing what we’re doing with Hadoop today, especially at this order of magnitude. The Experian Marketing Suite’s Identity Manager is the first real-time linkage engine that accepts data, links information together across an entire marketing ecosystem, and puts it into a usable format for a solid customer experience.” – Emad Georgy, SVP Global Software Development, Experian Marketing Services "It has been fascinating to see the growth in Data Impact Awards nominations every year – both in terms of total quantity and in the maturity and impact of each story shared," said Alan Saldich, vice president of marketing, Cloudera said in a press release. "This year's applicants represent organizations of all sizes, across all industries, and spanning locations around the globe, but they all have one thing in common: they're using data to do something amazing. I'm continuously impressed and humbled by our customers, and am glad for this opportunity to showcase their achievements." Follow the hashtags #DataImpact and #StrataHadoop on Twitter and other social media channels.  

Since Henry Ford invented the assembly line and mass automotive production began, the primary objective of all manufacturers and dealers has been to move new vehicle inventory off the lot year after year. But nowadays finding the right automotive customer can be a challenging task. Where do they live? How old are they? How much do they make? By leveraging data to answer these questions, manufacturers can market to the appropriate audience and manage inventory accordingly. In fact, a recent Experian analysis of the automotive market found that the top 10 states accounted for nearly 60 percent of all new vehicle registrations during the second quarter of 2015, led by California at 12.6 percent. The remainder of the top 10 included Texas (9.9 percent), Florida (7.9 percent), New York 5.7 percent), Pennsylvania (4.4 percent) Ohio (3.9 percent), Illinois (3.8 percent), Michigan (3.7 percent), New Jersey (3.5 percent) and Georgia (2.8 percent). Diving a bit deeper, the analysis also showed that through May 2015, nearly 50 percent of all new vehicle buyers fell within the 40-69 age range. Furthermore, 17.9 percent were between the ages of 50-59. The analysis also found that individuals with incomes from $50,000-$100,000 were the most active new car shoppers during the same time period, equating to nearly 36 percent of the market. Other findings include: Los Angeles and New York were the two DMAs with the highest market share for new vehicle registrations at 6.8 percent and 6.7 percent, respectively Toyota (13.5 percent), Ford (11.6 percent) and Chevrolet (10.8 percent) were the top 3 brands with highest new vehicle market share among retail buyers through Q2 2015 Entry-level CUVs accounted for the highest percentage of new vehicle registrations at 14.6 percent, followed by the small economy car (11.0 percent) and full-sized pickup trucks (10.7 percent) Leveraging data and analytics gives manufacturers and dealers a competitive advantage by enabling them to better understand the entire automotive market, specifically new vehicle trends. With these actionable insights, automotive companies will be positioned to make more confident inventory decisions and target specific consumers. And by better understanding whom they are targeting, manufacturers and dealers will be able to check their primary objective off the list.

There is no doubt data breaches have become a part of the Corporate and consumer consciousness. As data breaches have become more prevalent and companies are in need of assistance to prepare for and respond to a breach, industry analysts have taken notice of the experts in the marketplace like Experian. In its first report on data breach services, we are proud to have been named as a leader in The Forrester Wave™: Customer Data Breach Notification And Response Services, Q3 2015. The report by Forrester Research, Inc. covering the customer data breach industry independently evaluated vendors’ current offering, strategy and market presence to score the top players in the market. Each vendor was evaluated in 23 different areas, with Experian scoring the top marks possible in several categories, including response scale, call center, identity monitoring and remediation and credit monitoring and remediation. Although it is the first report like it in the industry, Experian has been around awhile serving clients for more than 10 years. There has been a lot of change since the market has matured, including the magnitude of breaches affecting now millions of people, the growth of a new industry in cyber insurance, and the vital need for consumers to have identity theft protection. On the topic of protection, the best type has fallen into debate, which has been a disservice to consumers in this age of data proliferation and breaches. Any time personally identifiable information (PII) has been exposed can possibly lead to identity theft and fraud so the most beneficial course of action is to enroll in identity theft protection, which includes credit monitoring. This provides consumers with alerts if there is a change in their credit report such as a new account opened in their name. If the individual feels it is fraudulent, they can seek assistance from a fraud resolution agent to rectify the situation and remove the account from their report. Over the course of a decade in business, millions of consumers have benefited from our ProtectMyID® product, and we are pleased it received a 5 out of 5 score in the report. However, while accolades are appreciated, our milestones speak for themselves with nearly 15,000 data breaches and more than 170,000 fraud cases handled to date. For more information on Experian Data Breach Resolution, visit Experian.com/databreach.

Financing my first car was a bittersweet feeling. I was thrilled at the thought of purchasing a new vehicle, yet I was dreading haggling the price with the dealer. As a millennial, I feared the rising prices for new cars, and knew that I needed to find a way to make the vehicle more affordable. That said, I decided to look at used cars. Clearly, I’m not the only car shopper going through this experience. Many consumers are exploring new options to keep their monthly payments down, whether it’s extending the length of their loan, or turning to leases. Sometimes it’s both. According to Experian Automotive’s Q2 2015 State of the Automotive Finance Market report, the average loan amount for a new vehicle reached $28,524, while the average loan amount for a used vehicle hit $18,671, a second quarter high and an all-time high, respectively. Subsequently, the increasing loan amounts also caused the average monthly payment for new ($483) and used ($361) vehicles to increase. Interestingly, the $122 difference in average monthly payment was also a second quarter high, furthering the need to make car payments affordable. As such, consumers continued to take out leases. During the second quarter, leasing accounted for 26.9 percent of all new vehicle transactions, reaching an all-time high. While leasing continues to be a popular option among car shoppers to keep monthly payments down, we’re beginning to see these consumers take it a step further. Sure 36-month term leases are still the most popular, however the percentage of leases extending past the 36 months into the 37- to 48-month range has increased by 18 percent. Furthermore, the average lease payment dropped $13 from a year ago, reaching $394. Findings from the report also showed that consumers continued to lengthen their loan terms, especially for used vehicles. The percentage of used vehicles financed for 73- to 84-months increased by 14.8 percent from Q2 2014 to reach 16.1 percent – the highest percentage of record. New vehicles financed for the same term length climbed 19.7 percent from the previous year to reach 28.8 percent. If the trend continues, we can only expect vehicles to become more expensive and harder to keep within budget. That said there are ways to keep monthly payments within reason. Just as I did, consumers will need to explore the different options available and work with the financing tool that best meets their needs. If they can do that, it will just be the sweet feeling of purchasing a car.  

New research from Experian Marketing Services, a recognized leader in data-driven marketing and cloud-based marketing technology, shows that email campaigns using the words “choice” or “choose” in the subject line are driving substantially higher engagement and revenue rates than average. As described in our recently released Q2 2015 Email Benchmark Report, these email campaigns drove 22 percent higher revenue per email, a 46 percent increase in transaction rates and a 117 percent increase in transaction-to-click rates. “Allowing customers to choose their preferred path is a smart and tangible way to increase engagement and ultimately their return on marketing investment,” said Spencer Kollas, vice president of global deliverability services at Experian Marketing Services. “Marketers know that consumers are the ones in control of their relationship today. What’s interesting about the trend our research uncovered is that consumers are responding to brands that explicitly give them that control; they are engaging and spending with brands that are taking action to empower them.” Value of mobile subscribers The Q2 2015 Email Benchmark Report features a special section on mobile subscribers that features the results of two analyses of two brands with ongoing SMS (mobile push) and MMS (mobile text) messaging programs. To conduct the analyses, Experian® attributed the brands’ transactions to their mobile campaign data on a subscriber level. In comparing mobile transaction rates to email benchmark data, Experian found that mobile transaction rates were more than 10 times higher than those for email campaigns. Further, SMS push/broadcast campaigns made up more than 95 percent of the volume, but pull messages provided much stronger transaction results. Interestingly, the results also showed that dual subscribers (both email and mobile) were 3.9 times more likely to complete transactions than email-only customers. “While mobile subscriber lists typically are much smaller than email lists, these subscribers form a loyal group of highly engaged customers,” said Kollas. “It is the sophisticated marketer that is able to use this type of information to continue to increase brand loyalty and customer engagement across multiple platforms.” Benchmarking email volume and mobile gains The Q2 2015 Email Benchmark Report details overall email marketing trends for the second quarter of 2015 as well as the key performance indicators (KPIs) that shaped the success of email programs over the past two years across six major verticals: business products and services, consumer products and services, media and entertainment, multichannel retailers, publishers, and travel. According to the analysis, email volume rose by 16.1 percent in Q2 2015 compared to the same period in 2014, yet subscriber response rates remained steady. Consumer products and services and multichannel retailers headed the surge in email volume gains. Two-thirds of the brands in these verticals increased their year-over-year volume in Q2 2015. While publishers and media and entertainment brands had an overall decrease in year-over-year volume in Q2 2015, one-third of brands under those categories actually increased their volume this quarter. Further, all of the industry verticals increased volume in Q2 compared to Q1 in 2015, except for media and entertainment. In comparing email opens and clicks by platform, Experian found that 52 percent of total email opens occurred on a mobile phone or tablet during Q2 2015, a slight increase from 51 percent in Q1. In comparing email opens and clicks by device type, Windows accounted for the largest percentage, with the iPhone® receiving the second largest number of clicks. IPhone clicks were particularly strong for media and entertainment and multichannel retailers. A complimentary download of the full report is available here: http://bit.ly/1K69bT6.

Forbes Magazine recently named Experian among the top 100 innovative companies in the world for the second year in a row. Forbes has a rigorous selection methodology that places an emphasis on what organizations’ investors see as the most innovative today, but also the companies that investors believe will continue to be the most innovative in the future; Forbes calls this methodology the Innovation Premium.  Put simply, it’s the expectation that a company will launch new products and services and enter new markets to generate growth. With this distinction, I am reminded of the many initiatives undertaken by Experian North America in the last year aimed at evolving its technologies and systems, all in an effort to deliver the highest-quality data, superior products, intelligent insights and best-in-class service to our customers. A few of these initiatives include: Experian Data Quality launched its first eCommerce offerings, allowing businesses of any size to quickly and easily see better value from their data assets. Experian Marketing Services transformed its marketing portfolio in the last two years – bringing together the synergies in the portfolio to deliver a differentiated proposition in the market. This transformation culminated with the launch of the Experian Marketing Suite, a marketing platform that unifies Experian’s unique capabilities in customer identity and recognition, consumer data, analytics and technology. Experian Consumer Services offered new apps to help consumers quickly and easily review and understand their Experian credit reports and FICO Scores. To ensure our ongoing commitment to data quality standards specific to consumer reported data, Experian created nimble technologies to identify business opportunities for clients and improve the quality of consumers’ credit reports. Experian Health introduced a number of new and innovative solutions to help hospitals, medical providers and patients address challenges, such as continuation of care, financial assistance, fraud and identity protection throughout the healthcare process. Our Business Information Services group introduced a new Global Data Network that provides businesses with insight into their international customers and vendors, enabling them to assess risk and become more competitive in the marketplace. To help companies manage risk and mitigate fraud, our Decision Analytics business recently launched a new dedicated enterprise Fraud and ID business in North America to more aggressively address the growing variety of fraud risk and identity management challenges businesses, financial institutions and government agencies face. In an effort to help its clients track loyalty rates, Experian Automotive reengineered its data sources to standardize a new loyalty measurement model at the manufacturer, brand and dealer levels. We’re proud that Forbes Magazine continues to view Experian as a forward-thinking and innovative company. But Experian isn’t resting on its laurels. We are continuing the ongoing process of looking at ways to serve our customers better by investing in innovation. In fact, Experian holds an annual innovation program that brings together talented employees from across our businesses to research, build and test new concepts that address emerging market challenges that can benefit from Experian’s data and insights. Data can be and must be used as a force for good. Match it with the proper technologies and systems, and we are in a position to help businesses, consumers, government and society overall.

On July 16, the CFPB published its “first ever” monthly report  providing a snapshot of complaints filed by consumers through the agency’s complaint portal.  For full disclosure, Experian is one of the top three companies that received the most complaints from February through April 2015.  But that is absolutely deceiving. In reporting the complaint data, CFPB’s own press release said the company-level information provided in the report should be considered in the context of company size, but then failed to provide any context needed to understand the numbers. Two of the more important points of context are that Experian is the largest consumer reporting agency in the United States and it touches more than 220 million consumers.  Experian delivers approximately 1 billion credit reports annually.  But this really is beyond the number of consumer files Experian maintains; it affects an entire industry. In a letter to CFPB’s Richard Cordray, the Consumer Data Industry Association has asked that CFPB re-think how it publishes monthly report in the future, including adding context to the data it publishes.  CDIA’s letter says that doing so would help the CFPB complaint portal live up to its stated goal to “…provide consumers with timely and understandable information to help enable them to make responsible financial decisions…” CDIA offers CFPB some worthy advice in its letter, citing back to the agency’s semi-annual report dated May 2014, where it disclosed that 29,600 complaints had been collected in the prior 18 months.  CDIA’s letter contains a chart showing examples of how CFPB could put these complaints into context so that readers could more clearly understand them and so that consumers could be better informed.  For more information, the letter is posted on CDIA's Website.  

Financier Worldwide moderates a discussion on improving decision-making and increasing value using Big Data analytics between Shanji Xiong at Experian DataLabs, Ken Elliott at HP and Shaheen Dil at Protiviti. FW: To what extent are you seeing an increased demand for Big Data analytics in today’s business environment? What overarching advantages does it offer to companies? Dil: Many organisations have made fundamental investments in Big Data infrastructures and capabilities and are now actively exploring the best ways to achieve return on these investments. Applications range from customer behaviour to people analytics, from ways to better understand risk to achieving operational excellence. As one would expect, these use cases vary greatly by industry. The consumer retail sector, for example, leads the pack in use of analytics to understand the customer domain, whereas financial services companies, banks and insurers have greatly advanced their ability to model risk. We are seeing an increased demand for analytics services from the companies that have narrowed their focus on specific uses, such as risk management, as it is easier to quantify return on investment in those cases. The advantages that these companies are realising are in line with many of the promises of Big Data – increased higher-quality input into decision-making processes from a variety of internal and external, structured and unstructured data. Elliott: The volume and variety of data coming into an organisation in various forms is continuing to explode and an increasing number of companies have more data than they can effectively analyse and exploit with traditional methods. Whether or not you call it ‘Big Data,’ taking advantage of this data requires new approaches in how this data is collected, stored, analysed, archived and governed. Data holds insights into business factors and customer behaviours and companies that first harness this data are able to gain a competitive advantage over those that do not. Xiong: According to Forbes, over the 12 month period of 2014, the demand for computer system analysts with Big Data expertise increased 89.9 percent and 85.4 percent for computer and information research scientists respectively. This highlights that organisations from all industries continue to invest in Big Data analytics to maintain and improve their competitive advantage. They need to be able to sift through large amounts of data, find patterns and distil the key takeaways in order to make better decisions, improve our society and in turn, drive our economy forward. “Big Data helps to prove more of the ‘why’ behind events discovered with traditional analytics, and this added dimension greatly aids in decision-making.” — Shaheen Dil FW: In what ways does the use of Big Data analytics deliver demonstrable results for businesses that conventional analytics and business intelligence solutions cannot? How does this translate into improved decision-making? Elliott: Traditional business intelligence solutions are highly structured and often focus on standardised reporting of internally available data. These solutions are well-suited for ‘referential’ analytics where the reporting of facts is critical – such as in finance or regulatory compliance – and focus more on ‘what’ has happened versus ‘why’. Big Data often originates from machines, sensors, logs, social interactions, audio, rich media and more. These sources often contain insights into ‘why’ things happen and what is potentially around the corner. Big Data analytics techniques can mine through massive amounts of all types of data to find hidden insights that would not have been possible with traditional methods. Xiong: The intelligent use of data assets helps businesses make better decisions. With it we can prevent fraud, verify identity, manage debt, and retain and expand customer relationships. Those businesses that fuel our economy can also use it to plan, target and execute strategies of all kinds, thus turning data into value-added insight. That’s the real promise of Big Data: giving researchers an unprecedented opportunity to look at their business problems from a fresh perspective and to capture the value hidden within their data assets. Dil: Even with the advent and adoption of Big Data analytics, we are still seeing conventional analysis and business intelligence solutions as a key portion of the equation. More companies are using Big Data in conjunction with these traditional sources of analytics to help better frame and add additional detail and context to existing analyses. Big Data helps to prove more of the ‘why’ behind events discovered with traditional analytics, and this added dimension greatly aids in decision-making as it helps to design better responses to addressing the required change. But it does not stop there. Predictive capabilities allow for preventive intervention with traditional operating models. How loyal are our clients going to be in the next two quarters? Should we spend $100 to keep a particular client or $150 to let them go? What should be the scope of our next internal audit based on the real-time signals we receive from our data? These questions can be answered using Big Data analytics. FW: How should a company go about ensuring that their Big Data datasets do not infringe on a third party’s intellectual property or contractual rights? What other potential liabilities exist in this context? Dil: One of the challenges in launching Big Data is managing risk. Traditional definitions of Big Data have focused on three Vs: Velocity, Variety and Volume. We typically add two more: Veracity and Value. The veracity of data must be managed carefully to ensure that we are not bringing in risk through either intellectual property infringements or privacy and confidentiality concerns. One way to protect an organisation from IP or contractual right risks is to implement robust data governance programs so that organisations understand the definitions and composition of data. The natural inclination to bring everything into a Big Data program must be balanced by caution – just because we can source the data does not mean we should always bring in those data sets. Thus the Value of including data must drive the decision on whether or not to include various data sets. The other complicating factor here is that many sources for Big Data are unstructured, making the detection of potentially sensitive or proprietary information even more difficult. As companies evolve their Big Data data sets, they will need to involve legal and general counsel. Xiong: Protecting an individual’s privacy and ensuring that a third party’s intellectual property rights are not infringed is critical. These aspects need to be safeguarded during every step of Big Data analytics. This includes data collection, data storage, data analysis and the execution of business strategies that are derived from Big Data projects. Having a transparent privacy policy and frequent communication with consumers about how their data is collected and used is in the best interest of any organisation. This should be an essential part of any Big Data initiative. When in doubt, consult your legal and compliance organisations. Elliott: It is critical to understand the legal right to use data that is being accessed by the various data service providers. Aside from the potential privacy issues associated with collecting data from audio, video and log analysis, many services such as web scraping are still being debated in courts and are being challenged as directly violating of terms of use. To reduce exposure, a company must have well-defined and functioning data governance collaboration between business, IT and legal leaders. Additionally, it is critical to manage the numerous point solution providers across the enterprise that are using or providing information as a service. Their oversight can pass liability to the company and expose the company to litigation. “There is a risk that the ability to collect data is outpacing the understanding of how to do so responsibly.” — Ken Elliott FW: In your opinion, when businesses adopt a potentially disruptive technology such as Big Data analytics, is there a chance they will fail to identify all the risks that need to be managed? How should companies address the legal and regulatory scrutiny surrounding data usage? Xiong: Like any disruptive technology, Big Data analytics has risks and every business needs to ensure they identify and manage those potential risks. By managing them, organisations will be able to minimise any potentially negative impact on their business. The most common risk is underestimating the investment and complexity of a Big Data initiative. The second risk is not properly protecting an individual’s privacy, and the third is aggressively implementing a business strategy derived from Big Data analytics without proper testing. As long as privacy rights are respected, vigorous security measures are in place to protect personal information, compliance protocols are carefully maintained and there remains a total commitment to data accuracy, the opportunities brought by Big Data should not be hindered. Elliott: Big Data has risen from the relatively recent expansion of the capability to store and process a greater variety and volume of data. As a result there has been an explosion of new sources, applications and devices that collect potentially private and proprietary information. There is a risk that the ability to collect data is outpacing the understanding of how to do so responsibly. This includes the collection, management, usage, security and archiving of potentially sensitive information. To ensure legal compliance, companies should establish formal data governance, document data management policies and procedures, establish an audit and review process and seek consultation from information governance professionals. Dil: With all the disruptive changes in the business environment today, including from new technologies, risk is constantly on top of corporate agendas, whether it be underestimating risks or the failure to properly align initial investment needs, understand business drivers or recognise a deteriorating business model. Understanding the critical assumptions underlying the corporate strategy, conducting contrarian analysis with those assumptions, identifying the vital signs in the business environment that would indicate whether one or more critical assumptions are either no longer valid or becoming invalid, and aligning intelligence gathering to focus on those vital signs, are ways to identify and monitor potentially disruptive risks. Data governance is another solution, but certainly not the silver bullet to cure all woes. Companies also need to focus on compliance with local statutory laws and regulations in the various jurisdictions in which they operate, many of which restrict the collection, handling and transfer of sensitive data. FW: To what extent are businesses building on their use of Big Data analytics to embrace Smart Data, which purports to filter out the ‘noise’ and identify valuable data? Do you believe more businesses will adopt the Smart Data approach? Xiong: We are, by and large, better when we can make sense of the world around us, and that world is being made more complex by the vast amount of information that’s out there. As the volume of data increases, it has become more challenging to identify and extract useful information or business intelligence from raw data. This can be like finding a needle in a haystack. In this sense, the data analyst has embraced Smart Data. Many advanced algorithms and software tools have been developed to help filter out the noise by analysing and visualising the data. This has helped businesses adopt the Smart Data approach in order to really benefit from Big Data analytics. Dil: The concept of Smart Data has been around since the initial advent of management reporting and decision support systems, so this is not a new demand; rather, it’s applying an older data management discipline to a new source of information flowing from Big Data initiatives. Even though hardware and software advances have made it cheaper to collect large sets of data, including the added ‘noise’, there are still fundamental costs to maintaining this data, including added time for analysis, and potential e-discovery or retention risks. As such, the need to continue to shrink data sets, even those defined as Big Data sets, will continue to drive organisations. Elliott: Extracting value from Big Data requires more efficient means of collecting and managing data, and most importantly analysing that data. The first part of the solution is to make Big Data available for analysis using cost effective means. Following this, shifting out the noise and identifying relevant data requires data mining and statistical techniques which can process massive amounts of data and reveal precisely which data elements are predictive or descriptive of business outcomes. Using analytics in this way further enables business intelligence development to focus on the Smart Data which is most relevant to business decision making. “The productivity increase from Big Data analytics will help us use data for good by benefiting people, our society and our economy.” — Shanji Xiong FW: What trends and developments in the Big Data analytics sphere do you expect to see in the coming years? In what ways do you believe this trend will transform business practices? Elliott: While a handful of data centric companies such as LinkedIn, Google and eBay have led the way, most others are still either experimenting with Big Data or planning their Big Data strategy. According to Gartner, through 2015, 85 percent of Fortune 500 organisations will be unable to exploit Big Data for competitive advantage. With limited capital investment and skilled resources, many companies are turning to third party Big Data discovery platforms as a quick way to validate and test their use cases. Given the rapidly evolving nature of Big Data techniques and technology, this trend toward service platforms is extending to more permanent Big Data platforms as a service. Pursuing Big Data platforms as a service allows organisations and IT to focus on their core business while enjoying more rapid insights at a lower total cost of ownership and much lower risk. Within these platforms, innovation in the Big Data analytics sphere is moving toward the expanded use of machine learning for automated analytics and integration with decision management systems to shorten the distance between Big Data and business results. Xiong: Over the last several years, organisations have invested significantly in data collection, storage and analytical platforms. In the future, their focus will be on developing impactful analytical intelligence and applying it to business processes. Data scientists with business acumen and solid analytical capability will play an instrumental role in this process. This presents tremendous opportunities for data scientists to have a positive impact on business and society. Powered by Big Data analytics, business will happen more in real-time and be tailored for individuals. Examples include consumers being able to design their own car online or having their medicine customised for their specific needs and delivered to them even before they know they need it. The productivity increase from Big Data analytics will help us use data for good by benefiting people, our society and our economy. Dil: Organisations are just beginning to take advantage of combining their internal data sets with external data, despite the risks. We believe this trend will continue for years to come, with more high-quality external data sets becoming commodities to assist in the analysis of real-world problems. These data sets might originate from entirely new sources, such as devices participating in the Internet of Things, to improve the ability of organisations to understand customers, competitors and performance improvement opportunities and to improve quality, compress time and reduce costs of providing goods and services. As data availability, consumption and analytics become ‘real time’, the transformation of business practices will evolve as businesses become better at understanding how best to leverage these new data sources for increasing value through predictive analytics.   Dr Shanji Xiong is the chief scientist of Experian’s DataLabs. Prior to his current role, he held senior positions with Morgan Stanley, FICO, HNC and ID Analytics. For the past 20 years he has been working in the Big Data area, developing analytical solutions for financial, telecommunication and insurance companies. Dr Xiong received his doctoral degree from Columbia University in Engineering Mechanics. He can be contacted on +1 (714) 830 7475 or by email: shanji.xiong@experian.com. Ken Elliott, Ph.D. is director of analytics within HP’s Analytics and Data Management arm at HP Enterprise Services. In his role, he effectively combines strategic thinking, leadership, analytic knowledge and technology to business process improvements, which deliver measurable corporate results. He has more than 25 years of experience delivering business intelligence and analytic solutions, which improve corporate performance. Mr Elliott holds a Ph.D. in Industrial Psychology with a focus on analytics. He can be contacted on +1 (512) 319 7355 or by email: kenneth.elliott@hp.com. Shaheen Dil is a managing director with Protiviti and is responsible for the Data Management & Advanced Analytics Solution. Ms Dil has more than 25 years of experience in all aspects of domestic and international risk management, including Basel qualification and compliance, capital management and stress testing for CCAR and DFAST, enterprise-wide risk governance and reporting, risk modelling and model validation, credit approvals and credit portfolio management. She can be contacted on +1 (212) 603 8378 or by email: shaheen.dil@protiviti.com. Originally Published: Financier Worldwide

Federal and local governments around the world are expected to spend $475.5 billion on technology products and services by 2019. From New York to Chicago to Rio de Janeiro, metropolitan centers around the world are looking for new ways to be “smart” – to become more sustainable, improve the efficiency of public services and citizens’ quality of life. Forward-thinking civic and business leaders are experimenting with massive amounts of data – and the tools and technologies to compile and examine it – in order to improve how efficiently and effectively cities are managed. But the explosion of data is not without obstacles. According to the research firm, Gartner, it may take a full decade or more before the maximum utility of government open data is realized. So-called, “smart cities” require more than data alone – they require technologies to collect and analyze huge amounts of information and they require cross-sector solutions that can be scaled to size. “Smart cities” require leaders to use Big Data for good – to make better decisions, drive smart growth and benefit society as a whole. The true smart cities will use Big Data to enable the preemption and prediction of urban issues, improving efficiency and quality of city services from healthcare to traffic management. If used properly, and in conjunction with tools that deliver actionable insights, smart cities will transform the lives of urban residents. The timing of this transformation couldn’t be better. The number of people living in cities worldwide is expected to increase to 6.3 billion by 2050 – up from 3.6 billion in 2010. To meet the demands of growing urban populations, more and more cities are taking up data-smart initiatives. In fact, at the state and local levels of government, alone, spending on information goods and services is projected to grow at a 3.3 percent rate between now and 2019, increasing to $70 billion. The initiatives vary in scope and focus, but the drive is the same – improve efficiency, save costs and generally improve the urban experience. In New Orleans, for example, leaders are responding to ongoing fiscal challenges with NOLAlytics – a unit spanning multiple departments focused on using data to improve the city’s services. The unit’s first project aims to reduce fire causalities and save costs. The Targeted Smoke Alarm Outreach program – a door-to-door smoke alarm campaign leverages data from the Fire department, Census and American Community Survey and the New Orleans Fire Department to prioritize outreach in neighborhoods that are least likely to have smoke alarms. Meanwhile in Singapore, leaders are building a network of sensors to collect and analyze data at bus stops, public parks, traffic intersections and other areas to improve public services with the goal of making them not only more responsive, but anticipatory of needs. And the General Service Administration has figured out a way to save $13 million annually in energy costs by using a proprietary data algorithm to monitor 180 buildings for malfunctioning exhaust fans. At Experian, we are also embracing the potential of Big Data to improve society and support smart cities. We are using data assets to glean insights and help consumers, financial institutions, healthcare organizations, automotive companies, retailers and governmental organizations make more informed and effective decisions. For example, with rising insurance costs, deductibles and copays, some people struggle to afford the out-of-pocket expense that can come with seeking medical treatment. Because of this, some consumers decide not to seek treatment, which could have negative effects on their health and overall well-being. Experian works with hospitals, medical offices and clinics to provide unique data and analytics to provide insight into each patient’s financial situation. By leveraging healthcare-specific predictive models, Experian enables healthcare organizations to easily and efficiently determine which patients qualify for financial assistance programs or help them set up a payment plan that fits within their current budget. And in Orange County, California, for instance, Experian worked with local officials to verify the county’s list of 260,000 inactive voters – those who had not cast a ballot in the last four years – against our extensive database of known addresses. Once Orange County had the proper addresses they were able to send out post cards for residents to either update their contact information or confirm that they had moved out of the county. The process saved the county $80,000 in the next election. And in our DataLabs, teams of data scientists with experience in machine learning and analytics are using data for good in bold new ways, developing data-driven solutions and pinpointing previously undetected strategies. By leveraging technologies, such as Hadoop, Spark, Hive and advanced machine learning techniques, we are able to crunch through massive amount of information and discover new insights to help local businesses and governments understand their customer and constitution base to provide better services. For example, the DataLabs is working with social media and de-identified data to better understand and predict consumer activity. By analyzing this data, our DataLabs can identify the generalized daily travel patterns of consumers, which can in turn be used in combination with the government’s open data to improve public services, from transportation planning to land use and public safety in large crowds. With the abundance of data, we have the potential to improve the efficiency of government and build smarter cities. To achieve such goal, we need to encourage data sharing, standardization of data, and the application of the data science to use Big Data for good and truly transform urban life. Originally Published: The Hill