Margin test

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

In 2014, sports analytics was a $125 million market. By 2021, its value is expected to balloon to $4.7 billion. But this market wasn’t always so lucrative or widely accepted. Back in 2002, the Oakland Athletics General Manager Billy Beane earned a trip to the Major League Baseball playoffs despite having a payroll of just over $40 million — $80 million less than major market teams like the New York Yankees. The key to the A’s success? Not just their scouts’ intuition, but sabermetric principles and rigorous – though at the time, overlooked – statistical analysis. “Moneyball” changed the way front offices across the sports world conducted business. From baseball to hockey and football to basketball, general managers are increasingly relying on analytics to make smarter decisions when scouting, drafting and trading players. They are now using data to build the very best possible teams. But a team’s success depends on more than compiling a roster of skilled players, it requires putting the best combination of talent into the game – and that means keeping players healthy. In an industry fixated on numbers, biometric measurements and workload metrics can offer just as much insight as the box score. At least that’s what companies like Kitman Labs and founder Stephen Smith believe. Building on his experience as a strength & conditioning coach for Leinster Rugby and a thesis on injury risk, Stephen created Kitman Labs’ Profiler tool in 2012 to highlight, manage and reduce the risk of injury in professional athletes. He believes analytics can be used to not only draft the best players, but maximize that investment by keeping them healthy. At Experian, we’re fascinated by the idea of using data to manage risks and maximize investments – in sports, but more importantly, in business. As a company, we are intimately familiar with the many ways Big Data is being used to solve strategic marketing and risk-management problems through an advanced data analysis process, research and development. Thereby, helping us utilize data as a force for good. In fact, every day, we are compiling, analyzing and transforming massive amounts of information into actionable insights. Whether those insights can help consumers secure an affordable loan, understand their credit score, or protect their identity; or for a business to manage risk, help prevent fraudulent transactions, and to ensure they are marketing their products and services to the right consumers at the right time and across the right channels. So we took a moment to sit down with Stephen and Kitman Labs to hear more about their work, compare the worlds of sports and business, share insights and examine what the future might have in store for both industries. Here are the highlights and takeaways from our conversation: What’s your mission at Kitman Labs? Our mission, quite simply, is to create competitive advantages through technology and analytics. We sell to professional and college sports organizations. Our clients are some of the most elite in sports. Our goal is to use our sports science backgrounds to help them win. As you may know, the movement to use analytics in sports has been enormous over the past 15 years, especially with data related things like Moneyball and statistical analysis. Our work is in this tradition, but a lot more components go into it. Risk management is a key focus for us at Experian, would this be an example of such a component? Yes. Risk management is exactly what we do in the world of sports. Our platform is designed to identify injury risks and help players stay on the field. Because anyone who follows sports will tell you injuries are an enormous problem. Every single year, teams lose player performances and huge amounts of money because of injuries. If there was a company out there that could help solve this issue, in a quantifiable way – that would be huge. We believe that’s what we’re building here. So how does it work? There are a number of different components. We have a screening tool that we use to run diagnostic tests on athletes as frequently as possible. That goes into our database system, called Profiler. We’ve taken our years of sports science and engineering experience to develop a system that pulls together all data points tracked by a team to objectively inform us of all stressors placed on athletes and how they are responding to these stressors. Using this information we can identify when certain players are at a higher risk of injury. As far as we’re concerned, this is the next frontier in sports analytics – this is the vanguard of sports technology helping teams to win. Is there one sport in particular that you’ve seen this take off in? As far as the United States goes, we are talking to teams in every sport, and have worked with such a wide variety of data and analyze it all uniquely. Our system can analyze enormous datasets in real time and process this information to find the slightest important variation outside the realm of normality, that coaches just do not have time to find themselves. But this isn’t a problem confined to sports. We’re solving a human problem. We’re in the business of human optimization and we are committed to delving into the minute details to help revolutionize the way this industry looks at these issues. At Experian we’re seeing first-hand the many ways data and insights can solve real life challenges for businesses and people alike. How are you seeing the data that you’re collecting transform your industry, with relation to sports science and sports medicine? Essentially, we’re turning that data into actionable insights that can be used every day. We’re giving the practitioners that work with these teams, the ability to make better, more informed, data-driven decisions to help improve the welfare of their teams, and ultimately, their performance. So what we’re doing in sports right now is no different than the majority of Big Data companies like Experian * * * We, of course, wholeheartedly agreed with Stephen’s final point. The cutting-edge work being done at Kitman Labs is exactly in line with what we’re doing at Experian. Today, we are turning data into actionable insights to help consumers, financial institutions, healthcare organizations, automotive companies, retailers and governmental organizations make more informed and effective decisions. For example, our DataLabs team provides a safe and secure environment to partner with our clients to enable breakthrough data experimentation and innovation. In our labs, we're able to combine Experian's data assets with those of our clients to present a larger picture and to experiment with new and innovative ways of analyzing that data to deliver greater competitive advantages. Just like Kitman Labs is using analytics to help sports teams make better decisions to manage risks around training, improve player and team performance and extend careers, Experian is using Big Data for good to help individual consumers and businesses make informed decisions, grow the economy and improve society.