What Big Data means for Business

what big data is used for and what big data reveals about us and how much data for big data and how big data is generated and how to learn big data for free
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JuliyaMadenta,Philippines,Researcher
Published Date:15-07-2017
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Insights on governance, risk and compliance April 2014 Big data Changing the way businesses compete and operate Contents Introduction Big opportunities, big challenges ................................ 1 What is big data? ......................................................... 2 Big data life cycle ............................................................ 5 Big data and analytics .................................................. 6 Big data drivers ........................................................... 9 Governance ............................................................. 10 Management ........................................................... 12 Architecture ............................................................ 14 Usage ..................................................................... 16 Quality .................................................................... 18 Security .................................................................. 20 Privacy ................................................................... 22 Conclusion Big data has big potential ......................................... 25 Next moves .............................................................. 26Introduction Big opportunities, big challenges The idea of data creating business value is not new, however, the effective use of data is becoming the basis of competition. Business has always wanted to derive insights from information in order to make better, smarter, real time, fact- based decisions: it is this demand for depth of knowledge that has fueled the growth of big data tools and platforms. Those leading the change are now including big data from both within and outside the enterprise, including structured and unstructured data, machine data, and online and mobile data to supplement their organizational data and provide the basis for historical and forward-looking (statistical and predictive) views. Big data will fundamentally change the way businesses compete and operate. Companies that invest in and successfully derive value from their data will have a distinct advantage over their competitors — a performance gap that will continue to grow as more relevant data is generated, emerging technologies and digital channels offer better acquisition and delivery mechanisms, and the technologies that enable faster, easier data analysis continue to develop. While the ability to capture and store vast amounts of data has grown at an unprecedented rate, the technical capacity to aggregate and analyze these disparate volumes of information is only just now catching up. This report explains what big data is and how you can benefit from using it in your business operations, especially in the field of analytics. Using our in-depth client knowledge, we outline some of the opportunities already grasped by global organizations. In addition, we also highlight some of the key risks — some you may already be aware of, but others that may be new to you when you join the big data revolution. Big data — Changing the way businesses compete and operate 1What is big data? Big data refers to the dynamic, large and disparate volumes of Evolving technology has brought data analysis out of IT backrooms, data being created by people, tools and machines; it requires and extended the potential of using data-driven results into every new, innovative and scalable technology to collect, host and facet of an organization. However, while advances in software and analytically process the vast amount of data gathered in order to hardware have enabled the age of big data, technology is not the derive real-time business insights that relate to consumers, risk, only consideration. Companies need to take a holistic view that profit, performance, productivity management and enhanced recognizes that success is built upon the integration of people, process , shareholder value. technology and data; this means being able to incorporate data into their business routines, their strategy and their daily operations. Big data includes information garnered from social media, data from internet-enabled devices (including smartphones and tablets), Organizations must understand what insights they need in order to machine data, video and voice recordings, and the continued make good strategic and operational decisions. The first part of the preservation and logging of structured and unstructured data. It is challenge is sorting through all of the available data to identify typically characterized by the four “V’s”: trends and correlations that will drive beneficial changes in business behavior. The next step is enriching this organizational information V olume: the amount of data being created is vast compared to • with that from sources outside the enterprise; this will include traditional data sources familiar big data sources, such as those created and stored online. Variety: data comes from different sources and is being created • In a business environment that constantly and rapidly changes, by machines as well as people future prediction becomes more important than the simple V elocit y: data is being generated extremely fast — a process that visualization of historical or current perspectives. For effective • never stops, even while we sleep future prediction, data analysis using statistical and predictive modeling techniques may be applied to enhance and support the V er acit y: big data is sourced from many different places, as a • organization’s business strategy. The collection and aggregation result you need to test the veracity/quality of the data of big data, and other information from outside the enterprise, enables the business to develop their own analytic capacity and capability, which for many years has only been available to a few The four V’s larger organizations. • Click stream • Active/passive sensor • Log • Unstructured • Event • Semi-structured • Printed corpus Volume Variety • Structured • Speech • Social media The impact of big data • Traditional Big To understand the impact of how data has transformed our data daily lives, look no further than how the movie rental experience has changed. When movies were rented from independent • Speed of • Untrusted generation Velocity Veracity neighborhood stores, the rental agent would base their • Uncleansed • Rate of analysis recommendations on which movies the customer said they liked and a large amount of their own opinion. Today, movie rental companies and content delivery services can utilize a vast array of data points to generate recommendations. By analyzing what was viewed, when, on what device (and even whether the content was fast forwarded, rewound or paused), as well as user activities such as internet searches, and browsing and scrolling within a webpage, recommendations can be tailored for millions of customers in real time andapproximately 75% of views at a leading provider are now driven by these recommendations. 2 Big data — Changing the way businesses compete and operateThe term Big Data has become a major theme of the technology Technology megatrends media, but it has also increasingly made its way into many Big data is generating an intense amount of attention among compliance, internal audit and fraud risk management-related businesses, media and even consumers, along with analytics, cloud- discussions. In EY’s Global Forensic Data Analytics Survey 2014, based technologies, digital channels and data visualization. These are 72% of respondents believe that emerging big data technologies can all part of the current diverse ecosystem created by the technology play a key role in fraud prevention and detection. Yet only 7% of megatrends. Some even herald the potential transformative power respondents were aware of any specific big data technologies, and of the current trends as rivaling that of the internet. Yet, as in the only 2% were actually using them. early days of the internet, there is uncertainty about just what big Forensic data analytics (FDA) technologies are available to help data is, its potential benefits and the associated risks. companies keep pace with increasing data volumes, as well as EY’s 2013 Global Information Security Survey results indicate that business and regulatory complexities; examples can include while adoption and use of big data is not yet widespread, there is real-time analytical processing engines that make rapid business growing confidence and familiarity with the technology. Respondents decisions, such as stopping a potentially improper payment or ranked big data technologies as being “around the corner” (i.e., those business transaction, or leveraging anti-fraud/anti-corruption that have been on organizations’ radar for a period of time but may monitoring controls that integrate data visualization, statistical not yet be implemented or widely adopted) as average in terms of analysis and text mining. Yet despite their availability, many level of importance, familiarity and confidence in their capabilities companies have not scaled up their data usage to take advantage to address related cyber risks. Organizations typically view these of these effective tools, and may be missing important fraud technologies as offering opportunities to improve their performance prevention and detection opportunities by not mining larger data and create competitive advantage. This is where familiarity and sets to more robustly monitor business activities. confidence in capabilities needs to increase today, as the importance of these technologies is likely to grow significantly in the near future. Emerging technologies and trends 70 Software applications Smartphones and tablets 60 Current technologies Current technologies Around the corner Web-based applications 50 On the horizon Social media (Size of data points represent importance to survey respondents) 40 Enterprise application store Supply chain management 30 Big data Around the corner Internet Bring your own cloud of things Digital money Cloud service brokerage 20 In-memory Cyber havens computing 10 On the horizon Source: Under cyber attack: EY’s Global 0 10 20 30 40 50 60 70 Information Security Survey 2013. www.ey.com/GISS2013 Familiarity with technologies and trends (% of respondents) Big data — Changing the way businesses compete and operate 3 Confidence incapabilities (% of respondents)Big data eliminates intuition Decisions can be made with a structured approach through data-driven insight, including: Customer and product • profitability Customer acquisition and • retention strategies Customer satisfaction • strategies Marketing segmentation • Operations and • performance management Supply chain and delivery • channel strategyBig data life cycle Creation Output Certain types of data have long been able to be captured, but this Although it is now easier and cheaper to capture, store and data has rarely been used effectively until now (e.g., the location of a process data, it is not useful unless the information is relevant; person at any point in time, the number of steps a person takes every it must also be readily available to the right people who need the day, a real-time history of credit card purchases). New technology appropriate input in order to make insightful decisions leading to such as advanced sensors and customized software can now record successful outcomes. this information for analysis. There are three key enablers: Changes in the way we communicate (e.g., social media vs. telephone Mobile — established mobile networks have allowed for easier • vs. text/SMS vs. email vs. letter) have also increased our ability to distribution of information in real-time investigate areas such as consumer sentiment. Social media increases Visual/interactive — technologies have brought the ability to • the speed at which data is generated; for example, a product launch review large and complex data sets into the realm of the average that is discussed live on a popular social networking site can business user generate a buzz in real-time and allow companies to gauge public reaction even before the launch event is over. Human r e s our c e — there is a new breed of employees with the • knowledge to handle the complexities of big data and with the ability to simplify the output for daily use Processing Extremely large volumes of data have traditionally not been captured Resources and processes and processed for various reasons, most notably because the cost to do so was far greater than the value of insights companies An important factor in being able to achieve big data success is could derive from its analysis. However, multiple factors and new having knowledgeable and competent resources. This extends technologies have lowered the cost and technology barrier for beyond the so-called data scientists who have deep knowledge and effective data processing, allowing companies of all sizes, to be able to experience in handling, analyzing and reporting on big data sets. unlock the value contained in different data sources. For instance, While these skillsets are indeed in high demand, success requires it is difficult for conventional relational databases to handle more than having a handful of specialists on the workforce. (R) unstructured data, so software frameworks like Hadoop , for While governments continue to call for the training of “data scientists,” distributed storage and parallel processing of large datasets have companies are taking advantage of fundamental skillsets that been introduced to process non-structured data at high speed; making already exist within their organization. Employees with the curiosity it easier to perform a more comprehensive analysis of big data. to ask the right questions and the ability to synthesize and leverage Many organizations are looking to the cloud to provide a storage new data points quickly are well suited to lead the big data revolution. solution that is agile and enables unparalleled scalability; however, In reality, they are the revolution, but they must be supported with these organizations need to ensure the governance and risk business processes that place value on gathering and using data, management practices on their cloud are appropriate for the type and that integrate data-driven decision making. of information being collected. Cloud computing enables companies to use prebuilt big data solutions, or quickly build and deploy a powerful array of servers, without the substantial costs involved in owning physical hardware. Big data — Changing the way businesses compete and operate 5Big data and analytics Big data poses both opportunities and challenges for businesses. In order to extract value from big data, it must be processed and analyzed in a timely manner, and the results need to be available in such a way as to be able to effect positive change or influence business decisions. The effectiveness also relies on an organization having the right combination of people, process and technology. By pure definition, analytics is the discovery and communication of meaningful patterns in data — but for business, analytics should be viewed as the extensive use of data, statistical and quantitative analysis, using explanatory and predictive models to drive fact-based business management decisions and actions. Analytics helps to optimize key processes, functions and roles. It can be leveraged to aggregate both internal and external data. It enables organizations to meet stakeholder reporting demands, manage massive data volumes, create market advantages, manage risk, improve controls and, ultimately, enhance organizational performance by turning information into intelligence. EY analytics value chain Continuous feedback loop Transaction/behavior history The goal is to use analytics to improve Relevant data Insights the e f ficienc y and effectiveness of Drive Perform Manage decisions every decision and/or action. analytics Rules/ data algorithms 1. Begin with leveraging leading tools and techniques to manage and extract relevant data from big data sources. Prescriptive analytics Advanced To determine which decision and/or action will produce the most 2. Applications of analytics can analytics effective result against a specific set of objectives and constraints range from historical reporting, Predictive analytics through to real-time decision Predictive Leverage past data to understand why something happened or to support for organizations based on predict what will happen in the future across various scenarios future predictions. Descriptive analytics Business intelligence Mine past data to report, visualize and understand what has 3. Use the insight generated by the already happened – after the fact or in real-time analysis to drive change. Mathematical complexity 6 Big data — Changing the way businesses compete and operateAnalytics can identify innovative opportunities in key processes, functions and roles. It Big data can be a creates a catalyst for innovation and change — and by challenging the status quo, it can help to create new possibilities for the business and its customers. Sophisticated techniques can powerful way to identify allow companies to discover root causes, analyze microsegments of their markets, transform opportunities, but processes and make accurate predictions about future events or customers’ propensity to buy, churn or engage. when combined with It is no longer enough for companies to simply understand current process or operations traditional organizational with a view on improving what already exists, when there is now the capacity to question if a process is relevant to the business, or whether there is a new way of solving a particular issue. information the volumes The key driver for innovation within organizations is to constantly challenge existing practices rather than consistently accept the same. of data collected can Most organizations have complex and fragmented architecture landscapes that make the be vast and traditional cohesive collation and dissemination of data difficult. New analytic solutions are playing an important role in enabling an effective Intelligent Enterprise (IE). An IE helps to create a storage methods can be single view across your organization by utilizing a combination of standard reporting and data visualization: prohibitively expensive Data from multiple source systems is cleansed, normalized and collated • and do not necessarily External feeds can be gathered from the latest research, best practice guidelines, • scale effectively. benchmarks and other online repositories Use of enhanced visualization techniques, benchmarking indexes and dashboards can • inform management and consumers via smartphones, laptops, tablets, etc., in-house or remotely All companies need to start thinking about collecting and using relevant big data. Data-driven decisions can reduce inefficiency between the business, legal and IT, optimize existing information assets and address disconnects between different functions of an organization. However, it is worth noting that the best data and the most advanced analytical tools and techniques mean nothing if they are not being leveraged by people who are asking the right questions. Big data, emerging storage technology platforms and the latest analytical algorithms are enablers to business success — not a guarantee of it. Big data — Changing the way businesses compete and operate 7Organizational information is typically historical, incomplete and inaccurate. For a forward-looking perspective (using statistical and predictive modeling) it needs to be enriched with external information. Big data drivers The benefits and risks of big data A key success factor for companies is the While there is no doubt that the big data revolution has created substantial benefits to businesses and consumers availability of relevant alike, there are commensurate risks that go along with using information at the big data. right time. The need to secure sensitive data, to protect private information and to manage data quality, exists whether data sets are big or small. However, the specific properties of big data (volume, variety, velocity, veracity) create new types of risks that necessitate a comprehensive strategy to enable a company to utilize big data while avoiding the pitfalls. This should be done in a prioritized fashion so that companies can start to realize the benefits of big data in step with managing the risks. The following pages look at the possibilities and risks associated with big data and give examples of how big data is being leveraged to solve some of the complex issues businesses face today. We identify traditional and new risks and considerations for the seven key steps to success: governance, management, architecture, usage, quality, security and privacy. Big data drivers Risks or considerations • Governance Ability to • Management compute/ analyze • Architecture Big data Availability • Usage of data success • Quality Need to deliver/extract • Security value • Privacy Big data — Changing the way businesses compete and operate 9Big data drivers Governance Good governance is vital Benefits to the success of Big data initiatives in any business; The MIT Center for Digital Business states “When it comes to big data, the ‘right’ it encompasses consistent governance model depends on the maturity level of the organization regarding data guidance, procedures and driven decisions.” It obviously also highly depends on if big data is used to create new clear management decision- business or to drive more sales. making. Organizations need To unleash the power of big data, first of all data must be available and made fit for to ensure standard and sharing. When it comes to, for example, medical data, respect for privacy and trust is inevitable. exhaustive data capture; they Standardization in governance structures, with an integrated combination of technical, need not protect all the data, organizational and legal measures and safeguards, will help to increase trust. This is but they need to start sharing especially important when integrating governmental, institutional “open” and data with in-built protections company data. with the right levels and An example of this in action is seen with a European association that aims to build up functions of the organization. a big data services platform for the health sector in their local region. It’s a unique collaboration between health institutions, government, education and knowledge institutions and major IT service providers, addressing both the clinical and research sides of the health sector. The core solution is comprised of a “vendor neutral hub” — a platform that works independently of vendors and data “owners” — where data can be captured, safely and durably stored, processed and distributed and finally, shared, if permitted by the data owner. This may be the case when patients are being treated by multidisciplinary teams or having treatment in various locations, or for research purposes using large sets of anonymous data. The framework offers solutions for the immediate need to access data, to substantially lower the cost of data storage and, more importantly, to do more with the rapidly growing amount of unstructured data. 10 Big data — Changing the way businesses compete and operate“Our top priorities are Risks and considerations working with large data volumes and improving Traditional risks the efficiency of our There is continued regulatory pressure on companies to meet a variety of policies • and laws (e.g., Basel II, MiFID, SOX). Compliance governance is an expensive and testing. By using larger complex problem to deal with, but failing to meet regulations can mean safety risks, hefty penalties, loss of reputation or even bankruptcy. data sets we hope to do In a global and continuously and rapidly changing legal and IT landscape it is • smarter internal audits, not always clear exactly what legal and regulatory compliance entails (Who is responsible? Who is liable?), or how best to translate abstract rules from laws into including more effective organizational and technical measures within a company. fraud detection.” Companies need to balance contradictive rules and regulations e.g., obligations • based upon the US Patriot Act and the EU Data Protection Directive (and its many Head of Internal Audit, Australia local implementations). New risks Managers will need to learn to embrace the evidence-based decision-making • process. Organizations have to redefine their understanding of “judgements” of the outcome of big data analytics. Data can be of great value, but companies have to consider ownership and privacy • issues before using big data results. In the case of medical data, it is sometimes not clear who is the owner of the data, but using the data without the right legal foundation or consent of the patient may cause big problems. Big data may bring about intellectual property issues, e.g. copyright and database • rights infringements. It will be a challenge to make sure that employees are not sharing inappropriate information, or too much data outside of the organization. Big data — Changing the way businesses compete and operate 11Big data drivers Management Integrating and moving data across the organization is Benefits traditionally constrained by Big data overcomes traditional restraints in a cost-effective manner and opens data storage platforms such opportunities to ingest, store and process data from new sources such as external as relational databases or social media data, market data, communications, interaction with customers via batch files with limited ability digital channels, etc. to process very large volumes By some estimates, more than 80% of the data within organizations is unstructured of data, data with complex and unfit for traditional processing. Using big data will enable the processing of this unstructured data and increased system intelligence which can be used to structure or without structure improve performance in sales, increase understanding of customer needs, reinforce at all, or data generated or the internal risk management function, support marketing initiatives and enhance received at very high speeds. fraud monitoring. Big data capability allows organizations to integrate multiple data sources with Organizations need to start relatively low effort in a short timeframe. Combined with a lower cost of storage managing data through per gigabyte, this enables organizations to build, for example, a federated view of customers by shifting customer data from various separate business departments into different sources, and a single infrastructure, and then to run consolidated analytics and reporting on it. integrating its usefulness via Big data technologies release organizations from the traditional accuracy vs. cost a range of technologies in challenge by enabling them to store data at the lowest level of detail, keeping all data the market. history under reasonable costs and with less effort. 12 Big data — Changing the way businesses compete and operateExample engagement: Risks and considerations management In response to the recent Dodd-Frank Traditional risks regulations, requiring financial There is a long implementation cycle for data warehousing and reporting solutions. • organizations to report all pre-trade communications data across the Challenges over unifying data definitions are made even more complex across • organization related to a trade under multiple business lines. query within short notice, EY has Modeling, storage and processing challenges arise from the growing volumes of data • developed a solution that allows with dynamic structures. organizations to harvest, index and link unstructured information related to internal communications data to New risks related trades. Simplified access to diverse sources of data and easy-to-ingest large amounts of • The solution is able to process large information may result in increasing amount of “noise” in data and decrease in the variety of unstructured pre-trade overall level of data quality. communications data sources (emails, instant messages, phone calls, etc.) Many new technology market players don’t have mature enterprise-ready capabilities • and applies a number of matching around implementation, support, training, etc. rules and fuzzy logic to match this New big data methods, architecture and volume variety impose additional risks of • data to financial transactions the lack of control and governance over data, and this requires additional organizational communications relate to: this enables focus. Under the context of the complex data landscape, it is especially important to the organization to fetch the full history establish and maintain data lineage. of communication events related to a specific transaction almost instantly. Organizations may struggle with finding the right skills and building internal • capabilities for handling big data as most of the technologies and methods are While addressing the regulatory relatively new, and market resources are in short supply. requirement, the solution also provides essential capabilities for rogue trader analytics as it effectively supplements traditional analytics models that leverage trade economics data with unstructured pre-trade communications data feed that significantly broadens the context and precision of analysis. Big data — Changing the way businesses compete and operate 13Big data drivers Architecture With big data, it has become possible to build Benefits an architecture which can Big data has brought a new paradigm to data architecture. In the past, data systems integrate massive volumes of were built with a predetermined set of data requirements. In the Big Data world, data data in various formats and storage platforms are not restricted to a predefined rigid data model, and data systems provide real-time analytics are capable of handling all kinds of structured and unstructured data. aimed at a consolidated Integration of unstructured data in particular can lead to improved analytics and customer view, or improved reporting. For example, a business goal of having a consolidated view of the customer profile across business functions and geographies is important for various reasons: fraud detection and other To make the business decision making process more intelligent • similar business goals. To enhance the monitoring of customer profiles for “red flags” (issues of concern • Data architecture should or opportunity) be prepared to break down To enable the company to offer more relevant services to their customers tailored to • internal silos, enabling the their specific needs sharing of key data sets Traditionally, organizations struggled to achieve this goal because their customer across the organization and data was lying in multiple systems and different file formats (PDFs, Word and Excel documents, charts, images, scans, videos, etc.): technology was seen as a limiting to ensure that learnings are factor to integrate this scattered and massive data and meet the goal. Big data brings being captured and relayed a solution to this by offering capabilities to integrate and analyze data coming from across to the right set of large variety of systems across the organization in an efficient and flexible manner. people in the organization in a Real-time fraud monitoring is a classic big data challenge, demanding the integration of large amounts of diverse, structured and unstructured high-velocity data that needs timely and accurate manner. to be analyzed in near-real time to realize the benefits. A global payments technology company recently stated that it has made an improvement of 130% in identifying fraud for debit transactions and 175% improvement in cases of credit card transactions by using big data technologies authorization model. Big data also offers additional capabilities such as deploying data storage/processing power over a grid of commodity hardware, with unconstrained scalability and flexibility to adapt to constantly changing data landscape. 14 Big data — Changing the way businesses compete and operateExample engagement: Risks and considerations architecture A top 10 global insurance company Traditional risks invested 300 million in a futuristic Increasing the volume of data puts a strain on infrastructure, resulting in slow • big data solution aimed at providing processing, storage problems and back up requirements. a single customer view across their enterprise; providing a 360 degree The inability to work with unstructured data reduces the quality of analytics and • view of their customer portfolio and reporting. consolidates customer interactions. Numerous data silos create the risk of poor data integrity, inconsistency and high • Before the big data solution, the implementation and maintenance budgets. organization struggled to create a single view of its customers across product and business systems because of New risks growing volumes, incompatible systems, More is not always better. More data can lead to an increased number of data • inconsistency of data and inability to quality issues, and confusion and lack of consistency in business decision making — process unstructured data. They are especially when conflicting information is present. now able to generate a single customer view which can be used in a consistent Integrated data architecture increases the challenges of data linkages and matching • manner across the enterprise, leading algorithms to distinguish items of relevance from piles of data. to effective sales recommendations, Increased complexity of architectural landscape and the growing amount of data • customized offerings and operational bring new challenges around data governance and data privacy. improvements. Lack of capabilities, both within organizations and externally, make it hard to keep • up with rapidly evolving hardware/software technology and implementation methods. Big data — Changing the way businesses compete and operate 15Big data drivers Usage The convergence of data availability and processing Benefits power is helping to unlock The weather used to be unforeseeable and ungovernable. Robust weather forecasts the potential of big data for models usually require hundreds of thousands of atmospheric variables that are most sectors and industries. constantly changing. With big data, some technology companies have emerged The results of big data can with the ability to provide historical weather data and better forecasting of extreme weather events. Based on billions of calculations and data points over the past several beneficial to a wide range decades, big data now makes it possible to improve weather predictions up to a of stakeholders across the month in advance. organization — executive With the advent of low-cost cloud computing environments and open data movements, management and boards, various big data weather forecasting ventures have arisen in recent years. Some of business operations and those new start-ups provide their services to corporate users (e.g., large-scale farmers, logistic companies) and some to retail customers directly. risk professionals, including Accurate weather data is beneficial for many organizations; for example, some legal, internal audit, finance companies have been using weather information to improve their business activities and compliance; as well as ranging from supply chain planning to advertising. customer-facing departments Supply chain management goes beyond just stocking more shovels ahead of a like sales and marketing. snowstorm. Retailers can now improve inventory management by leveraging new big data insights showing, for example, that after unusually cool weather, beer sales The key challenge is having will decrease in some cities while increasing in others. And, by combining real-time the ability to interpret the huge detailed analysis of current and historical weather data with personal data such as location, demographics and purchase history, retailers are able to further refine and amount of data that can be target their advertisements; i.e., consumer purchase patterns will change if today is collated from various sources. the first warm day after a week of cold temperatures. 16 Big data — Changing the way businesses compete and operateBig data brings value Risks and considerations to companies in diverse and unexpected ways. Traditional risks A key challenge is to know the right business questions to ask. • “Companies that are There are misunderstandings over what data is needed to make strategic or • moving ahead are operational decisions. Many organizations do not have the ability to analyze data timely enough to take doing an incredible • advantage of new insights. amount of analysis around selection and New risks recruitment processes Not considering information from outside the organization (e.g., weather) that is • relevant to answering bigger question is an ongoing concern. and staff turnover to There is a shortage of qualified “data scientists” globally for the near to mid-term. • figure out if they can Organizations can get overloaded and overwhelmed by trying to handle too • much data. identify what is The challenge of getting the right information to the right person at the right time is • happening with their expanded due to the sheer size of big data. workforce. The focus on The costs associated with managing and monitoring the quality, credibility and • integrity of big data can be prohibitive. data is a real winner for There is a necessity to temper the expectation that big data will solve everything. • HR if handled correctly.” Edward Lawler, Director of the Center for Effective Organizations and Distinguished Profession of Business at the University of Southern California Big data — Changing the way businesses compete and operate 17Big data drivers Quality The quality of data sets and the inference drawn from such Benefits data sets are increasingly becoming more critical For many years the health care ecosystem has embraced big data. With the ability to capture every patient touch point, the amount of data within the health care and organizations need to ecosystem has exploded. The evolution of new data sources and the ability to mash build quality and monitoring that data with existing data sources is evolving — big data is creating the possibility of functions and parameters new positive patient outcomes. for big data. For example, Some of these new data sources include the integration of disease registries, tissue registries and genomic information, and then aligning them with meaningful use correcting a data error can clinical standards. It is defining key care treatment approaches based on new genetic be much more costly than insights and clinical protocol matching algorithms, and defining focused patient care getting the data right the first treatment insights earlier within the care delivery process. time — and getting the data The value from these new big data insights will be priceless for the patient. The quality wrong can be catastrophic of the data will also have a direct effect on driving new key health care insights in creating high-quality outcomes while effectively managing costs. and much more costly to the organization if not corrected. 18 Big data — Changing the way businesses compete and operate

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