Embracing Real-time Analytics for Proactive Business Management


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To succeed in any economic climate, businesses need to combine intelligent agents, 3-D visualization and predictive analytics into a framework that can detect underlying issues with revenues, expenditures and profitability and proactively provide solutions.

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Embracing Real-time Analytics for Proactive Business Management

  1. 1. • Cognizant 20-20 InsightsEmbracing Real-time Analytics forProactive Business ManagementCompanies can achieve their strategic goals in any economic climateby combining 3-D visualization, intelligent agents and predictiveanalytics into a framework that recognizes underlying businessissues and proactively offers solutions. Executive Summary • A healthcare payer is losing membership due to layoffs among its client base, and its In these times of economic uncertainty, com- revenues are decreasing. The company needs panies must adapt quickly to changes in the to develop a comprehensive cost containment revenue stream and find ways to stay competitive methodology in pharmacy benefits, radiology and profitable, even with substantially reduced and other high-cost medical categories. To do staff due to lower revenues. To gain competitive this, it wants a real-time analytics system that advantage, most companies are digging deeper can identify and proactively provide solutions into their volumes of data and turning to real- to these problems and potentially replace some time analytics. of its labor-intensive operations in the claims Let’s consider some real scenarios that companies processing area. face today: • A casualty and property insurance company • A high-tech company wants to develop a micro- faces declining revenues due to the inability of chip that would perform advanced analytics in policy holders to pay their premiums because real time, to be used by the retail industry in of job loss. With pricing optimization a key digital signage. This microchip would decrease product differentiator in the marketplace, the the cost of advanced analytics by millions of company wants to use analytics to increase dollars and allow retail companies to conduct profitability by identifying and providing real-time marketing analytics campaigns. potential solutions in high-cost areas by deploying an early warning system. • A social media company wants to use real-time advanced analytics to improve its click- through • Aninternational consumer packaged goods rate by 20% by matching targeted advertise- company is experiencing decreased sales and ments to users. wants to gain back market share by utilizing cognizant 20-20 insights | april 2012
  2. 2. analytics in the areas of foresight, blind spots, and complex data sets. To truly appreciate IAVM, portfolio optimization and consumer insights. we should first understand the components that create its foundation. These include the following:These challenges can be addressed throughthe use of agile and resilient computer systemsthat can detect underlying issues with revenues, • Business Competency Model: One of the issues that the IAVM strives to resolve hasexpenditures and profitability and proactively existed since the beginning of the 21st century:provide insights to help companies adapt quickly leaders have a specific vision, or mental image,to continuously changing conditions. of where they want to take their companies, but there is no visualization mechanism toSuch systems depend on the utilization of three describe that vision to the rest of the company.scientific and technology techniques that have This became clear in 2000, in my work withbeen in the making for years: intelligent agents, a Fortune 100 company that was trying tothree-dimensional (3-D) visualization and data determine how to change course in a fiscalmining with predictive modeling techniques. These quarter without negatively impacting profits.three technologies form the core of the Intel- We developed a business model, which laterligent Agent Visualization Model (IAVM), a deci- became the business competency model (BCM),sion-support framework that allows companies that could respond to recessionary times byto efficiently design, build, deploy and update an bridging the gap between corporate strategyagile and robust enterprise analytics system that and operational decision-making. It accom-supports profitability under any economic climate plishes this by making sure the entire companyby understanding business changes and providing mirrors the executive management committeeproactive solutions to decision-makers. and that the committee is organized accordingAn essential concept of IAVM is that business to the corporate goals and vision of its leaders.data, like our universe, is three-dimensional. IAVM • Workforce Turnover Efficiency ratio: Theseeks to improve users’ ability to see patterns WTE is an asset management ratio that allowswithin business data by increasing their depth companies to design restructuring plans basedperception of traditional two-dimensional data on contributions to revenues. It was developedanalysis. This improved visualization is achieved in 2001, when a Fortune 100 company realizedthrough techniques such as self-populated maps it needed to lay off tens of thousands ofthat enable executives to quickly compare per- employees in order to stay profitable but didformance across different geographical regions. not have a metric or KPI to measure how eachSuch a 3-D dashboard would also include individual and group contributed to revenues.drill-down capabilities to allow further examina-tion into issues detected by the IAVM. • Weighted Outlier Variable: The WOV is a way to separate clusters of data and understandTo create agile systems using enterprise analytics, the driving factors for any changes. It wascompanies must focus on three main areas using developed in 2003, when I was designing fraudIAVM design and implementation: and abuse analytical detection models. To my surprise, probability theory and statistics had• Corporate goals answered the dispersion (standard deviation) part of the equation,1 and Albert Einstein had• The business model shown mathematically how to clarify driving• Metrics factors using an algebraic concept (quadratic equations) that had been around for overDoing so will result in responsive and flexible 2,000 years.systems that allow survival and prosperity even inharsh economic conditions. Companies that use • Depth perception studies: Business analyticsproven science and technology in their decision- visualization borrows from analytics meth-support systems will earn an advantage in the odologies and algorithms used in diagnosticmarketplace in good times and bad, since they imaging. I realized the link in 2004, when I waswill be able to quickly adapt to change without researching the area of neuroscience to betternegatively impacting their core business. understand diseases that both my parents were diagnosed with. Moreover, I realized thatIAVM Foundational Components cognitive science and medicine had foundThe IAVM framework is a culmination of 11 years of that depth perception (binocular summation)research and design of business analytics in large involves the brain making predictions about cognizant 20-20 insights 2
  3. 3. size, movement and distance. The result of • Intelligent agents: An intelligent agent is adding depth to our vision capabilities had software that is autonomous; interacts with been calculated to improve vision acuity by a other agents (is sociable); reacts to its environ- minimum of 140%. ment; and proactively tries to reach its goals by producing solutions. This technology allows• Commoditization of the statistics meth- for software to detect and suggest solutions odology: In 2007, I learned that the Analysis to business problems. Basically, advances in Services team at Microsoft Research Lab- technology (more data processed more quickly oratories had optimized regression and using smaller form factors) allow us to perform partition algorithms. From this, I realized that multiple calculations in a very short time. Intel- the statistics methodology had become a ligent agents are currently used in a number of commodity and that the additional key ingre- industries, such as in electrical grids to ensure dients were variable creation, visualization and a continuous flow of electricity to hundreds domain knowledge. of millions of consumers, as well as in large,• Three-dimensional visualization: In 2008, distributed commercial systems to detect and after seeing the work from the Visualization control intrusion. Group at the Lawrence Berkeley National Laboratory (see page 8), it became clear to Methodology me that 3-D visualization could be adapted In business, it sometimes seems easier to live with to business analytics to share strategic vision a familiar problem than implement an unfamiliar across the enterprise. solution. This is particularly true in corporate decision support systems; however, the 3-D visu-• Optimized delivery model: In 2009, I found alization of analytics clarifies underlying issues in that Cognizant’s business model is an optimal a way that anyone can understand. delivery model for the IAVM. There are three aspects of our model that are tailor-made for Current decision support systems are difficult the IAVM: our on-site/offshore ratio for solution and expensive to manipulate and seldom proac- delivery; our depth of analytics experience; and tively provide solutions to issues. On the contrary, our domain expertise in multiple industries.IAVM High-Level Framework Goal Assessment Corporate Alignment Clear definition of vision, goals BCM ensures that the organization and stakeholder responsibilities. supports the corporate goals and vision. 1 t Org Stag ge men Reaaniza e 2 Sta sess (BC lign tion s M a lA Pro ment l G oa ces s) Metrics Definition IAVM Implementation The WTE ratio ensures accurate Implementation Definition (WTE Design, build, test, Stage 6 measurement of how Stage 3 Metrics IAVM implementation, individuals and sub-organizations visualization and contribute to revenues and maintenance. ) profitability. Age s nt D ate Upd ) Sta esi gn KPI (WOV 4 ge ge 5 Sta Agent Design KPI Updates Interface, tasks and WOV separates clusters of data information agents. and clarifies driving factors in large and complex data sets.Figure 1 cognizant 20-20 insights 3
  4. 4. IAVM Continuous Improvement Process: Kaizen Analytics BCM Goals GIS Analysis Alignment 3-D Dashboard IAVM Visualization Ad Hoc Metrics WOV Reports WTEFigure 2IAVM is a decision-support framework that allows as costs have declined and ease-of-use hascompanies to efficiently design, build, deploy and improved to the point that anyone in the organi-update an agile and robust enterprise analytics zation can use these tools.system by understanding business changes andproviding proactive solutions to decision-makers. Step 1: Assess corporate goals and business rulesThe high-level process of IAVM involves six The first step in IAVM is to assess corporatedifferent steps that constitute a continuous goals and business rules. Before designing anyimprovement method, or kaizen analytics (see decision support system, the company needs afigures 1 and 2). clear understanding of the corporate goals andInstead of great technological breakthroughs, how those goals flow through the organization.the kaizen approach aims to involve the entire General statements of increased profitability andworkforce in a continuous improvement process. decreased costs must be translated into specificHence, most of the improvements are small and metrics that can be reported, measured andprocess oriented (like making shelves easier predicted. Discovering business rules is essentialto reach), but they involve the entire workforce during the assessment since these rules tend torather than a selected few, inspiring the enterprise mirror corporate compliance and workflow.as a whole to be vibrant and innovative. A good The conceptual design of the 3-D visualizationexample of how this works is at Toyota, whose begins within this phase because the visualizationemployees provide management with 100 times needs to mirror the corporate vision and goals.more suggestions for improvement than other For example, a soda manufacturer and distributorauto manufacturers. may want to see the aggregate visualization asBusinesses that want to improve their analytics a series of 3-D soda cans, or a retailer may wantcapabilities should follow the kaizen approach and to see the aggregate visualization as a categorymake business analytics available throughout the of consumer goods. These visualizations can beentire organization. In some companies, analytics self-populated maps like the ones used by theis limited to the purview of the few — statisticians, Lawrence Berkeley National Laboratory Visual-physicians, molecular engineers and actuaries — ization Group, with underlying geographical infor-often because it is seen as expensive and difficult mation system (GIS) and dashboard technologies.to interpret. This premise is no longer applicable, A 3-D visualization of the enterprise’s analytic cognizant 20-20 insights 4
  5. 5. and predictive capabilities will allow executives can be used in M&A, due diligence and financialand field staff to use the power of the human analytics.brain to its fullest potential. In today’s economy, companies like to say thatStep 2: Realign corporate structure human capital is their most important asset.The second step in IAVM is to evaluate the Indeed, the last 10 years have seen the develop-company’s organizational structure and make ment of a service economy and increased reliancerecommendations for how to better align the on the knowledge worker. As a result, the mea-company with its corporate goals. This is where surement of management efficiency in utilizingthe BCM comes in.2 The BCM is a three-pronged human capital has moved to the forefront ofstructure that aligns the company’s financial this benchmarking exercise; hence, it is essentialgoals and organizational model with strategic to develop a financial performance tool thatplanning, assessment tools and knowledge determines how an organization is managing itsmanagement (see Figure 3). Its leading feature is workforce.3its efficiency, allowing a company to turn around Asset management ratios measure the ability ofin a short time period, even one financial quarter. assets to generate revenues or earnings. As such,This type of agility is a necessary characteristic they complement liquidity ratios when analyzingfor any decision support system that involves financial performance. There are six other assethuman-computer interaction (HCI). management ratios: accounts receivable turnover, days in receivables, inventory turnover,4 days inStep 3: Define metrics inventory,5 operating cycle6 and capital turnover.The third step is to define metrics and determinehow they aggregate through the company in WTE is calculated by multiplying average dailyorder to predict and meet corporate goals. An salary (ADS) with the actual number of days toorganization must measure what it expects to fill an open position (TTF), dividing that sum bymanage and accomplish; otherwise, it has no the average number of days to fill a position (ATF)reference with which to work. The IAVM uses a and then dividing again by 10 (see Figure 4).7company’s current metrics and enhances themby using the WTE ratio, which measures the rela- WTE is useful for companies with a large numbertionship between the cost per employee and the of employees (over 10,000). These companiestimely management of project staffing. This ratio can be in different industries such as healthcare, manufacturing, financial services, telecommuni- cations and other services. Also, it can be used to measure performance efficiencies within anyBCM Framework organization, including but not limited to IT and business processes. Corporate Step 4: KPI updates using Executive the weighted outlier Committee The IAVM also uses the weighted outlier meth- odology8 to improve visibility into data patterns. An outlier is an observation that lies outside Knowledge Tools Strategy Management the overall pattern of a distribution in the data. Usually, the presence of an outlier indicates Workforce Turnover Efficiency™ Ratio IAVMFigure 3 Figure 4 cognizant 20-20 insights 5
  6. 6. some sort of problem. The weighted outlier The visualization architecture consists of threevariable (WOV) separates clusters of data while main layers: 3-D interactive visualization, a geo-simultaneously clarifying the driving factors in graphical information system and a dashboardlarge and complex data sets (see Figure 5). A with drill-down capabilities. The 3-D interac-weighted outlier creates variables that maximize tive visualization uses the medical conceptthe differences in the data, while simultaneously of binocular vision, which adds an additionalminimizing the similarities in the data to detect predictive variable to two-dimensional data.potential fraud. This effect could be described as“squeezing and pulling out” the potential fraud Business data is three-dimensional; however,from the data set. A significant WOV should also business analytics tend to be flat, or two-dimen-substantially increase the efficiency of a data sional, like an Excel table or chart. The differencemodel for fraud detection. between a 2-D analysis and a 3-D analysis is depth. Depth perception allows an individual toStep 5: Designing intelligent agents accurately determine the distance to an object.The design of the IAVM framework takes into In analytics, depth is referred to as dimensionalconsideration three different types of intelligent analysis. Dimensional analysis is used in engi-agents:9 neering, physics and chemistry to understand• Interface agent: Collects information from the characteristics of multi-dimensional data and users and delivers requested information. formulate hypotheses about the data that are later tested in more detail. In business analytics, we• Task agent: Performs most of the autonomous can create a 3-D variable that allows the end-user functions. For example, task agents calculate in to “see the depth” of the data. This variable is real-time the mean and standard deviation of called a 3-D vector analysis. This variable, when a specified value and then decide the outlier combined with cluster analysis and a visualization limits for an alarm script. Also, these agents tool, answers the recurring business question: may decide whether the solution is a potential How deep can I go into my data and see patterns data error, fraud issue, new pattern or risk in which sound business decisions can be made? management issue.• Information agent: Used for one-time retrieval The main goal is to increase the user’s under- of information that has reusable capabilities. standing of the data by adding depth perception (i.e., predictive modeling) to traditional 2-D dataStep 6: IAVM implementation/ analysis. This method, binocular summation,visualization increases visual perception by a minimum ofThe conceptual framework of the IAVM is depicted 140% in clinical studies.11in Figure 6 (next page).10Weighted Outlier Effect K=18 K= Kurtosis K=3 S= Skewness S= 2 S=27 X1 WOVFigure 5 cognizant 20-20 insights 6
  7. 7. IAVM High-Level Architecture User 1 User 2 User h Goals and Task Results Specifications Interface Interface Interface Agent 1 Agent 2 Agent k k Tas d ose Tas k Prop tion Solu Task Task Conflict Task Agent 1 Agent 2 Agent j Solution Information Information Integration Reply Request Info Collaborative Info Info Agent 1 Query Processing Agent 2 Agent n Query Answer Database Database Database 1 2 kFigure 6The IAVM uses this increased visual perception Other potential applications for real-time IAVMto its advantage. An example is a self-populated include but are not limited to:map that allows executives to determine potentialissues and solutions to achieve corporate goals • Retail: As a consumer browses through a(see Figure 7, next page). The geographical infor- store (brick and mortar or Internet), intelli-mation system gives the user a spatial dimension gent agents react to browsing and purchasingamong different geographical regions for com- patterns to recommend additional articles toparative analysis. The dashboard view should purchase. This output then can integrate withhave drill-down capabilities that allow users to a marketing campaign to send coupons thatexamine the root causes of the issues detected target the consumer’s preferences.by the IAVM. • Financial services: Early warning systems react to diverse credit card purchases, andReal-Time IAVM Applications investment mechanisms proactively detectTo fully understand the potential for IAVM,12 fraud and abuse.we must recognize how intelligent agents arecurrently used in the following industries: • Healthcare: Systems detect and proactively recommend diagnoses and treatment based on real-time clinical and claims data in a digital• Healthcare: As patient care becomes more hospital setting or in a claims processing clear- data intensive, intelligent agents are used in intensive care settings to administer inghouse. medication by proactively reacting to constant • Internet gaming companies: An inflation monitoring of vital signs. control tool acts as a central bank regulating the supply of money to control inflation in• Air traffic control: The volume and complexity virtual economies. of managing air traffic control systems requires the utilization of intelligent agents to avoid • Internet advertisement: Mobile agents detect collisions and manage departures and landings. patterns in user behavior and proactively com- municate with other agents to determine what• Manufacturing: Robotics has become one of advertisements to display. the main applications in the manufacturing industry, and intelligent agents are used to • Communications: Intelligent-agent technology react and proactively make decisions regarding efficiently transfers calls and detects potential quality control processes. outages. cognizant 20-20 insights 7
  8. 8. Self-Populated 3-D VisualizationsSource: Lawrence Berkeley National LaboratoryFigure 7Conclusion adapt during difficult economic conditions and flourish during strong economic times.The IAVM has multiple applications in analyticsaround big data for the high-tech, healthcare, As an added benefit, the IAVM proactively bringsretail, pharmaceutical, life sciences, CPG, banking potential solutions to issues based on sound andand financial industries. It can be used for M&A, proven mathematical and scientific methodsrisk management, financial analysis, corporate like standard deviation, risk detection, outlierasset management, restructuring, fraud detection analysis and visualization. It allows decision-mak-and best practices identification. This framework ers to gain confidence in their understanding ofincorporates proven business, scientific and why a goal-related issue has surfaced (or beentechnological methods and processes to provide detected), and why a specific solution has beencompanies with a flexible and robust decision recommended.support system that will allow them to rapidlyFootnotes1 Karl Pearson, “Contributions to the Mathematical Theory of Evolution — On the Dissection of Asymmetri- cal Frequency Curves,” Philosophical Transactions of the Royal Society of London, Vol. 185), 1894, pp. 71–85, The Royal Society.2 Alberto Roldan, “The Business Competency Model: Turning Around in a Quarter,” Business Analytics blog, April 10, 2008, http://atomai.blogspot.com/2008/04/enterprise-business-analytics-turning.html.3 Noel Capon, John U. Farley and Scott Heonig, “Determinants of Financial Performance: A Meta-Analysis,” Management Science, Vol. 36, No. 10, 1990, pp. 1,143–1,159; Bronwyn Hall, “The Relationship Between Firm Size and Firm Growth in the U.S. Manufacturing Sector,” Journal of Industrial Economics, Vol. 35, Issue 4, 1987, pp. 583–605; Edwin Mansfield, “Entry, Gibrat’s Law, Innovation, and the Growth of Firms,” The American Economic Review, Vol. 52, No. 5, December 1962, pp. 1,031–1,051, American Economic Associa- tion; and Robert Gibrat, Les Inégalités Economiques, Paris: Sirey, 1931.4 Inventory turnover is similar to accounts receivable turnover. It measures how many times a company turned its inventory over during the year. Higher turnover rates are desirable, as they imply that management does not hold onto excess inventories and that its inventories are highly marketable. Inventory turnover is calculated as follows: Cost of sales/average inventory.5 Days in inventory is the average number of days a company holds its inventory before a sale. A low number of inventory days is desirable. A high number of days implies that management is unable to sell existing inventory stocks. Days in inventory is calculated as follows: 365 or 360 or 300/inventory turnover.6 Operating cycle = number of days in receivables + number of days in inventory. cognizant 20-20 insights 8
  9. 9. 7 Average daily base salary (ADS) = average annual base salary/365 days. Time-to-fill days (TTF) = number of days to fill a particular position, job category or job code within a corporate unit. Average time-to-fill days (ATF) = average number of days to fill a position, job category or job code, enterprise-wide.8 Alberto Roldan, “The Weighted Outlier Variable: Data Mining for Fraud Detection.”9 Michael Wooldridge, Nicholas R. Jennings, “Intelligent Agents: Theory and Practice,” Knowledge Engineer- ing Review, Vol. 10:2, 1995, pp. 115-152; Jun Huang, N.R. Jennings, John Fox, “Agent-Based Approach to Health Care Management,” International Journal of Applied Artificial Intelligence, Vol. 9, Issue 4, 1995, pp. 401-420.10 This is an adaptation of the distributed systems architecture diagram from Katia Sycara, Dajun Zeng, “Coordination of Multiple Intelligent Software Agents,” International Journal of Cooperative Information Systems, Vol. 5, Nos. 2 and 3, 1996, World Scientific Publishing Co.11 Scott B. Steinman, Barbara A. Steinman and Ralph Philip Garzia, Foundations of Binocular Vision: A Clinical Perspective, McGraw-Hill Medical, June 26, 2000.12 Anand S. Rao, Michael Georgeff, “BDI Agents: From Theory To Practice,” Proceedings of the First Interna- tional Conference on Multi-Agent Systems, 1995; Barbara Hayes-Roth, Rattikorn Hewett, Anne Collinot, Luc Boreau, “Architectural Foundations for Real-Time Performance in Intelligent Agents,” Real-Time Systems, Vol. 2, Issues 1-2, May 1990; Peter R. Bonasso, James R. Firby, Erann Gat, David Kortenkamp, David P. Miller, Mark G. Slack, “Experiences with an Architecture for Intelligent, Reactive Agents,” Journal of Experimen- tal and Theoretical Artificial Intelligence, Vol. 9, Nos. 2-3, April 1, 1997, pp. 237-256, Taylor & Francis Ltd.About the AuthorAlberto Roldan is an Associate Director of the Enterprise Analytics Practice. He has over 20 yearsof experience in designing analytics solutions for organizations with large, complex and diversedatabases. Alberto specializes in adapting proven analytics techniques and methods in neuro-science, medicine, physics and chemistry to business analytics problems. He can be reached atAlberto.Roldan@cognizant.com.About CognizantCognizant (NASDAQ: CTSH) is a leading provider of information technology, consulting, and business process out-sourcing services, dedicated to helping the world’s leading companies build stronger businesses. Headquartered inTeaneck, New Jersey (U.S.), Cognizant combines a passion for client satisfaction, technology innovation, deep industryand business process expertise, and a global, collaborative workforce that embodies the future of work. With over 50delivery centers worldwide and approximately 137,700 employees as of December 31, 2011, Cognizant is a member ofthe NASDAQ-100, the S&P 500, the Forbes Global 2000, and the Fortune 500 and is ranked among the top performingand fastest growing companies in the world. Visit us online at www.cognizant.com or follow us on Twitter: Cognizant. World Headquarters European Headquarters India Operations Headquarters 500 Frank W. Burr Blvd. 1 Kingdom Street #5/535, Old Mahabalipuram Road Teaneck, NJ 07666 USA Paddington Central Okkiyam Pettai, Thoraipakkam Phone: +1 201 801 0233 London W2 6BD Chennai, 600 096 India Fax: +1 201 801 0243 Phone: +44 (0) 20 7297 7600 Phone: +91 (0) 44 4209 6000 Toll Free: +1 888 937 3277 Fax: +44 (0) 20 7121 0102 Fax: +91 (0) 44 4209 6060 Email: inquiry@cognizant.com Email: infouk@cognizant.com Email: inquiryindia@cognizant.com©­­ Copyright 2012, Cognizant. All rights reserved. No part of this document may be reproduced, stored in a retrieval system, transmitted in any form or by anymeans, electronic, mechanical, photocopying, recording, or otherwise, without the express written permission from Cognizant. The information contained herein issubject to change without notice. All other trademarks mentioned herein are the property of their respective owners.