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Sas business analytics

  1. 1. businessanalyticsinsights Brain trust Enabling the confident enterprise with business analytics Informed decision making • Business analytics for industries and SMBs • Analytics applied to processes • Essentials to get started
  2. 2. Editor-in-Chief Anna Brown Copy Editors Amy Dyson Trey Whittenton Chris Hoerter Editorial Contributors Kelly LeVoyer Greg Wood Anne Milley Michael Dowding Design Patrice Cherry Circulation Ellen Brandt Production Melody Fountain Copyright © 2010 SAS Institute Inc., Cary, NC, USA. All rights reserved. Limited copies may be made for internal staff use only. Credit must be given to the publisher. Otherwise, no part of this publication may be reproduced without prior written permission of the publisher and copyright owner. SAS and all other SAS Institute Inc. product or service names are registered trademarks or trademarks of SAS Institute Inc. in the USA and other countries. ® indicates USA registration. Other brand and product names are trademarks of their respective companies. 104447_S50296.0310
  3. 3. Contents|P1 contents 2 The impact of business analytics on performance and profitability Jim Goodnight 4 Business analytics: helping you put an informed foot forward Jim Davis 8 How organizations make better decisions Thomas H. Davenport 12 16 The art, act and science of knowing Thornton May 20 What business analytics means for small and medium businesses Matthew Mikell 23 ACCESS THIS REPORT ONLINE: Business analytics in action Gail Bamford, David Wallace, Mike Newkirk and Becca Goren Embedding analytics into processes Thomas Davenport, Jeanne Harris and Robert Morison 27 8 essentials of business analytics Jim Davis 30 The art of the possible: business analytics to measure corporate sustainability Alyssa Farrell
  4. 4. P2|Performance and Profitability The impact of business analytics on performance and profitability By Jim Goodnight, CEO, SAS With the rising complexity of global business, gut decisions and hunches no longer suffice. Successful responses to threats and opportunities now depend on rapid and smart execution. Let me state it plainly: Business analytics is the key to achieving these challenging objectives. Our world generated more data in 2009 than in the previous recorded history of mankind. A good deal of this data can be converted into useful information and competitive advantage – by applying the right analytics. The answers are out there – in the data we capture and store. Right now, that capture and storage is costing huge amounts of money. Analytics converts those tremendous costs into invaluable assets. Far more than mere reporting or dashboards or scorecards, business analytics is a discipline that digs deeper into these vastly larger sets of data to uncover the most important insights. It can mean “social network analysis” to study behaviors and relationships on multiple levels to uncover fraud. It can involve in-database analytics to optimize retail assortments or pricing. It can mean analyzing portfolios to manage risk positions. For example, with the right analytics, retailers can predict how many red sweaters they need in stock and how many smalls or larges they need based on local demographics. They can also determine optimal prices for hundreds of thousands of products at multiple locations. Pricing used to be an art. Now, giant retailers can zero in on the optimal price for all their SKUs and stores. Banks can determine the optimal amount of cash to keep in ATMs. Automakers can predict how many spare parts they’ll need on hand – and when. Harrah’s, a global casino operator, uses analytics to optimize its marketing and customer loyalty programs. Thanks largely to its use of analytics, Harrah’s ranks No. 1 in profits as a percentage of revenues and has increased its share of wallet from 36 percent in 1998 to 45 percent today.
  5. 5. Performance and Profitability|P3 In the Philippines, the Bureau of Internal Revenue used analytics to recoup $114 million in unpaid value-added taxes, a 400 percent ROI in the first year. In Sweden, they are using analytics to reduce the number of patients who die from clinical errors. In addition to reducing unnecessary deaths, they expect to save $10 billion in health care costs at the national level through their analytic efforts. 1-800-FLOWERS.COM changes prices and offerings on its Web site, sometimes hourly, because it uses analytics. It also uses analytic software to target print and online promotions with greater accuracy. And it uses analytics to optimize its marketing, shipping, distribution and manufacturing operations. The result: a $50 million reduction in costs last year. Here’s my advice: Take the time to learn about analytics. Take the time to discover how analytics can provide an objective view of your world, not only as it appears today but also how it’s likely to appear tomorrow. I’m not talking about gazing into a crystal ball. I’m talking about the capability of competitive organizations to develop and implement strategies today that are based on a careful analysis of their likely outcomes in the future. And here’s my crystal-ball view: The ability to predict future business trends with reasonable accuracy will be one of the crucial competitive advantages of this new decade. And you won’t be able to do that without analytics. ONLINE Business Analytics Knowledge Exchange Jim Goodnight has been at SAS’ helm since the company’s incorporation in 1976, overseeing an unbroken chain of revenue growth – a feat almost unheard of in the software industry.
  6. 6. P4|Face Forward with Business Analytics Business analytics: helping you put an informed foot forward By Jim Davis Most companies today have plenty of data. Creating intelligence and gleaning real insight from this data is what continues to elude organizations. Despite years of talk about scorecards and metrics, gut feelings and experience are often still the guides for making important, sometimes critical decisions, even though current research reveals a clear link between business performance and the use of business analytics. Why BI is not enough Business intelligence provides historical, metric-driven decision making – and answers questions like, how many units did we sell, what did customers buy and for how much? BI is characterized by the creation of simple rules and alerts and the distribution of known facts to systems and people. These decisions have a low transformational impact on the business. So what exactly is business analytics and how can it help? Business analytics is, simply put, the application of analytical techniques to resolve business issues. It provides organizations with a framework for decision making, helping organizations solve complex business problems, improve performance, drive sustainable growth through innovation, anticipate and plan for change while managing and balancing risk. BI is still a highly valuable part of your overall business analytics environment, however, offering an excellent general purpose backbone for ad hoc analysis and basic operational reporting. It sounds like a lot, but if you break it down it’s all about enabling effective decision making. Organizations make decisions every day, and these sit on a continuum from frequent, up to millions per day to transformative, which occur less frequently but greatly impact organizational strategy. The need for agile decision making has never been greater but unfortunately, IT infrastructure, people and processes are lagging behind. For example, BI can alert management on how many credit card transactions were completed on a given day. It can also develop a simple rule for automatic reporting, like reporting on transactions greater than $10,000 to the regulators. From a more strategic decision perspective, business analytics can help answer questions such as what new products should we offer and in what markets? Or relative to the example, which credit card transactions are likely to be fraudulent? Business analytics can predict this with certainty and automatically deny transactions – while reporting activities in real time.
  7. 7. Face Forward with Business Analytics|P5 A business analytics framework is not a monolithic and costly approach but rather provides for incremental growth to achieve strategic goals at any given stage of an organization’s value chain. Business analytics allows organizations to “face forward,” bringing insight to transformative decisions. It benefits all aspects of an organization’s value chain, including: • Inbound logistics: receiving, storing, inventory control and transportation scheduling. • Operations: including factors such as packaging, equipment maintenance, testing and all activities that add value from the raw material to final product. • Outbound logistics: the activities re quired to get the finished products to market, including warehousing and distribution management. • arketing and sales: activities that M lead a buyer to purchase the product, including channel selection, advertising, promotion, selling, pricing, retail management and shelf space optimization. • Service: activities that maintain a product’s value, including customer support, repairs, installation, training, spare parts management and more. 1 1 P orter, Michael E., Competitive Advantage : Creating and Sustaining Superior Performance. 1985. In this way, business analytics drives innovation and improves an organization’s speed of response to market and environmental changes. In the credit card scenario, business analytics can not only discover the causal factors of fraud, but also forecast accurately when it will occur again. The company can then change business processes accordingly. In the following report, you’ll hear from several experts about how business analytics can be applied to business problems across all types of organizations, industries and value chains. Perhaps then it will become part of your plan to outthink and out-smart the competition. A step toward business analytics Effective decision making requires a business analytics framework that incorporates the people, processes, technology and culture of an organization. This common framework provides flexibility across the entire range of analytical decision-making types from highly managed operational analytics (such as a setting a simple credit limit) to discovery-based analytics (such as credit fraud scenarios or setting dynamic credit limits). A business analytics framework is not a monolithic and costly approach, but rather provides for incremental growth to achieve strategic goals at any given stage of an organization’s value chain. It offers business-ready analytical applications with underlying technologies for key services like data management and quality, reporting and advanced analytics. ONLINE Business Analytics Knowledge Exchange Credit card fraud management
  8. 8. P6|Face Forward with Business Analytics Six questions about your company’s information The modern organization is awash in information – yet, too often, it falls short of the tools, methods and expertise it needs to derive the greatest value from this untapped asset. Information about the most important facets of the business – customers, processes, employees, competitors and more – is gathered but not analyzed, reported but not understood, guessed about rather than acted upon. But not with business analytics. Ask these questions of your company and join aggressive competitors by being a smart organization. 1. here should we leverage business analytics? Focus business analytics where you W already compete. The payoff is greatest where you are playing to your strength, not where you are playing catch-up. 2. hy now? Because the technology is ready. Because competitors are likely exploring W the possibilities of analytical competition, too. And because it’s always risky to delay capitalizing on a new business capability. 3. hat’s the payoff? Business analytics is all about anticipating the payoff in order to W maximize it. The analytics initiative succeeds when the business capitalizes on an opportunity that analytics reveals. 4. hat information and technology do we need? Most companies don’t lack for sufficient W data, but instead suffer from a lack of integration and a lack of quality. Without good data, you simply can’t do good analytics. 5. hat kind of people do we need? You need a variety of talented people: analytical W professionals who design and refine analytical algorithms, and perform data mining; analytical semiprofessionals who do substantial amounts of modeling and analysis but are unlikely to develop sophisticated new algorithms or models; analytical amateurs who need to understand something of the analytical basis for operations and decisions; and the analytical manager who focuses the work of analytical professionals. 6. hat roles must senior executives play? Committed senior executives provide the passion W and the resources to drive their organizations in an analytical direction. In virtually every successful firm, senior management sets an analytical strategy and continually pushes it forward. ONLINE
  9. 9. Face Forward with Business Analytics|P7 HSBC: fraud detection that exceeds aggressive goals With fraud levels surging around the world, banks are facing greater regulatory scrutiny, as well as risks associated with damaging publicity from fraud. The ability to correctly make split-second decisions on accepting credit card transactions – before fraud occurs – is more important than ever. Using SAS Fraud Management, part of the SAS Business Analytics Framework, HSBC prevents, detects and manages financial crimes by scoring and accepting or rejecting millions of transactions a day in real time – at the point of sale. ® As a result, the global financial services leader has achieved significantly lower incidence of fraud across tens of millions of debit and credit card accounts. “The proof is in our fraud numbers – our detection rates and our false positives – which continue to meet our aggressive goals,” said Derek Wylde, Head of Group Fraud Risk, Global Security and Fraud Risk for HSBC. ONLINE Jim Davis is Senior Vice President and Chief Marketing Officer for SAS.
  10. 10. P8|Better Decisions How organizations make better decisions The following article is an edited excerpt of an article distributed by the International Institute for Analytics. By Thomas H. Davenport Author and researcher Tom Davenport is the President’s Distinguished Professor at Babson College. His newest book is Analytics at Work: Smarter Decisions, Better Results (with Jeanne Harris and Robert Morison, from Harvard Business Press). Relatively few businesses and organizations have given full and proper attention to one of their most important activities: making decisions regarding key questions such as what strategies and business models to pursue, which products and services to offer, which customers to target, what prices to charge and what employees to hire. Organizations with poor decision processes and tools eventually encounter poor outcomes, and performance suffers. However, new analytics, decision automation tools and business intelligence systems make it possible to make better use of information in decisions. “Wisdom of crowds” approaches and technologies allow larger groups of people to participate meaningfully in decision processes. Organizations cannot afford to ignore these new options if they wish to make the best possible decisions. Given both negative and positive incentives to get better, one might expect that organizations would attempt to improve their decisions — that they would prioritize them, examine their current level of effectiveness, investigate new options for making them better and implement some of those options. In my survey and analysis of dozens of corporations, I found that while they are, indeed, doing some of these things,
  11. 11. Better Decisions|P9 In a survey and analysis of dozens of corporations, Davenport found that very few organizations have undertaken systematic efforts to improve a variety of decisions. very few organizations have undertaken systematic efforts to improve a variety of decisions. In this excerpt I describe some of the more frequent approaches used to intervene in decision processes. Analytics, testing and data Infrastructures predicated on analytics and data were among the most common decision-making frameworks among the surveyed firms. Eighty-four percent of respondents mentioned an analytical component in their decision improvement efforts and 66 percent mentioned efforts to improve data. The range of analytical techniques employed was quite broad. Scoring approaches based on statistical analyses (usually some form of regression analysis) were common. Other approaches included optimization, behavior-based customer targeting, statistical forecasting, prediction of various phenomena and the use of text analytics. Systematic testing was one form of analysis that was being used somewhat frequently by companies; 18 percent mentioned it specifically in interviews. One key virtue is that it creates a decision-oriented context from the start. If a test between two alternative Web page designs is performed, it is generally assumed that a decision to adopt the winning page will be made. Other analytical approaches may not have as clear a path to a decision. A prerequisite of virtually any form of analytics is high-quality data, so it is not surprising that data-oriented responses were also common. Sixty-six percent of respondents mentioned some issue involving data. The most common were: • Having difficulty in accessing data. • Creating a common data architecture. • Eliminating duplicate data. • ntegrating “master data I management.” • chieving “one version of the truth” A in functional or process areas. • Dealing with too much data. • Gathering data from channel partners. • Creating new metrics. Not surprisingly, many organizations reported that they needed to change business processes to make better decisions.
  12. 12. P10|Better Decisions Technology support – and overrides – for decisions Several firms surveyed mentioned specific analytical software, testing software, data warehouses and Web analytics/ reporting software. Two other technologies were mentioned frequently: specialized information display technologies and business rule engines. Thirty-eight percent of companies in the study mentioned some use of specialized information displays such as scorecards and dashboards. These tools, typically found in the business intelligence category, allow decision makers to see only the information that they need to make a decision. Several firms mentioned using specific display approaches not generally supported by conventional BI tools, including the “A3” format for displaying key issues in a particular business domain. Some companies are using neuroscience principles to guide how information is presented and digested. This may be a bellwether of future attempts to link information and decision making. Another popular decision technology involves using business rules to enable automated or semiautomated decision processes — sometimes in conjunction with analytics (e.g., scoring-oriented applications). Many organizations employ business rules but allow humans to override the recommended decisions when appropriate. Changes in business processes Not surprisingly, many organizations reported that they needed to change business processes to make better decisions. Forty-three percent mentioned process changes of some type. For instance, some described process changes around supply chain management in an IT firm, lease processing in an auto financing firm, financial processes in health insurance or new product development processes. Several organizations mentioned changes for decisionoriented processes made in the context of Six Sigma programs. However, some decision-focused analysts noted that their original goal wasn’t necessarily to identify and implement process changes, and that they had to work with other groups to accomplish them. As one head of an analyst group at an IT firm commented, “We didn’t initially have the franchise to do process improvement — our thing was analytics. But it kept coming up on our projects. So we eventually just made it a part of our standard approach.” Decision-oriented methods and tools Several organizations reported that one aspect of their decision processes was an overarching, strategic management approach to guide all aspects of their efforts. Most of these initiatives are well-known approaches to business and management. • An insurance company adopted enterprise risk management. • he Six Sigma approach to process T quality and decision outcomes was implemented at a financial payments firm and a staffing firm. • financial services firm uses the A “net promoter score” for customer satisfaction decisions. • n economic decision analysis A approach, popularized and taught by Stanford’s Engineering School and the Strategic Decisions Group, is used by an oil company. In addition, three responding organizations developed analytically focused decision processes that have been widely used in IT systems development, but are not widely known in the decision-making or analytics literature. Sometimes called “agile methods” or “rapid prototyping,” they involve the creation of a series of short-term deliverables, and frequent review of them by the client and stakeholders for the decision. The organizations that use this approach found that it led to results that better fit the decision-makers’ requirements, and at a faster pace.
  13. 13. Better Decisions|P11 Conclusion From my research, it’s clear that organizations recognize the importance of improving decisions. Although the survey was not a random sample, individuals in 90 percent of organizations surveyed identified some attempt to improve decisions through better processes. Second, organizations employ a variety of intervention types to improve decisions across analytics, culture and leadership, and data. The most successful organizations adopted multiple interventions at once to improve a decision. As a result, analysts — previously responsible for data gathering and analysis — are morphing into consultants who may be responsible for framing decisions, process redesign, communication and education programs, and change management — all in addition to the traditional analysis functions. Organizations seeking to implement decision improvements should become familiar with these common intervention types and create ongoing capabilities to deliver them. Analytics improves decisions Davenport’s research found the most common types of decisions improved by analytics include: • ricing decisions (consumer goods, P industrial goods, government contracts, maintenance contracts, etc.). • ecisions to target consumer segments D (by retailers, insurers, credit card firms). • erchandising decisions (brands M to buy, quantities and allocations). • ocation decisions (for bank L branches or where to service industrial equipment). • reatment protocols for health care. T • roduct development for P pharmaceutical firms. ONLINE Order it now – Analytics at Work: Smarter Decisions, Better Results • tudent performance in educational S organizations. Read the full International Institute for Analytics research • valuating marketing approaches E (in both consumer and B2B environments). Engage with analytic leaders and researchers • iring decisions. H • ehicle routing decisions. V Analysts — previously responsible for data gathering and analysis — are morphing into consultants who may be responsible for framing decisions, process redesign, communication and education programs, and change management — all in addition to the traditional analysis functions.
  14. 14. P12|Business Analytics in Action Business analytics in action How are key industries deriving value from their business analytics implementations? By Gail Bamford, David Wallace, Mike Newkirk and Becca Goren HEALTH CARE According to the World Health Organization, global health spending totalled more than US$4.1 trillion in 2007, with $639 as the total health expenditure per person. That number will only grow in ways that affect businesses and citizens. Despite these huge investments, health care quality is uneven and resistant to changes and improvements. How can we enhance health care delivery while controlling those costs? It starts by carefully measuring and monitoring the quality of that care – a complex task perfectly suited for business analytics. Here’s how some forward-thinking health care institutions are delivering better quality of care more efficiently. Maine Medical Center Named to US News and World Report’s “America’s Best Hospitals” list for orthopedics, heart care and gynecologic care, Maine Medical Center uses SAS Business Analytics to understand key patient care metrics – and sustain a quality-driven culture. The data-driven approach has produced excellent results: • ncreased compliance on medication I reconciliation by more than 50 percent in a nine-month period. • ramatically reduced the rate of hosD pital-acquired infections by measuring where infections originated and what admission conditions closely correlated with acquired infections. • mproved government/industry acI creditation/compliance by incorporating national guidelines into key metrics. • eveloped new methods for caring for D stroke patients while controlling costs. By taking better care of these patients, the hospital expects fewer complications, which will reduce costs. Karolinska Institute The Karolinska Institute in Sweden needed a way to examine the effects of drugs, other treatments and lifestyle factors on patients with rheumatoid arthritis. Using SAS Business Analytics, the Institute has deployed a Web-based patient self-help application and predictive modeling to determine which treatments will be most effective for certain segments of RA patients.
  15. 15. Business Analytics in Action|P13 BANKING In a challenging economic and regulatory climate, bankers must be especially vigilant. Two key indicators of a bank’s health are net charge-offs (NCOs) – the value of loans written off as uncollectable – and nonperforming loans (NPLs) that are in default or delinquent more than 90 days. In the past two years in the US, bank NCOs have soared by an average of more than 350 percent across all institutions, with institutions holding assets of $5 billion or less showing growth of almost 500 percent. NPLs as a percentage of average loan balances have risen more than 278 percent at US banks with $1 billion or more in assets. How can financial institutions improve their collections and protect their bottom line? 1 Business analytics can provide the insights that institutions need to reduce both loan writeoffs and the cost of collections activities. First, models created within a business analytics framework can identify likely candidates for workouts and loan modifications. Second, business analytics can optimize collections activities to improve the probability of success and maximize self-treatment among debtor segments. It starts with three basic steps. • leanse and integrate. Cleanse and C standardize third-party credit and customer data, enrich it (e.g., add geocoding tags) and integrate it into a single data store. • nalyze and score. Develop scoring A models to analyze debtor-customer segment data against objectives, including “maximize profits” or “minimize writeoffs” or against constraints, such as loan types, outstanding balances or days delinquent. • ptimize and execute treatment O strategies. Analytical models help collections teams understand who is most likely to respond, which communication channels work best and how much payment to expect. Collections optimization driven by business analytics delivers the results that institutions need to improve their profitability. Optimizing collections A leading Australian financial institution previously relied on instinct when contacting delinquent customers. Since introducing SAS for collections optimization, it has achieved a 300 percent ROI in less than six months. A debt purchasing firm based in the UK uses SAS to predict debt portfolio performance. This enables the firm to make quicker decisions on acquiring new debt portfolios at the right prices, collect more from each portfolio and grow revenues by 50 percent annually. 1 Source: SNL Financial
  16. 16. P14|Business Analytics in Action Meaningful ROI with Business Analytics One SAS customer increased company profitability by accurately predicting product demand and customer behavior – more than doubling its forecasting accuracy. It found that for every 1 percent reduction in forecast variance, it saved $200,000. Another manufacturer improved two seemingly competing objectives. It simultaneously reduced inventory by 20 percent, eliminating millions of dollars of holding costs, yet improved service levels, which directly and positively affected customer satisfaction. MANUFACTURING From diapers to jet engines and almost everything in between, manufacturing expertise is a competitive differentiator for companies that follow optimal practices and methodologies to attack inefficiencies and eliminate waste. Business analytics is essential in these settings to improve production and sales planning, enhance the supply chain, reduce inventory, streamline logistics and much more. For example, with demand forecasting, business analytics can be a key contributor to a manufacturer’s success. Better forecasts deliver ROI by: • Reducing inventories. • Improving order fulfillment rates. • Shortening cash-to-cash cycles. Many manufacturers struggle with optimally managing and forecasting their raw materials requirements, workin-process (WIP) inventory and finished goods inventories. Without the right mix of raw materials, production plans fall apart and customer orders are delayed (or, worse, canceled). Missing WIP forecasts similarly leads to inefficient schedules and a crippling misallocation of finished stocks – not having the right quantities of the right goods at the right time and in the right places. While the data is often available to prevent, identify and correct these imbalances and inefficiencies, it is usually not integrated, analyzed and shared across the organization. Data management technologies can bring together islands of information such as point-of-sale (POS) data and historical shipment data. Once that data is aggregated, business analytics models and tools can accurately forecast the demand for products by family, individual SKU, geography, customer type, etc. With a clear and accurate demand picture, manufacturers can properly allocate raw materials across plants and regions – all optimized by distribution channel – to create complete roll-ups in master planning schedules. TELECOMMUNICATIONS You’ve likely experienced it before – your cell phone loses service one too many times, so you switch providers. Low barriers to churning mean providers must vigilantly and carefully invest to maintain and increase their service quality and customer satisfaction rankings. After all, your satisfaction keeps them in business. Network managers typically receive error reports and alarms after a network device fails. The team addresses the stream of trouble tickets, but never gets insight into underlying causes or trends for outages. The result: long call-resolution times. With business analytics and approaches such as predictive fault analysis, network managers can analyze performance to pre-empt failures. They can analyze trouble tickets and optimize corrective services, shortening times you are without coverage. Strong data management, including data quality and reporting capabilities – all key underpinnings for business analytics – can help quickly identify
  17. 17. Business Analytics in Action|P15 One large telco service provider used SAS to identify emerging issues (an average of two weeks prior to failure) and double the percentage of tickets resolved within 48 hours. service and network issues. Business analytics helps to: • dentify and remove duplicate trouble I tickets. • nderstand faults and performance on U a macro level. • etermine which services have the D highest fault rates. In addition to analyzing network performance, predictive analytics technologies can help evaluate demand, faults and systems to improve resource utilization and quality of service (QoS). A telco provider can then identify when and where network resources are deployed and quality/performance variations over time. Business analytics allows network and service managers to better understand causes and impacts of failures. They can prioritize and pre-empt outages, optimize repairs and mitigate risk with answers to key questions: • ow significant is each factor H influencing network faults degradation? Gail Bamford is a SAS Global Industry Marketing Manager for Public Sector. David Wallace is a SAS Global Industry Marketing Manager for Financial Services. Mike Newkirk is a SAS Global Industry Marketing Manager for Manufacturing. Becca Goren is a SAS Global Industry Marketing Manager for Communications, Media and Entertainment. ONLINE Health care providers keep pace with change • hich network faults are tied to a given W trouble ticket? • hich faults are related and what are W their impacts? Armed with predictive fault analytics, a telco provider can limit the times you lose a signal and continually improve overall service, allowing it to keep your business. The standard for clinical data analysis and reporting ONLINE Get the full stories on: Maine Medical Center Karolinska Institute Solutions for better risk management Compete in manufacturing Invest wisely, communications service providers
  18. 18. P16|The New Know The art, act and science of knowing An excerpt from The New Know 1 By Thornton May The Internet makes self teaching — and lifelong learning — the rule rather than the exception. Historians ultimately will come to consensus on what to call the time period between the frenzy that was the dot-com bubble and the period before society finally enters the data cloud. For want of a better phrase, I call the 20year interregnum we currently inhabit (1995 – 2015) the Age of Little Information. I come to this label not because the age exhibits a lack of information. Quite the contrary, it is during this epoch that information — previously locked away in analog form — is becoming widely digitized. The New Know has changed our reality along 10 fundamental dimensions. Futurist Thornton May positions analysts as heroes of the age we are about to enter in his new book, The New Know: Innovation Powered by Analytics. C opyright 2009 by John Wiley Sons, Inc. All rights reserved. Reprinted with permission. 1 New Know Reality #1: You will be expected to do something with information. All this newly digitized information has had, relatively speaking, little impact on behavior and little impact on organizational outcomes. We are now exiting a historical moment of undermanaged and only occasionally acted-upon information to an environment requiring much more active, much more intense, much more aggressive information management. You as an executive will be held much more accountable for your data management behaviors. You will be expected to transform “data lead” into “knowledge gold” via the expeditious
  19. 19. The New Know|P17 sensemaking leading to efficacious action. In the Age of Little Information, we were data vegetarians. In the New Know we will have to become information and knowledge carnivores. New Know Reality #2: There really is more to know. The New Know will be awash with data. Processing power doubles every 18 months. Storage capacity doubles every 12 months. Bandwidth throughput doubles every nine months. There is more to know. Organizations are having trouble keeping up — and, sadly, the fact that there are more facts arriving at a faster rate of speed is not even the tip of the cognitive iceberg. Like the “fog of war,” info warriors speak of the “fog of facts” (e.g., confusion about what information is to be believed, what information sources are credible and what version of reality is to be acted on). In a world of multiple sources of information and 24-hour decision making, the very character of information is changing. A “fact” is no longer a “fact.” New Know Reality #3: You will have to know more about knowing. One of the major changes defining the new competitive environment is the requirement to know more about knowing, what experts sometimes refer to as metacognition. Society is about to undergo a tectonic shift in how it thinks about thinking. Driving this cognitive plate shifting are the RSS feeds, podcasts, blogs, old-media headlines and evening news programs, which are increasingly filled with images and instances of current-generation leaders being asked by dissatisfied nextgeneration voters, customers and shareholders: “What were you thinking?” Looking beneath the surface, they are really asking: “How were you thinking? Via what processes, using what data and assisted by what tools did you arrive at your course of action?” New Know Reality #4: Brain science and decision science are converging. Scientists do not know how the brain works — yet. But they are sneaking up on it. Readers may be surprised to learn that neuroscience has been around for over 100 years. Neuroscience has progressed to the point that we at least know what we do not know.
  20. 20. P18|The New Know Carly Fiorina, former CEO at Hewlett-Packard, believes that distilling truth from overwhelming amounts of information is the essence of leadership. To some extent, it is a simple truism that the brain is involved with all things that comprise our human existence. It follows, loosely, therefore, that understanding the brain will help us understand the human condition more fully. The big news is that the brain possesses innate qualities that influence individual experience and opinions. There are things that can be known— that need to be known by executives seeking to maximize value from the knowledge assets available to the enterprise. New Know Reality #5: The environment is changing our brain. The information flood should be viewed as a permanent macroenvironmental change. Thinking in Darwinian terms, what adaptive pressures does this environmental change place on us? “Daily exposure to high technology — computers, smart phones, video games, search engines — stimulates brain cell alteration and neurotransmitter release, gradually strengthening new neural pathways in our brains while weakening old ones. Because of the current technological revolution, our brains are evolving right now — at a speed like never before.” New Know Reality #6: Information management Is the essence of leadership. Low-cost communications give rise to almost toxic levels of spin, hype and empty rhetoric. Leaders are able to cut through all the noise. Does your organization filter its data? Carly Fiorina, former CEO at Hewlett-Packard, believes that distilling truth from overwhelming amounts of information is the essence of leadership. She believes that all of us are overwhelmed with information, and what sets great leaders apart is their ability to cut through the clutter and distinguish the truly important from the merely interesting. New Know Reality #7: A more connected world. One of the transformational elements moving society to the New Know is something analysts at Forrester Research call the “groundswell.” Josh Bernoff, Vice President at Forrester, contends: “There’s so much information flowing out of the groundswell, it’s like watching a thousand television channels at once. To make sense of it, you need to apply some technology, boiling down the chatter to a manageable stream of insights.” The new scarce resource in the next economy will be the human attention needed to make sense of information. The question is: How will we be able to keep up?
  21. 21. The New Know|P19 New Know Reality #8: Math matters. Mathematics is now so widely accepted as the arbiter of truth in the modern world that it has become the backbone of disciplines ranging from physics (of course) to economics and sociology. Backing up a statement with mathematics gives it an aura of validity, even if the topic has to do with something as mathematically messy as human behavior. However, many otherwise “normal” executives have a pathological aversion to math. This is not just unfortunate, it is dysfunctional. Some intuition about numbers, counting and mathematical ability is basic to almost all animals. People use math to make decisions every day. “In an age where you need to be numerate to do almost anything (from building bridges to conquering disease), governments anxiously compare their performance in mathematics with that of competitor nations.” New Know Reality #9: There are significant downsides to not knowing. Success requires materially expanding what you know and adding precision and efficiency to the processes (analytics) whereby you come to know. Here is a metaphor to keep in mind as you think about the New Know. If you are locked in a room with an elephant, it is useful to know where it will step. Every key process in your enterprise is locked in a room with an elephant — a critical process, serving a critical customer. Business analytics tells you where that elephant will step. New Know Reality #10: Knowing can change the world. If knowledge is power, then “knowledge about power should be especially empowering,” says John Murrell, the very-much-in-the-know editor of Good Morning Silicon Valley. For instance, using 15,000 meters, a subset of National Grid Customers will be able to access their energy — use information via the Internet, by a thermostat readout, or through text messaging, and use the data to change their consumption patterns. Program participants are expected to save 5 percent, or about $70 a year, on their energy bills. Change advocates from all fields of endeavor are excited about the possibility of putting new information in front of people in the hopes of changing behavior. If you are locked in a room with an elephant, it is useful to know where it will step. Every key process in your enterprise is locked in a room with an elephant — a critical process, serving a critical customer. Business analytics tells you where that elephant will step.
  22. 22. P20|Business Analytics for SMBs What business analytics means for small and medium businesses An interview with Matthew Mikell, SMB Global Product Marketing Manager The Wine House discovers $400,000 in ‘lost’ inventory Economic times may be tough, but Bill Knight, owner and President of The Wine House, is toasting a 100 percent return on his investment in SAS. The first day its SAS application was live, the brick-and-mortar and Internet retailer discovered 1,000 items of wine that hadn’t moved in more than a year. “We had a huge sale to blow it out, generating $400,000 in capital in one weekend,” Knight said, “and just in time, because in today’s economy, we’d be choking on that inventory.” Using SAS, The Wine House has reduced its aged inventory by 40 percent. “Now I can get the answers I need and base decisions on facts rather than gut intuition,” says Knight. “I’ve got less money tied up in inventory, I know who our best customers are, how to market to them and can monitor the effectiveness of our marketing. Our ROI with SAS has been well over 100 percent in less than a year, so my return on investment has been fantastic.” ONLINE When it comes to business analytics, it sometimes seems like only major enterprises garner the spotlight. That’s somewhat understandable given the complexity and scope of their analytical challenges and the nature of their highprofile brands. But the fact is, far more small to medium businesses (SMBs) are poised to implement business analytics solutions. In the US, these companies have revenues of less than $500 million. In Europe, the SMB category comprises companies with a maximum of EUR 450 million (about US$611 million). While in the Asia Pacific region, SMB often refers to both employee numbers and revenue, and range between 200 and 250 employees and $200 million and $500 million in revenue. In many ways, these businesses are striving for the same goals to grow their business through innovation, and need the same sophisticated functionality scaled appropriately to their processes. In this QA, Matthew Mikell, SAS Global Product Marketing Manager, shares his perspectives on what business analytics means to SMBs. Q. What are some of the unique challenges that SMBs face with respect to business analytics? A: SMBs primarily face the issue of scale. At SAS we have heard four general constraints when listening to organizations that are SMBs: 1) Decision-making style Transitioning from gut instinct to factbased framework can be difficult in part because the former approach has likely served the successful SMB very well. Most SMBs have Excel experts who can generate some great static charts and graphs — and I wouldn’t ever want to denigrate the value those reports provide. But there’s so much more value that can be derived from in-depth analyses. Once SMB executives get a real glimpse of the insights that are lurking beneath the surface of their transaction data, their willingness to adopt business analytics increases pretty quickly. 2) Cash flow In addition to a shift in decision-making style, cash constraints can pose very real obstacles for an SMB that wants to mature in this area. Considering the business analytics framework helps improve margins, retain key customers and grow share of wallet in their markets. However, the long-lasting return on investment far outweighs the capital required to undergo the transition.
  23. 23. Business Analytics for SMBs|P21 SMB executives – often owners or people with lengthy tenures – worry about letting go of the information flow and empowering people to make decisions that were previously reserved for executives. 3) IT resources and infrastructure More than 80 percent of SMBs with about 100 employees have only four dedicated IT staffers. They’re stretched thin, and that can make it very difficult to expand the IT mandate beyond critical business operations into managing business analytics environments. 4) Business analytics maturity SMBs must have an appreciation for the level of skills required to meet overall strategic goals through business analytics. Research from Aberdeen Group suggests that SMBs without the relevant skill sets are poorly positioned to drive value from an analytical solution. It reports that SMBs using some sort of analytical applications perform at a higher level than their competitors that do not. 1 The main SMB challenge for moving to business analytics is the understanding of its impact on these four critical areas, and building a capability that is costeffective and remains flexible and easy to use. Q. Why should SMBs adopt business analytics? A: It essentially boils down to competitive pressures. SMBs need to continually innovate. If you’re an SMB that isn’t constantly seeking to optimize every possible aspect of the operation, you’re at a disadvantage. Internally, employees need these tools to be productive. Otherwise, it’s gut-based decisions, or cutting and pasting from multiple tools. The truth is what brought you to where you are typically won’t take you to the next level. But it’s very difficult, culturally, to walk away from what’s made you successful. SMB executives – often owners or people with lengthy tenures – worry about letting go of the information flow and empowering people to make decisions that were previously reserved for executives. Q What’s the best way for SMBs to tackle the adoption of business analytics? A: Of course, every company differs – particularly at the SMB size. But we’ve found that there is a general approach to the adoption of business analytics. The first step is to ensure you have sponsorship from company executives. Clearly lay out the business analytics benefits and return to the management team. This transparency is key at the SMB level as SMB executives are traditionally heavily involved in analyses, reporting and the decision-making process. Make it clear how business analytics will resolve a compelling issue or attract and retain customers, for example. 1 A berdeen Group, 2009, Beyond Spreadsheets: The Value of BI and Analytics.
  24. 24. P22|Business Analytics for SMBs The second strategy is to focus on a particular business process or issue. Don’t introduce business analytics as a broad, unfocused utility for general usage. This will occur naturally as you solve more focused issues, building up confidence in fact-based decision making as a core competency. Some of the typical issues that we see being solved with business analytics include improving customer data quality for improved marketing, invoicing or customer service, or improved product pricing and packaging analysis to drive a higher market share. Finally, don’t rest on your laurels. Capitalize on your initial success to broaden deployment to other areas of the organization. Those adoptions move faster once you can point to a successful track record in another area. Q. What’s the difference between business analytics for large enterprises vs. SMBs? Q. Can you share some examples of how SMBs have been able to capitalize on business analytics? A: In a nutshell, it’s about scale. Deployment and support strategies will have a different nature. What’s more interesting to me, however, is the important commonality: functionality. Business analytics in SMBs is not about presenting a subset of functionality but rather surfacing the right functionality for the problem at hand, and opening up to more as the business requires it. Despite their size, SMBs face similar challenges to make better and more informed decisions to continue innovating in their markets. It is therefore essential to provide a rich set of features and a very high level of technology usability. A: Sure. We’ve worked with an energytrading company that enables staff to predict what today’s electricity and gas purchases will sell for months later when consumers buy. Business analytics supplies that intelligence to traders in a cleaner, faster and more accurate way. A collection agency uses SAS Business Analytics to analyze bad-debt portfolios before acquiring those assets. This is a quantum leap forward from its previous model, which was simply buying any debt assets for as little as possible and hoping to collect successfully. A player in the secondary-ticket market uses SAS to develop a deeper understanding of the needs of its thousands of customers. By segmenting them and catering to psychographics, the company can optimize how frequently it contacts the customers and improves loyalty. ONLINE Software for SMBs Some of the typical issues that we see being solved with business analytics include improving customer data quality for improved marketing, invoicing or customer service, or improved product pricing and packaging analysis to drive a higher market share. Matthew Mikell leads Global Product Marketing for SMB markets and software-as-a-service (SaaS) offerings at SAS, supporting strategic planning, messaging and product offerings through direct and indirect channels.
  25. 25. Analytics at Work|P23 Embedding analytics into processes In their latest book, Analytics at Work: Smarter Decisions, Better Results, Thomas Davenport, Jeanne Harris and Robert Morison show how companies apply analytics in their daily operations. This excerpt, ‘Embedded Analytics in Action,’ explores what to consider when infusing analytics into business processes. We see examples of analytics at work within core processes in a variety of business areas. Statistical analysis has been a feature of supply chain and logistics management for decades, starting with the techniques of statistical process control (SPC) and total quality management (TQM). Real-time analytics are helping guide call center workers in their interactions with customers. And analytics are well established in the engineering and simulation sides of product design. Among business support functions, analytics are essential to many facets of finance, common in the management of technology operations, and relatively new to human resources (though of enormous potential there). In corporate development, key decisions — for example, regarding mergers and acquisitions—may benefit greatly from analytics, but few companies take a process approach to such activities. Reprinted by permission of Harvard Business Press. Excerpted from Analytics at Work: Smarter Decisions, Better Results by Thomas Davenport, Jeanne Harris and Robert Morison. All rights reserved. Consider the example of UPS to whet your appetite for embedding analytics in your core business processes. As a logistics company, UPS lives and breathes the “traveling salesman problem”—how to reach a variable series of destinations most efficiently with the right delivery capacity, and often in designated time windows, every day. The solutions naturally demand very sophisticated and industrialized analytics: for capacity planning of aircraft and truck fleets, for routing packages through its distribution network, and for scheduling and routing delivery trucks. For a company this steeped in analytical applications, the frontier is moving closer to real-time, dynamic adjustments. For example, UPS is experimenting with algorithms to adjust the order of deliveries as conditions (e.g., road closures, extraordinary customer need) change. Making processes analytical The effects of analytics on the operations of a process can be profound, and over time you may want to reengineer the overall business process and revamp its information systems to capitalize on the potential for analytics-based improvement. But you can start embedding analytics without a major overhaul. For processes that rely extensively on enterprise systems, it may be possible to simply start taking advantage of the analytical capabilities that are already included in the software. However, many process analytics initiatives will require tools, techniques, and working relationships that are likely to be new and unfamiliar at first. We have found that implementing analyticsenabled processes requires applying four major perspectives.
  26. 26. P24|Analytics at Work The effects of analytics on the operations of a process can be profound, and over time you may want to reengineer the overall business process ... but you can start embedding analytics without a major overhaul. The first is process implementation. Occasionally a business may create a new analytically enabled process or rebuild a process from scratch, but most often you are adding capability to and altering an existing process. Especially given the iterative nature of many analytical applications, it’s essential to measure baseline process performance first and to run the enhanced process in parallel to the original (perhaps as a pilot or test) in order to refine the new process and measure its performance and value. In some cases, process simulation can yield insights about how the process might perform even before implementation. Next, organizations should consider model implementation. Much of the distinctive work of process analytics centers on designing, developing and iteratively refining statistical algorithms and descriptive or predictive models or rule-based systems. If you are going to industrialize important decision processes, it is important that the rules, assumptions and algorithms in your model are correct. Analytical projects generally require different tools and development methodologies from those employed in more traditional systems development. And, of course, this work is performed by business analysts and programmers with special skills in statistical methods and modeling. Third is systems implementation. The analytical system must be incorporated into the set of systems and technologies supporting the business process. In building these interfaces, it helps to employ process-oriented technologies, including capabilities of ERP systems, workflow and document management systems. And integrating and testing the new systems and interfaces is critical given analytics’ reliance on a broad range of quality data and the fact that analytics-based decisions may dramatically change process flow. Human implementation is the fourth perspective. Often the greatest implementation challenge, especially when analytics is new to the process and the people performing it, is on the human side. Only people can tell if an embedded application is resulting in good decisions, so be sure to involve them in developing, managing and monitoring the assumptions and results of any embedded model. Another important factor is developing the right mix of automated and human decision making and enabling process performers to trust and use their new analytical information and sometimes tools.
  27. 27. Analytics at Work|P25 Embedding analytics into processes starts with a robust analytical architecture that provides an accurate, timely, standardized, integrated, secure and reliable information management environment. SAS and Accenture: Making business analytics work for you SAS and Accenture have joined the forces of their best and brightest to help more organizations reap the benefits of an analytic approach. The new Accenture SAS Analytics Group combines Accenture’s domain and industry experience with SAS’ analytic strengths to provide the services (best business practices, proof of concepts), technology (both industry and cross-industry offerings) and support (competency centers, certification programs) to help companies reach their competitive potential – more efficiently and cost-effectively. ONLINE Accenture SAS Analytics Group All four perspectives must mesh: process flow and decisions are enabled or controlled by analytical models, other information systems interface with the models and provide clean data feeds, and people perform the process better with the help of embedded analytics. If you lack clear business goals, specifications or momentum, be prepared to demo or pilot the concept, to work with stakeholders to define targets and set ambitions, and to make the business case for investing in prerequisite assets, often starting with data. IT’s role in embedding analytics into business processes Technology is an integral part of most business processes today. So the best route to embedding analytics into processes is often through the technologies and applications that employees routinely use to do their jobs. Embedding analytics into processes starts with a robust analytical architecture that provides an accurate, timely, standardized, integrated, secure and reliable information management environment. Scorecards and applications that monitor and alert based on predetermined thresholds are the norm these days, but too many remain as standalone applications. An industrial-strength IT architecture makes it vastly easier to weave analytics into ongoing work processes in three ways: 1. Automated decision applications. These sense online data or conditions, apply codified knowledge or logic, and make decisions — all with minimal human intervention. Technology is best suited to automate decisions that must be made frequently and rapidly, using any kind of information (data, text, images) that is available electronically. The knowledge and decision criteria used in these systems need to be highly structured. The factors that must be taken into account (the business problem’s dimensions, conditions and decision factors) must be clearly understood and not subject to rapid obsolescence. The conditions are ripe for automating the decision when experts can readily codify the decision rules, a production system automates the surrounding process and high-quality data exists in electronic form. Business activities that benefit from automated decisionmaking applications include fraud detection, solution configuration, yield optimization, recommendation/realtime offers, dynamic forecasting and operational control (like monitoring and adjusting temperature).
  28. 28. P26|Analytics at Work 2. Business applications for operational and tactical decision making. Analytical managers rely on analytical applications (whether custom developed or from third parties) that are integrated directly into Web applications or enterprise systems for tasks such as supply chain optimization, sales forecasting and advertising effectiveness/planning. Recommendation, planning and “what-if” applications can incorporate near real-time information and multiple models to dynamically optimize a solution while factoring in conflicting goals like profitability and customer satisfaction. Analytical business applications are best suited to welldefined, periodic tasks in which most of the information needed is predictable and available electronically. Since the data, knowledge and decision criteria are typically less defined and/or more fluid than those of a fully automated application, they require industry and functional expertise. 3. Information workflow, project management, collaboration and personal productivity tools. Most information work is done through personal productivity tools like Microsoft Office. As vendors increase the analytical quotient of their collaboration and productivity tools, analytics become more accessible to analytical amateurs throughout the enterprise. One consumer products company found that its elaborate modeling tool was ignored by nearly everyone until the findings were distilled into a monthly deck of ten PowerPoint slides and e-mailed directly to the sales force. As platform vendors align their products to work together more seamlessly, a manager needn’t know that his Excel spreadsheet is using the company’s ERP system to prepare his forecast. These tools and applications work best for less structured information with less defined decision criteria. To address the growing need to embed analytics into processes, both specialty applications vendors and the major platform vendors are building more analytical functionality directly into their tools and applications. Software companies are building more industry-specific, process-driven applications. Major platform providers like Oracle are embedding analytics into their products by building statistical functions directly into their enterprise data warehouse products. ERP vendors, which are including more sophisticated analytical features, remain a powerful way to integrate industry best practices into business processes. And Microsoft, Oracle, SAP and SAS continue to quietly embed more sophisticated analytics and business intelligence capabilities into their applications and tools.
  29. 29. 8 Essentials|P27 8 essentials of business analytics Find out what business analytics can do for you – and how to get started By Jim Davis Leading banks use business analytics to predict and prevent credit fraud, saving millions. Retailers use business analytics to predict the best location for stores and how to stock them. Pharmaceutical firms use it to get life-saving drugs to market more quickly. Even sports teams are getting in on the action, using business analytics to determine both game strategy and optimal ticket prices. But these advanced business applications tell only part of the story. What’s going on inside these market-leading companies that sets them apart? They have committed to deploying their people, technologies and business processes in new ways. They have committed to a culture that is based on fact-based decisions – which helps them anticipate and solve complex business problems throughout the organization. By embracing an analytical approach, these companies identify their most profitable customers, accelerate product innovation, optimize supply chains and pricing, and identify the true drivers of financial performance.
  30. 30. P28|8 Essentials And you can too. Get started with business analytics by taking these eight essential actions: 1. mprove the flow and flexibility I of data. High-quality data must be integrated and accessible across your organization. It should also be structured in a flexible way that allows your analysts to discover new insights and provide leaders the information they need to adjust strategies quickly. Strengthening and flexing the data backbone of your enterprise will pay off when you need to change business processes quickly in response to market shifts, regulatory or stakeholder demands. 2. Get the right technology in place. Take an enterprise approach to data management and analytics to effect better decisions. Remove disconnected silos of data, technology or expertise. Your technology portfolio should include: • ptimized data stores to support O core business processes and discovery. • ata integration and data quality D software. • nalytical software with the means A to effectively deploy, explore and share results in a meaningful way. • ntegrated analytical applications I designed to solve defined issues quickly. When selecting technologies, consider “risk-to-value”: Can the technology be applied to help reduce costs and increase revenue? And getting the right technology in place doesn’t have to mean a complete overhaul. 3. Develop the talent you need. Develop or recruit analytic thinkers who seek and explore the right data to make discoveries. To make analytics work, analysts must also be able to communicate effectively with leaders and link analytics to key decisions and the bottom line. 4. Demand fact-based decisions. An analytical company makes a wide range of decisions. Some are ad hoc; some are automated; some are transformative. The common thread? Evidence backs them all. Managers encourage asking the right questions of the data to get maximum insight. How results are deployed is also important – through operation systems such as customer relationship management applications or real-time fraud applications to interactive dashboards, data movies, in databases – wherever needed to ensure decision makers have the information they need when they need it (and in the way they can best consume it). 5. Keep the process transparent. Transparency implies openness, communication and accountability; it is key to successful business analytics projects. The value delivered from an By embracing an analytical approach, these companies identify their most profitable customers, accelerate product innovation, optimize supply chains and pricing, and identify the true drivers of financial performance.
  31. 31. 8 Essentials|P29 investment in business analytics must be visible and measureable. Who the analysts are and what they’re seeking to accomplish should be clearly communicated to the business, as should their findings. 8. Revise your strategies – often. Your competitors will often duplicate your analytical initiatives. Staying ahead requires continuous review of strategy and development of new skills and capabilities. Top five benefits of business analytics 6. evelop an analytical center D of excellence. Create a centralized team approach – an analytical center of excellence (ACE) – which promotes the use of analytics and associated best practices. Your implementation of an ACE will depend on your organization’s maturity and requirements, but the most effective implementations address all elements of the organization’s analytic infrastructure: people, process, technology and culture to support the business’ strategy and operations. Get started now. Find important questions that need answering and problems that need to be solved. Answer these questions, solve these problems and create value for the organization. By creating small wins in any business, function or department, over time your company will become an analytical competitor. 1. mproving the decision-making I process. 7. Transform the culture. A strong analytical culture has executive sponsorship and encourages creativity. Experimentation should be seen as part of learning, and employees should be given permission to fail as they learn from trying new things. Jim Davis is Senior Vice President and Chief Marketing Officer for SAS. When Computerworld asked 215 IT and business professionals to name the key benefits of business analytics software, they received a wide range of responses. The five most popular were: 2. peeding up the decision-making S process. 3. etter alignment of resources with B strategies. 4. Realizing cost efficiencies. 5. esponding to user needs for R availability of data on a timely basis. ONLINE ONLINE Defining business analytics white paper:
  32. 32. P30|Art of the Possible The art of the possible: business analytics to measure corporate sustainability By Alyssa Farrell In the abstract, business analytics presents a range of powerful options to uncover meaningful insights that promote action. And that promise is compelling to virtually any organization. But the case becomes even more persuasive when we consider how it can be applied to one of the fastest-emerging issues in corporations today: sustainability and the corporate “environmental footprint.” Today, companies are seeking to strengthen the so-called “triple bottom line” that conceptually expands the traditional financial framework to encompass rigorous reporting on the organization’s performance on sustainability issues such as the carbon footprint, community development, occupational safety and dozens of other metrics.
  33. 33. Art of the Possible|P31 In a report from the Economist Intelligence Unit, researchers report that the top three motivations for sustainability initiatives are brand enhancement, revenue growth and cost savings – in other words, outcomes that have a direct impact on profitability. Environmental protection only placed fourth on the list, amply demonstrating that pragmatism and not altruism is the dominant motivator. Three planning challenges Unfortunately, significant barriers have impeded decisive corporate action. In the first MIT Sloan Management Review Business of Sustainability Survey, researchers articulated three major roadblocks. The first is a basic lack of information upon which to base sustainability efforts and decisions. Despite the high profile for sustainability, managers often find themselves forced to speculate about drivers of sustainable performance and lack a deep understanding of issues that are relevant for their industry. Accessing, interacting with and analyzing the fundamental data about energy, water and waste is a nonnegotiable premise for effective sustainability. Second, companies often have conflicting definitions of precisely what sustainability means to their organizations. This makes it extremely challenging to develop a meaningful business case for sustainable investments and presents an often insurmountable barrier to the effective cross-functional collaboration that is necessary for success. Third, without that business case based on accepted definitions, companies struggle with precisely how to measure the ROI of sustainability efforts. What’s more, tangible and intangible costs and benefits abound in the sustainability discipline – but they can be especially challenging to forecast because the goals for greenhouse gas emissions reductions established by governments are often in 10- and 20-year time horizons, far exceeding the typical oneto three-year payback period. Traditional reporting and analysis can often fall short when attempting to predict future impacts of sustainability investments. Business analytics plays a critical role by enabling the organization to balance today’s ROI objectives with longer planning horizons. These challenges are not uncommon for emerging business issues. Sustainability is a new discipline for most organizations, one where there isn’t a generation of tested and proven models to call upon and modify. As a result, many organizations forego the effort to model the intangible benefits that may result from sustainable practices. Or, they minimize important externalities such as environmental or societal costs and benefits – all of which can become tangible with business analytics. Business analytics at work: gaining energy efficiency at Poste Italiane Group The art of the possible is already in practice at leading organizations today. The Poste Italiane Group uses software from SAS to analyze energy efficiency in more than 250 facilities, including those with the highest energy consumption – such as data processing centers, executive centers and the largest branches. Their analysis has identified best practices that led to an immediate reduction in energy consumption and a 7 percent reduction in CO2 emissions. Future developments involve correcting operation and maintenance behaviors for the systems and indirectly for the buildings. ONLINE
  34. 34. P32|Art of the Possible SAS and corporate sustainability Sustainability has remained a top priority with SAS precisely because of its potential to deliver tremendous business value. It’s not just the right thing to do; it’s the smart thing to do. In addition to employee engagement practices, from health care to expanded job opportunities, SAS has made great progress in reducing its environmental footprint. For example, a 1-megawatt solar array is providing clean, renewable energy to the public energy grid for the local utility. Several construction projects at SAS offices around the world utilize low-environmental-impact design principles. Notably, SAS is pursuing Leadership in Energy and Environmental Design (LEED) certification for a new conference facility and a new cloud computing facility located at its global headquarters. ONLINE For more information on SAS and sustainability, check out the Corporate Social Responsibility Report: The ROI matters Despite these challenges, creating the strongest possible business case is an essential mandate for today’s sustainability directors. That’s because although few observers fail to see the importance of efforts to reduce carbon output and minimize environmental impact, these benefits are highly unlikely to achieve primacy in profit-driven enterprises. In a report from the Economist Intelligence Unit, researchers report that the top three motivations for sustainability initiatives are brand enhancement, revenue growth and cost savings – in other words, outcomes that have a direct impact on profitability. Environmental protection only placed fourth on the list, amply demonstrating that pragmatism and not altruism is the dominant motivator. However, while the pro forma income statement in the analysis is paramount, the attention organizations are paying to sustainability matters is definitely not merely pro forma. The actions, when implemented, are far-reaching and transformational. For example, GE announced that its Ecoimagination program to reduce environmental impact generated a $17 billion revenue stream and reduced costs by more than $100 million since 2005. And the US Army reports that 80 percent of its construction meets Leadership in Energy and Environmental Design (LEED) standards, reducing its energy costs by 8 percent. Delivering green analytics Transformational organizations require a combination of descriptive and predictive insight – the ability to track meaningful green indicators, validate strategies and costs before investing, identify causal relationships and forecast outcomes. And in these areas, business analytics can make the difference. Such a business analytics framework can empower the organization to: • Measure sustainability activities using accepted methodologies and protocols. • eport on environmental perforR mance to shareholders and regulators. • mprove sustainability metrics using I analytical techniques such as optimization, forecasting and data mining to deliver metrics that matter. • educe resource usage by accurateR ly forecasting resource requirements needed to reach desired outcomes for a department or enterprise.
  35. 35. Art of the Possible|P33 With business analytics, we start to see the “art of the possible” with respect to sustainability. You can measure emissions and resource consumption throughout a value chain or product life cycle. You can ensure regulatory compliance. And you can build green strategies with predicted ROI. You can determine which conservation efforts or greenhouse-gas reduction s ­ trategies will have the greatest impact – physically and financially. And you can identify ways to profit from environmentally respectful goods and services. Undoubtedly, embracing sustainability initiatives will lead to meaningful — sometimes profound — changes to processes and culture. This transformation can be an exciting opportunity to innovate and redefine, to explore new business models and markets. By providing the right information and insights, business analytics can be a key enabler of strategic sustainability initiatives. Accessing, interacting with and analyzing the fundamental data about energy, water and waste is a nonnegotiable premise for effective sustainability. ONLINE Read the full white papers: The Business of Sustainability: What it Means to Managers Now. MIT Sloan Management Review. Fall 2009 Management Magnified: Sustainability and Corporate Growth. Economist Intelligence Unit, 2009 Measure and improve performance with sustainability management Alyssa A. Farrell, Manager of Sustainability and Performance Management Solutions, has responsibility for SAS’ sustainability solutions. Farrell works with customers to understand best practices and solutions for managing their business with environmental responsibility in mind.
  36. 36. SAS INSTITUTE INC.  WORLD HEADQUARTERS SAS CAMPUS DRIVE  CARY,  NC  27513  USA SAS® Business Analytics Software Data Management | Analytics | Reporting | Targeted Business and Industry Solutions What if you could increase revenue by 66% using your data to make confident, fact-based decisions? You can. SAS gives you The Power to Know. ® SAS Business Analytics software helps organizations across every industry discover innovative ways to increase profits, reduce risk, predict trends, and turn information assets into true competitive advantage. for a free research paper SAS and all other SAS Institute Inc. product or service names are registered trademarks or trademarks of SAS Institute Inc. in the USA and other countries. ® indicates USA registration. Other brand and product names are trademarks of their respective companies. © 2010 SAS Institute Inc. All rights reserved. 54446US.0310