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Shifting from business hindsight to insight to foresight

Shifting from business hindsight to insight to foresight

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  • 1. Real AnalyticsShifting from business hindsight to insight to The crunchy questions haunting the business requireforesight a combination of hindsight, foresight and insight. ByIn 2010, many organizations began to see information investing in a balance of information management,automation outweigh business process automation as performance management and advanced analytics,their highest priority area1. In the reset economy, analytics organizations can make small steps, smartly madeoffered improved visibility to drive operational efficiencies, to capture measurable results. These can span fromas well as a platform for growth by addressing heart-of- improving fragmented customer relationships by analyzingthe-business questions that could guide decisions, yield omni-channel interactions, to providing an integratednew insights and help predict what’s next. It seemed enterprise view of risk and finance. This is the essencelike a no-brainer. But companies quickly discovered of real analytics: delivering business value through thethat the journey is complicated – requiring a clear continuous build-out of core information disciplines.analytics vision aligned to the business strategy, severallayers of supporting capabilities and the fortitude to While getting the right answers is critical, manyembed analytical thinking across multiple facets of the organizations don’t normally know the right questionsorganization. In 2011, leading organizations are launching to ask to get there. Powerful new tools and supportingbroad initiatives with executive-level sponsorship, ready infrastructure have removed most technical constraints,and eager to achieve their vision via real analytics. but analytics initiatives continue to suffer from the lack of a clear vision and commitment to embed analytics-basedData volumes continue to explode, doubling every 14 approaches into how work is performed. Real analyticsmonths2. Regulators are demanding deeper insight into can add knowledge, fact-based predictions and businessrisk, exposure and public responsiveness. Public and prescriptions – but only if applied to the right problems,private organizations alike are feeling increased pressure to and only if the resulting insight is pushed into action.achieve profitable growth. New signals are evolving thatcontain crucial information about companies and markets You can’t drive your car with only the rear view mirror –– including sensor-laden assets, unstructured internal data equivalent to historical reporting. You use the view outand external sentiments shared via social computing3. of the windshield and the dashboard gauges – EnterpriseCisco estimates that the amount of data flowing over the Performance Management (EPM) and performanceinternet each year will reach 667 exabytes by 20134. The dashboards. In fact, many drivers take advantage ofmagnitude and complexity of global businesses have made navigation systems fed by GPS to see the road ahead andit even more difficult for leaders to uncover hidden insight. direct the next turns. That’s like going from descriptive analytics to predictive and prescriptive. That’s moving from hindsight to insight to foresight for the business. 1
  • 2. History repeating itself?Over the past two decades, companies have invested heavily in back-office systems to automate their business processes.Information investments were typically siloed, static, historical and focused only on operational reporting for pockets ofthe business. Real analytics is focused on a more holistic, forward-looking approach, positioning information as an asset tosupport effective business decision and action. What were the challenges? What’s different in 2011? ERP-based information • Large-scale packaged technologies form the • ERP providers have invested in adding information repositories foundation of many organizations’ system platforms to their solution sets, including perform- footprints. Embedded reporting and perform- ance management and some advanced analytics ance management tools were leveraged to tools. These are largely integrated into the core try to meet information needs. However, process automation solutions. solutions were mostly backwards-looking, with minimal real-time dashboarding and • Integration between internal and external systems limited advanced analytics. has been eased by adoption of open architecture standards and advancements in transactional and • Most organizations have a hybrid applica- view-based integration tools. tion landscape, with multiple ERP instances and tens or hundreds of ancillary systems that execute end-to-end business processes. Visibility confined to the ERP transactional store was insufficient for true business insight, but integration to other systems was costly and complex. Business intelligence/ • Performance improvement has been a critical • Leading organizations have adopted a combination reporting/data warehousing part of the real analytics journey, but it is not of performance improvement, information manage- sufficient because it lacks vehicles to guide ment and advanced analytics to meet the needs of insight and foresight. the business. • Organizations often faced multiple isolated, • Enterprise-wide governance is a critical dimension competing initiatives buried within business of real analytics, allowing for visibility across and units, functions and geographies – creating beyond organizational boundaries. confusion and multiple versions of the truth. • Real analytics efforts are embedded in business • Results of information efforts were only loosely processes with executive and management linked to operations and decision making, support, with continuous feedback loops so limiting the amount of value realized. that actual performance can guide the next iteration of analysis. • Technical constraints forced the segmenta- tion of information repositories into discrete, • A combination of improvement in storage, federated views. Complex operations, inte- processing and network performance, as well as grated views or even traversing of data advanced new options for dealing with complex sets were compromised. calculations on large data sets (e.g., high-per- formance information appliances, column-based • Analysis was characterized by small datasets in-memory databases, distributed computing with variables between 10-20 and limited cases tailored for data processing). For example, a (<100), driven by unrealistic assumptions that large consumer credit card issuer recently datasets were linear, normal and independent. analyzed two years of data (73 billion transactions Data universes were restricted to static data across 36 terabytes of data) in 13 minutes. In the snapshots interred by a handful of tools (e.g., past this transaction would have taken more than SQL, SAS). one month5. • Analysis now routinely handles massive datasets with millions of variables and billions of cases, increasingly in real time. Tools such as PMML, DMQL, SPSS and DMX allow the focus to be on exploratory analysis to discover relevant patterns, trends and anomalies in data, without having an explicit goal in mind. 2
  • 3. Technology implicationsReal analytics represents a combination of information management, performance improvement and advancedanalytics. Each of these capabilities has a number of critical underlying technical implications, with interdependenciesrequiring an enterprise information architecture spanning the entire stack. Topic Description Information Tools for establishing trusted foundational data are essential. These include master data management management for maintaining data correlation, consistency of semantic meaning, providing matching services to identify and link identical entities and enabling bidirectional updates across systems of record. Data quality is also a concern, requiring tools to monitor, analyze, report and scrub. Finally, tools to manage data governance are needed, and should be tightly linked with master data and data quality solutions. Performance This drives to the heart of monitoring, reporting and recommending action by combining historical improvement reporting, business intelligence and dashboards. Technical implications include report design, business rule development, business process integration and the development of dashboards and scorecards. Increasingly, performance management solutions also include mobile delivery channels, either through Web-based outputs or dedicated applications6. Advanced analytics Advanced analytic tools enable predictive modeling, embedding analytics into business processes, discovery and information visualization7. This work typically involves advanced statistical modeling and correlation of widely disparate data sets, requiring access to internal and external data. Infrastructure Complex analysis on large data sets requires a high performance computing environment. Options in 2011 include on-premise appliances, in-memory column-based databases and cloud-based options for elasticity and distributed processing. 3
  • 4. Lessons from the frontlines They first analyzed over 16 million claims and mergedShining a light on addressable markets data from multiple sources which had never beenWhile a specialty insurer’s sales were growing 20 percent harmonized – both within the company and fromannually with 80 percent customer retention, the external business credit and “firmographic” sources.company had captured only six percent of the annual Then, using advanced modeling techniques such aspotential in its market. In order to grow the business, decision trees, association rules, logistic regression andthe company wanted to better understand its customer Benford’s analysis, the company developed five setsbase with the goals to improve retention among current of new rules capable of identifying potentially false orpolicyholders using targeted communication and cross- inappropriate claims. The organization added the newselling, as well as identify potential new customer rules to their existing process in order to improve bothsegments that were more likely to purchase its policies. the accuracy of its warranty claim reviews and the return on investment from the operation. The post-analysisTo supplement what the insurer already knew about predictive benefits were more than three times largerits customers, third-party market segment information than initial expectations. Also, as an unexpected benefit,(biographic, demographic, psychographic), enhanced the company was able to create a list of pre-approvedcensus and other external data were used to append labor operations and parts for a given repair accordingnearly 300,000 policies and 150,000 customers. to specific make, model and year – drastically reducingCluster analysis was used to identify primary customer the need for reviewing and interpreting every claim.groupings and segments, and, finally, decision treeanalysis was completed to differentiate those segments Delivering on the premiumthat produced the highest value for the company. A leading insurance provider saw its core business beingThey added a market penetration study to compare pushed to deliver more personalized services at lowerexisting and potential market share by segment. costs – while facing increased transparency for demands, growing commoditization of its product offerings andFor the first time, the insurer had a unified view of its overall slowing industry growth. Their response? A multi-customer bases as well as insights on customer behavior, year analytics program to increase sales effectivenesspreferences and lifestyles – all useful in creating new and operational efficiency, increase customer retentionup-sell, cross-sell and retention strategies and focusing and better support executive decision making. Predictivegrowth on specific consumer segments and regions. modeling driven by online, agent and customerAs a result, the company was able to increase product feedback was the cornerstone of the effort – with apurchase loyalty and growth among key customer core analytics competency center built to support needssegments and attained measurable improvements, across business units. The results speak for themselves:including migrating core segments to higher profitability. improved policy retention by 300 basis points, increased acquisition rates on abandoned quotes by 200 basisAdvanced auto(motive) analytics points and advances in customer satisfaction rates.By replacing manual, rule-based warranty claim reviewswith scientific, automated methods, an automakersignificantly improved its warranty claims adjudicationprocess with the ability to preemptively identifypotentially false or inappropriate (improper, exaggerated,embellished) claims. The legacy warranty claimsadjudication process relied heavily on manual reviews,exception reports and static rules. The automaker neededa more scientific approach to adjudication that couldhelp it identify potentially fraudulent or false activity in amore efficient, automated manner and also enhance itsrule set with more sophisticated statistically groundedrules that are too complex for manual processes. 4
  • 5. Where do you start? • Right fit analytics. Match statistical models andFew organizations are starting from scratch when it comes analytics techniques to the job at hand. Overpoweredto real analytics. Many organizations have decades of solutions waste time and money. Underpoweredexperience with information-related initiatives in various solutions can miss important insights. Buy what youforms. Because of its wide scope, however, real analytics need and use what you buy – across tools and services.initiatives require special attention to dependencieson in-flight efforts. While specific steps vary company • Accelerate insights. Automate delivery of theby company, some fundamental principles apply: information people need to do their work and automate responses whenever possible, so that• Crunchy questions. Start by laying out specific, action is taken with more certainty and at the heart-of-the-business questions. Prime your business lowest possible cost. leaders with ideas from other industries showcasing how unstructured and external data can be applied in • Behavior change. Recognize that a big part of practical terms. Then prioritize according to what drives the impact of real analytics will be creating a fact- value, where returns will likely be higher and the degree based culture that embraces its repercussions, to which results can be made actionable. allowing analytics capabilities and outputs to be embedded into operational processes across the• Start where you are. Assess your current capabilities enterprise and up and down the organization. and get a clear picture of the gap between what your organization can do and what it needs to do. Think in • New talent. Institutionalizing real analytics will require terms of both technical capabilities and organizational new skills, including pockets of creative design, deep depth. Grade yourself, prioritize projects aligned to mathematics, statistics and behavioral change skills. crunchy questions and fill the cracks – both with Develop a strategy for how to locate it, develop it and small, focused efforts and with some cross-functional retain it. investments (e.g., enterprise data management). 5
  • 6. Contact Bottom lineJohn Lucker Complexity is growing, providing organizations with more data to manage, more decisions to make and less overallPrincipal certainty. Some business problems are like puzzles, with pieces dispersed across internal and external players,Deloitte Consulting LLP captured in structured and unstructured forms. Competitive advantage will come from winning the race for clarityjlucker@deloitte.com and precision, and from building the institutional skills to quickly solve the next puzzle that crosses your executives’ desks. Other business problems are mysteries, where the clues may or may not be within your grasp. These requireLearn more empowered leaders who understand the business issue, who can work with specialized resources to model theThis is an excerpt from problem and who have the analysis tools to recognize and act on patterns that might lead to the solution.Tech Trends 2011 – Thenatural convergence Puzzles and mysteries are the purview of real analytics. Both start with a clear understanding of the business problemof business and IT. and a commitment to make the answer actionable once it is clear. Though the magic that happens in between isVisit www.deloitte.com/ anything but simple, these two steps are the biggest factors to achieve effective results. Look in the mirror, state yourus/2011techtrends to intent for making analytics real, and start digging up your crunchiest questions.explore other toptechnology trends. Endnotes 1 Additional information is available in Deloitte Consulting LLP (2010), “Depth Perception: A dozen technology trends shaping business and IT in 2010”, http://www.deloitte.com/us/2010technologytrends, Chapter 7. 2 InfoNIAC.com. 487 Billion Gigabytes of Digital Content Today to Double Every 18 Months. Retrieved February 3, 2011, from http://www.infoniac. com/hi-tech/digital-content-today-to-double-every-18-months.html 3 Additional information is available in Deloitte Consulting LLP (2010), “Depth Perception: A dozen technology trends shaping business and IT in 2010”, http://www.deloitte.com/us/2010technologytrends, Chapter 11. 4 The Economist. ”Data, Data Everywhere.” Economist [Online]. February 25, 2011. http://www.economist.com/node/15557443 5 The Economist. “A Different Game.” Economist [Online]. February 25, 2011. http://www.economist.com/node/15557465 6 Additional information is available in Deloitte Consulting LLP (2011), “Tech Trends 2011: The natural convergence of business and IT”, http://www.deloitte.com/us/2011techtrends, Chapter 9. 7 Additional information is available in Deloitte Consulting LLP (2011), “Tech Trends 2011: The natural convergence of business and IT”, http://www.deloitte.com/us/2011techtrends, Chapter 1. This publication contains general information only and is based on the experiences and research of Deloitte practitioners. Deloitte is not, by means of this publication, rendering business, financial, investment, or other professional advice or services. This publication is not a substitute for such professional advice or services, nor should it be used as a basis for any decision or action that may affect your business. Before making any decision or taking any action that may affect your business, you should consult a qualified professional advisor. Deloitte, its affiliates, and related entities shall not be responsible for any loss sustained by any person who relies on this publication. As used in this document, “Deloitte” means Deloitte Consulting LLP, which provides strategy, operations, technology, systems, outsourcing and human capital consulting services. These entities are separate subsidiaries of Deloitte LLP. Please see www.deloitte.com/us/about for a detailed description of the legal structure of Deloitte LLP and its subsidiaries. © 2011 Deloitte Development LLC. All rights reserved. 6