Analytic Transformation | 2013 Loras College Business Analytics Symposium


Published on

Loras College is proud to present our annual Business Analytics Symposium on March 27, 2014 at the Grand River Center in Dubuque, IA. Industry experts will share their insights about the evolving field of business analytics opportunities. Learn about everything from best practices when analyzing data to the importance and benefits of building a culture of analytics within your organization.

To learn more, secure your seat or to take advantage of group discounts visit

Published in: Business
1 Like
  • Be the first to comment

No Downloads
Total views
On SlideShare
From Embeds
Number of Embeds
Embeds 0
No embeds

No notes for slide
  • Speaker notes:Life can be a lot easier than it is today. What if it were easier to: Explore the Why and the How behind the What?Visualize ”the best course of action?Analyze and act in real-time ?Do “what-if analysis with the slide of your finger?Update your forecasts every day or every hour? How much value could you business generate by making sure every business decision-maker could easily use the kind of advanced analytics and visualizations usually reserved for PhD statisticians to better understand the business, market and customers?
  • What’s the next big wave look like?
  • Big-Data is really just a continuation of a long standing data trend by supplementing existing information management systems with technologies and access methods more tailored to true ad-hoc business analytics and data scientists.What it also does is re-emphasise the value of information
  • Speaker Notes:The challenge with big data is that 80% of it is unstructured and the majority of it is “noise”. 80% of the information you need to understand why there are changes in your business or market, how you should address those changes and what else might be on the horizon is located in unstructured information—much of it outside of the organization. And the signal to noise ratio is high – not all that information is valuable. So you need to be able to sort through the noise and focus on the important nuggets.
  • Main Point: Combine, Explore, EvolveThey did this with our Warranty Discovery Solution, built with Oracle Endeca Information Discovery.With this solution and its underlying technology you can:Easily combine information to swiftly start the investigationEasily explore all the different paths and relationships to find the root causeEasily evolve the application to keep pace with the changing investigationLet’s look at each in turn
  • … found itself accused in 2010 of making cars that accelerated out of control, killing 34 people. The press had a field day. And the CEO, in the middle here, had to testify in a congressional hearing about how Toyota would fix the problem.But the real problem was they didn’t know if there actually was a problem with the cars. They just knew they were being accused.Toyota is a huge OBIEE customer. And very happy with it. But, there was no report for “all the vehicles that have a problem we never thought would happen.” So, how could they figure this out?
  • After a thorough investigation, Toyota was vindicated. The Transportation Secretary said, “The verdict is in. There is no electronic-based cause for unintended high-speed acceleration in Toyotas. Period.”Proving a negative – that the cars didn’t have an electronic problem – was tough. And the Big Data Discovery app played a prominent role in exonerating Toyota.As I mentioned, Toyota is a happy OBIEE customer. Still are. But they said building a discovery app with BI tools would have taken over a year. With EID it took 12 weeks.But how is this done? How is discovery technology different from traditional analytics technology? It comes down to one idea: a way to analyze data before it’s fully organized.
  • point of this slide is to explain the rationale behind Big Data – the fact that It is data that is now at the core of every business. If you can collect the right data, apply the right type of analysis to that data at the right time you can generate tremendous business value. Oracle used advanced analytics to control its Americas Cup trimaran. This meant that for any given wind speed the craft could travel at 3X the prevailing wind speed. Result was the Americas Cup. The use of 250 data sensors, the collection of over 40,000 data points, the use of Oracle Data Mining to analyze the data and make recommendations ensured the Americas Cup was won by Oracle-BMW.This is a great story for Oracle and a great example of Big Data at work and what can be achieved if you focus on the data.
  • Now we’re going to look at some use cases in more detail. Because of the variety of opportunities, we can’t show all the possible use cases. Instead, we’re going to show some representative examples. Even if your industry or company is not shown directly, we hope that you will see something relevant that’s applicable to you, or something that will give you ideas that you can use.The single biggest success factor for big data projects is having a good business case. So what we’re going to do here is offer three different sets of examples oriented around different types of business case. Some projects set out to reduce costs, others to increase revenue and others to provide some kind of new innovation with new products or programs not currently available. All these approaches have the potential to bring long term value to any organization and keep you ahead of your competition.
  • Smartphones and tablets are game changers for engagement because people carry them everywhere they go. The goal of pervasive analytics can be realized when deploying analytics and insights to a mobile device.
  • Simplify and increase the frequency of access to business information. Reach out to broader constituencies, fostering adoption by users that refrain from using, or physically can't explore (for example due to usually being away from a desk), traditional BI tools. Allow for more pervasive deployments: to more roles, embedded in more business processes, to less tech-savvy users, and in more locations. Make processes leaner by allowing on-location uninterrupted workflows.
  • Consistency across user experience. Scorecard for communicating goals, integration with search, Office. Generate production reports, utilize location data, run ad-hoc queries and of-course self-service dashboards…
  • CPG Customer started by pushing 4 reports to mobile devices over a year ago – the primary audience initially was Executives. The information was used and commonly referenced in meetings to ensure decisions were being made off from numbers that were not potentially modified prior to the meetings. The ability of having the information on mobile devices
  • Organization: Trane, heating and air conditioning systems manufacturerKey players: Trane sales operations and CIO organization; solution provider CynergySituation: Sales operations saw an opportunity to help dealers sell by replacing clipboards with direct engagement through tablets. Solution: Build TraneMap, an iPad order management application with rich content, interactive modules, and analytics. The app works offline and syncs when back online later. Sources of business value: selling or upselling the best solution based on real inputs of “load,” customer priorities, and house layout; capturing data and analytics at the point of customer engagement Business impact: Improve the “ring to ching” sales close rate from 35% to 65%. Increasethe product mix by 3% and average revenue by 22% for dealers using the app. Dealers using TraneMap recognized a 30% increase in revenue over the same period the previousyear. Provide analytics into future inventory requirements, which products are pitched but not selling, and what content results in the best sales. Road map: Focus on programs to increase tablet adoption by Trane dealers. Extend the app beyond the Trane “Comfort Specialist” level of dealers to include all Trane dealers.
  • Speaker Notes:The reality of today , is that too much crucial information is delivered too late. When this happens, your organization can miss the train – market opportunities are missed, customers are lost, revenue and profits can suffer. 53% Of executives say too much crucial information is delivered too late** **(AberdeenGroup – January 2012, survey of 247 executives - Data Management for BI – Big Data, Bigger Insight, Superior Performance)
  • Here’s an example of a customer who achieved a huge ROI with Oracle Real-Time Decisions.Oracle Real-Time Decisions Significantly Increases Revenue By Improving Closure Rates And Transaction ValuesForrester Total Economic Impact of Oracle Real-time Decisions, July 2011 found that one company in the financial services industry experienced the risk-adjusted ROI of 986% with a payback period of 3 months. The company has operations in 50 US states and more than 20,000 employees. It has completed several initiatives using RTD, including:1. Shopping cart abandonment rate reduction — online and call center channels. one percentage point lift in the closure rate during the original sales cycle. This equates to roughly $54.4 million in additional revenue over three years. 2. Post abandonment follow-up email campaign. This resulted in approximately a one percentage point lift in the conversion rate compared with a control group consisting of a single, static message. This totals $56.4 million over three years. 3. Optional program enrollment, i.e., electronic funds transfer (EFT), paperless statements, etc. This increase applies to all new sales, not just those attributable to the use of RTD, and is additive to the increased closure rate benefit. This results in more than $41.1 million over the life of the study. 4. Retention event identification and resolution strategies.
  • And of course stock prices….
  • Massive amounts of poll data. But few used it.When the major networks couldn’t make heads or tails of the election, New York Times’ Nate Silver called the presidential election in every state.
  • A lot of research has been done to explore how different visual constructs can add meaning to raw data. Stephen Few and Edward Tuft are a couple of more well known authors and practitioners of visualization techniques that can be application to BI and analytics.
  • Target: (this could be the story of any retailer)Colleagues from the Marketing Dept stop by and asked a statistician an odd question – “If we wanted to find out if a customer is pregnant, even if she didn’t want us to know, can you do that”?Shopping hapits are like routinesThere are, however, some brief periods in a person’s life when old routines fall apart and buying habits are suddenly in flux. One of those moments is when you are pregnant – right around the time of child birth….Most retailers collect information when we check out If you use a credit card or a coupon, or fill out a survey, or mail in a refund, or call the customer help line, or open an e-mail we’ve sent you or visit our Web site, we’ll record it and link it to your Guest ID,” Pole said. “We want to know everything we can.” Also linked to your Guest ID is demographic information like your age, whether you are married and have kids, which part of town you live in, how long it takes you to drive to the store, your estimated salary, whether you’ve moved recently…The statistician from Target used that Customer data and determined 25 things a women who is pregnant in her second tri-mester (prenatal vitamins, unscented lotions, scent free soaps, etc) and gave women in their customer database a pregnancy prediction scoreAbout a year after Pole created his pregnancy-prediction model, a man walked into a Target outside Minneapolis and demanded to see the manager. He was clutching coupons that had been sent to his daughter, and he was angry, according to an employee who participated in the conversation. “My daughter got this in the mail!” he said. “She’s still in high school, and you’re sending her coupons for baby clothes and cribs? Are you trying to encourage her to get pregnant?” The manager didn’t have any idea what the man was talking about. He looked at the mailer. Sure enough, it was addressed to the man’s daughter and contained advertisements for maternity clothing, nursery furniture and pictures of smiling infants. The manager apologized and then called a few days later to apologize again. On the phone, though, the father was somewhat abashed. “I had a talk with my daughter,” he said. “It turns out there’s been some activities in my house I haven’t been completely aware of. She’s due in August. I owe you an apology.”
  • Analytic Transformation | 2013 Loras College Business Analytics Symposium

    1. 1. 2013 ANALYTICS SYMPOSIUM February 12, 2013 Grand River Center Dubuque, Iowa Analytic Technology Trends Amy Mayer Vice President Capgemini Rich Clayton Vice President of Business Analytics Oracle Corporation
    2. 2. 3 Financial Markets Commodities Consumer Confidence Volatility is Primary Driver for Deeper Insights
    3. 3. 4 Why & How Visualize the Best Course of Action? Analyze & Act in Real-Time What-if Analysis Update Forecasts Daily or Hourly BETTER DECISIONS, FASTER ACTION
    4. 4. Agenda 5 • Big Data Discovery • Mobility • Real Time Decisions • Predictive Analytics
    5. 5. 6 t r e n d Big Data Discovery
    6. 6. 7
    7. 7. 8 Even Dilbert Has a Perspective…
    8. 8. 9 Big Data Is About… Tapping into diverse data sets Finding and monetizing unknown relationships Creating data driven business decisions 9
    9. 9. 10 MEDIA/ ENTERTAINMENT Viewers / advertising effectiveness COMMUNICATIONS Location-based advertising EDUCATION & RESEARCH Experiment sensor analysis CONSUMER PACKAGED GOODS Sentiment analysis of what’s hot, problems HEALTH CARE Patient sensors, monitoring, EHRs Quality of care LIFE SCIENCES Clinical trials Genomics HIGH TECHNOLOGY / INDUSTRIAL MFG. Mfg quality Warranty analysis OIL & GAS Reserve Capacity estimation, Drilling exploration sensor analysis FINANCIAL SERVICES Risk & portfolio analysis AUTOMOTIVE Auto sensors reporting location, problems RETAIL Consumer sentiment Optimized sales & marketing LAW ENFORCEMENT & DEFENSE Threat analysis - social media monitoring, photo analysis TRAVEL & TRANSPORTATION Sensor analysis for optimal traffic flows Customer sentiment UTILITIES Smart Meter analysis Big Data Impacts Every Industry ON-LINE SERVICES / SOCIAL MEDIA People & career matching Web-site optimization
    10. 10. 11 Four Dimensions of Big Data • Exponential growth in data volumes demand a different approachVolume • Exploring real-time dataVelocity • All types and forms of data leveraged to enrich & create additional valueVariety • The (value) density of the data also has to be consideredValue
    11. 11. 12 80% of the information you need is unstructured
    12. 12. 13 Analytic Processes Must Change DISCOVER PLAN PREDICT ANALYZE DATA DECISIONS
    13. 13. 14 Big Data Discovery Easily EXPLORE all the different paths to find the root cause Easily EVOLVE the application to keep pace with the changing investigation Easily COMBINE information to swiftly start the investigation
    14. 14. 15Copyright © 2012, Oracle and/or its affiliates. All rights reserved.15
    15. 15. 16 “The verdict is in. There is no electronic-based cause for unintended high- speed acceleration in Toyotas. Period.” Ray LaHood Transportation Secretary February 9, 2011 16
    17. 17. 18 Where is Your Big Data Opportunity? “In a big data world, a competitor that fails to sufficiently develop its capabilities will be left behind.” McKinsey Global Institute INNOVATE INCREASE REVENUE LOWER COSTS
    18. 18. 19 t r e n d Mobility
    19. 19. 20 ACCESSAnytime Anywhere 15% Today’s workforce works anytime and anywhere (this number is expected to triple by 2016) ~ Forrester Research
    20. 20. 21 Access Anywhere Mobile access doubles adoption potential
    21. 21. 22 Many Styles of Information Access
    23. 23. 24
    24. 24. 25 Improve Business Results TraneMap: iPad Order Management app with interactive modules and analytics Business Impact: • Increase sales close rate from 35 to 65% • Increase product mix by 3% • Increase average revenue by 22% Source: Forrester Research
    25. 25. 26 t r e n d Real-time Decisions
    26. 26. 27 of executives say too much critical information is delivered too late
    27. 27. 28 Browsing History Purchase History Social Stats Target Audience Predictive Modeling + + + = σ Real-Time Decisions For Retail Banking
    28. 28. 30 t r e n d Predictive Analytics
    29. 29. 31 Predicting IPO Returns
    30. 30. 32Copyright © 2012, Oracle and/or its affiliates. All rights reserved.32 Nate Silver Source:
    31. 31. 33 Add Meaning to Data With Visualization
    32. 32. 34 How Companies Learn Your Secrets NY Times Article, Feb 16, 2012
    33. 33. 35 t r e n d The Deciding Factor: Big Data and Decision Making
    34. 34. 36 The Market View Capgemini commissioned the Economist Intelligence Unit to survey over 600 business leaders, across the globe and industry sector, about the use of Big Data in their organizations. Specifically looking at: Their use of big data today and planned in the next 3 years The advantages they have seen The issues they have in using it of participants are C-level and board executives43%
    35. 35. 37 The Economist Intelligence Unit Survey: (1 of 2) The Deciding Factor: Big Data and Decision Making What we found: 85% Say the issue is not about volume but the ability to analyse and act on the data in real time 62% Believe we have a long way to go when it comes to automating operational and tactical decisions 75% Believe their organizations to be data-driven 42%Survey respondents say that unstructured content is too difficult to interpret
    36. 36. 38 26% is the level of performance improvement already seen from the application of big data analytics 41% is the level of performance improvement expected in the next 3 years 62% Dispute the proposition that most operational / tactical decisions that can be automated have been automated The Economist Intelligence Unit Survey: (1 of 2) The Deciding Factor: Big Data and Decision Making
    37. 37. 39 Shortage of skilled people to analyze the data properly Too Many Data Silos – Data is not pooled for the benefit of the entire organization Time it takes to analyze large data sets The Economist Intelligence Unit Survey: (1 of 2) The Deciding Factor: Big Data and Decision Making
    38. 38. 40 Q & A