The document summarizes key points from a 2013 analytics symposium. It discusses trends in big data discovery, mobility, real-time decisions, and predictive analytics. Big data allows tapping diverse data sets to find unknown relationships and make data-driven decisions. It impacts many industries. Real-time data and decisions are important as over 80% of executives say critical information is delivered too late. Predictive analytics and visualization help add meaning to data. Mobility increases access and analytical collaboration anywhere.
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Analytic Transformation | 2013 Loras College Business Analytics Symposium
1.
2. 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
4. 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
5. Agenda
5
⢠Big Data Discovery
⢠Mobility
⢠Real Time Decisions
⢠Predictive Analytics
9. 9
Big Data Is AboutâŚ
Tapping into diverse data sets
Finding and monetizing
unknown relationships
Creating data driven business
decisions
9
10. 10
MEDIA/
ENTERTAINMENT
Viewers / advertising
effectiveness
COMMUNICATIONS
Location-based
advertising
EDUCATION &
RESEARCH
Experiment
sensor analysis
CONSUMER
PACKAGED
GOODS
Sentiment analysis
of whatâs 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
11. 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
14. 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
16. 16
âThe verdict is in. There is
no electronic-based cause
for unintended high-
speed acceleration in
Toyotas. Period.â
Ray LaHood
Transportation Secretary
February 9, 2011
16
17. 17
Data Driven: Achieve the Impossible
VARIABLES / SEC40k
SENSORS250
MORE DATA40x
DRIVE DECISIONS
DEEP ANALYTICS
REAL TIMEBig
Data
AMERICAS CUP#1
18. 18
Where is Your Big Data Opportunity?
âIn a big data world, a competitor that fails to sufficiently
develop its capabilities will be left behind.â
McKinsey Global Institute
INNOVATE
INCREASE
REVENUE
LOWER
COSTS
25. 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
34. 35
t r e n d
The Deciding Factor:
Big Data and Decision Making
35. 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%
36. 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
37. 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
38. 39
Shortage of skilled people to analyze the
data properly
Too Many Data Silos â 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
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 âthe best course of action?Analyze and act in real-time ?Do âwhat-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?
Whatâs 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 ânoiseâ. 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 informationâmuch of it outside of the organization. And the signal to noise ratio is high â 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 investigationLetâs 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 didnât 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 âall the vehicles that have a problem we never thought would happen.â So, how could they figure this out?
After a thorough investigation, Toyota was vindicated. The Transportation Secretary said, âThe verdict is in. There is no electronic-based cause for unintended high-speed acceleration in Toyotas. Period.âProving a negative â that the cars didnât have an electronic problem â 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 itâs fully organized.
http://www.sail-world.com/USA/Americas-Cup:-Oracle-Data-Mining-supports-crew-and-BMW-ORACLE-Racing/68834The point of this slide is to explain the rationale behind Big Data â 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 weâre going to look at some use cases in more detail. Because of the variety of opportunities, we canât show all the possible use cases. Instead, weâre going to show some representative examples. Even if your industry or company is not shown directly, we hope that you will see something relevant thatâs 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 weâre 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 dashboardsâŚ
CPG Customer started by pushing 4 reports to mobile devices over a year ago â 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 âload,â customer priorities, and house layout; capturing data and analytics at the point of customer engagement Business impact: Improve the âring to chingâ 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 âComfort Specialistâ 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 â 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 â January 2012, survey of 247 executives - Data Management for BI â Big Data, Bigger Insight, Superior Performance)
Hereâs 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 â 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 pricesâŚ.
Massive amounts of poll data. But few used it.When the major networks couldnât make heads or tails of the election, New York Timesâ 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 â âIf we wanted to find out if a customer is pregnant, even if she didnât want us to know, can you do thatâ?Shopping hapits are like routinesThere are, however, some brief periods in a personâs life when old routines fall apart and buying habits are suddenly in flux. One of those moments is when you are pregnant â right around the time of child birthâŚ.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 weâve sent you or visit our Web site, weâll record it and link it to your Guest ID,â Pole said. âWe want to know everything we can.â 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 youâve moved recentlyâŚThe 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. âMy daughter got this in the mail!â he said. âSheâs still in high school, and youâre sending her coupons for baby clothes and cribs? Are you trying to encourage her to get pregnant?â The manager didnât have any idea what the man was talking about. He looked at the mailer. Sure enough, it was addressed to the manâs 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. âI had a talk with my daughter,â he said. âIt turns out thereâs been some activities in my house I havenât been completely aware of. Sheâs due in August. I owe you an apology.â