Analytics 3.0 Measurable business impact from analytics & big data

Analytics 3.0: Measurable Business Impact From Analytics & Big Data

Featuring analytics expert Tom Davenport, author of Competing on Analytics, Analytics at Work, and the just-released Keeping Up with the Quants

OCTOBER 15, 2013

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OCTOBER 17, 2012

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OCTOBER 15, 2013

Today’s Speaker

Tom DavenportPresident’s Distinguished Professor, Management & IT, Babson College

Author, Keeping Up with the Quants

Analytics 3.0: Measurable Business Impact From Analytics & Big Data

OCTOBER 15, 2013

Analytics 3.0Measurable Business Impact From Analytics & Big Data

Tom Davenport

Babson/MIT/International Institute for Analytics

Harvard Business Review/SAP Webcast

15 October 2013

More Words on Big Data?

Big data beginsat online firms

& startups

No technical ororganizational

infrastructure to co-exist with

Working wonders forGoogle, eBay, & LinkedIn

…but what abouteveryone else?

What happens in big companies when

IT & analytics arewell-entrenched?

Findings show evolutionof a new analytics

paradigm

The Rise of Big Data

“Big Data in Big Companies” Study

8 | 2013 © Thomas H. Davenport All Rights Reserved

� How new? “Not very” to many; continually adding data over time UPS—Started building telematics

capabilities in 1986

� Excited about new sources of data, new processing capabilities

� Familiar rationales for big data: Same decisions faster—Macy’s, Caesars Same decisions cheaper—Citi Better decisions with more data—United

Healthcare Product/service innovation—GE, Novartis

� Need new management paradigm

Analytics 1.0│Traditional Analytics

1.0 Traditional Analytics• Primarily descriptive

analytics and reporting

• Internally sourced, relatively small, structured data

• “Back office” teams of analysts

• Internal decision support


Analytics 1.0│Data Environment


Analytics 1.0│Other Technologies

� Standalone spreadsheets

� BI and analytics “packages”

� ETL tools

� OLAP cubes

� On-premise servers


Analytics 1.0│Ethos

► Stay in the back room—as far away from decision-makers as possible—and don’t cause trouble

► Take your time—nobody’s that interested in your results anyway► Talk about “BI for the masses,” but make it all too difficult for anyone

but experts to use► Look backwards—that’s where the threats to your business are► If possible, spend much more time getting data ready for analysis

than actually analyzing it► Stay inside the sheltering confines of the IT organization


Analytics 2.0│The Big Data Era

1.0 Traditional Analytics

Big Data2.0

• Primarily descriptive analytics and reporting


• “Back room” teams of analysts


• Complex, large, unstructured data sources

• New analytical and computational capabilities

• “Data Scientists” emerge

• Online firms create data-based products and services


Analytics 2.0│Data Products

► Google—Search, AdSense, Books, Maps, Scholar, etc., etc.

► LinkedIn—People You May Know, Jobs You May Like, Groups You May Be Interested In, etc.

► Netflix Cinematch, Max, etc.► Zillow Zestimates, rent

Zestimates, Home Value Index, Underwater Index, etc.

► Facebook People You May Know, Custom Audiences, Exchange


Analytics 2.0│Ethos

► Be “on the bridge” if not in charge of it► “Agile is too slow”► “Being a consultant is the dead zone”► Develop products, not presentations

or reports► Information (and hardware and

software) wants to be free and shared► All problems can be solved in a

hackathon► “Nobody’s ever done this before!”


Analytics 2.0│Data Environment


Analytics 3.0│Fast Business Impact for the Data Economy


Fast Business Impact for the Data Economy

Big Data2.0

3.0• Primarily descriptive










• A seamless blend of traditional analytics and big data

• Analytics integral to running the business; strategic asset

• Rapid, agile insight delivery

• Analytical tools at point of decision

• Industrialized decision-making at scale

Analytics 3.0│Fast Business Impact for the Data Economy


Fast Business Impact for the Data Economy

Big Data2.0

3.0• Primarily descriptive





• A seamless blend of traditional analytics and big data

• Analytics integral to running the business; strategic asset

• Rapid, agile insight delivery

• Analytical tools at point of decision

• Industrialized decision-making at scale





Today


Analytics 3.0│Competing in the Data Economy

► Every company—not just online firms—can create data and analytics-based products and services that change the game

► Use “data exhaust” to help customers use your products and services more effectively

► Start with data opportunities or start with business problems? Answer is yes!

► Need “data products” team good at data science, customer knowledge, new product/service development

► Opportunities and data come at high speed, so quants must respond quickly


Analytics 3.0│Data Types

Website activity

Clickstream logs

Social Feeds

Spatial GPS

TwitterLinkedIn

Blogs

Videos

ImagesRSS

Text messages

Cloud

Hosted applicationsMobile devices

Device sensors

XML

Email

Articles

Documents

Presentations


Analytics 3.0│Data Management Environment


Analytics 3.0│Technologies and People


► Analytical “apps”► Integrated and embedded models► Focus on data discovery► Heavy use of visual analytics► Faster technology and analytical methods ► Blended data science/analytics/IT teams► Chief Analytics Officers and their ilk► Use of prescriptive analytics

23

Analytics 3.0│Everything’s Much Faster!

► In-memory analytics► From 2-3 hours to prioritize customers

at Hilti to 2-3 seconds► From 22 hours to optimize all prices at

Macy’s to 20 minutes

► In-database processing► Propensity scoring for all customers in

seconds, not weeks, at Cabela’s► From 30 variables to 5000 in model

predicting revenues for InterContinental Hotels Group

Analytics 3.0│Everything’s Much Cheaper!

24

Cost/PerformanceCost/Performance► Some organizations using big data technologies just to save money

► Hadoop useful as short-term “persistence layer” or “discovery platform”—but requires expensive and specialized skills

► Not directly comparable yet to data warehouses in terms of hygiene


► $2B initiative in software and analytics► Primary focus on data-based products and

services from “things that spin”► Will reshape service agreements for locomotives,

jet engines, turbines► Gas blade monitoring in turbines produces 588

gigabytes/day—7 times Twitter daily volume► Marketing new industrial data platforms and

brands like “Predicity” and “Datalandia”

GE 3.0


► Primary focus on improving management decisions

► “Information and Decision Solutions” (IT) embeds over 300 analysts in leadership teams

► Over 50 “Business Suites” for executive information viewing and decision-making

► “Decision cockpits” on 50K desktops► Real-time social media sentiment analysis for

“Consumer Pulse”► Financial restatements in seconds versus

several days in the past► P&L’s by brand and retailer on the fly

Procter & Gamble 3.0

► CEO Joe Jimenez: “If you think about the amounts of data that are now available, bioinformatics capability is becoming very important, as is the ability to mine that data and really understand, for example, the specific mutations that are leading to certain types of cancers.”

► “IT has become a very important part of drug discovery”

► Programs at Novartis Institutes for Biomedical Research in bioinformatics, quantitative biology, computational biology

► Big user of big data tools


Novartis 3.0

Schneider National 3.0

Has invested heavily in sensors to automate data collection on trucks, trailers and intermodal containers

Quality of decisions has improved as a result of sensor data

Prescriptive analytics are changing job roles and relationships

Sensor data related to safety predicts drivers at risk of safety accident for preventative conversations


Monsanto 3.0

FieldScripts program uses data from field testing and Monsanto research to recommend what corn hybrids to plant where

Genotypes and phenotypes of plants add up to tens of petabytes of data for analysis

Field photographs analyzed to determine correct watering, fertilizer

Paid almost $1B for The Climate Company, which gathers and analyzes weather data for agriculture

Embarking on data and analytics education programs for farmer customers


Problematic Issues 3.0

• Labor intensiveness of data science work• Privacy/security implications• How to get to more sophisticated

analytics with big data• Integration with processes and systems• Need for integrated architectures,

governance, transition processes• Implications of people shortage (if there

is one) and ways to address it

30

Recipe for a 3.0 World

Start with an existing capability for data management and analytics

Add some unstructured, large-volume data

Throw product/service innovation into the mix

Add a dash of Hadoop and a pinch of NoSQL

Cook up data in a high-heat convection oven

Embed this dish into a well-balanced meal of processes and systems

Promote the chef to Chief Analytics Officer



Questions?

OCTOBER 17, 2012

To ask a question … click on the “question icon” in the lower-right corner of your screen.

Thank you for joining us!

This webinar was made possible by the generous support of SAP.

Learn more at www.SAP.com

OCTOBER 15, 2013

Analytics 3.0 Measurable business impact from analytics & big data

Business

big data analytics integral

descriptive analytics

big data complex

data economytraditional1

data environment10

data environment16

data ready

big data eratraditional1