CNME Big Data Symposium “Big Data is here! Now what do we do?”€¦ · A.T. Kearney/CNME Big...

“Big Data is here! Now what do we do?”

May 2013Federico Mariscotti – A.T. Kearney

CNME Big Data Symposium

A.T. Kearney/CNME Big Data Symposium/May 2013 2

There is a growing awareness that use of advanced analytics to Big Data can drive companies’ performance

■ Mankind has collected ~2.7 ZB of digital data already.

• In 1986 the amount of data was equivalent to 1 CD per human,

• today we are at ~600 CDs

■ Companies overwhelmed with data:

• 2.5 quintillion bytes created daily

• 90% of the world’s data created in the past 2 years

■ 80% of enterprise data sits unused as text and other unstructured data

■ Companies who consider themselves data-driven are 5% more productive and 6% more profitable than their competitors

Big data is here

Source: Wikipedia, IDC White Paper “The Expanding Digital Universe”, A.T. Kearney


Scale of Information Use of Information

Big Data is as much about the growth of information itself, as it is about the ability to effectively use that information

Big Data definition

Source: IDC, Forrester, Gartner, Press search, A.T. Kearney Analysis

Big data

New Types of Information

Unstructured data, geo, sensors

Increasing Granularity

Store-level, individual consumer

Real-Time

Volume of transactional information, supply chain

visibility

Ubiquitous Access

Access to the consumer, to trading partners, and

across functions

Decision Analytics

Analysis of unstructured data, embedded “business

intelligence”

Immediate Action

Tailored consumer experience, promo-pricing action, “fluid” supply chain


Sensors, RFID, GPS/GSM modules and Audio / Video Analytics solutions are generating tons of data automatically

■Many of James Bond’s gadgets are today’s reality:

Automated Data generation

Source: Wikipedia, ebay, nemesysco , VACAM, A.T. Kearney

• Advanced object classification or face recognition in pictures or video streams

• RFID chips, small like a rice corn for ~0.03 EUR

• “Emotion detection” software in Call Centers

• GPS/GSM devices for ~60 EUR

• Smart Meters


Big data

Organizations don’t lack data; rather, they lack the analytical capability to analyze and transform it into actionable outcomes

Elements of Big Data

• Industry can’t keep up with the deluge of data and is challenged in its ability to rapidly turn its data into accurate forecasts and true insights

Velocity

• 80% of enterprise data exists in non-structured and non-relational form as text, email, images, video, surveys, call logs, and social media

Variety

• Organizations struggle to make effect use of data of the data at their disposal but instead they find that they are buried in it

Volume

• Except for hi-tech internet companies, companies across industries are struggling to analyze and interpret data

Complexity

1. eBay Study: How to build trust and improve the shopping experience2. Stand-alone boxes are boxes not in direct combination with light-grey or other color shapes excl. arrowsSource: Gartner – Real World Lessons from Big Data Deployment, A.T. Kearney Survey, AFT, Harvard Business Review


Except for hi-tech internet companies at the leading edge, companies across industries are struggling with Big Data

Elements of Big Data

Stage 1 Impaired

Limited measurement and insights into business performance

Stage 2 Reactive

Early adoption of analytics with variable results focused on backward looking view of performance

Stage 3Anticipatory

Analytics used to create transparency into past and potential future performance drivers

Stage 4Predictive

Analytics enables dynamic forward-looking insights with quantified trade-offs to drive performance and influence behavior

Stage 5Transformative

Analytics used to drive innovation and continuous improvement to increase competitive differentiation

InternetFinance &

Banking

Telcos Consumer ElectronicsBeverage

Retailers

Source: A.T. Kearney


To be successful, organizations need to consider a holistic approach to Big data and build differentiating analytics

Business questions that can be addressed at various stages of maturity

Analytical Maturity Techniques Business questions

Predictive/Transformative Analytics

• Enables dynamic forward-looking insights with quantified trade-offs to drive performance

• Requires high quality integrated data and complex mathematical capabilities

• Optimization • What’s the best that can happen?

• Simulation • What would happen if . . . ?

• Predictive Modeling • What will happen next?

Anticipatory Analytics

• Creates transparency into past and potential future performance drivers

• Systems and processes in place to perform a range of descriptive analytics based on uniform metrics

• Segmentation Analysis • What are the unique drivers?

• Statistical Analysis • Why is this happening?

• Sensitivity Analysis • What if conditions change?

Reactive Analytics

• Provides static, historical view of business performance

• Supported by basic scorecards and static reports populated by semi-automated data feeds

• Query / Drill Down • Where is the problem?

• Ad Hoc Reporting • How many, how often, where?

• Standard Reporting • What happened?



In a recent project big data allowed to reduce an inventory reduction of > 1 bn USD by “digging deeper”

■ Client: North American retail pharmacy

■ > 7,000 stores

■ > 60,000 SKUs

■ Client had applied traditional approaches in the past but limited to “representative basket”

■ Big Data approach: “Rapid Reverse Logistics” on single SKU at single store detail level

■ Result: > 1 bn USD inventory reduction while service level maintained/ improved

■ Big Data Environment hosted by A.T. Kearney

• 100 TB storage

• 240 processor cores

• 3,072 GB memory


Rapid Reverse Logistics powered by Big Data Client example


A big furniture trading company is pursuing their vision of a cross-silo, cross-value-chain “Forest to Customer” optimization

■ Where to plant / cut the trees ■ Where to saw/plane them ■ Where to print the manual,produce the packaging, the screws and the Allen key

■ Where to put it all together ■ How to ship it to the stores ■ To have the right products at best price in the right quantitiesin the right stores

Source. A.T. Kearney


Yet, analytical results can strongly be influenced by irrational behavior. Which option would you choose?

Example Subscriptions

Source: Dan Ariely “Predictably Irrational” (2008)


And if there were just two option, which option would you choose?




Human behavior is often irrational. Advanced analytics can make it “predictively irrational”


■ The introduction of a „decoy“ leads to dramatically different results

■ Irrationality can be statistically predicted by random tests

■ Without deeper understanding of such effects, analytics can deliver misleading accuracy

68%32%

16%

84%0%

Revenue with 10,000 customers

$1,14 Million $0,8 Million +43%



But blind faith in analytics can lead to curious results

Book price (1/2)

Source: http://schmalenstroer.net/blog/2011/04/wenn-algorithmen-amok-laufen/


.. and a source of increasing embarassment if left unchecked

Book price (2/2)

Source: http://schmalenstroer.net/blog/2011/04/wenn-algorithmen-amok-laufen/


Yet, the perfect prediction of the US election results before the ballot was cast, shows the immense power of using data

Nate Silver predicting 2012 US Election Result with Big Data

Poll performance

� Nate Silver hit a perfect 50 for 50 in his state by state predictions

� With this method he outperformed the major US polls & expert forecasts

� Accuracy of forecasts:

Background

a. Note: The closer the dart is to the center, the closer the prediction was to the actual outcome. Darts below the center underestimated Obama's electoral vote share, while darts above overestimated it. Blue darts represent predictions made by liberal pundits, while red darts represent predictions made by conservative pundits.Source: 1.Mashable.com: Triumph of the nerds2.Fivethirtyeight Blog

Case Study – Variety

• Statistician Nate developed prediction model to forecast the results of the 2012 US Presidential Elections based on publicly available data

• While dealing with imperfect data, his approach is around cleaning out human bias

Methodology

Stage 1: Weighted polling average

Stage 2: Adjusted polling average

Stage 3: Regression (non-poll variables)

Stage 4: Snapshot

Stage 5: Election day projection

Stage 6: Error Analysis

Step 7: Scenario Analysis

� Recency, sample size, past accuracy rating

� E.g. when a poll is leaning to one side

� E.g. effect of incumbent status of 1 candidate

� Combined adjusted polling & regression

� E.g. error is higher where the polls disagree

� Trend analysis of current result

� Probabilistic assessment of outcomes

a


Success requires a focus data strategy, organizational effectiveness, and practical analytics experience

Key Principles

Data Science Organization and Governance

■ Dealing with governance in an era where shared and open data is necessary

■ Embracing data science from a process point of view

■ Building an analytics toolkit

Analytics Strategy

■ Starting small with projects and POCs for quick wins that drive immediate value

■ Developing an agile culture for scaling analytics across the organization

■ Developing talent and organizational maturity

Big Data and Advanced Analytics Practical Experience

■ Creating advanced analytics engines that combine cross-functional and inter-organizational data sets into a single space

■ Automating data lifecycle -machine collects, processes, and analyzes information (using sophisticated techniques) before humans interact with the data

CNME Big Data Symposium “Big Data is here! Now what do we do?”€¦ · A.T. Kearney/CNME Big...

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