6. December 2016 2016

Grab‘n Go: Session 18Analytics: Motoren i fremtidensdatadrevne virksomheder

#deloittegng

Advanced Analytics is hyped…

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Machine

Learning

Advanced

Visualization

Artificial

Intelligence

Simulation

?

Real-time

Analytics

Sentiment

Analysis

Image

Recognition Business Case Data Technology Competences Examples

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Hypothesis Data New Technology InsightData Scientist

The winning formula of being data-driven

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Hypothesis Data New Technology ActionData Scientist

The winning formula of being data-drivenThe potential does not lie in the insight but in the action

From Descriptive Predictive

Typical transition journey towards advanced insight

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Ad

ded

Valu

e

Business User Data scientist

Descriptive

Diagnostic

Predictive

Prescriptive

Operational reporting• Standardized, static

reports (e.g. Monday morning Weekly Sales report)

• In-system reports (Open orders)

KPI reporting• Integrated

Performance reporting• Setting the basis for

integrated performance management

• Interactive dashboards for providing the right level of insight

Visual data exploration• Visualizing data &

relationships• Cause explanation

and anomaly detection

Simulation & Optimization• Simulate and

experiment with possible scenario’s

• Find the best solution out of many

• Generate structured business cases

Predictive Modelling• Forecasting and

predicting future outcomes

• Modelling and understanding correlations and causalities

Analytical Sophistication

Proje

ct

ap

proach

Êxperiment(lab)

Agile

Structured

From BI Advanced AnalyticsBusiness should not have what they ask for – but what they need…

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I would like to understand my

revenue broken down by a

number of dimensions…

I would like to know the

sickness leave broken down

by organization and time…

Traditional BI

Advanced Analytics

I would like to understand what is

causing my UK revenue to fall and

be able to be notified when and why

this is starting to happen so that I

can stop it before it becomes a

problem…

I would like to be able to know what

causes longer sickness leave

among specific departments to be

able to prevent people from being

stressed…

Types of Data Driven Business

1

2

3

Old Business New DataData used to diversify products or services to improve business – but the product itself remains as-is

Transforming Business Data is an integrated part of the product or services –product development is changed to support the transformation

Data NativeThe business is 100% based on data and the business originates from a data insight.

Core BusinessDo same thing better

Transformation

Find Inspiration

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Types of Data Driven Business

Data-enabled differentiation

Product Innovators Enhance their products and services with data

Systems InnovatorsUse data to integrate multiple product types

Data brokering

Data ProvidersGather and sell raw data without adding too much

value to it

Data Brokers Combine data from

multiple sources and sell insights

Data-based delivery networks

Value Chain Integrators Share data to extend

product offerings or reduce costs

Delivery Network Collaborators

Share data to foster marketplaces and enable

advertising

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From data to insight to action

What happened?

Why did it happen?

What will happen?

What do we want to happen?

Descri

ptive

Pre

dic

tive

Pre

scri

ptive

Business outcome

Data Knowledge

• Reduce cost• Improve efficiency• Automation• Prediction• New services• Improve products• New products• General insights• …

Action

?

Plan your actions

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BIG DATA = “ALL DATA”

New Technologies

Data Driven Business

New Data Types

New Sources

New Data

New Insight

New Decision making

New Scenarios

Draw your data landscape

Draw your data landscape

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Internal

CRMERP

R&DStr

uctu

red

External

Un

str

uctu

red

Data sources

Volume Variety

Velocity Variability

Shared Data Alternate Data

Privacy

Meta Data

Master Data

EssentialsConsider

Data Catalogue

Data characteristics, the V’s

Calculated Data

The Big Data industry

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The Open Source Community

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Machine

Learning

Hadoop

Advanced

Visualization

Artificial

Intelligence

Jupyter

Simulation

Scala

?

SPARK

Traditional Data Management

Big Data technologies

?

R

MATLAB

Real-time

Analytics

Python

Sentiment

Analysis

Image

Recognition

?

Amount of data is now fit for purpose with Machine Learning algorithm

Processing power is now general availableAlgorithms are democratized due to open

source

Jupyter Notebook - The Data Scientists “Swiss knife”

The Open Source Community

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The Jupyter Notebook is a web application that allows

you to create and share documents that contain live

code, equations, visualizations and explanatory text.

Uses include: data cleaning and transformation,

numerical simulation, statistical modeling, machine

learning and much more.

The Notebook has support for over 40 programming

languages, including those popular in Data Science

such as Python, R, Julia and Scala.

Notebooks can be shared with others using email,

Dropbox, GitHub and the Jupyter Notebook Viewer.

Code can produce rich output such as images, videos,

LaTeX, and JavaScript. Interactive widgets can be used

to manipulate and visualize data in realtime.

Leverage big data tools, such as Apache Spark, from

Python, R and Scala.

Advanced Analytics - Machine Learning Analyse patterns in data

Model

Can we based on earlier examples identify the shape?

Training data based on known patterns are used to build ML models that can identify similar patterns in data

The strength of the model determines the accuracy in prediction on identifying similar patterns in new data

Model

0,980,01 0,03 0,18 0,27 ………………………………………

New classifiers and examples are

added to the training data to strengthen the

model and to be able to identify new patterns

?

False PositiveFalse Negative

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Process overview of analysis

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Illustration

Prediction

Modelling

Data

Time Line

Tim

e C

on

su

mp

tio

n

Insight Driven OrganisationAsking the right questions

ActionDecision

Human inputDescriptiveWhat happened?

Human inputDiagnosticWhy did it happen?

PrescriptiveWhat should be done?

Decision Support

Decision Automation

Human inputPredictiveWhat will happened?

Feedback and

learning

Cognitive computingDecides what should be done, learn and improve from outcome.

Data

Data Information Insight Outlook Non-IDO IDO

Past What has

happened?

Why and how did it

happen?

Present What is currently

happening?

What is the next

best action?

Future

What is going to

happen?

What does

simulation tell us;

the options; the

pros and cons?

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The Data ScientistCreating the “Purple People”

Technical &

AnalyticalBusiness &

Communication

Business Technical

Traditional Analytics Advanced Analytics

There’s massive confusion about what a data scientist

actually is. For some, a person who can manage spread

sheets and do basic reporting might qualify. For others, the

data scientist definition speaks to a rare blend of statistical

sophistication, data management skills and business acumen.

Professionals who can deliver data-backed insights that

create business value - not just number crunchers - are

especially hard to find.

The shortage of data scientists coupled with growing amount

of data has led organisations to use artificial intelligence

technologies to solve complex problems in new ways and

make more effective decisions. Knowledge about artificial

intelligence is increasingly becoming a critical asset.

WHO ARE THE “PURPLE PEOPLE”? SKILLS SHORTAGES MACHINES AND ARTIFICIAL INTELLIGENCE

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The Data ScientistCreating the “Purple People”

Sto

ryte

lling

Busin

ess a

cum

enT

echnic

al skill

sD

ata

analy

sis

These skills may be all present in a very highly skilled individual or be complementary skills with a team; however it is this blend of skills within a capability

which is critical for success. Consider creating a ‘Purple Team’ for every analytics project you embark on.

“PURPLE CAPABILITIES”

Advanced Analytics Examples

Annual Accounts

Store Location

Shockwaves in flows

Social media

Learning

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Control on Annual Accounts

Annual accounts for

smaller companies.

727 companies controlled

manually – 24 with error

in property fields

Can we build a model that can

predict same error in new

annual accounts?

Basic data from fiscal year

no strong indicators.

Additional data and data from

previous fiscal year

strong indicators found

Model

First model: prediction

on 65%

Analysed 10.000 new

financial accounts and

identified 173 financial

accounts with error

Added 173 identified

annual accounts to

training set and trained

model

Model +

Updated model: prediction

on ~ 92%

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Store Location

City-Level Store Location Reduction Top City-Level Store Expansion

Optimal No. of Shops

Current No. of Shops

Problem: Why does the productivity drop when we accept many clients?

Service workflow in a small biotech company

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• Biological samples come in• Laboratory treatment in several stages: cleaning, preparation• Large equipment, terabytes of data• Weeks of computations, report writing and data delivery

Inventory and workflow data in Excel sheets

When did different events occur?Biotech: basic analysis

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0

50

100

150

200

19 23 25 27 30 34 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65 67 69 71 73 75 77 79 81 83 85

Rate

[sam

ple

s/w

eek]

Week

TotalFlow

During this period the workerscomplained and were often sick

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200

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600

800

1000

1200

19 23 25 27 30 34 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65 67 69 71 73 75 77 79 81 83 85

No.

sam

ple

s

Density

Clients in

Weeks of lower

productivityGood Good

Products/results out

Something flows, capacity is inflexibleModel: painfully simple – but useful

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projects

deliveryreception

Flow: 𝑞 =𝑁items

𝑡𝑖𝑚𝑒[1/s]

Density: 𝜌 =𝑁items

𝑙𝑒𝑛𝑔𝑡ℎ[1/m]

Velocity: 𝑣 =𝑙

𝑡[m/s]

flow = velocity × density: 𝑞 = 𝑣 ∙ 𝜌,1

𝑠=

𝑚

𝑠

1

𝑚

𝑥1 𝑥2

“The accountant’s equation“ is bookkeeping: any change of stock must balance the net flux.

Problem: why does the flow start to stall, compromising delivery dates?Answer: phantom traffic jams (density shockwave)

Observations, simulations and properties

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No bottlenecks!!

Real traffic flow dataJapanese Highway Authority

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Do not often exceed your critical density.Recommendations for the biotech company

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0

20

40

60

80

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0 100 200 300 400 500 600 700 800 900 1000 1100 1200

flow

[sam

ple

s/w

eek]

density [samples in workflow]

Fundamental traffic diagram

• Optimal delivery rate is 65 samples analysed per week.

• Do not process much more than 650 samples per week

• Breakdown predicted if > 2000 samples are to be processed per week.

Does Twitter activity predict box office sales?

Twitter and Hollywood box office revenue

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The early rise in social activity gives away the coming storm.

The viral property modelled as an epidemics

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Liable to buy a ticket

Moviegoer

Losing interest

Opening night in theatres

Example: Avengers

• Opening revenue est. 623 M $

• Model revenue est. 750 M$

• 1 tweet ~ 750 $ at the box office.

Data science: supervised vs unsupervised learning- a look ”under the hood”

Machine learning

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Iris setosa

Iris versicolor

Iris virginica

Fisher's Iris Data

Sepal length

Sepal width

Petal length

Petal width

Species

5.1 3.5 1.4 0.2 I. setosa

4.9 3.0 1.4 0.2 I. setosa

4.7 3.2 1.3 0.2 I. setosa

4.6 3.1 1.5 0.2 I. setosa

5.0 3.6 1.4 0.2 I. setosa

… … … … …

A classic data set from 1936 by the statistician Ronald

Fisher is often used as a benchmark for learning

algorithms. It is called the Iris flower dataset.

When learning is supervised you have expert

information on the categorization of your data, and you

optimize the algorithm to replicate those labels.

Unsupervised learning is pure exploration.

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Hypothesis Data New Technology ActionData Scientist

The winning formula of being data-driven

6. December 2016

Michael Winther & Jacob Bock Axelsen

Deloitte Consulting