BIG DATA: BIG OPPORTUNITY OR BIG HEADACHE?10 ROADBLOCKS TO IMPLEMENTING BIG DATA ANALYTICS 1. Budget 2. IT know-how 3. Business know-how 4. Data clean-up 5. The storage bulge 6. New

Copy r ight © 2012, SAS Insti tute I nc . All r i ghts r eserved.

BIG DATA:

BIG OPPORTUNITY OR BIG HEADACHE?

Peter Dorrington

SAS


FIRST, A FEW WORDS ABOUT SAS

(Who do, after all, pay my salary)

(Post conference narrative annotations to this presentation are in green italics)


2011

PERFORMANCE

A LEADING PROVIDER OF

ADVANCED ANALYTICS SOFTWARE

12% growth in total

revenue over 2011

36 consecutive

years of revenue

growth

24% of 2011

revenues invested

into R&D

For 37 years, we have focused on

giving our customers…

Being privately owned means we can afford to reinvest

in R&D, not focus on quarterly share price / dividends


WHY DOES SAS CARE ABOUT BIG DATA?


THE VISION WE HAVE ALWAYS UNDER-PINNED DECISION-MAKING

Organizations are inundated with data – terabytes and

petabytes of it. To put it in context, 1 terabyte contains

2,000 hours of CD-quality music and 10 terabytes could

store the entire US Library of Congress print collection.

Exabytes, zettabytes and yottabytes definitely are on the

horizon.

The hopeful vision of big data is that organizations will

be able to harvest and harness every byte of relevant

data and use it to make the best decisions. Big data

technologies not only support the ability to collect large

amounts, but more importantly, the ability to understand

and take advantage of its full value.

This is the vision – reality is somewhat different


BUT DO YOU

REMEMBER THIS? SINGLE CUSTOMER VIEW (SCV)

“A complete SCV is not currently available in any of the

interviewed organisations. Most have a partial

implementation of some of the data and / or some of the

channels ...”

From a study

this year

A Market Study by Henley Business School

in association with SAS UK and Ireland

When I joined SAS UK as Head of

CRM in 2000, this was already old

news. Over a decade later, with all

the advances in data management

and analytics, it is still an issue.

The danger is that ‘Big Data’ will

make the challenge greater by

adding new data sources and

aspirations before we have fully got

to grips with our current reality.


OUR PERSPECTIVE BIG DATA IS RELATIVE, NOT ABSOLUTE

When volume, velocity and variety of data exceeds an

organization’s storage or compute capacity for accurate

and timely decision-making.

The explosion of data isn’t new. It continues a trend that

started in the 1970s. What has changed is the velocity of

growth, the diversity of the data and the imperative to

make better use of information to transform the

business.

‘Big Data is…’

Big data is really just ‘more data, from more

sources’ . Most organizations already have ‘large

data’. (I regularly use Companies House data of 5.3

millions rows; far more than Excel can deal with.

Some of our customers are using 5.3 billion rows of

data and doing so very effectively)


Based on 450 responses from 109 respondents who report practicing Big Data analytics;

4.1 responses per respondent on average.

Source: TDW I Big Data Analytics Report, 4 th Quarter 2011, Philip Russom

Structured data ( tables, records )

Semi-structured data ( XML and similar standards )

Complex data ( hierarchical or legacy sources )

Event data ( messages, usually in real time )

Unstructured data ( human language, audio, video )

Web logs and click streams

Social media data ( blogs, tweets, social networks )

Other

Spatial data ( long / lat coordinates, GPS output )

Machine-generated data ( sensors, RFID, devices )

Scientific data ( astronomy, genomes, physics )

“Which of the following data types are you collecting

as Big Data and/or using today?”

BIG DATA

SOURCES BIG DATA IS EVERYWHERE

It’s happening

already, a

significant

challenge will be in

working out how to

manage all these

sources


Source: IDC Digital Universe Study, sponsored by EMC, May 2010

THE SCALE OF THE CHALLENGE

It’s not hard to

imagine a future

of super-cheap,

ubiquitous,

connected ‘chips

with everything’;

the data growth

curve is

potentially

exponential.

Will the future be

‘Even Bigger

Data’?

But how much of

this data is going

to be useful in

any given

context?


VOLUME

VARIETY

VELOCITY

VARIABILITY

TODAY THE

FUTURE

DA

TA

SIZ

E

SO WHAT? NOT ALL DATA IS EQUAL

COMPLEXITY

- terabytes, petabytes and up

- from all kinds of sources

- some historic, others real-time

in fits-and-starts, as well as

- smooth flowing & of also

dubious quality

- and often without context

or clear value

The

challenge is

to find

relevance

from within

this ‘data

deluge’


IMPLICATION WE WILL NEED TO RETHINK DATA MANAGEMENT

Where data integration, data quality, metadata

management and data governance are designed and

used together. The traditional extract-transform-load

(ETL) data approach augmented with one that minimizes

data movement and improves processing power.

From standalone

disciplines to

integrated

processes

- There is no meaningful way we can store all this data

(with today’s technologies), never mind build an OLAP

cube from it.

- For example, the Large Hadron Collider at CERN

products 15 petabytes of data per year: they can only

store a subset of this and that only by distributing the

storage around the world using multiple hubs.

- Now add real-time data feeds into the mix…


BIG DATA & ANALYTICS

Data without analysis has only transactional value


Big

Analytics

Business

Intelligence (BI)

BIG DATA, BI

AND ANALYTICS

Large Data

Reacti

ve

Analy

tics

Pre

dic

tive

Analy

tics

TRADITIONAL VIEW MY DEFINITIONS:

Predictive (Proactive)

Analytics:

• Optimisation - How do we

do things better? What is

the ‘best’ decision?

• Predictive Modelling - What

will happen next? How will it

affect me?

• Forecasting - What if the

trend(s) continue?

• Statistical Analysis - Why is

it happening? What am I

missing?

Reactive Analytics

(Business Intelligence):

• Alerts - When should I

react? What action is

needed now?

• Query Drilldown (OLAP) -

Where exactly? How do I

find the answers?

• Ad Hoc Reports - How

Many? How Often?

• Standard Reports - What

happened? when?

All have a role to play Pretty much all

organisations have

‘large data’


Big Data

Analytics

Big

Analytics

Big Data

BI

Business

Intelligence (BI)

BIG DATA, BI

AND ANALYTICS WHAT CHANGED?

Large Data Big Data

Reacti

ve

Analy

tics

Pre

dic

tive

Analy

tics

Not much has changed when moving from ‘Large Data’ to

‘Big Data’: BI is still BI, Analytics is still Analytics – applying

BI to Big Data does not make it inherently analytical


OUR PERSPECTIVE BIG DATA ANALYSIS: A PROMISE AS YET ONLY

PARTIALLY FULFILLED / ADOPTED

The true value of big data lies not just in having it, but in

harvesting it for fast, fact-based decisions that lead to

real business value.

‘There’s gold in

them thar hills’

- Just like mining for gold (a deliberate pun about data

mining) – you have to work for the reward, it is rarely found

just lying on the surface and if it was it wouldn’t be rare and

therefore valuable.

- The problem with ‘low hanging fruit’ is that everyone can

see it and reach for it – your competition included. Your

unique Intellectual Property (what you know, and what you

know about what you know) may be the only thing that

ultimately sets you apart.


SO WHAT’S

STOPPING YOU?

10 ROADBLOCKS TO IMPLEMENTING BIG DATA

ANALYTICS

1. Budget

2. IT know-how

3. Business know-how

4. Data clean-up

5. The storage bulge

6. New data centre workloads

7. Data retention

8. Vendor role clarification

9. Business and IT alignment

10. Developing new talent

All of these

are solvable

- Mary Shacklett. TechRepublic, Nov 2012

- Develop a ‘plain English’ business case with £value

- Figure out what you need to do, then what

capabilities are needed & how obtained

- Partner with those who do have the skills

- Face up to the problem & prepare to invest

- Store only what you have to or is relevant

- Monitor & analyze workloads and

Plan accordingly

- (see Storage Bulge)

- Identify who can offer more than

‘canned analyses / reports | Partner

- Design a strategy around business,

not IT goals & objectives

- If at all possible, develop in-house talent using

consistent architectures, rather than buy-in skills


HEALTH WARNING BOYD: BRING YOUR OWN DATA?

How many views of

how many data

sources, using how

many tools on how

many devices?

FINANCE DIRECTOR SALES DIRECTOR OPERATIONS DIRECTOR

- Imagine what would happen if the whole leadership team

turns up to a meeting with their own sets of data

- Implement a strategy that provides a consistent data

strategy / foundation

- Bring Your Own View of one set of Data


THE VALUE OF BIG DATA


OPPORTUNITY OR

THREAT? WHAT BUSINESS LEADERS SAY ABOUT BIG DATA

Should probably

ask “strength or

weakness?”

- Opportunities / Threats are often external – not under our

control

- Strengths/Weaknesses are internal – we decide where we

want to be strong

- Perhaps the internal debate should be able how the value

of big data can provide an organisation with new strengths

- In particular, proprietary IP based on data is very hard for

competitors to replicate, whereas products typically are not.


EXAMPLE MARKETING & CUSTOMER ACQUISITION

Same / better result

for less investment

- This has been going on for years: by understanding

customers / segments better, we can focus our

investment on just those most likely to respond, this ‘lift’ in

response rates improves RoI

- Big data has the potential to know more about customers

& develop better models for more customers


THE POTENTIAL THE UK’S CORPORATE GOLD


IMPLICATION IMPACT EVERY PAGE OF THE ANNUAL REPORT

Cut losses from fraud by 30% in retail banking

Improved retention rates by 40~%, and increase product

holding by customers by 10%. (Retail)

Increased the number of customers by 1.7m pa

assisting to a 15% compound annual growth rate in just 2

years

Increased sales by 40% by identifying customers sales,

and matching the best salespeople to close the opportunity.

Increased customer purchase by 65% through data integration and effective targeting.

Maintain bad debt of <0.05%, compared to the industry

norm of 3.45%.

Reduced number of financial reports by 82% -

providing key fiscal information for rapid decision making.

The applications of analytics to address business

challenges / opportunities are not restricted to just

one function


WHAT IS YOUR

ISSUE?

Time Orient

Observe

Act

MARKET

OPPORTUNITY

Decide

- OODA - Colonel John Boyd

Confidence

100% Sampling

- When you need to be able to reach a decision

& act faster than the competition

- When you need to consider

lots of scenarios

- When you need to see the whole

picture, not just a sample of it


HOW TO… BIG DATA ANALYTICS

(Based on SAS technologies)


Domain Expert

Makes Decisions

Evaluates Processes and ROI

BUSINESS

MANAGER

Model Validation

Model Deployment

Model Monitoring

Data Preparation

IT SYSTEMS /

MANAGEMENT

Data Exploration

Data Visualization

Report Creation

BUSINESS

ANALYST

Exploratory Analysis

Descriptive Segmentation

Predictive Modeling

DATA MINER /

STATISTICIAN

IDENTIFY /

FORMULATE

PROBLEM

DATA

PREPARATION

DATA

EXPLORATION

TRANSFORM

& SELECT

BUILD

MODEL

VALIDATE

MODEL

DEPLOY

MODEL

EVALUATE /

MONITOR

RESULTS

How can we

create

strategic

advantage?

THE ANALYTICS

LIFECYCLE THERE IS STILL A PLACE FOR A STRUCTURED APPROACH

In my opinion, you start out with identifying what question you need an answer to


DISTRIBUTED COMPUTING

Almost all Big Data solutions run in grid environments –

chunking up the task to share across many processors


IN-DATABASE ANALYTICS

Doing the ‘analytics’ in the database keeps it close

to the data and in an easily managed environment


IN-MEMORY ANALYTICS ARCHITECTURE

But doing analytics in-memory allows for vast

improvements in speed & enables ‘train of thought’

development of new questions / answers


USAGE EXAMPLES


WHAT IF YOU

COULD…

• . . . predict the buying behavior and decision criteria of your prospects weeks before your competition

. . . gain first-mover advantage by introducing new

products and services to micro-segments that haven't

been identified by anyone

. . . evaluate the impact of your marketing campaigns

hourly and make adjustments in real-time

• . . . Improve customer experience scores that grow products per customer, reduce attrition, and leverage the power of customer recommendations for new business


RETAIL

Big, general purpose retailers have 10,000s of SKUs across

tens of stores – having the right amount / mix of stock, at the

right price is critical to protecting (slim) margins. The challenge

is to adjust pricing as quickly as the market changes – not

monthly or weekly, but daily, or even hourly.


TELCO

Two big issues: the market is saturated (very few ‘new’ customers) and is

commoditized (customers driven by price and ‘customer experience’).

Network failures directly impact the latter whereas just providing the

infrastructure does not make money. Some Telco's are looking at their IP

and working out how they can use it to grown new revenue

streams


HEALTH CARE

In a recent case, the DNA of MSRA bacterium was sequenced in 48 hours

for a cost of £50; we are much closer to personalised health plans than

many would think. Even leaving the genetic issue to one side, it is possible

to use analytics to predict healthcare needs and therefore opportunities to

intervene before the chronic becomes acute.


BANKING

If you have ever had a credit card transaction declined, then you will know

that the card issuers are working hard to identify 100% of the potential fraud,

whilst at the same time not generating ‘false positives’ – declining genuine

transactions because the detection models are incomplete or unresponsive

to individual consumer behaviour is bad for business


PUBLIC SAFETY

Lots of what goes on in this sector is kept, quite rightly, under wraps but

there are case studies from all over the world where police forces are

starting to anticipate where crime hotspots are/will develop and fix

policing strategy accordingly


INSURANCE

Telematics in cars for insurance is already available in the UK. Because

insurers get a better picture of individual driving patterns they can adjust

their risk calculations accordingly and offer individual (and competitive)

prices to better / safer drivers


FINANCIAL

SERVICES

Risk is at the heart of all financial services; banks and insurers just need to

know how to price it correctly. In the example of ‘stress testing’ banks are

now asked to consider the impacts of a wide range of scenarios on their

business. The ability to run lots and lots of different risk scenarios directly

impacts price and tactically allows more responsiveness

- heading off problems before they become unmanageable


UTILITIES

An incredible commoditised, mature, competitive

sector – leveraging IP is one way it is responding


IN CONCLUSION


ADOPTING BIG DATA ANALYTICS IS NOT WITHOUT

CHALLENGES

Source: The Current State of Business Analytics: Where Do We Go From Here?

Prepared by Bloomberg Businessweek Research Services, 2011


BUT PLENTY TO

GET EXCITED

ABOUT!

“Problems cannot be solved by the same level of

thinking that created them.” - Albert Einstein

• Open Data

• The power to analyse more

• Lots and lots of solutions…..

….. Framing the problem

• Knowledge systems

• Interpretation of data


FURTHER

READING http://www.sas.com/reg/wp/corp/46345

Copy r ight © 2012, SAS Insti tute I nc . All r i ghts r eserved. www.SAS.com

THANK YOU!

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http://www.sas.com/

Documents

BIG DATA: BIG OPPORTUNITY OR BIG HEADACHE?10 ROADBLOCKS TO IMPLEMENTING BIG DATA ANALYTICS 1. Budget 2. IT know-how 3. Business know-how 4. Data clean-up 5. The storage bulge 6. New