cebit big data 2012 - david cummins, senior analytics manager, pwc

Senior ManagerPwC Australia

Integrating Big Data into Business Practice

David Cummins

Version 2

2 October 2012

Integrating Big Data Analytics into Business Practice

Information advantage is an essential component of building competitive advantage in any industry


Benefits are typically delivered in two ways

“Analytics is the use of comprehensive datasets together with models, algorithms, statistics and technology to generate insights, support decisions and drive benefits”

Drive efficiency & productivity

Drive efficiency & productivity

Drive growth & margin

Drive growth & margin

• Revenue & market share

• Customer profitability

• Bundling and segmentation

• Stop profit leakage

• Marketing effectiveness

• Take out costs

• Improve efficiency

• Optimise networks

• Improve control

• Workforce productivity

“The volume and variety of data is rapidly increasing – leading to information overload”

“Leading firms to formulate and adopt a comprehensive strategy to leverage data for insights and value”

“The data comes from the organisations own data repositories, external data sources, or conducting proprietary research ”

“Leading firms design and build competency in Information Management, Business Intelligence and Analytics to embed repeatable solutions and drive value”


Various forces are driving interest and adoption

Pressure on costPressure on cost � In a time of austerity, inefficient processes leading to high costs to generate essential

management reports are no longer acceptable.

� In a time of austerity, inefficient processes leading to high costs to generate essential

management reports are no longer acceptable.

Explosion of dataExplosion of data� Businesses today are inundated with more data than they can handle, ranging from

critical transactional data to social media feeds. Managing and harnessing the power of

these sources of information is key

� Businesses today are inundated with more data than they can handle, ranging from

critical transactional data to social media feeds. Managing and harnessing the power of

these sources of information is key

Re- balancing of

intuition vs fact

Re- balancing of

intuition vs fact� Businesses can no longer rely on “gut feel” and intuition to be successful. Key decisions

need to be supplemented with fact based insight based on deep data analytics

� Businesses can no longer rely on “gut feel” and intuition to be successful. Key decisions

need to be supplemented with fact based insight based on deep data analytics

Speed to actionSpeed to action� Staff are required to make decisions using out-dated information which could lead to

sub-optimal outcomes. Access to up-to-date information will help drive better decision

making and improved operational outcomes

� Staff are required to make decisions using out-dated information which could lead to

sub-optimal outcomes. Access to up-to-date information will help drive better decision

making and improved operational outcomes

Risk and

regulation

Risk and

regulation� Increasing amounts of external regulation and internal board reporting has placed

pressure on companies to deliver complete and accurate reports on a routine basis.

� Increasing amounts of external regulation and internal board reporting has placed

pressure on companies to deliver complete and accurate reports on a routine basis.

Technology

Advancement

Technology

Advancement

� Innovations in computing including high performing hardware for data analytics,

specialist software applications and mobile handset technology have resulted in a flood of

new technologies hitting the market

� Innovations in computing including high performing hardware for data analytics,

specialist software applications and mobile handset technology have resulted in a flood of

new technologies hitting the market

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The type of data that we can exploit has progressively moved from structured to semi-structured to unstructured data

Cost per Iteration

Value of Insight

Average Cost Per

OutputA

B

C

Unstructured

Database

Websites

Audio

Video

Sensors

Social Media

Structured

Static

Real-Time

This creates tremendous efficiency benefits and greatly reduces costs


Leading organisations typically deploy analytics capability as part of a wider Information Management strategy

Internal

Databases

External

data

extracts

Structured

Source

Documents &

Reports

Log files,

social

media

Unstructured

New sources – social media and other beds of data becoming available for new, richer and more real-time insight

Big Data

• Analysis of large

volumes of

unstructured data to

uncover correlations,

trends, new facts,

patterns, etc

• Integration of data across varying

sources and formats into a

consolidated relational database

Data Warehouse

Data Integration

and Modelling

Digitisation – more and more data captured, available and untapped

Analytics &

Insights

Forward looking – what happened is interesting, what will happen more so...

Predictive

• Statistical modelling of data to

forecast outcomes

• Scenario modelling to understand

cause-effect

Non-Predictive

• Automated reports

• Ad hoc query analysis

• Dashboards, thresholds and trends

Hyperconnectivity – a growing thirst for data and information to be available anytime and anywhere

Display

Audience

Customers Management

Operations

Channels

@

http://

Mobile devices E-mail

Printer/Fax HTTP

Access

•Self-service reporting

•Standard published

reports

•Personalised

dashboards

Integrating Big Data Analytics into Business Practice2

PwC Technology Forecast 2012

Reshaping the workforce with the new

analytics


Case Study 1: Centralised Marketing Platform /Integrated Marketing Management

Client had tripled its customer base to roughly 45 million customers in just 5 years. Data volumes and complexity proliferated. The marketing team had to transform their processes to handle:�Decentralised and manual marketing processes� Contractors running manual database queries to generate campaign lists�Slow , costly and error prone process that resulted in large campaign backlogs�Campaign lacked relevance and timeliness�Duplicated and un-coordinated campaigns resulting in “ contact stress” for customers�Customers had become targets of too many initiatives suffering from “ Campaign Fatigue”�Performance measurability was limited�Difficulty determining which campaigns and channels were most effective

• Analysed data from 20 transaction systems processing more than 1 billion call detail records (CDR’s) and 200TB of customer information•Ran a pilot project for 2-4 weeks over 5 campaigns based on live data to trial insight from analysis.

• Focussed on the company's medium business segment which is a complex, highly fragmented market of 175,000 customers• Segmented its mainstream and premium customers into microsegments in order to determine the right channel treatment and solutions for each customer segment. (Right Price-to-Value proposition)

• The company now conducts more than 250 campaigns a month• Revenue generated from direct marketing campaigns increased by 25%

resulting from highly sophisticated propensity models• Reduced its operation costs by 90% eliminating manual efforts to

develop and run campaigns• Campaign cycle time have reduced from 40 days to 2 • Achieved customer retention by 22% over a period of 2 years through

targeted intervention• The marketing department can now focus on more strategic tasks such a

optimising the planning and execution of more personalised campaigns


Case Study 2: Using telematics to manage agricultural efficiency

How it Works • Four Large agricultural companies, Class, John Deere, New Holland and Agco, are now marketing telematics as a way to manage risk and cut costs

• All of the machinery such as tractors and plows are outfitted with sensors and are linked to a farm owner or insurance company via GPS

• Farmers can monitor data such as how quickly workers are finishing fields, how much field is done, etc

• Insurance companies can use the same technology to see how oftenthe machinery is being used, how safely it’s being driven, and how effectively farmers are taking care of their land. This can impact pricing decision and claims management.


Case Study 3: Enterprise Wide Customer Centric Business Transformation

Transition from a business-focused monopoly to a customer-oriented competitor in a newly liberalised market. Market leader with more than 85% of the local telephony market. Serves more than 10million customers and employs approx 25,000 staff�The company experienced customer attrition and rapidly declining market share (5-10% of their market share had eviscerated) as new carriers emerged�Customers were switching carriers and more than a million customers had walked out of the door� Manual paper based, labour intensive

costly business practices and no single view of the customer

� Inability to predict customer behavior, retain long-term valuable customers and measure success

There was an urgent need to create a customer focused and operationally efficient organisation i.e. needed an operating model that supported an enterprise wide analytics platform. Key highlights of the solution were:•A new solution infrastructure to focus on customer analytics•Created highly sophisticated churn, retention and segmentation models that directly tap into the data warehouse• There was a convergence of marketing, risk and financial data to create a “ Single Customer View”•Robust and scalable analytical platform that enabled “ Operationalising Analytics” into business processes.• A clearly defined “Environmental Strategy” for analytics i.e. provisioning for operational and discovery analytics•Use of differentiated hybrid modelling techniques such as predicative models, anomaly detection, social network analysis and user defined business rules

• 22% improvement in customer retention rates amounting to a dollar savings of ( US$55M) annually

• Exceeded customer retention goal by 47%• Dramatically improved modeling times from 10 hours to 3 minutes• Gained US$50 million in total value through a lift in

revenue and reduced customer acquisition costs


What should I do next?


• There is a risk where analytics is driven out of a strong technical

emphasis

• a common issue is the focus on delivering a new capability (shiny box syndrome)

rather than identifying how it can assist the business

• potential to not meet the requirements for the business

• Start with understanding the business objectives

• mission statement

• key performance indicators

• Engage early across the entire development life cycle

• partner with the business across all stages of the development life cycle

• Understanding business objectives and requirements will determine the

types of reporting, analyses and predictive models will be required

Understand current business requirementsWhat do your business data consumers expect out of analytics?


• Differentiate between adoption of new technology that adds value vs

following the herd!

• Understand the true cost of implementing Big Data

• pilot projects are the tip of the iceberg! Consider what it would take to industrialise the

pilot

• Adopt a fail-fast policy for new technology – do not be afraid of trial and

error

• State up-front success criteria for any Big Data pilots

• Abandon technology or pilots when it is clear that they will not meet your success

criteria

Hype vs Business ValueIf everyone else is doing it does this mean I should as well?


• Is Data Scientist the new must-

have role?

• How can you cross-train or upskill

existing team members with Big

Data skills?

• Does the type of analysis require

the specialist skills?

• Don’t be afraid to go externally for

specialist skills in order to

bootstrap your Big Data initiative

Identify the new skills required for Big Data

Data

Analysts

Business

Analysts

Customer

Analytics

Data Scientist

Project Management Solution Architecture

Testing Infrastructure

Technical

Delivery


• If your IM strategy hasn’t been touched in a year then it is time to dust it off and

revisit it

• What has to change in order to incorporate Big Data?

• How do will you identify and prove the value of use cases?

• Does your development life cycle need to change in order to incorporate Big Data?

• Ensure that your IM strategy has a plan for data governance that includes

reuse and repurpose of data

• Big Data creates the ability to merge data sets and key concepts in order to build new insights

• Implication is that there is a need to repurpose data for multiple applications

• External data sources will require the application of protocols and standards

• Benchmark your strategy against comparable industry reference architectures

as well as peers

Review your IM Strategy


• Engage your business users to find suitable use cases

• Determine the value of the insight to the business and the risk of not doing

• Identify what constitutes success and what is your exit criteria

• Have a plan to transition early success into enterprise readiness

Prove Big Data works in your organisation


1. Understand where Big Data can be mapped to current business

requirements

2. Understand what is hype and what can really benefit the business

3. Identify new skills required for Big Data and adjust your plan to

acquire talent

4. Undertake a review of your current Information Management strategy

to understand how Big Data is to be positioned

5. Prove Big Data can work in your organisation

- Identify potential use cases

- Design a proof of concept

6. Streamline the transition of successful PoC to enterprise readiness

In summary...

cebit big data 2012 - david cummins, senior analytics manager, pwc

Documents

beds of data data

integration of data

data repositories

variety of data

type of data

external data sources

data andbecoming available

deep data analytics