Where have you been all my life? How the financial services industry can unlock the value in Big Data

Where have you been all my life?

How the financial services industry can unlock

the value in Big Data

Agenda

• Why should I care?

• What is Big Data?

• Is Big Data for me?

• What will it take?

The numbers don’t lie, the stats behind Big Data are compelling

Why should I care

Financial Services firms spend more on unstructured data infrastructure than any other industry, costs are expected to

rise annually by

9%

[1]

A survey conducted by IDC says that Around

2/3 ^rd

information technology and business executives surveyed by PwC believe that Big Data has significant potential to create

1. David Rogers and Don Sexton. "Marketing ROI in the Era of Big Data" The 2012 BRITE/NYAMA Marketing in Transition Study 2012

2. IDC Big Opportunities and Big Challenges: Recommendations for Succeeding in the Multibillion-Dollar Big Data Market, doc #237885, November 2012.

almost

30%

significant potential to create business advantage

Source: PwC 5^thAnnual Digital IQ survey

Sitting back is not an option!

Revolutionary changes in mobile, social media and ecommerce

require companies to rethink data all over again to stay relevant.

Why do we

Internet/

Mobile Data

Large Volumes

Why Should I care?

If the answer to any of these questions is “Yes” then you should start thinking about

Collect what you don’t have, Analyze what you need, Discard what you don’t need and

Distribute what is relevant

Why do we need to this

now?

High End Modeling Internal Data and

Third-Party Data

should start thinking about Big Data now and not later.

Remember to…

What is Big Data?

Big Data encompasses structured, semi-structured and

unstructured information from demographic and behavorial

information about consumers to product reviews and commentary;

blogs; content on social media sites; and data streamed 24/7 from mobile devices, sensors, and technical devices

Using Big Data to get the right information to identify the right markets and customers at the right time enables

institutions to make the right strategic decisions.

Trying to unlock the power of Big Data without data analytics is like trying to harness the power of the Internet without a search engine.

..

Historically, while financial institutions collected copious amounts of data, they were unable to use that data to generate

meaningful information in a timely manner

This fragmented their view of business insights

What is Big Data?

08 January 2013

business insights

Because they were unable to develop Big Data analytics and process the data in real time, they had difficulty predicting and responding to changing business needs and arising opportunities As a result, business

opportunities and related growth were tied to a much slower roadmap. This value chain is at the foundation of Big Data

We have observed three key attributes of Big Data—volume, velocity, and variety—which we refer to as the “3 Vs.” Traditional data management practices cannot accommodate these attributes.

What is Big Data?

• FS firms grapple with the need to store large amounts of structured and unstructured data.

Volume

• Firms have begun to gain a competitive advantage by focusing on the ability to process more trades at a faster rate than their competitors.

Velocity

• Unstructured social media data—including tweets, status updates, blogs, tags, pins, and

videos ─ is needed to keep up with evolving needs of the customer .

Variety

Financial firms are looking to transform their organizational culture to foster innovation while staying cost competitive.

Managing exponential growth of structured and unstructured data:

• Digital data is used to analyze customer demands and growing business needs

• Market data in variable formats

IT and Operations both need to deliver high end processes and

solutions while being cost effective:

• IT and Ops are looking to identify opportunities to reduce costs and reduce large data processing

Is Big Data for Me?

• Market data in variable formats needs to be processed and analyzed

• Regulatory demands are pushing for a scalable platform to address ad-hoc queries to provide transparency

reduce large data processing requirements

• IT is looking to create more flexibility to respond to business requests

through document centric databases

• Business demands IT to provide

enhanced user interface where IT does

not need to be involved

Recognizing that Big Data is an innovation that delivers business insights and

opportunities, we see leading institutions now beginning to utilize the power of Big Data to compete in the market.

Big Data is not a technology problem

A large asset management firm invested in standing up big data infrastructure but could not grow it because the business did not “see” the value

Big Data is not a A large mortgage servicing organization agreed that solving Is Big Data for Me?

Big Data is not a panacea

A large mortgage servicing organization agreed that solving issues like poor quality of data and inadequate data governance cannot be solved by Big data

Big Data is used as an enabler

A large Investment Bank already had a number of departments leveraging Big Data but had no common direction at the

enterprise level. Standardized tools and approaches were needed

Big data without the right foundation

A mid cap insurer in the mid west started a data warehousing program 5 years ago, and are just starting to see ROI. But the organization could not scale to meet the demands of the

business

How can financial institutions apply Big Data to take advantage of

opportunities and manage risk

Is Big Data for Me?

Customer Impact

• Leverage Big Data technology to extract unstructured information

Investment Management

• Develop and deploy a

distributed “compute farm”

Risk and Regulation

• Use Big Data distribution technologies to integrate both structured and

unstructured data from firm- unstructured information

from customer-service systems

• Utilize Big Data distribution technologies to integrate data from both traditional and internal structured sources and external unstructured sources

• Perform predictive analytics on payment and statement information to forecast buying patterns

distributed “compute farm”

that executes mortgage models

• Create functionality that allows a mortgage analyst to submit a model for

processing

• Integrate data throughout the lifecycle of the loan—

from origination, to

servicing, to close—for better quality and consistency

unstructured data from firm- wide sources

• Measure risk and exposures by applying Big Data to integrate external market data into internal transaction data

• Develop stress strategies and perform parallel simulations.

• Build dynamic data

structures to increase agility when faced with changing compliance and reporting requirements

You can start the Big Data

journey at any level of maturity, but you should reach a certain level before you can invest

Decisions are based on

MaturityHigh

Full Information Management value

realization

Level 4 Integrated Excellence

Level 5 Information Premium Big Data

Experimentation can start here

You should be here before you

can invest in a production ready Big Data

solution

What will it take

based on integrated internal and external fully simulated business operations Analytic Models sit

on top of enterprise databases with enhanced data quality

Data is managed centrally in one or more

integrated

repositories with repeatable

processes Data is managed in

disparate relational databases with little integration Data is mostly

managed in excel spreadsheets

MaturityLow Level 1

Limited

Level 2 Evolving

Level 3 Functional Excellence can start here

Financial institutions are finding it challenging to enable data oriented business capabilities with Big Data but are finding ways to overcome these barriers.

Barriers to adoption Big Data solutions

Confusion around which problem Big Data is intended to solve

Lack of the right tools; or having too many

Define a business statement that will describe the Big Data vision.

Develop and deploy common infrastructure execution environment(s^).

What will it take

Being unprepared to drive a shift in organizational mindset

Lack of the right tools; or having too many tools in house for Big Data

Undefined or Ill-defined data policies

Think through the ways that the business model, processes, and people skills should change.

Measure value realization in small segments and in every step.

Create a data policy that addresses how long certain types of data should be retained, who should be entitled to see it, and the appropriate field of use.

environment(s^).

Not sure how to determine if the capability delivered value once implemented

Slide 11

What does it take to turn information into growth?

Upfront

• Recognize that

Big Data Is Not A Technology Problem.

• Prepare their organization to

Face the Big Data Storm

• Educate your business leaders around both the

Value and the How-To

• Make sure that the

Strong Governance

What will it take

08 January 2013

can be met, and that costs can be minimized by standardizing common technologies, tools, and processes.

Ongoing

• Be prepared to

Fail Early

• Recognize that Big Data technologies are still evolving and

Require Careful and Ongoing Needs Assessments

• Rather than focusing solely on external data, strive to achieve the full promise of Big Data by

Combining Third-Party Data With Internal Data Assets.

• Look at Big Data as an

Enterprise Asset

Once the opportunity has been identified, adopt an approach that will allow your organization to experiment before going down the SDLC track

1. Ideate - Out of the box thinking to enable business capability, build the idea, test the idea, then build it to production.

2. Incubate -

What will it take

2. Incubate - Create a small scale execution environment to conduct Proof of Concept (POC) activities on ideas that passed the ideate phase.

3. Productionalize -Transition the POC developed in the incubate phase to the standard software development process.

4. Realize - Realize the value of the capability and operate it in production. Continuously test the value delivered, enhance as needed and retire timely as it becomes obsolete .

Thank You!

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