tim jennings, ovum - business at the speed of thought - bi symposium 2012

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Business at the speed of thought: a manifesto

26th Nov 2012

Tim Jennings, Research Fellow and Chief Analyst, Ovum IT

Email: [email protected]: +44 7715 748947Twitter: @tjennings

mailto:[email protected]

© Copyright Ovum. All rights reserved. Ovum is an Informa business.2

Agenda

Industry transformation and the data-driven enterprise

The culture of decision making

Data management and quality

Evolving the analytical architecture


Industry transformation – Henry Milward & Sons

Established 1730 in Redditch, Worcs.

Manufacturer of needles and fish hooks

Cottage industry became mechanised, first using a water-driven scouring mill

Introduction of steam power enabled further automation

Local market for glove makers, saddle makers and weavers expanded into a global market

Canal and then rail network provided easier distribution


Industry transformation – Samuel Greg & Co.

Cotton mill founded 1782 at Quarry Bank Mill, Styal, near Manchester

Arkwright’s Spinning Jenny enabled move from domestic spinning (once patents had been challenged or expired)

Machinery powered initially by water wheel. Boulton and Watt steam engine acquired 1810.

Samuel Greg was a pioneer of the factory system, and built on-site accommodation

Greg expanded the business, building four further cotton factories in the area


Common factors

New and improved power sources Mechanization

Factory system and division of labour

Dramatic increase in network bandwidth

and speed

Increase in knowledge transfer

Improved processes for raw materials,

chemicals & fabrication


The data-driven enterprise in financial services

Banking core system renewal to enable real-time processing and standardization

Transaction analysis for fraud detection and risk management

Basel III and Solvency II require integration of large and disparate data sources to assess risk exposure & liquidity in real time

Telematics are being applied to analyse driving behaviour and offer pay-as-you-drive policies

High frequency trading relies entirely on technical capabilities, rather than fundamentals of the stocks involved


The data-driven enterprise in telecoms

In a highly competitive telecoms market, customer experience, customer satisfaction and personalization are key to success

Telcos are providing new and enhanced services such as payment services, identity, and mobile wallets

Telco services becoming integrated into industry value chains in areas including healthcare, energy, and public sector

Telecom operators have evolved to triple-play and quad-play service bundles, incorporating mobile, digital & broadband

New competition from Over-The-Top (OTT) players offering VOIP calls, messaging, and videoconferencing services


The data-driven enterprise in energy and utilities

The industry is making a huge transition from analogue to digital, with smart meters and smart grids transforming the value chain

Utilities will have to deal with an exponential growth in data, and information architecture complexity

Data enables utilities to increase efficiency, optimize maintenance, predict demand, and cross-sell additional services.

e.g. every 1% gain in accuracy in forecasting customer demand can save a large energy company £130m

Data also used to add value for the utilities’ business and residential customers


The data-driven enterprise in media & broadcast

Convergence of traditional broadcast channels and Web is causing massive disruption, with reduced entry barriers

Audience behaviour is changing dramatically with new devices and new channels; consumers demand a unified and personalized experience

Massive volumes of digital data enables sophisticated real-time targeting of editorial and advertising content

Restructuring of internal and supply chain architectures using metadata-enhanced digital file based workflow, enables new levels of automation


The data-driven enterprise in government and public sector

Fiscal austerity driving increased process efficiency and automation

Analytics being used to predict citizen behaviour and optimize resource allocation

Crime report analysis and event correlation used to assist law enforcement

Sensor data (M2M) used to manage digital city infrastructure and relieve traffic congestion

Citizen engagement across online, mobile and social channels

Education transforming back-office processes and embracing e-learning


The data-driven enterprise in healthcare and life sciences

Electronic health records enable better continuum of care through higher availability of patient data

Analytics and decision support systems generate valuable insights into how to improve clinical and operational outcomes

Pharma sales reps using tablets for drug detailing, linked to optimized sales and marketing campaigns

Pharma & biotech using electronic notebooks and automated data acquisition to increase collaboration, and improve data quality

Mobile devices enable new interactions between patients, doctors and insurers


Common factors

Automation, optimization and process efficiency

Primacy of the customer and

citizen experience

Widespread use of analytics for

decision support

Connected, always on society – customers,

employees, environment

Convergence of operational technology and

information technology

Information as thekey differentiator

for business


Your view?

Do you believe that you already work for a data-driven enterprise?

Yes No

Continue the conversation on Twitter with hashtag #bisymp


THE CULTURE OF DECISION-MAKING

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Infographics abound!

Dr. No

From Russia With Love

Goldfinger

Thunderball

The World is Not Enough

Die Another Day

Casino Royale

Quantum of Solace

Source: The Guardian

Number of kills by James Bond in different movies

= 10 = 1


We expect information at our fingertips

Distribution of economic activity in the information and

communication sector

Source: Statistics Netherlands


New data-driven possibilities emerge

Map of geolocated Tweets to find the most frequently

travelled routes in UK cities

Source: The Guardian, Eric Fischer


Why Big Data changes nothing … and everything!

BIG DATA

FAST DATA

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Mapping capabilities to the decision making process

Data collection

Data integration

Business process

Process analysis

Performance insight

Collaboration

Action

Do we have the right information available? Is it accurate and trusted?

Can we successfully integrate data from multiple sources and make it consistent?

Have we got effective reporting on operational business processes?

Do we successfully gain insight into performance against business objectives?

Is it easy to share information and collaborate with colleagues and partners?

Does our information translate directly into action?

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Data privacy and trust will be the big issue

Governance problem first, technology second

Understand the relationship with your customers & business partners

Keep communications in context

Don’t catch your customers by surprise

The law still trying to catch up

How Companies Learn Your Secrets


DATA MANAGEMENT AND QUALITY


Variety of data sources

Call records

Website clickstreams

Location/ GPS data

Sensor data

Meter readings

Telematics

XML feeds

Log files

POS transactions

Financialtransactions

Transactionaldata

Customercorrespondenc

eEmails

Public data sets

Video images/CCTV

Audio callrecordings

Personal records

Forms dataSocial media

feeds

MP3s

Geophysical data


Implications of analysing big, fast data

Structure of data may be unknown in advance, or vary across data set

Data sources that have previously been discarded can now be utilized to create business insight and value

Analysing all data versus sampling. Get a “good enough” picture of all the data

Iterative and exploratory analysis versus structured and repeatable analysis

Data integration is critical to create the most powerful insights

Democratization of data

Governments focus on “open data”, Publicly available APIs from Google, Facebook,


Data stewardship questions for Big Data

Can we, should we “control” this data?

Are there limits to how much we should know?

Can we just keep piling up data forever?

Can we cleanse terabytes of data?

Do we still need “good” data?


Is data quality necessary for Big Data?

The App

How mission-critical?

Regulatory compliance impacts?

What degree of business impact?

The Data

The 4V’s (volume, variety, velocity, value) determine what approaches to quality are feasible

Techniques

Profiling vs. cleansing


EVOLVING THE ANALYTICAL ARCHITECTURE


Technology perspective

Traditional BI architectures and methodologies are not suited to many of these new use cases

A platform approach is required that is capable of handling a broader range of big and fast data scenarios across the whole lifecycle

New database approaches are required to handle the volume, variety, and velocity of Big Data

Specialized databases vs. enhancements to existing database tools

Architectural changes required for many aspects of data handling, including storage, archiving, ETL, DW, analytics

Data processing capabilities must become a ‘first-class citizen’ in the data center

Organizations must balance cost, performance, and value in selecting the appropriate architecture and tools for a given application


New processing scenarios will require data processing, BI and analytics architectures to evolve

Scale

Lat

ency

PBytesGBytes

Bat

chIn

tera

ctiv

eR

eal-

tim

e

Legacy Batch

Traditional SQL

TBytes

NoSQL

Advanced SQL

In-Memory SQLReal-time

events

CEP

Long-running events

Fast Data

Big Data


Emerging directions for Big Data and analytics architecture

Social data

Sensor data

Big Data Toolsand PlatformMultimedi

a

HadoopCluster

TransactionalData

EnterpriseData

Warehouse

Business Intelligenceand Analytics

Common Interfaceand Management Layer


Early Development Commercialization Ecosystem Formation

2003 - 2005 2009 2011 2012 2013

First Advanced

SQL platforms emerge

Hadoop emerges

Other NoSQL

platforms emerge

Cloudera intros

comm’l Hadoop support

Major vendors enter Big

Data market

Tooling emerges

2nd wave Advanced

SQL platforms emerge

Infrastructure vendors enter

market

Internet firm early adopters

Enterprise early adopters (FS, Telecoms, Media)

Mainstream adoption begins

2015

Market begins to mature

Market timeline for the data-driven enterprise


Key takeaways

Continued evolution of information management, and a step change in business technology is making possible the

data-driven enterprise

Organizations must consider how information can optimize every single aspect of their business

BI, DW and analytics architectures are evolving rapidly and big and fast data should become a

first-class citizen in the data center

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tim jennings, ovum - business at the speed of thought - bi symposium 2012

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