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© Copyright Ovum. All rights reserved. Ovum is an Informa business1
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
© 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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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Your view?
Do you believe that you already work for a data-driven enterprise?
Yes No
Continue the conversation on Twitter with hashtag #bisymp
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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
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We expect information at our fingertips
Distribution of economic activity in the information and
communication sector
Source: Statistics Netherlands
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New data-driven possibilities emerge
Map of geolocated Tweets to find the most frequently
travelled routes in UK cities
Source: The Guardian, Eric Fischer
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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
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DATA MANAGEMENT AND QUALITY
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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
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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,
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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?
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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
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EVOLVING THE ANALYTICAL ARCHITECTURE
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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
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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
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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
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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
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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
1
2
3
© Copyright Ovum. All rights reserved. Ovum is an Informa business32
www.ovum.com
Tim Jennings, Research Fellow and Chief Analyst, Ovum IT
Email: [email protected]: +44 7715 748947Twitter: @tjennings
© Copyright Ovum. All rights reserved. Ovum is an Informa business33
www.ovum.com
Tim Jennings, Research Fellow and Chief Analyst, Ovum IT
Email: [email protected]: +44 7715 748947Twitter: @tjennings
© Copyright Ovum. All rights reserved. Ovum is an Informa business34
www.ovum.com
Tim Jennings, Research Fellow and Chief Analyst, Ovum IT
Email: [email protected]: +44 7715 748947Twitter: @tjennings