presumption of abundance: architecting the future of success

Grab some coffee and

enjoy the

pre-show

banter

before the top of the

hour!

H T Technologies of 2015

HOST: Eric Kavanagh

THIS YEAR is…

ANALYST:

Dr. Claudia Imhoff CEO, Intelligent Solutions

ANALYST:

Dr. Robin Bloor Chief Analyst, The Bloor Group

GUEST:

Gary Spakes Senior Manager, SAS TH

E LINE UP

INTRODUCING

Dr. Claudia Imhoff

Copyright © Intelligent Solutions, Inc. 2014 All Rights Reserved

President, Intelligent Solutions, Inc. Founder, Boulder BI Brain Trust (BBBT) A thought leader, visionary, and practitioner, Claudia Imhoff, Ph.D., is an internationally recognized expert on analytics, business intelligence, and the architectures to support these initiatives. Dr. Imhoff has co-authored five books on these subjects and writes articles (totaling more than 150) for technical and business magazines. She is also the Founder of the Boulder BI Brain Trust (BBBT), an international consortium of independent analysts and experts. You can follow them on Twitter at #BBBT or become a subscriber at www.bbbt.us.

Email: [email protected] Phone: 303-444-6650 Twitter: Claudia_Imhoff

Claudia Imhoff

7


Agenda

§  Extending the Data Warehouse Architecture

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A Complex BI Environment

9

Multiple user devices

Multiple output formats

Multiple deployment options

Sophisticated analytics + complex analytic workloads Multiple data sources

Increasing data volumes & data rates

DW historical data

Web & social content

Sensor data

Operational data

Text & media files

Decision management

Data management

Data integration

Data analysis

Decision management

Slide compliments of Colin White – BI Research, Inc.


The Extended Data Warehouse Architecture (XDW)

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Traditional EDW environment

Investigative computing platform

Data refinery

Data integration platform

Analytic tools & applications

Operational real-time environment

RT analysis engine

Other internal & external structured & multi-structured data

Real-time streaming data Operational systems

BI services Slide created by Colin White – BI Research, Inc.


Use Case: Real Time Operational Environment

Embedded or callable BI services:

§  Real-time fraud detection §  Real-time loan risk assessment §  Optimizing online promotions §  Location-based offers §  Contact center optimization §  Supply chain optimization

Real-time analysis engine: §  Traffic flow optimization §  Web event analysis §  Natural resource exploration

analysis §  Stock trading analysis §  Risk analysis §  Correlation of unrelated data

streams (e.g., weather effects on product sales)

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RT analysis platform

Other internal & external structured & multi-structured data

Real-time streaming data

Operational systems

RT BI services


Data Provisioning Use Case: Data Integration

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§  Heavy lifting process of extracting, transforming to standard format and loading structured data – mostly batch

§  Physically consolidates data into “trusted” EDW sets for analysis

§  Invokes data quality processing where needed

§  Employs low-cost hardware and software to enable large data volumes to be combined and stored

§  Requires more formal governance policies to manage data security, privacy, quality, archiving and destruction



Data refinery



Data Provisioning Use Case: Data Refinery

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§  Ingests raw detailed structured and unstructured data in batch and/or real-time into a managed data store

§  Distills data into useful business information and distributes the results to downstream systems

§  May also directly analyze certain types of data

§  Also employs low-cost hardware and software to enable large amounts of detailed data to be managed cost effectively

§  Requires (flexible) governance policies to manage data security, privacy, quality, archiving and destruction



Data refinery



Traditional EDW Use Cases

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Most BI environments today §  New technologies can be

incorporated into the EDW environment to improve performance, efficiency & reduce costs

Use cases §  Production reporting §  Historical comparisons §  Customer analysis (next best offer,

segmentation, life-time value scores, churn analysis, etc.)

§  KPI calculations §  Profitability analysis §  Forecasting


Data refinery




RT analysis engine Operational systems

BI services


Investigative Computing Use Cases

New technologies used here include: §  Hadoop, in-memory computing,

columnar storage, data compression, appliances, etc.

Use cases §  Data mining and predictive

modeling for EDW and real-time environments

§  Cause and effect analysis §  Data exploration (“Did this ever

happen?” “How often?”) §  Pattern analysis §  General, unplanned

investigations of data

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Data refinery




RT analysis engine


Operational systems

BI services


All Components Must Work Together

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analytic models analyses

New sources of data Enterprise DW

Analytic tools

Investigative computing platform Data refinery Operational systems

existing customer

data

next best customer offer

3rd party data location data social data

feedback

RT analysis engine call center dashboard or web event stream

Slide created by Colin White – BI Research, Inc.


Four Forms of Analytics

Based on Delen, Dursun and Demirkan, Haluk, “Decision Support Systems, Data, information and analytics as services,” from Elsevier, published online May 29, 2012

Business Analytics

Descriptive (Reactive)

Prescriptive (Proactive)

Predictive (Proactive)

What happened? What is happening?

• Business reporting • Dashboards • Scorecards • Data warehousing

Well-defined business problems and opportunities

What will happen?

• Data mining • Text mining • Web/media mining • Forecasting

Accurate projections of the future states

and conditions

What should I do? Why should I do it?

• Optimization • Simulation • Decision modeling • Expert systems

Best possible business decisions

and transactions

Out

com

es

Ena

bler

s Q

uest

ions

Diagnostic (Reactive)

Why did it happen?

• Behavioral analysis • Cause and effect analysis • Correlations

Cause and effects of changes in business

activities

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Enterprises Must Evolve Their Analytical Thinking

§  Select few §  IT managed §  Reflecting the business §  What & why? §  Within the four walls §  Command/control §  Discrete activities §  Configured §  A conscious thought §  Tactical necessity

Expanding to From

§  Empowered many §  Business led §  Driving the business §  What could & should? §  The world around us §  Sense/respond §  Embedded everywhere §  Composed §  In everything we do §  Strategic advantage

*From IBM 18

INTRODUCING

Dr. Robin Bloor

Robin Bloor, PhD

It’s Not Really About Analytics…

There’s nothing new in Data Science

Nearly all the mathematical techniques have been known for decades – some for

centuries

SO WHY IS THERE SO MUCH EXCITEMENT?

It’s About Disruption

§  Cloud deployments

§  Multicore chips

§  CPU/GPU merging

§  Commodity servers

§  Commodity storage

§  On-chip processing

§  Memory-based architectures

§  Virtual networks


§  Cloud deployments

§  Multicore chips

§  CPU/GPU merging

§  Commodity servers

§  Commodity storage

§  On-chip processing

§  Memory-based architectures

§  Virtual networks

§  Massively scalable software

§  Hadoop + the key-value revolution

§  Schema on read

§  Marshalling unstructured data

§  Data availability and the market for data

§  Event data

§  Big data tools and architecture


u  Moore’s Law gave us a 10x speed increase every 6 years

u  Technology disruption is now giving us a 1000x or more speed increase whenever we want it – as long as we make sensible technology selections

u  This impacted analytics first because that’s where the biggest workloads were

It’s About Speed

The Industrialization of Data

Hadoop(Staging

Area)

DataAssaying

Servers

The Cloud

DesktopsMobile

Devices

IoT

DataExploration

DataCapture

The Prospecting Domain

Real-TimeActioning

DataManagement

HadoopArchive

DataServing

DataLife Cycle

Mgt

SystemManagement

AppsAppsAppsDataStoreData

StoreDataStores

u  We can speed up all the technologies in the end-to-end data chain

u  Data analytics that took days can now take minutes

u  Analytics that took months can be done in hours

u  We can process data in flight

u  So it’s not about re-thinking analytics, it’s about re-thinking how we use it

It’s About a Much Bigger Data Universe

It’s About “Different” Analytics

u  Our human control system works at different speeds: •  Operational control •  Almost instant reflex •  Considered response

u  Organizations will gradually implement similar control systems

u  This suggests a data-flow- based architecture

The Corporate Nervous System(s)

u  Mentation •  The Brain

u  Fight or Flight •  Sympathetic Nervous

System u  Operational Control

•  Enteric Nervous System •  Parasympathetic Nervous

System

Note that these three systems integrate. It would be bad

news if they didn’t.

The New World of Analytics

Ultimately, this is the direction we are heading in

The speed barriers have been torn

asunder

NOW WE HAVE TO BUILD IT

INTRODUCING

Gary Spakes

The Archive Trifecta: •  Inside Analysis www.insideanalysis.com •  SlideShare www.slideshare.net/InsideAnalysis •  YouTube www.youtube.com/user/BloorGroup

THANK YOU!

presumption of abundance: architecting the future of success

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