Extending BI with Big Data Analytics

Fastest Time to New Insights

© 2014 Datameer, Inc. All rights reserved.

Extending Analytics Beyond BI!

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Claudia Imhoff President, Intelligent Solutions, Inc. 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

About Our Speaker!

Azita Martin @datameer CMO Azita Martin is Chief Marketing Officer at Datameer with extensive marketing leadership experience at high-growth start-ups and category-creating public companies like Salesforce and Siebel. Azita has global responsibility for scaling all aspects of Datameer’s product and corporate marketing, including defining go-to-market strategy, driving thought leadership, and increasing brand awareness and customer acquisition. Prior to Datameer, Azita built and led marketing teams for both fast-growing start-ups and major public companies, including Get Satisfaction, Moxie Software, LiveOps, Salesforce, Siebel and SGI.

#datameer @datameer

About Our Speaker!

Matt Schumpert @datameer Senior Director, Solutions Engineering Matt has been working in the enterprise infrastructure software space for over 14 years in various capacities, including sales engineering, strategic alliances and consulting. Matt currently runs the pre-sales engineering team at Datameer, supporting all technical aspects of customer engagement from initial contact through roll-out of customers into production. Matt holds a BS in Computer Science from the University of Virginia. #datameer @datameer

About Our Speaker!

Copyright © Intelligent Solutions, Inc. 2014 All Rights Reserved

Agenda

§  Extending the Data Warehouse Architecture §  Use Cases §  Major Trends and Examples

7


Disruptive Forces

§  Deployment Options

§  Mobile Work Force

§  Advanced Analytics

§  Big Data

§  Data Management

BUT disruption does not have to mean CHAOS!

8


Next Generation BI

Based on a concept by Shree Dandekar of Dell Slide compliments of Colin White – BI Research, Inc.

New business insights

Reduced costs

New technologies

Enhanced data

management

Advanced analytics

New deployment

options

Next generation

BI

DRIVERS

TECHNOLOGIES

9


A Complex Environment

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.

10


Next Generation – Extended Data Warehouse Architecture (XDW)

Traditional EDW environment

Investigative computing platform

Data refinery

Data integration platform

Analytic tools & applications

Operational real-time environment

RT analysis platform

Other internal & external structured & multi-structured data

Real-time streaming data Operational systems

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

11


Agenda


12


Systems of Record

§  Remember – It all starts here! §  Transactional systems generate most of the data used for all other

activities – operational processes, BI & analytical capabilities, etc.

§  The point here is a reminder: §  Extend OLTP systems of record as a “key” source of data §  Many companies do not (or can not) leverage data they already

have in their operational systems

13

Operational systems

RT BI services


Real-time streaming data


Use Case: Traditional EDW

14

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




Operational systems

RT BI services

real-time models & rules


Use Case: Data Refinery

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

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

May also directly analyze certain types of data

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


15


Use Case: Investigative Computing

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 and discovery (“Did

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

of data

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Data refinery






Operational systems

RT BI services


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)

17





Operational systems

RT BI services


All Components Must Work Together

18

analytic models analyses

Analytic tools & apps


Data refinery Operational systems

existing customer

data

next best customer offer

3rd party data location data social data

feedback

RT analysis platform call center dashboard or web event stream

Slide created by Colin White – BI Research, Inc.





Agenda


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1. What is the IoT?

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Investigative Computing: Hadoop Example

§  Predictive Analytics to Reduce Patient Re-admittance

o  Goal is to predict the likelihood of hospital re-admittance within 30 days after discharge

o  Patients with congestive heart failure have a tendency to build up fluid, which causes them to gain weight

o  Rapid weight gain over a 1-2 day period is a sign that something is wrong

o  Heart patients at home have a scale that wirelessly transmits data (uses iSirona) to Hadoop where an algorithm determines risk of re-admittance and alerts a clinician

o  All home monitoring data will be viewable in the EMR via an API to Hadoop

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“If Hadoop didn’t exist we would still have to make decisions

about what can come into our data warehouse or the electronic

medical record (and what cannot). Now we can bring

everything into Hadoop, regardless of data format or

speed of ingest. If I find a new data source, I can start storing it the day that I learn about it. We

leave no data behind.”

Source: Hortonworks


2. Evolution of Analytics

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§  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


Four Forms of Analytics

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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


Predicting the Future

§  Netflix uses predictive analytics to produce “House of Cards” = most streamed piece of content in 40 countries §  Netflix knew it was a hit BEFORE filming began by analyzing 30 M “plays” a day, 4 M ratings, etc.

§  They also analyzed the director’s track record, Kevin Spacey’s appeal, reaction to the British version, etc.

§  Benefit? To breakeven, Netflix needed to gain 565,000 more members. They brought in more than 17 Million!

§  Downside – impact on quality, diversity, even creativity?

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3. Making Analytics More Consumable

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§  Use of BI for decision making continues to be a high priority for organizations §  Recent survey1 of 2,500 CIOs showed 83% of CIOs see BI &

analytics as the way to enhance an organizations’ competitiveness

§  But reach of BI is often restricted to those users with experience to exploit analytics for business benefit §  59% of users say that they miss information that might be

of value to their jobs because they can not find it §  27% of managers time is spent searching for information §  50% say the information they obtain has no value to them

§  BI must be more easily understood and consumed! §  You need an architecture

1 “IBM Global CIO Study: The New Voice of the CIO”


Making BI More Consumable – Information Consumers

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Access Integrate Manage Report

Analyze Deliver

Make it easy to access and

Blend data

Make DM solutions fast to deploy & easy to manage

Make BI tools easy to use

Make BI results easy to consume

& enhance

Office product integration Portal integration + search

Business glossary & data lineage BI automation

Mobile BI Collaborative BI

Data visualization


Making BI More Consumable – Information Producers

27

Access Integrate Manage Report

Analyze Deliver

Make it easy to access & blend data

Make DM solutions fast to deploy & easy to manage

Make BI tools easy to use

Make BI results easy to consume

& enhance

Customizable BI components

Ad hoc visual analysis tools Investigative BI workbench

Cloud computing BI sandboxes

Investigative BI platform

Data virtualization Big data

connectors Data blending


Getting Started

§  Education is mandatory §  This is not just training on BI tools §  Education includes how to think analytically, how to interpret

results, who to ask for help §  Advanced BI analysts (business analysts, data scientists, etc.)

must evangelize value of analytics

§  Many business people don’t know where to get training §  May be embarrassed to ask for it or intimidated by it §  May not even know what BI resources are available or what data

is available

28 From www. business-help.org


Getting Started

§  Governance still has an important role §  Determine whether data used is “governed” (e.g., in a data

warehouse or MDM environment) or “ungoverned” (e.g., individual spreadsheets, external source)

§  IT must have monitoring and oversight capability §  BI/DW builder needs to administer and manage infrastructure §  Must be able to monitor the environment §  Must have oversight into the environment

§  Note: LOB IT or experienced information producers may have to take on some previously traditional central IT roles §  Security of data, adherence to privacy policies

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Use Cases!

Social Media

Mobile Ads

Web Logs

CRM

Product Logs Transaction

Call Center

Are keywords related to customer segments?

Which campaign combinations accelerate conversion?

Which product features drive adoption?

What content works be best for each lead segment?

Which features do users struggle with?

What behavior signals churn?

Understand Your Customer Journey!

Call Center

Public Data

CRM

Web

Reduced customer churn by 50%

Reduce Customer Churn!

Public Data

Connected Home

Energy Consumption

Data

Time & cost savings for IT Reduced false alarms

User Behavior

Internet of Things!

Improved customer experience

Household Data

7 Billion lbs. reduction In CO2 Output

$500M/Year in Energy Savings

Energy Consumption

Data

Smart Meter

Smart Meter Analytics!

Demo

@Datameer www.datameer.com

For the webinar:http://bit.ly/1zxY3Nl

!

Data & Analytics

Extending BI with Big Data Analytics