1.
JOHN CHOATE – PMMS SIG CHAIR
JAMES HAIGHT - BLUE HILL RESEARCH
RAGHU BANDA - SAP
BIG DATA 2014 UPDATE & BUSINESS ANALYTICS (BASIC‘S)
SESSION #1

2.
 The MARKET ( 2011 – 2017 )
 Forecast – Components – 2013 Actual
 Why Big Data? (Big 3: B – T – F)
 Big Data Sponsorship – “C” Level Action
 Big Data Focus Areas
 Priority of Need
 Infrastructure Priorities
 The 4 V’s - Revisited
 Top 10 Trends for 2014
PRESENTATION CONTENT - BIG DATA 2014 UPDATE

3.
 What is Analytics / Business Analytics
 Market Projection
 The 4 Key Types
 Domains of Analytics
 Capability Needs
 Making Analytics Work – 10 Steps!
 Building an Approach
 Key Take Away’s
PRESENTATION CONTENT - ANALYTICS

4.
BIG DATA MARKET FORECAST : 2011 - 2017

5.
• Hadoop software and related hardware and services;
• No SQL database software and related hardware and services;
• Next-generation data warehouses/analytic database software and related hardware and services;
• Non-Hadoop Big Data platforms, software, and related hardware and services;
• In-memory – both DRAM and flash – databases as applied to Big Data workloads;
• Data integration and data quality platforms, tools and services as applied to Big Data deployments;
• Advanced analytics and data science platforms, tools and services;
• Application development platforms, tools and services as applied to Big Data use cases;
• Business intelligence and data visualization platforms, tools and services as applied to Big Data use cases;
• Analytic and transactional applications and services as applied to Big Data use cases;
• Cloud-based Big Data services including infrastructure, platform and software delivers as a service.
• Other Big Data support, training, and professional services.
BIG DATA PRODUCTS & SERVICES

6.
BIG DATA MARKET FORECAST (SUB-TYPE) : 2011 - 2017

7.
BIG DATA 2013 MARKET - ACTUAL
Big Data Adoption Barriers
 A lack of best practices for integrating
Big Data analytics into existing business
processes and workflows.
 Concerns over security and data privacy
in the wake of numerous high-profile
data breaches and the ongoing NSA
scandal.
 Continued “Big Data Washing” by
legacy IT vendors leading to confusion
among enterprise buyers and
practitioners, as well as “political” factors
that make it difficult for enterprise
buyers to engage new vendors.
 A still volatile and fast developing
market of competing Big Data vendors
and, though to a lesser degree in 2013,
competing technologies and
frameworks.
 A lack of polished Big Data applications
designed to solve specific business
problems.
Big Data Growth Drivers
 Both mega-IT-vendors and pure-play Big
Data vendors took steps to better articulate
their product & services roadmaps and
larger visions for Big Data in the enterprise,
creating greater confidence from enterprise
buyers.
 The products and services related to Big
Data continued to mature from a features
perspective in 2013, further spurring
adoption. Big Data technologies also took
important steps towards greater enterprise-
grade capabilities in 2013, critical for mass
enterprise adoption. These steps included
better privacy, security and governance
capabilities, as well as improved backup &
recovery and high-availability for Hadoop
specifically.
 Partnerships also played an important role
in maturing the Big Data landscape in 2013.
Of particular importance are a number of
reseller agreements and technical
partnerships between Big Data vendors and
non-Big Data vendors, the results of which
that make it easier for practitioners to adopt
and integrate Big Data technologies.

8.
Business
 Opportunity to enable innovative new business models
 Potential for new insights that drive competitive advantage
Technical
 Data collected and stored continues to grow exponentially
 Data is increasingly everywhere and in many formats
 Traditional solutions are failing under new requirements
Financial
 Cost of data systems, as a percentage of IT spend, continues to grow
 Cost advantages of commodity hardware & open source software
KEY DRIVERS BIG DATA *
* http://hortonworks.com/blog/7-key-drivers-for-the-big-data-market/

9.
BIG DATA SPONSORSHIP
“C” LEVEL ACTION

10.
 Customer Centric Outcomes
 Operational Optimization
 Risk / Financial Management
 New Business Models
 Employee Collaboration
BIG DATA FOCUS AREAS

11.
1. A Greater Scope of Information
2. New Kinds of Data and Analysis
3. Real Time (HANA) Information
4. Data influx of New Technologies
5. Non-traditional forms of Media
6. Large Volumes of Data (Big Data!)
7. The Latest Buzz words
8. Social Media Data
PRIORITY OF NEED FOR BIG DATA

12.
INFRASTRUCTURE PRIORITIES FOR BIG DATA
 Information Integration
 Scalable Infrastructure
Storage
 High Capacity Warehouse
 Security and Governance
Scripting and Development Tools
Columnar Databases
Complex Event Processing
Workload Optimization
Analytic Accelerators
Hadoop / Map Reduce
No SQL Engines
Stream Computing

13.
THE 4 “V’s” (REVISITED)
VELOCITY
Data in Motion: Streaming data within fractions of a second to make “Real Time” (HANA) Decisions
VOLUME
Data at Scale: Terabytes to Zeta bytes (Big Data)
VARIETY
Data in Many Forms: Structured, Unstructured, Text & Multi Media
VERACITY
Data Uncertainty: Managing the reliability and predictability of imprecise data types.
Gartner Model

14.
VOLUME
500+ Million records
Terabytes to Zetabytes
VELOCITY
Data in Motion
Streams
VARIETY
Structured, Semi – structured,
Unstructured
VALUE
Store everywhere
Billions of Records
10’s of TB’s of Data
“REAL TIME”
Text Processing & Search
Sentiment Analysis
High-Value
Low Volumes
of Low Value data
THE 4 “V’s” & In Memory (HANA)

15.
Big Data and Analytic Top 10 Trends for 2014
Copyright Oracle - 2013
1. Business Users Get Hooked on Mobile Analytics
2. Analytics' Take to the Cloud
3. Hadoop-Based Data Reservoirs Unite with Data Warehouses
4. New Skills Bolster Big Data Investments
5. Big Data Discovery is the Secret to Workforce Success for HCM
6. Predictive Analytics Lend Fresh Insight into Big Data Strategies
7. Predictive Analytics Bring New Insight to Old Business Processes
8. Decision Optimization Technologies Enhance Human Intuition
9. Business Leaders Embrace Packaged Analytics
10. New Skills Launch New Horizons of Analysis

16.
What is Analytics?
WHAT IS BUSINESS ANALYTICS?
Analytics is the discovery and communication of meaningful patterns in data.
Analytics uses data visualization to effectively communicate insight.
Business Analytics (BA) is comprised of solutions used to build analysis models and simulations to
create scenarios, understand realities and predict future states.
Business analytics includes;
 Data Mining
 Predictive Analytics
 Applied Analytics
 Statistics
According to market research firm IDC, the business analytics software market grew by 14.1 percent in 2011
and will continue to grow at a 9.8 percent annual rate, to reach
$50.7 billion in 2016, driven by the focus on Big Data.

17.
TYPES OF ANALYTICS
“Business Intelligence”, or BI reporting
 More the real time (HANA) the better!
 Form of dashboard reporting or any other conventional reporting
 Simply “analytics”
“Descriptive Analytics”
 Gain insight from historical data with reporting, scorecards, clustering etc.
 Terms such as profiling, segmentation, or clustering fall under descriptive analytics.
Example:
 How many different segments of buyers are we dealing with? Where are they, and what
do they look like?
 How do high value customers differ from our other Customers?

18.
TYPES OF ANALYTICS
PREDICTIVE : Analyze current and historical facts to make predictions about future, or otherwise unknown,
events.
 Need carefully structured statistical models, which will return “scores” that define likelihood of customers
behaving a certain way.
 In terms of complexity, this is the most demanding type of analytics
EXAMPLES:
 Predict market trends and customer needs (CRM)
 Customized offers for each segment & channel (CRM)
 Predict how market-volatility will impact business (CRM)
 Foresee changes in demand and supply across entire supply chain (SCM)
 Proactively manage workforce by attracting and retaining talent (HCM)
Optimization:
 Requires a complex type of modeling, where “what if” type of questions are answered.
 Type of analytics calls for different types of data in comparison to typical predictive modeling

19.
BASIC DOMAINS WITHIN ANALYTICS
Behavioral Analytics
Cohort Analytics
Collections Analytics
Contextual Data modeling
Financial Services Analytics
Fraud Analytics
Marketing (Customer) Analytics
Pricing Analytics
Retail Sales Analytics
Risk and Credit Analytics
Supply Chain Analytics
Talent (Human Resources) Analytics
Telecommunications
Transportation Analytics
DOMAIN
(1) A group of computers and devices on a
network that are administered as a unit with
common rules and procedures. Within the
Internet, domains are defined by the IP address.
All devices sharing a common part of the IP
address are said to be in the same domain.
(2) In database technology, domain refers to the
description of an attribute's allowed values. The
physical description is a set of values the attribute
can have, and the semantic, or logical, description
is the meaning of the attribute.

20.
 Query and Reporting
 Data Mining
 Data Visualization
 Predictive Modeling
 Optimization
Simulation
Natural Language Text
Geospatial Analytics
Streaming Analytics
Video Analytics
Voice Analytics
ANALYTICS CAPABILITY NEEDS

21.
1. Expand where feasible and effective!
2. Integrate across the organization
3. Bring to specific tasks: Strategy/Planning, Finance, Marketing, Sales, IT, Ops/SCM,
Product Development, Customer Service, & HR
4. Use the tools: Spreadsheets, KPI’s/Dash boards, Forecasting, Queries, General Stats,
data/Text Mining, Simulations, Models, Optimization, Web Analytics, & Data visualization
5. Create data strategy that includes “Real Time” access to data.
6. Deploy necessary Technology
7. Develop formal data-management processes
8. Secure Executive Buy In
9. Deliver and Communicate Value
10. Hire and train the right analytic talent
EFFECTIVE STEPS TO MAKE ANALYTICS WORK

22.
BUILDING AN ANALYTIC APPROACH / ROADMAP / TEAM
Analytics Structure &
Change Management
Centralized Analytics Structure
 Modern IT is a business enabler and
strategic partner
 IT can take leadership to framework the
centralized analytics team, since data
and data management is essential to
analytics
Decentralized Analytics Structure
 Data architects, analysts distribute cross
the business functions, the more
dynamic CoE (Center of Excellence) is
facilitated to share the progress and
best practices
Analytics Tips
 Out-of-the-box analytics (RDS) with a
heavy focus on results
 Increased demand by users and
continued data model development
analytics
 Make it stick: Integrate the analytics
practitioners into everyday business
rhythms, also commit the measurement
 Agile Analytics: A series of user-driven
deliverables, with frequent outputs and
check-in
Analytics KPIs & Maturity
 The path to analytic maturity has three
key areas — leadership, breaking down
silos, and developing and keeping talent .
 The maturity of the organization is based
on exploring the quality data, asking the
effective question, exploring the end-to-
end business process, building the
practical analytics model, measure the
KPIs.
Analytic Business Cases
 Quick Win: Communicate and initiate
the business case base on business
priorities buy-in & support from
shareholders to deliver near-term
results
 Strategic Project: Capture the hinder-
sight, insight and foresight, enable the
business to solve problems timely and
approach new market promptly.
 Expansion: Cross-functional, multiple
analytic disciplines are required to solve
the wide variety of problems an
organization faces, while enabling the
greatest analytic bandwidth.
 Transformation: Organizational change
and analytics capability expand effort
cross-functional track, evaluate and
measure the result, the analytics culture
has been nurtured, the key processes
have been optimized, the organization
has been transformed into agile, high-
performance business.

23.
 Analytics support business intuition with data decisions
 Don’t expect an analytical model to give you “the answer”
 Simpler is Better
 The simplest approach that solves your problem is usually the best one
 There is no correlation between analytic complexity and business value
 Really understand the Customer’s Business Problem you’re trying to solve
 Apply the 5 Why’s approach
 Small steps lead to big wins!
 POC as a 1st step!
TAKE AWAYS

24.
 #1 BIG DATA 2014 UPDATE & ANALTICS BASIC’S
 #2 TYPES OF ANALYTICS – July 28
 #3 INDUSTRIES / X INDUSTRIES
LINE OF BUSINESS (LOB) – Aug TBD
 BUSINESS PERSPECTIVE
 TECHNOLOGY PERSPECTIVE
UPCOMING SESSIONS IN ANALYTICS SERIES

25.
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26.
JOHN CHOATE – PMMS SIG CHAIR
JAMES HAIGHT - BLUE HILL RESEARCH
RAGHU BANDA - SAP
BIG DATA 2014 UPDATE & BUSINESS ANALYTICS (BASIC‘S)
SESSION #1