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

Big Data Analytics

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Looking at what is driving Big Data. Market projections to 2017 plus what is are customer and infrastructure priorities. What drove BD in 2013 and what were barriers. Introduction to Business Analytics, Types, Building Analytics approach and ten steps to build your analytics platform within your company plus key takeaways.

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Page 1: Big Data Analytics

JOHN CHOATE – PMMS SIG CHAIRJAMES HAIGHT - BLUE HILL RESEARCH

RAGHU BANDA - SAP

BIG DATA 2014 UPDATE & BUSINESS ANALYTICS (BASIC‘S) SESSION #1

Page 2: Big Data Analytics

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

Page 3: Big Data Analytics

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

Page 4: Big Data Analytics

BIG DATA MARKET FORECAST : 2011 - 2017

Page 5: Big Data Analytics

• 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

Page 6: Big Data Analytics

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

Page 7: Big Data Analytics

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.

Page 8: Big Data Analytics

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/

Page 9: Big Data Analytics

BIG DATA SPONSORSHIP“C” LEVEL ACTION

Page 10: Big Data Analytics

Customer Centric Outcomes

Operational Optimization

Risk / Financial Management

New Business Models

Employee Collaboration

BIG DATA FOCUS AREAS

Page 11: Big Data Analytics

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

Page 12: Big Data Analytics

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

Page 13: Big Data Analytics

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

Page 14: Big Data Analytics

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)

Page 15: Big Data Analytics

Big Data and Analytic Top 10 Trends for 2014Copyright 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

Page 16: Big Data Analytics

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.

Page 17: Big Data Analytics

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?

Page 18: Big Data Analytics

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

Page 19: Big Data Analytics

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.

Page 20: Big Data Analytics

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

Page 21: Big Data Analytics

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

Page 22: Big Data Analytics

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.

Page 23: Big Data Analytics

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

Page 24: Big Data Analytics

#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

Page 25: Big Data Analytics

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Page 26: Big Data Analytics

JOHN CHOATE – PMMS SIG CHAIRJAMES HAIGHT - BLUE HILL RESEARCH

RAGHU BANDA - SAP

BIG DATA 2014 UPDATE & BUSINESS ANALYTICS (BASIC‘S) SESSION #1