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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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JOHN CHOATE – PMMS SIG CHAIRJAMES HAIGHT - BLUE HILL RESEARCH
RAGHU BANDA - SAP
BIG DATA 2014 UPDATE & BUSINESS ANALYTICS (BASIC‘S) SESSION #1
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
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
BIG DATA MARKET FORECAST : 2011 - 2017
• 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
BIG DATA MARKET FORECAST (SUB-TYPE) : 2011 - 2017
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.
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/
BIG DATA SPONSORSHIP“C” LEVEL ACTION
Customer Centric Outcomes
Operational Optimization
Risk / Financial Management
New Business Models
Employee Collaboration
BIG DATA FOCUS AREAS
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
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
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
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)
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
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.
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?
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
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.
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
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
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.
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
#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
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JOHN CHOATE – PMMS SIG CHAIRJAMES HAIGHT - BLUE HILL RESEARCH
RAGHU BANDA - SAP
BIG DATA 2014 UPDATE & BUSINESS ANALYTICS (BASIC‘S) SESSION #1