Big Data Analytics


Published on

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.

Published in: Technology
1 Like
  • Be the first to comment

No Downloads
Total views
On SlideShare
From Embeds
Number of Embeds
Embeds 0
No embeds

No notes for slide
  • Better ways of managing your business is one of the key drivers behind the whole Big Data Movement although it’s not the only one.

    For example, leveraging Big Data has enabled new innovative business models, for example analyzing social media feeds, or web log data and by analyzing Big Data it can give real competitive advantage.

    But there are also technical reasons, for example, the amount of data that is being collected continues to grow exponentially and appearing many different formats and, frankly, conventional database solutions were finding it hard to cope. New technologies for handling the data needed to be found if it was going to be processed.

    There are also financial reasons in that as data increased in volumes, lower cost methods of processing needed to be found.
  • Big Data Analytics

    2. 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. 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. 4. BIG DATA MARKET FORECAST : 2011 - 2017
    5. 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. 6. BIG DATA MARKET FORECAST (SUB-TYPE) : 2011 - 2017
    7. 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. 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 * *
    10. 10.  Customer Centric Outcomes  Operational Optimization  Risk / Financial Management  New Business Models  Employee Collaboration BIG DATA FOCUS AREAS
    11. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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
    25. 25. Join ASUG or Learn About Your Membership Benefits Stay in the Know, Subscribe to ASUG Newsletters Visit Visit for independent, unbiased, and customer-focused coverage of SAP.  TWITTER:  FACEBOOK:  LINKEDIN: STAY IN TOUCH!