Business Intelligence for Big Data Analytics

Business Intelligence for Big Data Analytics

1. Some concepts

   • Data Science
     • is
       • data analysis using scientific methods.
       • the extraction of useful information from accumulated data.
   • Data mining
     • Used for
       • knowledge discovery in databases. I mean, new information is searched for in large amounts of data sets
     • Help to
       • find rules for set tasks and to solve 4 problems: the prediction, classification, clustering and interconnectivity.

2. BUSINESS INTELLIGENCE AND ANALYTICS

   • IT-authored production reports.
     • Object
       • It was to inform a broad array of information consumers and analysts.
         • But now...
           • Business users and analysts
             • demand
               • Access to interactive styles of analysis and insights from advanced analytics, without IT or data science skills
                 • So...
                   • Platforms have to be user-friendly for analysts and business users to find insights quickly, or by IT to create quick analyzes providing more timely business requirements and benefits.
                     • Having as consequences
                       • BI and analytics vendors introduce self-service data preparation and smart data discovery and pattern detection capabilities
               • Access to self-service capabilities.
   • Gartner Magic Quadrant´s definition of BI and analytics
     • "A software platform that delivers 13 critical capabilities across three categories: to enable, produce and consume, in support of four use cases for BI and analytics."
       • This capabilities help to...
         • enable BI leaders to support a range of functions and use cases from system-of-record reporting and analytic applications to decentralized self-service data discovery.
   • How to evaluate sellers by the way to use
     • There are 4 use cases:
       • 1. Centralized BI provisioning: supports a workflow from data to IT-delivered-and-managed content.
         • 2. Decentralized analytics: supports a workflow from data to self-service analytics
           • 3. Governed data discovery: supports a workflow from data to self-service analytics to systems-of-record, ITmanaged content with governance, reusability and promotability;
             • 4. OEM/embedded BI: supports a workflow from data to embedded BI content in a process or application.
               • Also, with 13 critical capabilities:
                 • Business user data mashup and modelling, internal platform integration, BI platform administration, metadata management, cloud deployment, development and integration, free-form interactive exploration, analytic dashboards and content, IT-developed reporting and dashboards, traditional styles of analysis, mobile, collaboration and social integration and embedded BI
   • Study of Gartner, Inc
     • SAS and the Tableau were recognized as the world's greatest leaders in the field of business intelligence and analytics platforms
       • SAS Institute Inc.
         • Offers an array of components within its Business Intelligence and Analytics suite combining expertise in statistics and predictive modelling with innovative visualization
           • Strengths
             • Support for complex analytic use cases
             • Functionality and product quality
             • Can be deployed to meet the needs of a diverse set of use cases. It´s a platform that is able to meet both the enterprise IT needs and business self-service needs
             • The availability of integrated business-user data preparation capabilities
           • Weaknesses
             • License cost for SAS customers
             • Customers have difficulty in migrating to the latest release of the SAS platform components that they have deployed
             • It has yet to have its full impact on the perceived ease of use
       • Tableau
         • It have transformed business users' expectations about what they can discover in data and share without extensive skills or training with a BI platform
           • Strengths
             • It has clearly defined the market in terms of data discovery, with a focus on "helping people see and understand their data."
             • It aggregates product score, with particular strengths in the decentralized and governed data discovery use cases.
             • It has been able to grow and scale without a significant impact on discounts extended or customer experience
             • The highest scores in terms of breadth and ease of use along with high business benefits realized.
             • It is an R&D-driven company
           • Weaknesses
             • It has a limited product line focused on data discovery
             • IT-developed reports and dashboards, traditional styles of analysis, metadata management, development and integration, BI platform administration, embedded BI and collaboration are rated as weaker capabilities
             • It faces competitive threats from every other vendor in the market that is also focused on delivering self-service data discovery and visualization capabilities
             • It offers limited advanced analytics capabilities.

3. ANALYTICS: BUSINESS VISUALIZATION

   • The organizations collect all types and amounts of data, the industrial sector doesn´t matter. But, traditional architectures and existing infrastructures are not designed to deliver the fast analytical processing needed for rapid insights
     • Data visualization
       • Helps to
         • explore and make sense of data because uncover insights buried in data
     • SAS Visual Analytics
       • Help to
         • Build reports that enable collaborative and engaging discussions that can drive deeper insights and better decisions
         • Reduce the number of tools that should be used and the number of systems that IT must maintain.
     • SAS Visual Statistics
       • provides
         • an interactive, intuitive, dragand-drop, web-browser interface for creating descriptive and predictive models on data of any size rapidly.
       • is designed for
         • statisticians, data scientists and business analysts who want to visually and instantly interact with and analyze complex data.

4. GRID: FASTER PROCESSING

   • GRID
     • Helps to
       • organizations to create a managed, shared grid computing environment for processing large volumes of data and analytic programmes.
       • IT greater flexibility to meet service level commitments by easily reassigning computing resources to meet peak workloads or changing business demands
     • Example
       • SAS Grid Manager