BI, Analytics and Big Data A Modern-Day Perspective By: Elad Israeli, Co-Founder, SiSense www.sisense.com
Business Intelligence (Analytics) A set of theories, methodologies, processes, architectures, and technologies that transform raw data into meaningful and useful information for business purposes.
This is a Report (= a query)
This is a Dashboard (= several queries)
and BI/Analytics is: The ability to create a new report, dashboard or just get a new analytic question answered in real-time, or at least in-time.
What is Big Data? A collection of data sets so large and complex that it becomes difficult to process using onhand database management tools or traditional data processing applications Due to its technical nature, the same challenges arise in Analytics at much lower volumes than what is traditionally considered Big Data.
..so Big Data Analytics is: The same as Small Data Analytics, only with the added challenges (and potential) of large datasets (~50M records or 50GB size, or more) Challenges, such as: Data storage and management De-centralized/multi-server architectures Performance bottlenecks, poor responsiveness Increasing hardware requirements
BI and Analytics Projects
Approaches to The Challenge 1. Project-Specific: The development of a specific dashboard/report An isolated initiative, with no forward-looking implications from the prospect s perspective 2. Solution-Oriented: The development of a specific dashboard/report, with future ones (known or unknown) in mind
E.K.G: Solution-Oriented vs. Project- Specific BI/Analytics (Solution-Oriented) New Report New Report Time Report/Dashboard Project (Project-Specific) New Report New Report New Report Time
BI/Analytics E.K.G New Report = Answer To New Question = New Insight New Report New Report Time The rate at which new reports are introduced into critical processes should increase over-time, due to: Completed integration, customization & adaptation Time for training to sink in Adoption (more users generating reports)
How Raw Data Becomes Insight Connect To Source Load & Store Clean & Standardize Grant Access ETL / Data Management Define Queries Format The Report Share the Report Respond to Feedback BI/Analytics/Visualization
Data Warehouse Clean and accurate data recognized as the only real business truth A central repository of data which is created by integrating data from one or more disparate sources Stores current as well as historical data
Existing Data Landscapes With an existing Data Warehouse The data is in its detailed form (raw data) The data clean (was already processed) The data is usually only directly accessible to IT The data is centralized (single version) Data Marts or OLAP Cubes (optional) Without an existing Data Warehouse The data is in its detailed form (raw data) The data is located in multiple places The data may be dirty (i.e. entry-errors) The data is accessible to whoever owns the application/database The data is not centralized ETL DW Operational DB Application DB Files Operational DB Application DB Files Owner: IT Owner: IT or Business
Traditional BI/Analytics Architectures (Old-School)
Traditional BI/Analytics Architectures Centralized / Data Warehouse Non-Centralized / No DW End-Users (Business) End-Users (Business) Data Marts or OLAP Cubes DW Summarized De-centralized Clean Structured Detailed Dirty Unstructured Detailed Dirty Unstructured Detailed Dirty Unstructured Owner: IT Owner: IT or Business
Traditional Architectures - Comparison Centralized / DW Non-Centralized / No DW Approach Solution-oriented Project-specific Data Quality & Accuracy Higher Lower Scalability Higher Lower Single Version of the Truth Yes No Initial Investment Higher Lower Level of Detail Summarized Granular Owner IT IT or Business (optional) Implementation Time Longer Shorter Technical Complexity Higher Lower Advantage / Disadvantage
Modern-Day BI/Analytics Architectures
Modern-Day BI/Analytics - Focus Self-Service Empower business users of varying skill-levels Keep IT in control, without becoming a bottleneck Agility Fast turnaround for new requirements Scalability Handle large, or rapidly growing volumes of data Handle fast, unpredictable usage patterns and adoption
Modern BI/Analytics How? Full-Coverage Solution Provide all functionality required, from data management, ETL and end-user analytics Utilize modern technology Columnar databases In-Chip analytics technology Support for 21 st century chip-sets
Architecture: With a Data Warehouse Modern Traditional End-Users (Business) End-Users (Business) ElastiCube DW Detailed Centralized Clean Structured Detailed Dirty Unstructured Marts or OLAP Cubes DW Summarized De-centralized Clean Structured Owner: IT Owner: IT
Modern vs. Traditional (DW) Centralized / DW SiSense Architecture Approach Solution-oriented Solution-oriented Data Quality & Accuracy High High Scalability High High Single Version of the Truth Yes Yes Initial Investment Higher Lower Level of Detail Summarized Granular Owner IT IT or Business (optional) Implementation Time Longer Shorter Technical Complexity Higher Lower Advantage / Disadvantage
Architecture: Without a Data Warehouse Modern Traditional End-Users (Business) End-Users (Business) ElastiCube Detailed Centralized Clean Structured Detailed Non-Centralized Dirty Unstructured Owner: IT or Business Detailed Dirty Unstructured Owner: IT or Business Detailed Dirty Unstructured
Modern vs. Traditional (No DW) Non-Centralized / No DW Modern Architecture Approach Project-oriented Solution-oriented Data Quality & Accuracy Lower Higher Scalability Lower Higher Single Version of the Truth No Yes Initial Investment Lower Lower Level of Detail Granular Granular Owner IT or Business (optional) IT or Business (optional) Implementation Time Short Short Technical Complexity Lower Lower Advantage / Disadvantage
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