BI Introduction

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Information about BI Introduction
Data & Analytics

Published on September 22, 2014

Author: TarasPanchenko

Source: slideshare.net

Description

Business Intelligence (BI) introduction: what is it about, technologies, OLAP as a BI core, drill-down & slice-and-dice, ETL - BI basics and examples.

Business Intelligence (BI) Lecturer: PhD Taras V. Panchenko Associate Professor @ Theory and Technology for Programming Chair @ Cybernetics Faculty @ National Taras Shevchenko University of Kyiv

Introduction Computers are useless. They can only give you answers. Pablo Picasso … Meaning that asking questions and true creativity are things that computers aren't capable of yet.

BI – Def(s) Business intelligence (BI) is the ability to apprehend the interrelationships of presented facts in such a way as to guide action towards a desired goal. Hans Peter Luhn, IBM, 1958 BI is the transformation of raw data into meaningful and useful information for business analysis purposes. Wikipedia

BI – Def(s) Business intelligence (BI) BI is the transformation of raw data into meaningful and useful information for business analysis purposes. • BI can handle enormous amounts of unstructured data to help identify, develop and otherwise create new strategic business opportunities • BI allows for the easy interpretation of volumes of data • Identifying new opportunities and implementing an effective strategy can provide a competitive market advantage and long-term stability Wikipedia

BI – Def(s) Business intelligence (BI) is an umbrella term that includes the applications, infrastructure and tools, and best practices that enable access to and analysis of information to improve and optimize decisions and performance. Gartner BI is a set of methodologies, processes, architectures, and technologies that leverage the output of information management processes for analysis, reporting, performance management, and information delivery. Research coverage includes executive dashboards as well as query and reporting tools. Forrester

The Problem • Try to model and analyze activity of the bank – Develop the model: clients, accounts, currency, transactions, … – Possible questions to system/model from analyst • Performance (analytical query speed) • Dynamic reports & ad-hoc analysis • or: analyze sales of products by regions in time • Is RDBMS the best solution? … for Multidimensional model …

Multidimensional Analysis • Multidimensional (hyper-)cube:

Problem of Relational Database Model • Most notably lacking has been the ability to consolidate, view, and analyze data according to multiple dimensions, in ways that make sense to one or more specific enterprise analysts at any given point in time. This requirement is called “multidimensional data analysis.” E.F. Codd

Limitations: lack of … analytics • Until recently, the end-user products that had been developed as front-ends to the relational DBMS provided very straightforward simplistic functionality. The query/report writers and spreadsheets have been extremely limited in the ways in which data (having already been retrieved from the DBMS) can be aggregated, summarized, consolidated, summed, viewed, and analyzed. E.F. Codd

BI (or – partially – OLAP) • Is the solution • The only one “point of truth” – Contains all information about business (… or any subject area) in one place • Gives analytical & reporting means – Speed (performance) – Flexibility (many instruments)

BI is about • Decision Support Systems • Business Analytics • Complex & Comprehensive, Intelligent Reporting • Multidimensional Analysis (real-time) • “OLTP -> OLAP” – is the part of strategy – OLAP is the core of BI

BI core: Multidimensional engine (model, storage) • Multidimensional (hyper-)cube:

ETL = OLTP  OLAP • OnLine Transaction Processing System – accounting of transactions (E)xtract (T)ransform (L)oad • OnLine Analytical Processing System – gives analytical, intelligence (to transactional data)

OLAP (~Def.) • Is an approach to answering multi-dimensional analytical queries swiftly • Technology for information processing for quick answering on multidimensional analytical queries • Allows consolidation and analysis of data in a multidimensional space • Is not stand-alone! – but based on OLTP data

OLAP Applications • Business reporting – Sales – Marketing – Management etc. • Financial Reporting • Budgeting • Forecasting • Planning • Business Process Management … in any business (any subject area)

The Difference • OLTP: Operations – RDBMS • Large number of short transactions • 3NF, ER-model – ACID Atomicity, Consistency, Isolation, Durability – Business Process – Online, real-time info • OLAP: Information – Multidimensional • Complex queries involve aggregations • Sparse n-dim. spaces – Aggregates precalculated – Analytical Data Warehouse – Large historical data storage

Transaction vs. Analytical Approach Transaction Systems Analytical Systems Technology OLTP OLAP Data visualization Grid (Table) Pivot Table End-user visual querying QBE Cube browsing (drill-down, slice & dice) Query language SQL MDX + XMLA

OLTP vs. OLAP OLTP System – Online Transaction Processing (Operational System) OLAP System – Online Analytical Processing (Data Warehouse) Source of data Operational data; OLTPs are the original source of the data. Consolidation data; OLAP data comes from the various OLTP Databases Purpose of data To control and run fundamental business tasks To help with planning, problem solving, and decision support What the data Reveals a snapshot of ongoing business processes Multi-dimensional views of various kinds of business activities Inserts and Updates Short and fast inserts and updates initiated by end users Periodic long-running batch jobs refresh the data Queries Relatively standardized and simple queries Returning relatively few records Often complex queries involving aggregations Processing Speed Typically very fast Depends on the amount of data involved; batch data refreshes and complex queries may take many hours; query speed can be improved by creating indexes Space Require-ments Can be relatively small if historical data is archived Larger due to the existence of aggregation structures and history data; requires more indexes than OLTP Database Design Highly normalized with many tables Typically de-normalized with fewer tables; use of star and/or snowflake schemas Backup and Recovery Backup religiously; operational data is critical to run the business, data loss is likely to entail significant monetary loss and legal liability Instead of regular backups, some environments may consider simply reloading the OLTP data as a recovery method

BI includes • ETL procedure (= Extract – Transform – Load) – often: Data Warehouse, via Data Marts • OLAP Multidimensional Storage & Engine – Ad-hoc questions & multi-purpose querying • Reporting – flexible, interactive, dynamic, effective, … • Data Mining – clustering, associations, trends (time analysis), predictions, …

BI core is OLAP (engine & storage) • Multidimensional (hyper-)cube:

OLAP Concepts • Flexible Information Synthesis • Multiple Data Dimensions / / Consolidation Paths (i.e. Multidimensional Conceptual View)

Data Consolidation • Dimension hierarchy

OLAP Characteristics • Dynamic Data Analysis • Four Enterprise Data Models – Categorical, Exegetical, Contemplative, and Formulaic Models (укр.: накопичення фактів – інтерпретація – аналіз – висновки, моделювання, прогнози, …) • Common Enterprise Data • Synergistic Implementation

OLAP Server Mediating Role

OLAP Product Evaluation Rules 1. Multidimensional Conceptual View 2. Transparency 3. Accessibility 4. Consistent Reporting Performance 5. Client-Server Architecture 6. Generic Dimensionality 7. Dynamic Sparse Matrix Handling 8. Multi-User Support 9. Unrestricted Cross-dimensional Operations 10. Intuitive Data Manipulation 11. Flexible Reporting 12. Unlimited Dimensions and Aggregation Levels Codd E.F., Codd S.B., and Salley C.T., “Providing OLAP to User-Analysts: An IT Mandate”,

Main OLAP features • Drill-down – Drill-up – Drill-through • Slice and dice – Pivoting

OLAP Multi-dimensional Modes • MOLAP = Multi-dimensional – Pure OLAP • ROLAP = Relational – OLAP requests -> relational backend • HOLAP = Hybrid – Aggregates – MOLAP – Basic facts – ROLAP – Inconsistences possible!

OLAP cube example

OLAP cube slicing (1)

OLAP cube slicing (2)

OLAP dicing

OLAP drill-up  drill-down

OLAP pivoting

BI Solutions. Client Sales

BI Solutions. Sales by territory

BI Solutions. Plan/Sales Analysis

BI core = OLAP engine & storage • Multidimensional (hyper-)cube:

BI Introduction • Business Intelligence enhances business (or any other area) vision and understanding • OLAP is a core of BI • BI includes – ETL (OLTP  Data Warehouse with Data Marts  OLAP) – OLAP Multidimensional Storage & Engine – Reporting (multi-purpose, comprehensive) – Data Mining (clustering, associations, trends (time analysis), predictions, …)

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