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1 O.L.A.P. (Online Analytical Processing) By creating doubt you may find certainties. Certainties do not create enterprise. Doubt and questions do. Dedicated.

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Presentation on theme: "1 O.L.A.P. (Online Analytical Processing) By creating doubt you may find certainties. Certainties do not create enterprise. Doubt and questions do. Dedicated."— Presentation transcript:

1 1 O.L.A.P. (Online Analytical Processing) By creating doubt you may find certainties. Certainties do not create enterprise. Doubt and questions do. Dedicated to Dr. Ing. Andrea Fraschetti, my uncle, a Ferrari man who personally circuit tested, because he had doubts, a racing car he designed and died doing so Release 13 August 2012

2 2 Thank you to the following reviewers of this presentation Mr. Dean Tallam – Senior Manager of SciFinance, Inc "...SciFinance takes complex mathematical models and translates them into something a computer can solve, allowing banks to flexibly change pricing models as they introduce new products." Newsweek International Ing. Filippo Heilpern - Consultant in BD & International, Corporate Executive Dr. Ignazio Palau – Consultant in BD & International, Corporate Executive My son Lorenzo Some, among others, sources Introduction to OLAP - Slice, Dice and Drill! - Hari Mailvaganam BUSINESS INTELLIGENCE for DUMMIES – Swain Scheps (2008) Data Warehousing Part 1 : OLAP and OLTP – Mike Brunt OLAP Workshop : Basic overview of OLAP Concepts – Keith Laker MS SQL Server 7.0 OLAP Services – Microsoft Inc.

3 3 Your multidimensional business query Given what are my needs, where can I find in 3 areas/regions (France, Europe, South America) and from 2 countries (India and China) the offers that reflect the needs of the industry whom I can fulfill with my acquired educational skills ? This personal question describes both the data that you need to examine and the way you need the data structured Some of the questions contained in the above query : What is my product ? (“…my needs…”) Where can I sell it ? (“…3 areas/regions…and from 2 countries..”) Who wants to buy it (“… the offers…”) How much ? (you are too green, forget about it for the moment….) YOUR ANSWER TO THIS QUERY ? FOOD FOR YOUR THOUGHTS

4 4 OLAP is working with data & information - in business terms - without needing to understand the underlying storage mechanism as well as having the ability of intelligently and transparently working with the different types of business rules that exist within any organisation and sustain/support them

5 5 It has also been defined as Fast Analysis of Shared Multidimensional Information Fast Delivers information to the user at a fairly constant rate. Most queries should be delivered to the user in five seconds or less. Analysis Performs basic numerical and statistical analysis of the data, predefined by an application developer or defined ad hoc by the user. Shared Implements the security requirements necessary for sharing potentially confidential data across a large user population. Multidimensional Not bi-dimensional, not tri-dimensional, multidimensional Information Accesses all the data and information necessary and relevant for the application, wherever it may reside and not limited by volume

6 6 Keep well embedded in your mind the two terms SHARED MULTIDIMENTIONAL

7 7 Multidimentional Data Model DIMENSIONS descriptive cathegories MEASURES quantitative values SHARED MULTIDIMENTIONAL

8 8 O.L.A.P is an approach that may quickly provide answers to analytical queries that are multi-dimensional in nature. Think at the queries you have about your future : What do I need ? What do I want ? What does the market offer ? What is my offer to the market ? What are the skills that I can bring to the market ? How can I match these with the offer ? How do I find the sources of the offer ? When and how do we “tango”, the offer and me ? The typical applications of OLAP are in business reporting for sales, marketing, management reporting, business process management (BPM), budgeting and forecasting, financial reporting, etc. In your case finding a challenge which you will love !

9 9 It is used extensively by Intelligence Services and Intelligence Agencies (a prime example, the E.C.H.E.L.O.N evesdropping program from the N.S.A. in the US, that along with the F.B.I., just detected massive intrusions in Obama’s and McCain’s campaigns data bases)

10 10 Databases configured for OLAP employ a multidimensional data model, allowing for complex analytical and ad-hoc queries with a rapid execution time. They borrow aspects of navigational databases and hierarchical databases that are speedier than their relational kin (proche). The output of an OLAP query is typically displayed in a matrix (or pivot) format. The dimensions form the rows and columns of the matrix; the measures, the values.

11 11 OLAP Data Model In an OLAP data model, information is conceptually viewed as cubes,which consist of descriptive categories (dimensions) and quantitative values (measures). The multidimensional data model makes it simple for users to formulate complex queries, arrange data on a report, switch from summary to detail data, and filter or slice data into meaningful subsets. Cubes is an easy expression to describe a form. In the real business world OLAP can be multi-dimentional & multifaceted with 5,6,7,…x… dimensions and measures

12 12 To simplify Dimension is WhatTime Geography Product Channel Organization Scenario (budget or actual) Measure is How Much€ Sales Unit Sales Inventory Head counts Income Expenses Profits/Losses

13 13 Multidimentional Data Model MEASURES : quantitative DIMENSIONS : values descriptive cathegories

14 14 OLAP environment is centred around use of the term “business intelligence” where the emphasis is on “online” or active access “dynamic” “analytical” in terms of the reports that are generated.

15 15 online WHAT ? dynamic WHAT ? analytical WHAT ?

16 16 Online a.Live access to data rather than static reporting. b.Analytic queries are submitted against the database in real time, and the results are returned in real time. Analytical processing i.Easily navigate multidimensional data to perform unpredictable ad hoc queries and display the results in a variety of different layouts ii.Transparently manage business rules across dimensions and cubes iii.“Drill through” levels of detail to uncover significant aspects of data iv.Rapidly and efficiently obtain the results of sophisticated data calculation and selection across multiple dimensions of data

17 17 A few definitions A metadata repository is a database of data about data (metadata). The purpose of the metadata repository is to provide a consistent and reliable means of access to data. The repository itself may be stored in a physical location or may be a virtual database, in which metadata is drawn from separate sources. Metadata may include information about how to access specific data, or more detail about it, among a myriad of possibilities. A data warehouse is an Enterprise reporting solution. It will typically hold all historical data for the company for all time. A datamart is a smaller version of the data warehouse. It's going to hold a year or two's worth of information, and may not hold all the tables in the data warehouse. While the data warehouse is for the enterprise, a datamart is typically for a department’s use. Source

18 18 Online Analytical processing Output

19 19 One standard transactional report or query will ask the following question : When was order number shipped? This simple, down-to-earth, two-dimensional query reflects basic mechanics/data of doing business. a.Date of shipment b.Order Number It involves simple data selection and little or no calculation processing. It can be answered directly from the transactional system without any impact other operations. No organisation can survive without this basic level of information.

20 20 OLAP systems – on the other hand - allow an organization’s to answer a much broader multi-dimentional range of business queries about the data they are collecting in their transactional systems: i.How do same quarter sales for our top 10 most profitable products across EMEA Region for this quarter compare with sales a year ago? ii.What are the differences in the product-sales mix between Regions Scandinavia, North, Central and South Europe, in context to the global European sales mix? iii.What are our forecast units, unit price per service, unit cost per product, sales, cost trends, and profit for the next 12 months? iv.In what ways does the mix vary by salesperson, and what is the relative performance of our salespeople? v.What are, year to date, the products making up to 40% of our gross profit for each Region over the period 2006 to 2008?

21 21 Two illustrations of OLAP scenarios/architecture that can allow broad multi-dimentional business queries

22 22 Figure 1. Data Model for OLTP

23 23

24 24 Two dimentional transactional query vs. broader multi-dimentional queries The main differences between a simple two dimentional transactional query and broader multi-dimentional queries are : i.the fact that the latter are much more analytical and quite complex, ii.that the answer to one question often leads immediately to another question as the user follows a train of thought in addressing a business problem/issue or scouting for an opportunity.

25 25 OLAP is designed to make it easy for end users to ask broader multi- dimentional range of analytical queries and enhance its day-to-day use without requiring: Assistance from the IT department Programming skills Technical knowledge about the organization of the database The results of queries also need to be rapid so that the analyst’s train of thought is not interrupted and the value of the analysis is not diminished. Time and reaction time is of essence in any business scenario. Information is old the minute it is generated. If it is generated late it could be obsolete.

26 26 A typical multidimensional business query For each region of France, what was the percentage change in revenue for our top 15% products, over a rolling three-month period this year starting March compared to the same period last year? This rather simple business question describes both the data that the user wants to examine and they way he wants the data structured (i.e.: structural form of that data). Business users typically want to answer questions that include terms such as what, where, who, when and, above all, how much ! You find the following essential questions contained in the above query : What products are selling best? (“…top 15%…”) Where are they selling? (“…each region France…”) When have they performed the best? (“…over a rolling period….starting March…”) How much ? (“…percentage change in revenue…”)

27 27 Your multidimensional business query Given what are my needs, where can I can find in 3 areas/regions (France, Europe, South America) and from 2 countries (India and China) the offers that reflect the needs of the industry whom I can fulfill with my acquired educational skills ? This personal question describes both the data that you need to examine and the way you need the data structured Some of the questions contained in the above query : What is my product ? (“…my needs…”) Where can I sell it ? (“…3 areas/regions…and from 2 countries..”) Who wants to buy them (“… the offers…”) How much ? (you are too green, forget about it for the moment….) YOUR ANSWER ?????FOOD FOR YOUR THOUGHTS GOOD HUNT WOLF PACK !


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