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Principles Operational v Analytical Systems Data Warehousing & Data Mining Sheffield Hallam University 1.

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Presentation on theme: "Principles Operational v Analytical Systems Data Warehousing & Data Mining Sheffield Hallam University 1."— Presentation transcript:

1 Principles Operational v Analytical Systems Data Warehousing & Data Mining Sheffield Hallam University 1

2 A customer walks into a bank, and speaks to a cashier. The cashier uses his/her computer to answer the queries / fulfil the actions. List five typical queries / actions 1. __________________________________ 2. __________________________________ 3. __________________________________ 4. __________________________________ 5. __________________________________ 2

3 What similarities can you spot about the nature of the data involved in your five queries / actions? Hint: volume, up-to-dateness (“currency”), level of detail l List four similarities 1. __________________________________ 2. __________________________________ 3. __________________________________ 4. __________________________________ 3

4 What general characteristics do you think can be stated about the IT application that the cashier uses to handle the customer queries / actions? Hint: Complete the following sentence: The application is oriented towards l List four characteristics 1. __________________________________ 2. __________________________________ 3. __________________________________ 4. __________________________________ 4

5 Operational Systems 1.High volume of transactions 2.Small processing per transaction 3.Frequent updating of data 4.Data is always current 5.Transaction driven 6.Predictable query types 7.Static structure 8.Content varies 9.High accuracy 10.High availability 11.Mature support Table 1: Attributes of an Operational System 5

6 Explanation of points 7 & 8 on the previous slide How often does the underlying design of the bank’s database have to change (tables, relationships, integrity rules etc)? Just thinking about the current data (not the archive data), how much bigger /smaller will the bank’s volume of data be in one year’s time? Just thinking about the current data, how much of that data will have different values in one year’s time? Do you understand why we said “Static structure; content varies” ? 6

7 Operational systems are generally based on Relational Database systems. § Very highly optimised towards fast writing / retrieval of small items of data (eg Oracle, DB2, SQL-Server, MySQL = very long established, huge $$ research investments) § Highly optimised towards using Relationships to fetch related data (eg Customer Name, and current balance) These reasons make Relational Databases extremely quick Operational Systems 7

8 Operational systems are generally based on Relational Database systems (cont...) §The database itself can enforce Referential Integrity (eg cannot delete customer name & address if they still have an account open) Relational design: Only store each data item in one place (eg if customer changes address, only one copy to change) These reasons make Operational applications much easier to write. Operational Systems 8

9 The literature tends to use the term On-Line Transactional Processing (OLTP) for what we have described as “operational” systems. Transactional = On-line : This term is a bit historic... originally most systems processed batches of data non-interactively (cheques are one of the few batch-oriented systems left now). Now systems all tend to be on-line / interactive Since the industry calls it OLTP, we will too. 9

10 Management have different needs to that of the operational side of the business. Managers are much more concerned with trends and totals and are generally not so concerned with the finer details. What they want are reporting systems that: l Give quick access to summaries of data l Have data structures that are business oriented l Allow users to explore the data l Give them control over report writing Management Reporting 10

11 The manager of the bank wants to analyse the effectiveness of last month’s business List five typical queries 1. __________________________________ 2. __________________________________ 3. __________________________________ 4. __________________________________ 5. __________________________________ Nb: TRY to stick with just data drawn from the Cashier system. But you will find this hard. Later we will see that it is a feature of Analytical system that they integrate data from many sources 11

12 What similarities can you spot about the nature of the data involved in your five queries? Hint: volume, up-to-dateness (“currency”), level of detail l List four similarities 1. __________________________________ 2. __________________________________ 3. __________________________________ 4. __________________________________ 12

13 What general characteristics do you think can be stated about analytical applications? Hint: Complete the following sentence: The application is oriented towards l List four characteristics 1. __________________________________ 2. __________________________________ 3. __________________________________ 4. __________________________________ 13

14 Analytical Systems 1.Small volume of transactions 2.Often huge processing per transaction 3.Data output level is summary 4.Data routinely added to, but infrequently changed 5.Analysis driven 6.Flexible results structure 7.'Fairly accurate' better than no result 8.Medium availability 9.Requires different database tools Table 2: Attributes of an Analytical System 14

15  Data is stored in structures easy for business users to understand (not constrained by Relational rules)  Data held in duplicate if this makes access easier/quicker (eg can hold summary/totals of data too)  Out-of-date data held (with timestamp) as well as new (allows examination of historical trends) These sort of systems are referred to as OLAP systems or On-Line Analytical Processing Systems. On-line Analytical Processing (OLAP) The Management Data is better stored in a Data Warehouse. 15

16 16 The diagram below shows how Data is converted into Business Intelligence InformationKnowledge Structure Analyse Apply DataIntelligence The Process Flow

17 The diagram below shows the relationships between the various components: Database Data Warehouse OLTP OLAP SQL Server, Oracle, Ingres, Informix, DB/2 etc SAS Warehouse Administrator, SQL Server etc Application Software, SAP, SQL Server, Oracle, Spreadsheets etc SQL Server Analysis Services, Crystal Analysis, Oracle Discovery etc Cleansing/Staging

18 Database Data Warehouse OLTP OLAP SQL Server, Oracle, Ingres, Informix, DB/2 etc SAS Warehouse Administrator, SQL Server etc Application Software, SAP, SQL Server, Oracle, Spreadsheets etc SQL Server Analysis Services, Crystal Analysis, Oracle Discovery etc Cleansing/Staging Structure Analyse Apply InformationKnowledgeDataIntelligence

19 A Data Warehouse is... "... where data is specifically structured for query and analysis performance and ease-of-use" Kimball, 2002 Definition 19

20 Data Warehouses offer the flexibility needed to cope with the Management demands. A major issue is that often there are many differing OLTP systems and other data storage media. The Data Warehouse offers the opportunity to gather these together into one system with a unified structure. Because data is stored in a simplified aggregated format it allow reports to be written by staff who have a lesser computing background. Why a Data Warehouse? 20

21 OLAP versus OLTP If a report is designed to obtain information from an OLTP system it will generally be: Slow to produce the answer Complicated to write Slow down the operational system Need complicated formulas for grouping data 21

22 OperationalAnalytical DataIndividual ItemsSummarised Data RelationsSimple ChainsComplex/Unknown TimePresentPast AccessRecord-at-a-timeMany records ApproachSupport a transactionExplore a domain ImplementationRelationalFollow the module! Summary 22


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