BUSINESS ANALYTICS AND DATA VISUALIZATION. ITM-761 Business Intelligence ดร. สล ล บ ญพราหมณ

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1 1 BUSINESS ANALYTICS AND DATA VISUALIZATION ITM-761 Business Intelligence ดร. สล ล บ ญพราหมณ

2 2 การท าความด น น ยากและเห นผลช า แต ก จ าเป นต องท า เพราะหาไม ความช วซ งท าได ง ายจะเข ามาแทนท และจะพอกพ นข นอย างรวดเร วโดยไม ท นร ส กต ว แต ละคนจ งต องต งใจและเพ ยรพยายามให ส ดก าล ง ในการสร างเสร มและสะสมความด พระบรมราโชวาทพระบาทสมเด จพระเจ าอย ห ว พระราชทานแก ผ ส าเร จการศ กษาจากโรงเร ยนนายร อยต ารวจ ณ อาคารใหม สวนอ มพร ว นท 14 ส งหาคม 2525

3 Overview: The Business Analytics (BA) 3 The use of analytical methods, either manually or automatically, to derive relationships from data business analytics (BA)includes the access, reporting, and analysis of data supported by software to drive business performance and decision making

4 4

5 The Business Analytics (BA) Field: An Overview 5 MicroStrategy sclassification of BA tools: The five styles of BI 1. Enterprise reporting 2. Cube analysis 3. Ad hoc querying and analysis 4. Statistical analysis and data mining 5. Report delivery and alerting

6 The Business Analytics (BA) Field: An Overview 6 SAP s classification of strategic enterprise management Three levels of support 1. Operational 2. Managerial 3. Strategic

7 7 The Business Analytics (BA) Field: An Overview Executive information and support systems Executive information systems (EIS) Provides rapid access to timely and relevant information aiding in monitoring an organization s performance Executive support systems (ESS) Also provides analysis support, communications, office automation, and intelligence support

8 Online Analytical Processing (OLAP) 8 On-Line Analytical Processing (OLAP) is a decision support tool that allows users to analyze different dimensions of multidimensional data. Designed for executives looking to make sense out of their information, OLAP structures data hierarchically to reflect the real dimensionality of the enterprise as understood by the users. Users can pivot, filter, drill down and drill up data and generate numbers of views with simple mouse manipulations.

9 9 OLAP structure created from the operational data is called an OLAP cube. the cube holds data more like a 3D spreadsheet rather than a relational database, allowing different views of the data to be quickly displayed

10 10 In multidimensional OLAP (MOLAP) databases, cubes are created and stored physically, whereas in relational OLAP (ROLAP) databases, cubes are virtually created, based on a star or snowflake schema

11 11 Star and snowflake schemas

12 The OLAP Report 12 one of the most internationally authoritative sources of information on OLAP products and applications, defines OLAP in five keywords: Fast Analysis of Shared Multidimensional Information, or FASMI for short Fast The system is targeted to deliver most responses to users within about five seconds, with the simplest analyses taking no more than one second and very few taking more than 20 seconds

13 13 Analysis The system can cope with any business logic and statistical analysis that is relevant for the application and the user, and keep it easy enough for the target user

14 14 Shared The system implements all the security requirements for confidentiality and, if multiple write access is needed, concurrent update locking at an appropriate level. Not all applications need users to write data back, but for the growing number that do, the system should be able to handle multiple updates in a timely, secure manner

15 15 Multidimensional The system must provide a multidimensional conceptual view of the data, including full support for hierarchies and multiple hierarchies Information The capacity of various products is measured in terms of how much input data they can handle, not how many gigabytes they take to store it

16 OLAP versus OLTP 16 OLTP concentrates on processing repetitive transactions in large quantities and conducting simple manipulations OLAP involves examining many data items complex relationships OLAP may analyze relationships and look for patterns, trends, and exceptions OLAP is a direct decision support method

17 17 OLTP (on-line transaction processing) Major task of traditional relational DBMS Day-to-day operations: purchasing, inventory, banking, manufacturing, payroll, registration, accounting, etc. OLAP (on-line analytical processing) Major task of data warehouse system Data analysis and decision making

18 18 Distinct features (OLTP vs. OLAP): User and system orientation: customer vs. market Data contents: current, detailed vs. historical, consolidated Database design: ER + application vs. star + subject View: current, local vs. evolutionary, integrated Access patterns: update vs. read-only but complex queries

19 OLTP vs. OLAP 19 OLTP OLAP users clerk, IT professional knowledge worker function day to day operations decision support DB design application-oriented subject-oriented data current, up-to-date detailed, flat relational historical, summarized, multidimensional isolated integrated, consolidated usage repetitive ad-hoc access read/write lots of scans index/hash on prim. key unit of work short, simple transaction complex query # records accessed tens millions #users thousands hundreds DB size 100MB-GB 100GB-TB metric transaction throughput query throughput, response

20 Codd srules for OLAP Systems 20 In 1993, E.F. Coddformulated twelve rules as the basis for selecting OLAP tools.

21 Codd srules for OLAP Systems (cont.) 21 Multi-dimensional conceptual view Supports EIS (Executive Information System) slice and dice operations and is usually required in financial modeling. Transparency Is part of an open system that supports heterogeneous data sources. Furthermore, the end user should not be concerned about the details of data access or conversions.

22 Codd srules for OLAP Systems (cont.) 22 Accessibility Presents the user with a single logical schema of the data. OLAP engines act as middleware, sitting between heterogeneous data sources and an OLAP front-end. Consistent reporting performance Performance should not degrade as the number of dimensions in the model increases.

23 Codd srules for OLAP Systems (cont.) 23 Client-server architecture Requires open, modular systems. Not only the product should be client/server but the server component of an OLAP product should allow that various clients could be attached with minimum effort and programming for integration Generic dimensionality Not limited to 3D and not biased toward any particular dimension. A function applied to one dimension should also be able to be applied to another

24 Codd srules for OLAP 24 Dynamic sparse matrix handling (null values) Related both to the idea of nulls in relational databases and to the notion of compressing large files, a sparse matrix is one in which not every cell contains data. OLAP systems should accommodate varying storage and data-handling options Multi-user support Supports multiple concurrent users, including their individual views or slices of a common database

25 Codd srules for OLAP Systems (cont.) 25 Unrestricted cross-dimensional operations All dimensions are created equal, so all forms of calculation must be allowed across all dimensions, not just the measures dimension Intuitive data manipulation (slicing and dicing (pivoting), drill-down, consolidation(drill-up), etc) Users shouldn't have to use menus or perform complex multiple step operations when an intuitive drag and drop action will do

26 Codd srules for OLAP Systems (cont.) 26 Flexible reporting Users should be able to print just what they need, and any changes to the underlying model should be automatically reflected in reports. Unlimited dimensions and aggregation levels Supports at least 15, and preferably 20, dimensions

27 Codd srules for OLAP Systems (cont.) 27 There are proposals to re-defined or extended the rules. For example to also include Comprehensive database management tools Ability to drill down to detail (source record) level Incremental database refresh SQL interface to the existing enterprise environment

28 OLAP operations 28 Roll-up Takes the current aggregation level of fact values and does a further aggregation on one or more of the dimensions. Equivalent to doing GROUP BY to this dimension by using attribute hierarchy. Decreases a number of dimensions -removes row headers

29 29 Drill-down Opposite of roll-up. Summarizes data at a lower level of a dimension hierarchy, thereby viewing data in a more specialized level within a dimension. Increases a number of dimensions -adds new headers

30 30 Slice Performs a selection on one dimension of the given cube, resulting in a sub- cube. Reduces the dimensionality of the cubes. Sets one or more dimensions to specific values and keeps a subset of dimensions for selected values

31 31 Dice Define a sub-cube by performing a selection of one or more dimensions. Refers to range select condition on one dimension, or to select condition on more than one dimension. Reduces the number of member values of one or more dimensions

32 Categories of OLAP Tools 32 OLAP tools are categorized according to the architecture used to store and process multidimensional data. There are four main categories: Multi-dimensional OLAP (MOLAP) Relational OLAP (ROLAP) Hybrid OLAP (HOLAP) Desktop OLAP (DOLAP)

33 1) Multi-dimensional OLAP (MOLAP) 33 Use specialized data structures and multi-dimensional Database Management Systems (MDDBMSs) to organize, navigate, and analyze data. Data is typically aggregated and stored according to predicted usage to enhance query performance. This allows users to view different aspects of data aggregates such as sales by time period, geography, or product. The storage is not in a relational database

34 34 Use array technology and efficient storage techniques that minimize the disk space requirements through sparse data management. Provides excellent performance when data is used as designed, and the focus is on data for a specific decision-support application. Traditionally, require a tight coupling with the application layer and presentation layer.

35 MOLAP Available tools 35 Hyperion, Executive Viewer, CFO Vision, BI/Analyze, PowerPlay, Business Objects, Genita, Holos, MS OLAP Services, Pilot, ProCube

36 36 Typical Architecture for MOLAP Tools

37 37 MOLAP utilizes a proprietary multidimensional database to provide OLAP analyses. The main premise of this architecture is that data must be stored multidimensionallyto be viewed multidimensionally Data from various operational systems is loaded into a multidimensional database through a series of batch routines.

38 38 Once this atomic data has been loaded into the multidimensional database, the general approach is to perform a series of calculations in batch to aggregate along the dimensions and fill the multidimensional array structures. Then indices are created, and hashing algorithms are used to improve query access time

39 39 MOLAP is a two-tier, client/server architecture. The multidimensional database serves as both the database layer and the application logic layer. In the database layer, it is responsible for all data storage, access, and retrieval processes. In the application logic layer, it is responsible for the execution of all OLAP requests. The presentation layer integrates with the application logic layer and provides an interface through which the users view and request OLAP analyses. The client/server architecture allows multiple users to access the same multidimensional database

40 40

41 41 MOLAP Advantages Excellent performance since pre-aggregation provides quicker response time Availability of extensive libraries of complex functions for OLAP analyses Optimal for slice and dice operations Performs better than ROLAP when data is dense

42 42 MOLAP Disadvantages Usually more than 90% of cells are empty -issue with sparsity Limited in the amount of data it can handle, since all calculations are performed when the cube is built. Therefore, it is not commonly used above GB - scalability problem Difficult to change dimension without reaggregation

43 43 MOLAP Disadvantages (cont) Data must be copied and moved into data stores Originated from query tools, thereby lacking the architecture Requires additional investment since cube technology is often proprietary and does not already exist in organizations Lacks security and administration features which RDBMSs can bring

44 2) Relational OLAP (ROLAP) 44 Fastest-growing style of OLAP technology due to requirements to analyze ever increasing amounts of data and the realization that users cannot store all the data they require in MOLAP databases. The traditional OLAP's slice and dice functionality is equivalent to adding a WHERE clause in the SQL statement. The design may be structured in the form of a star or its variations ROLAP performs dynamic multidimensional analysis of data stored in a relational database, rather than in a multidimensional database

45 45 Supports RDBMS products using a metadata layer - avoids need to create a static multi-dimensional data structure - facilitates the creation of multiple multidimensional views of the two-dimensional relation. A typical use of ROLAP is for large data size that is infrequently queried, such as historical data

46 46 To improve performance, some products use SQL engines to support the complexity of multidimensional analysis, while others recommend, or require, the use of highly denormalized database designs such as the star schema.

47 ROLAP Available tools 47 Discover 3 from Oracle, DSS Agent from MicroStrategy, MetaCubefrom IBM Informix, Platinum Beacon from Platinum, Brio, Business Objects, Cognos Powerplay

48 48 Typical Architecture for ROLAP Tools

49 49 ROLAP accesses data stored in a data warehouse (relational database) to provide OLAP analyses OLAP is a three-tier, client/server architecture. The database layer utilizes relational databases for data storage, access, and retrieval processes. The application logic layer is the ROLAP engine which executes the multidimensional reports from multiple users. The ROLAP engine integrates with a variety of presentation layers, through which users perform OLAP analyses

50 50

51 51 ROLAP Advantages Well known environments (relational database) Can leverage functionality that comes with relational database with ROLAP technologies Can be used with data warehouse and OLTP systems No pre-aggregation is needed -avoid the data explosion effect that some MOLAP implementations incur with large scale models

52 52 ROLAP Advantages (cont.) Can handle large amounts of data -the limitation is the data size of the underlying relational database. OLAP itself has no limitation on data amount Full security and administration is provided through RDBMS Performs better than MOLAP when the data is sparse Performance is getting better by adding more OLAP functions and employing various storage and query optimization techniques

53 53 ROLAP Disadvantages Performance can be slow, since each ROLAP report is a SQL query in the relational database Does not have complex functions that are provided by OLAP tools Limited by SQL functionality Hard to maintain aggregate tables in the data warehouse

54 3) Hybrid OLAP (HOLAP) 54 Hybrid On-Line Analytic Processing (HOLAP) is a mixture of MOLAP and ROLAP technologies. For summary type query, HOLAP leverages cube technology for faster performance. When detail information is needed, it can drill through from the cube into the underlying relational database. Cubes stored as HOLAP are smaller than equivalent MOLAP cubes and respond quicker than ROLAP cubes for queries involving summary data. HOLAP storage is generally suitable for cubes that require rapid query response for summaries based on a large amount of base data

55 55 in order to deliver the combined strengths of MOLAP and ROLAP technologies, HOLAP systems must comply with the following rules Fast access at all levels of aggregation (MOLAP requirement) Easy aggregate maintenance (MOLAP requirement) Compact aggregate storage (MOLAP requirement) - for high-level aggregates in order to economize disk space

56 56 Dynamically updated dimensions (ROLAP requirement) -real time access to the data itself and to rapidly changing structures Multidimensional view based on RDBMS metadata (ROLAP requirement) -should point to the appropriate RDBMS tables and automatically generate required SQL statements when modifying the multidimensional view. It reduces development time and maintenance

57 HOLAP Available tools 57 Express from Oracle, IBM DB 2 OLAP Server, Microsoft OLAP Services, Sagent Holos

58 58 Typical Architecture for HOLAP Tools

59 59 HOLAP Advantages Combined advantages of both MOLAP and ROLAP (for a full list, look at the MOLAP and ROLAP sections) Can combine the ROLAP technology for sparse regions and MOLAP for dense regions. Also ROLAP for storing the detailed data and MOLAP for higher-level summary data

60 60 HOLAP disadvantages Complex -HOLAP server must support both MOLAP and ROLAP engines and tools to combine both storage engines and operations Functionality overlap -between storage and optimization techniques in ROLAP and MOLAP engines

61 4) Desktop OLAP (DOLAP) 61 Desktop On-Line Analytic Processing (DOLAP) is single-tier, desktop-based OLAP technology. It is able to download a relatively small hypercube from a central point, usually from data mart or data warehouse, and perform multidimensional analyses while disconnected from the source.

62 62 Data sets are limited to the boundaries defined by the user with no access to granular data. In general, cubes contain summarized data, organized in a fixed structure of dimensions. Therefore, it is ideal for well-understood, recurring analytic questions and reporting

63 63 As with multi-dimensional databases on the server, OLAP data may be held on disk or in RAM, however, some DOLAP products allow only read access. Most vendors of DOLAP exploit the power of desktop PC to perform some, if not most, multidimensional calculations.

64 Available tools 64 Cognos, Business Objects, Brio, Crystal Decisions, Hummingbird, Oracle

65 65 Typical Architecture for DOLAP Tools

66 66 DOLAP advantages User friendly -user can pivot and manipulate data locally from the returned result set stored on the desktop Excellent query performance -it collects, aggregates, and calculates data in advance of the analysis Low cost per seat and maintenance Useful for mobile users who cannot always connect to the data warehouse Easiest to deploy among all OLAP approaches.

67 67 DOLAP disadvantage Limited functionality and data capacity

68 Reports and Queries 68 Reports Routine reports Ad hoc (or on-demand) reports Multilingual support Scorecards and dashboards Report delivery and alerting Report distribution through any touchpoint Self-subscription as well as administrator-based distribution Delivery on-demand, on-schedule, or on-event Automatic content personalization

69 Reports and Queries 69 Ad hoc query A query that cannot be determined prior to the moment the query is issued Structured Query Language (SQL) A data definition and management language for relational databases. SQL front ends most relational DBMS

70 Multidimensionality 70 Multidimensionality The ability to organize, present, and analyze data by several dimensions, such as sales by region, by product, by salesperson, and by time (four dimensions) Multidimensional presentation Dimensions Measures Time

71 Multidimensionality 71 Multidimensional database A database in which the data are organized specifically to support easy and quick multidimensional analysis Data cube A two-dimensional, three-dimensional, or higherdimensional object in which each dimension of the data represents a measureof interest

72 Multidimensionality 72 Cube A subset of highly interrelated data that is organized to allow users to combine any attributes in a cube (e.g., stores, products, customers, suppliers) with any metrics in the cube (e.g., sales, profit, units, age) to create various two-dimensional views, or slices, that can be displayed on a computer screen

73 73 Multidimensionality

74 Multidimensionality 74 Multidimensional tools and vendors Tools with multidimensional capabilities often work in conjunction with database query systems and other OLAP tools

75 Multidimensionality 75

76 Multidimensionality 76 Limitations of dimensionality The multidimensional database can take up significantly more computer storage room than a summarized relational database Multidimensional products cost significantly more than standard relational products Database loading consumes significant system resources and time, depending on data volume and the number of dimensions Interfaces and maintenance are more complex in multidimensional databases than in relational databases

77 Advanced BA 77 Data mining and predictive analysis Data mining Predictive analysis Use of tools that help determine the probable future outcome for an event or the likelihood of a situation occurring. These tools also identify relationships and patterns

78 Data Visualization 78 Data visualization A graphical, animation, or video presentation of data and the results of data analysis The ability to quickly identify important trends in corporate and market data can provide competitive advantage Check their magnitude of trends by using predictive models that provide significant business advantages in applications that drive content, transactions, or processes

79 Data Visualization 79 New directions in data visualization In the 1990s data visualization has moved into: Mainstream computing, where it is integrated with decision support tools and applications Intelligent visualization, which includes data (information) interpretation

80 80

81 81

82 Traffic in Madrid Housing and poverty

83 Data Visualization 83 New directions in data visualization Dashboards and scorecards Visual analysis Financial data visualization

84 Geographic Information Systems (GIS) 84 An information system that uses spatial data, such as digitized maps. A GIS is a combination of text, graphics, icons, and symbols on maps

85 85 Geographic Information Systems (GIS) As GIS tools become increasingly sophisticated and affordable, they help more companies and governments understand: Precisely where their trucks, workers, and resources are located Where they need to go to service a customer The best way to get from here to there

86 Geographic Information Systems (GIS) 86 GIS and decision making GIS applications are used to improve decision making in the public and private sectors including: Dispatch of emergency vehicles Transit management Facility site selection Drought risk management Wildlife management Local governments use GIS applications for used mapping and other decision-making applications

87 Geographic Information Systems (GIS) 87 GIS combined with GPS Global positioning systems (GPS) Wireless devices that use satellites to enable users to detect the position on earth of items (e.g., cars or people) the devices are attached to, with reasonable precision

88 Geographic Information Systems (GIS) 88 GIS and the Internet/intranets Most major GIS software vendors provide Web access that hooks directly to their software GIS can help the manager of a retail operation determine where to locate retail outlets Some firms are deploying GIS on the Internet for internal use or for use by their customers (locate the closest store location)

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