CISB594 – Business Intelligence Business Analytics and Data Visualization Part I

Reference Materials used in this presentation are extracted mainly from the following texts, unless stated otherwise.

Objectives At the end of this lecture, you should be able to: Describe business analytics (BA) and its importance to organizations List and briefly describe the major BA methods and tools Describe how online analytical processing (OLAP), data visualization, and multidimensionality can improve decision making Describe advanced analysis methods CISB594 – Business Intelligence

Introduction Many organizations have amassed vast amounts of data that employers can use to unlock valuable relationship to enable organization to compete and perform successfully Using analytical tools, organizations enable decision analysis through access to all relevant data and information

The Business Analytics (BA) : An Overview The Essentials of BA Analytics : The science of analysis. Business analytics (BA) Provides the analysis procedures to BI, tracking data and analyzing them for competitive advantage. Broad category of applications and techniques for gathering, storing and analyzing to help users make better business and strategic decisions Allows for automating the thinking for decision making

The Business Analytics (BA) : An Overview Many tools can be used – the results will be presented in a form of reports, predictions, alerts or graphical presentations More advanced applications of BA includes financial modeling, budgeting, resource allocation and competitive intelligence

The Business Analytics (BA) : An Overview Example : An analytic application used for credit card scoring for a loan application Calculate a credit worthiness score Automatically accept or deny the loan application Select the loan limit Select which credit card product/deal to suit the applicant

The tools and techniques of BA Three major categories of BA analytic tools and techniques Information and knowledge discovery Decision Support and Intelligent System Visualization Illustrated in the following diagram

The Business Analytics (BA) Field: An Overview

The Business Analytics (BA) Field: An Overview Vendors classify BA tools in several different ways. MicroStrategy’s classification of BA tools: Enterprise reporting - used to generate highly formatted static reports meant for broad distribution to many people Cube analysis – used to provide simple OLAP multidimensional slice and dice analytical capabilities to business managers Ad hoc querying and analysis – used to allow power users to query a database for answers Statistical analysis and data mining - statistical, mathematical and data mining tools are used to perform predictive analysis and to determine cause-and-effect correlations Report delivery and alerting – report distribution engines to send full reports/alerts to internal/external users, based on subscriptions and schedules or threshold events

The Business Analytics (BA) Field: An Overview Vendors classify BA tools in several different ways. SAP’s classification of strategic enterprise management Three levels of support Operational – SAP R/3 mainly supports transaction processing on the operational level Managerial – middle managers can use SAP/R3 to access all reports, arranged by functional areas Strategic - SAP SEM (Strategic Enterprise Management)

The Business Analytics (BA) Field: An Overview Major Capabilities of BA Tools Drill-down The investigation of information in detail (e.g., finding not only total sales but also sales by region, by product, or by salesperson). Ad Hoc Analysis Analysis made at any time, and with any desired factors and relationships Slicing and dicing Rearranging data so that they can be viewed from different perspectives Exception Report Using reports to highlight deviation larger than threshold

Online Analytical Processing (OLAP) Variety of activities usually performed by users in online system – usually involving generating and answering queries, requesting ad-hoc reports, conducting statistical analysis and building visual presentation An information system that enables the user, while at a PC, to query the system, conduct an analysis, and so on. The result is generated in seconds

Online Analytical Processing (OLAP) OLAP versus OLTP 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

Online Analytical Processing (OLAP) Types of OLAP Multidimensional OLAP (MOLAP) OLAP implemented via a specialized multidimensional database (or data store). It summarizes transactions into multidimensional views ahead of time Data are organized into cube structure that users can rotate; particularly suitable for financial summaries

Online Analytical Processing (OLAP) Types of OLAP Relational OLAP (ROLAP) The implementation of an OLAP on top of an existing relational database Extracts data from relational database Tends to be used on data that has a large number of attributes, where it cannot be easily placed into a cube structure. Example, customer data as oppose to financial data. Web OLAP – accessible from Web Browser Desktop OLAP – low priced, simple OLAP, performs local analysis from database

Online Analytical Processing (OLAP) Four types of processing that are performed by analysts in an organization: Categorical analysis – static analysis based on historical data Exegetical analysis – also based on historical data, and it adds the capability of drill-down analysis (ability to query further down into data to determine the detail data that were used to obtain the derived value) Contemplative analysis – allows user to change a single value to determine its impact Formulaic analysis - allows changes to multiple variables OLAP tools are designed to support all of the above activities

Online Analytical Processing (OLAP) OLAP Products Evaluation Rules: Codd’s 12 Rules for OLAP Multidimensional conceptual view for formulating queries Transparency to the user Easy accessibility: batch and online access Consistent reporting performance Client/server architecture: the use of distributed resources Generic dimensionality 7. Dynamic sparse matrix handling 8. Multiuser support rather than support for only a single user 9. Unrestricted cross- dimensional operations 10. Intuitive data manipulation 11. Flexible reporting 12. Unlimited dimensions and aggregation level

Reports and Queries The oldest activities of OLAP and BI are using reports and queries. Reports Routine reports Ad hoc (or on-demand) reports Multilingual support Scorecards and dashboards Report delivery and alerting Report distribution through any touch point Self-subscription as well as administrator-based distribution Delivery on-demand, on-schedule, or on-event Automatic content personalization

Reports and Queries Ad hoc query A query that cannot be determined prior to the moment the query is issued . User might need to place such a query after seeing a report

Multidimensionality Multidimensionality Raw and summary data can be organized in different ways for analysis and presentation 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) Three factors are considered in multidimensional presentation Dimensions – products, salespeople, business units Measures - money, sales, sales volumes, head count Time – daily, weekly, monthly, quarterly

How multidimensionality works A manager wants to know the sales of a product (by unit or dollar) in a certain geographic area, by a specific salesperson, during a specific month. The answer to such question can be provided fast if the data is organized in multidimensional database or if query or related software products are designed for multidimensionality. This will allow users to navigate through the many dimensions and levels of data via tables or graphs and are able to make quick interpretations, such as uncovering significant deviations or important trends.

Multidimensionality 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 higher-dimensional object in which each dimension of the data represents a measure of interest Provides an opportunity to retrieve decision support information in an efficient way.

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

Multidimensionality More on cube Example : A database contains transaction information relating company sales of products (p) to a customer (c ) at different store (s) locations. The data cube formed from this database is a three dimensional representation, with each cell ( p, c, s). The cube can be used to retrieve information within the database about, for example which store should be given a certain product to sell in order to make greater profit.

Multidimensionality

Multidimensionality 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

Advanced Business Analytics While OLAP concentrates on reporting and queries, a more sophisticated way of analyzing data and information is needed Users today will want to perform statistical and mathematical analysis such as hypothesis testing, multiple regression, churn prediction and customer scoring models. Such investigation cannot be done with basic OLAP and will require special tools, including data mining and predictive analysis – hence, advanced business analytics

Advanced Business Analytics A major step in managerial decision making is forecasting or estimating the results of different alternative courses of actions Two methods that can be used for advanced business analytics are Data mining and predictive analysis Data mining Predictive analysis

Advanced Business Analytics Data mining Tools that would automatically extract hidden, predictive information from databases, search for pattern in large transaction database. OLAP can only answer questions you are certain to ask, whereas data mining answers questions you don’t necessarily know you should ask (to be discussed further in the next chapter) 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

Data Visualization Data visualization A graphical, animation, or video presentation of data and the results of data analysis Visual technologies can condense 1000 numbers in one picture and make decision support applications more attractive and understandable 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

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

Data Visualization

Data Visualization

Now ask if … You are now be able to: Describe business analytics (BA) and its importance to organizations List and briefly describe the major BA methods and tools Describe how online analytical processing (OLAP), data visualization, and multidimensionality can improve decision making Describe advanced analysis methods CISB594 – Business Intelligence