1. Information systems and management in business Chapter 8 Business Intelligence (BI)

2. 8.1 Introduction  Overview Businesses and organizations gather large volumes of business data via their operational systems The data is typically kept in relational databases or large data warehouses In practice this data is left in their relevant databases, archived or discarded when it has no further operational value Traditional management and executive information systems are not geared to analyzing the data in a manner which is capable of discovering business value that lies hidden within such large volume of data BI systems are geared to fit this role

3. 8.2 Business Intelligence (BI) Vs Knowledge Management  knowledge management (KM) The process and strategies with which an organization creates, capture, store, use, distribute, and share its intellectual assets is a concept that is typically referred to as knowledge management (KM) [32 and 33]  BI definition The process of accessing and analyzing vast volumes of business that is created by operational systems and stored in various relational databases, data warehouses or data marts using complex analytical tools or technologies in order to enhance the effectiveness of the business decision making process

4. 8.2 Business Intelligence (BI) Vs Knowledge Management  The BI Triangle Three key areas (BI triangle) need to carefully evaluated and managed in order to create an effective and beneficial BI environment  The business value of BI  BI technologies  Business intelligence issues of concern

5. 8.2 Business Intelligence (BI) Vs Knowledge Management  The BI Triangle  The business value of BI Business intelligence has the potential to add value to businesses in a number of key business areas some of which include  Competitiveness  Responsiveness  Customer’s satisfaction & experience  Creating business opportunities

6. 8.2 Business Intelligence (BI) Vs Knowledge Management  The BI Triangle  BI technologies  Primarily there are two key business intelligence technologies Data analysis  Data mining  OLAP Data technologies

7. 8.2 Business Intelligence (BI) Vs Knowledge Management  The BI Triangle Business intelligence issues of concern  A number of issues need to be taken into consideration and appropriately evaluated prior to embarking on a business intelligence project Direct Vs Indirect Data Feed Silo Vs Centralized BI Approach Context Empowerment

8. 8.3 Business Intelligence Key Data Technologies  BI key data technologies Online Transaction processing (OLTP) Data warehouses Data marts

9. 8.3 Business Intelligence Key Data Technologies  Online Transaction processing (OLTP) overview Operational data is gathered using various operational systems  FSIS, TPSs, ERP, CRM, SCM, etc.. The process of storing, retrieving and manipulating operational data using various operational information systems is known as online transaction processing (OLTP) Accuracy and speed are critical factors for OLTP OLTP are typically designed with performance - transactions speed in mind OLTP associated databases employ a process known as normalization for structuring transactional data in order to deliver on the speed and accuracy goals

10. 8.3 Business Intelligence Key Data Technologies  Data Warehouses What is a Data Warehouse?  A data warehouse is basically a centralized repository of a business’s or an enterprise’s various operational data such as finance, HR, inventory and so forth [40 and 44]  Data in a data warehouse is read only and none volatile (historical) where as in operational systems (OLTP systems), it is current and regularly changing [41 and 56]

11. 8.3 Business Intelligence Key Data Technologies  Data warehouses advantages The ability to facilitate data analysis and reporting a way from operational systems Data centralization  Unified and a comprehensive view of the business or the organization The ability to employ data modeling techniques and servers technologies  Optimized for speeding up reporting and data querying

12. 8.3 Business Intelligence Key Data Technologies  What is ETL? Short for extraction, loading and transformation A critical part of a data warehouse architecture ELT is a process which involves extracting data from operational systems and loading it into a data warehouse

13. 8.3 Business Intelligence Key Data Technologies  Data Marts A data mart is typically a very small type of data warehouse which is used to keep transactional data of a particular business function, operation or a geographic location as opposed to keeping an entire organizational data [36, 37 and 38]

14. 8.4 Business Intelligence Categories  There are three categories of business intelligence [23] Strategic  Used by executive and senior managers  Historical data sourced from operational systems  Months – decision latency Tactical  Used by middle managers  Historical data sourced from operational systems  Days, weeks or months – decision latency Operational  Used by front line workers such as call center agents and sales executive  Fresh and real or near real time data  Few seconds, minutes or hours– decision latency

15. 8.6 Key Business Intelligence Technologies  What is Data Mining? Generally data mining is defined as searching and analyzing large volumes of data in order to identify patterns and relationships and to find useful information [48, 49, 50 and 51]

16. 8.6 Key Business Intelligence Technologies  Data Mining Scope Generally, the data mining analysis process falls into a number of categories [21, 26, 27] Examples  Classification Analyzing the data in order to identify predictive information  Regression Similar to classification except that it is limited to working with continuous quantitative data [21]  Association Analyze the data in order to discover hidden patterns or correlation that exists in the data  Clustering Entities that have similar characteristics are grouped together

17. 8.6 Key Business Intelligence Technologies  The Data Mining Process Four steps process  Analysis request  Request processing Data mining application  Typically involve some data modeling based on statistical or machine learning techniques  Data retrieval OLTP, data warehouses, data marts  Analysis presentation

18. 8.6 Key Business Intelligence Technologies  Data Mining Techniques (Algorithms) overview When we talk about data mining algorithms we are basically referring to the statistical and machine learning techniques that are used to perform the data analysis which discover information in the data or make prediction from the data There are a number of techniques which data mining employ for its predictive (classification or regression) or descriptive analysis (clustering or association) of the data  Artificial neural networks, decision trees, nearest neighbor method and rule induction [3, 9 and 26]

19. 8.6 Key Business Intelligence Technologies  Data Mining In Practice The data mining vendors provide solutions (products) that often incorporate a number of different analytical techniques  A single product may have the capability to perform classification, regression, association as well as clustering using various algorithms such as neural networks, CART and nearest neighbors [21, 26] This feature is essential for building users confidence with using the generated data model

20. 8.6 Key Business Intelligence Technologies  Online Analytical Processing Concept overview What is OLAP?  Generally, OLAP may be simply defined as a category of software applications or technologies which are designed to support the decision making process through providing a visual, speedy, interactive and a multi perspective (dimensions) view of the dataxx

21. 8.6 Key Business Intelligence Technologies  OLAP Process Architecture Multidimensional data modelling and storage is a key component of the OLAP process architecture An OLAP server is at the centre of architecture  Performs all the data manipulation, computation and analysis required in order to satisfy all analysis queries received from its clients OLAP Clients – the third component of the architecture  Typically present the analysis output in a multidimensional dimensional highly visual presentational formats

22. 8.6 Key Business Intelligence Technologies  OLAP Activities There are a number of activities which an OLAP client could deploy in order to analyze a multidimensional data structure with OLAP [57]  Slice and dice Slicing and dicing is basically about the ability to break up large data into slices that could then be broken further into smaller chunks (dicing) in order to get a further insight into it  Drill down  Analyzing the data from a hierarchal perspective

23. 8.7 Customer Relationship Management (CRM)  Customer relationship management definition A business philosophy or a strategy that is focused in understanding and anticipating customer’s needs in order to create a strong and a profitable relationship

24. 8.7 Customer Relationship Management (CRM)  The Business Value of CRM CRM places the customer at the centre of its architecture Having a business strategy which puts the customer at the center of this is likely to positively affect the profitability and the competitive position of the business Providing a service that understands, anticipates and satisfy customer’s needs is an enabler to the process of retaining existing customers and potentially attracting new ones

25. 8.7 Customer Relationship Management (CRM)  How to realize the CRM benefits A great deal of planning and careful budgeting Appropriate training Enterprise wide early involvement Choosing the appropriate implementation process A through understanding of need for customization and the potential problems that may be associated with it. A high degree of commitment and support from the top of the business management hierarchy

26. Chapter 8 Problems Solving Skills Development  Visit the book’s Web site www.halaeducation.com & select module 8 www.halaeducation.com  Perform Chapter 8 associated skills development through their respective skills development exercises link