Key Concepts of ERP, Data Warehouse & Data Mining. CA. A.Rafeq

Transcription

1 Key Concepts of ERP, Data Warehouse & Data Mining CA. A.Rafeq

2 Agenda I. Key features and benefits of an ERP software II. ERP needs BPR and Teamwork III. Key aspects of implementing ERP software IV.Date Warehousing and Data Mining key concepts 2

3 I. Key Features & Benefits of using ERP Software 3

4 Key Features of ERP Replaces island of computing Various applications Differing platforms Absence of version controls Absence of integration High level of redundancies 4

5 Key Features of ERP Package Key feature Key concept Seamless availability of information across all the users and across enterprise. Information captured once and at the source. Increased risk - loss of integrity 5

6 Benefits of ERP Extending BETTER and EFFICIENT customer service... Enables management to take faster and better decisions ERP helps enhance: productivity performance efficiency ERP implementation enables lower operating costs 6

7 Key benefits of Implementing ERP Integrates the Entire Enterprise from Supplier or Vendor to Customer covering Financials Logistics and HRD and 7

8 Information Integration key concern If enterprises are not careful in implementing an ERP package, The dream of information integration can turn into a nightmare. 8

9 Capabilities of ERP On-line, Real time Use of Parameters Implementation models Customization of Reports Interface/ integration tools Scalable 9

10 Security and Configuration Ensuring security Confidentiality Integrity and Availability of information is a matter of key importance Greater the Technology, Higher the risk ERP Software has best of breed features of security but these need to be configured as per enterprise requirements. 10

11 II. Implementing ERP software requires BPR and Team work 11

12 Implementing ERP needs Teamwork 12

13 What is a Process? For Example for an Airlines: Process is not about selling tickets but about moving people to their destinations 13

14 BPR: Start with a Clean Slate Abandoning longestablished procedures Looking afresh at the work required for customer Product or service and Deliver value to the customer 14

15 Effective BPR: key aspects of ERP implementation Transforming core activities from one based on functions To one based on processes Accounting Order processing and Marketing and Manufacturing Fulfilling customer expectations 15

16 BPR requires Re-Organizing Work Flows Re-organize as per few fundamental work-flows Involves breaking down functions and their hierarchy 16

17 III. Key aspects of implementing ERP software 17

18 System Implementation Access security Data conversion Automated interfaces Change control Interaction with functional owners and training the users Development of procedures and implementation of control reports 18

19 Access Security Operating System Functions Logical Access Security Telecomm unications \Networki ng Modules Database Applicatio ns 19

20 Data Conversion Verification and reconciliation Integrity of information Complete Consistent Correct Authentic, Authorized and Available 20 20

21 Some tips for ERP implementation Implementing an ERP package is both a challenge and an opportunity. Assess the risks involved Evolve strategy Formulate appropriate procedures Map business process correctly Develop staff competencies and skills 21

22 ERP implementation requires collaboration ERP is one area of IT, which has major impact: Financial and Business-wise ERP implementation requires effective collaboration of all the key stakeholders 22

23 IV. Date Warehouse and Data Mining Key Concepts 23

24 What is a Data warehouse? Collection of data from various sources (Internal and External) and placing them in a form suitable for further processing. Data warehousing a huge collection of data from diverse sources. Unique features are that it is big in terms of size of: Data storage Systems Complexity of administration Risks involved Investments 24

25 Defining Data Warehouse Warehouse is a SUBJECT ORIENTED, INTEGRATED, NONVOLATILE, TIME VARIENT collection of data in support of management decisions. 25

26 Why Enterprises use DW? Drawing inference from collected data over a long period of time Provides better and sharper image of their operations Data provides competitive advantage Meets increasing need to analyze business issues from different perspectives Facilitates multidimensional analysis of data 26

27 Warehouse topology overview Flat Files Sales Analysis Operational Systems Staging Area Inventory Reporting Operational Systems Normalized Data Warehouse Purchase Mining 27

28 DW Implementation DW is not just storing of huge data in one place. Data needs to be stored, processed and summarized before being used as information. DW is technology, a process and an IS implementation philosophy with far reaching implications. 28

29 Evolution of Customer Centric Data Usage Evolution of customer-centric data usage Changing business realities Data extraction from heterogeneous dis-organized data Business models Interactive decision support system 29

30 Implementation Issues Huge size of data which is diverse in nature. Need for data and quality of data Dynamic impact of technology changes Finding the right model for data as per needs. 30

31 Key issues of Data Load and Maintenance Strategy Possible automation of a process Requirement of data cleansing Access Options Job specifications and resource requirements Amount of processing required for various data sources Contingency plans 31

32 DW Performance Tools Choosing the right tools: Latest indexing technologies Advanced RDBMS features Query capabilities Aggregation capabilities Support for sampling 32

33 Key Issues For Successful DW DW is an enabling technology requires extensive consultancy and implementation. Only as good as the people who make it. System integration is very important. Selection of a right combination of tools Business rules within which DWA is to function is very critical Choosing right Implementation partners with adequate domain knowledge of organization and project management skills is critical. 33

34 Reasons for Failure of DW project Lack of meticulous planning and execution. Cost overruns caused by wrong estimation of hardware/network resources Time overruns and changing priorities Scope escalation Lack of focus on main problems Non-cooperation by one or more user groups 34

35 MIS through DW and DM DW and DM are relatively new. Growing sophistication of technology and the need for predictive analysis with What if simulations. MIS in a DW environment has become more pertinent to decision-making process. DW and DM contributes to effective MIS 35

36 ROI for Warehouse What is value of. More rapid access to data More reliable Reporting More flexible data presentation Difficult to quantify the benefits. ROI : LEAP OF FAITH??? 36

37 What is Data Mining? Data Mining refers to the process of extracting hidden information Generating several types of analytical reports which are usually not available in the original transaction processing systems. 37

38 Definition of Data Mining Automated extraction of hidden predictive information from large databases 38

39 Summary I. Key features and benefits of an ERP software II. ERP needs BPR and Teamwork III. Key aspects of implementing ERP software IV. DW and DM key concepts 39

40 Hope you had a great learning experience. 40