MIS Business Intelligence Roadmap by Larissa T. Moss and Shaku Atre

1. MIS�Business Intelligence Roadmap�byLarissa T. Moss and Shaku Atre Presented by: Line, Gerd and Andreas

2. Content Definitions Presentation of the Model Practical Use of BI (example: DNV) Criticism and benefits Discussion/Questions Gerd Vi vil konsentrere oss om hovedpunktene, ikke detaljer!!!! Vi vil ikke snakke om rollefordeling Gerd Vi vil konsentrere oss om hovedpunktene, ikke detaljer!!!! Vi vil ikke snakke om rollefordeling

3. Definitions Business Intelligence �the gathering and analysis of information from human and published sources about market trends and industry developments that allow for advanced identification of risks and opportunities in the competitive arena� Business Performance solutions Gerd http://www.reduct.com/About_Ai/business.htm Business Intelligence (BI) is a code-name for a range of technologies which unlock knowledge that is hidden in business data and tell us how to use it more effectively. The overall objectives of BI are to improve planning, productivity, service quality, and profitability by making information and data more comprehensible and using business knowledge more effectively. Business Intelligence helps us to use business data for competitive advantage. Gerd http://www.reduct.com/About_Ai/business.htm Business Intelligence (BI) is a code-name for a range of technologies which unlock knowledge that is hidden in business data and tell us how to use it more effectively. The overall objectives of BI are to improve planning, productivity, service quality, and profitability by making information and data more comprehensible and using business knowledge more effectively. Business Intelligence helps us to use business data for competitive advantage.

4. Definitions BI vs. Traditional systems Business need => business opportunity Cross organizational developement From operational requirements to strategic information Business analysis =>system analysis Incremental development (waterfall deployment) rather than �big bang� Gerd 5 minGerd 5 min

5. Presentation of the Model Line 3 minLine 3 min

6. Stage 1 - Justification �Assess the business need that gives rise to the new engineering project� Business Cases Assessment Definitions of problems and opportunities Line Svarer p� hvorfor man trenger et nytt system Kostnads vurdering � lage en kostnadsramme Fordeler med � l�se problemet � hvorfor kan man ikke fortsette med det som er I dag Utnytte mulighetene - Line Svarer p� hvorfor man trenger et nytt system Kostnads vurdering � lage en kostnadsramme Fordeler med � l�se problemet � hvorfor kan man ikke fortsette med det som er I dag Utnytte mulighetene -

7. Stage 2 - Planning Enterprise Infrastructure Evaluation Technical Infrastructure Nontechnical infrastructure Project Planning Detailed planning Line In this stage, the firm would develop a strategic and a tactical plan. These plan would in the end show us how the project would be accomplished and deployed. This stage has 2 steps and those are Enterprise Infrastructure Evaluation and Project planning. Let�s start with stage 2 first: Step 2 - Enterprise Infrastructure Evaluation: must be created in order to support the cross-organizational initiatives� Consists of two components: Technical Infrastructure � hardware, software, database management systems, operating systems� Nontechnical Infrastructure � meta data, enterprise logical datamodel� Step 3 � Project Planning: There might be changes to scope, budget, technology, business representatives, sponsors� this can change the projects success That�s why it�s very important to have a detailed plan and the project should be closely watched and reported.Line In this stage, the firm would develop a strategic and a tactical plan. These plan would in the end show us how the project would be accomplished and deployed. This stage has 2 steps and those are Enterprise Infrastructure Evaluation and Project planning. Let�s start with stage 2 first: Step 2 - Enterprise Infrastructure Evaluation: must be created in order to support the cross-organizational initiatives� Consists of two components: Technical Infrastructure � hardware, software, database management systems, operating systems� Nontechnical Infrastructure � meta data, enterprise logical datamodel� Step 3 � Project Planning: There might be changes to scope, budget, technology, business representatives, sponsors� this can change the projects success That�s why it�s very important to have a detailed plan and the project should be closely watched and reported.

8. Stage 3 � Business Analysis �Detailed analysis of the business problem or business opportunity is performed, which provides a solid understanding of the business requirements for a solution�. Project Requirements Definition Functional, data, historical, security and performance Data Analysis Source data, Data quality, Data cleansing Application Prototyping Objectives, scope, deliverables, participation, tools Meta Data Repository Analysis Usage, req., security, capture, delivery, staffing Andreas BUSINESS ANALYSIS STAGE Step Four: Project Requirements Definition (p.106) + activities p.119 In this step we are supposed to define the requirements for each deliverable. Scoping is one of the most difficult tasks for BI applications. The desire to have everything instantly is difficult to curtail, but keeping the scope small is one of the most important aspects to defining the requirements for each deliverable. These requirements should be expected to change throughout the development cycle as more is learned about the possibilities and the limitations of the technology. Step Five: Data Analysis p.126 + activities p.142 The biggest challenge to all BI projects is the quality of the source data. The bad habits developed over decades are difficult to break, and the damage resulting from the bad habits is very time consuming and tedious to find and correct. In addition, data analysis in the past was confined to one line of business user�s view and was never reconciled with other views in the organization. This step will take a significant percentage of time from the entire project schedule. Step Six: Application Prototyping p.150 + activities p.163Also called system analysis. This means that we analyze the functional deliverable(s). This is best done through prototyping. Today there are tools and new programming languages, which enable the developers to relatively quickly prove or disprove a concept or idea. It also allows the users to see the potential and the limits of the technology. This gives them an opportunity to adjust their delivery requirements and their expectations.Step Seven: Meta Data Repository Analysis p.170 + activities p.187 Analysis of what meta data you have (technical meta data in addition to the business meta data). These data are usually captured in a modeling CASE (Computer Aided Software Engineering) tool. This meta data needs to be mapped to each other and needs to be stored in a repository. Meta data repositories can be purchased or built. In either case, the requirements for what type of meta data to capture and to store have to be documented in a meta model. In addition, the requirements for delivering meta data to the users have to be analyzed. Andreas BUSINESS ANALYSIS STAGE Step Four: Project Requirements Definition (p.106) + activities p.119 In this step we are supposed to define the requirements for each deliverable. Scoping is one of the most difficult tasks for BI applications. The desire to have everything instantly is difficult to curtail, but keeping the scope small is one of the most important aspects to defining the requirements for each deliverable. These requirements should be expected to change throughout the development cycle as more is learned about the possibilities and the limitations of the technology. Step Five: Data Analysis p.126 + activities p.142 The biggest challenge to all BI projects is the quality of the source data. The bad habits developed over decades are difficult to break, and the damage resulting from the bad habits is very time consuming and tedious to find and correct. In addition, data analysis in the past was confined to one line of business user�s view and was never reconciled with other views in the organization. This step will take a significant percentage of time from the entire project schedule. Step Six: Application Prototyping p.150 + activities p.163Also called system analysis. This means that we analyze the functional deliverable(s). This is best done through prototyping. Today there are tools and new programming languages, which enable the developers to relatively quickly prove or disprove a concept or idea. It also allows the users to see the potential and the limits of the technology. This gives them an opportunity to adjust their delivery requirements and their expectations.Step Seven: Meta Data Repository Analysis p.170 + activities p.187 Analysis of what meta data you have (technical meta data in addition to the business meta data). These data are usually captured in a modeling CASE (Computer Aided Software Engineering) tool. This meta data needs to be mapped to each other and needs to be stored in a repository. Meta data repositories can be purchased or built. In either case, the requirements for what type of meta data to capture and to store have to be documented in a meta model. In addition, the requirements for delivering meta data to the users have to be analyzed.

9. Stage 4 - Design �Conceive a product that solves the business problem or enables the business opportunity� Database Design Reports, design, performance, DMS, staffing ETL Design Tools, ETL staging, ETL process flow, performance, reconciliation Meta Data Repository Design Existing MDR, MDR products, interfaces, staffing Andreas DESIGN STAGE Step Eight: Database DesignOne or more databases will be storing the business data in detailed or aggregated form, depending on the reporting requirements of the users. Not all reporting requirements are strategic, and not all of them are multi-dimensional. The database design schema must match the access requirements of the business.Step Nine: Extract/Transform/Load (ETL) Design p.212 This process is the most complicated process of the entire BI project. It is also the least glamorous one. ETL processing time frames (batch windows) are typically small. Yet the poor quality of the source data usually requires a lot of time to run the transformation and cleansing programs. To finish the ETL process within the available time frame is a challenge for most organizations.Step Ten: Meta Data Repository Design p. 238 If a meta data repository is purchased, it will most likely have to be extended with features that are required by your BI applications. If a meta data repository is being built, the database has to be designed based on the meta model developed during the previous step. Andreas DESIGN STAGE Step Eight: Database DesignOne or more databases will be storing the business data in detailed or aggregated form, depending on the reporting requirements of the users. Not all reporting requirements are strategic, and not all of them are multi-dimensional. The database design schema must match the access requirements of the business.Step Nine: Extract/Transform/Load (ETL) Design p.212 This process is the most complicated process of the entire BI project. It is also the least glamorous one. ETL processing time frames (batch windows) are typically small. Yet the poor quality of the source data usually requires a lot of time to run the transformation and cleansing programs. To finish the ETL process within the available time frame is a challenge for most organizations.Step Ten: Meta Data Repository Design p. 238 If a meta data repository is purchased, it will most likely have to be extended with features that are required by your BI applications. If a meta data repository is being built, the database has to be designed based on the meta model developed during the previous step.

10. Stage 5 - Construction �Build the product, which should provide a return on investment within a predefined time frame� ETL Development Data extracts, ETL tool, ETL process dependencies, testing, tec. considerations Application Development Prototyping results, Access and analysis tools, skills and training, scope and project req., web and technical considerations. Data Mining Market, data, data mining tool, staffing Meta Data Repository Development MDR product support, custom-built MDR, staffing Andreas CONSTRUCTION STAGE Step Eleven: ETL Development p. 260 Many tools are available for this process, some sophisticated and some simple. Depending on the data cleansing and data transformation requirements developed during the Data Analysis step, an ETL tool may or may not be the best solution. In either case, pre-processing the data and writing extensions to the tool capabilities is frequently required.Step Twelve: Application Development p. 282 Once the prototyping effort has finalized the functional delivery requirements, true development can begin on either the same user access and analysis tools, such as OLAP tools, or on different tools. This activity is usually performed in parallel to the meta data repository and ETL activities.Step Thirteen: Data Mining p. 302 Many organizations do not use their BI databases to their fullest capability. In fact, usage is often limited to pre-written reports, some of them not even new types of reports, but replacements of old reports. The real payback for BI applications comes from the business intelligence hidden in the organization�s data, which can only be discovered with data mining tools.Step Fourteen: Meta Data Repository DevelopmentIf the decision is made to build a meta data repository rather than to buy one, a separate team is usually charged with the development process. This becomes a sizable sub-project of the overall BI project. Meta Data Repository Development p.320 Andreas CONSTRUCTION STAGE Step Eleven: ETL Development p. 260 Many tools are available for this process, some sophisticated and some simple. Depending on the data cleansing and data transformation requirements developed during the Data Analysis step, an ETL tool may or may not be the best solution. In either case, pre-processing the data and writing extensions to the tool capabilities is frequently required.Step Twelve: Application Development p. 282 Once the prototyping effort has finalized the functional delivery requirements, true development can begin on either the same user access and analysis tools, such as OLAP tools, or on different tools. This activity is usually performed in parallel to the meta data repository and ETL activities.Step Thirteen: Data Mining p. 302 Many organizations do not use their BI databases to their fullest capability. In fact, usage is often limited to pre-written reports, some of them not even new types of reports, but replacements of old reports. The real payback for BI applications comes from the business intelligence hidden in the organization�s data, which can only be discovered with data mining tools.Step Fourteen: Meta Data Repository DevelopmentIf the decision is made to build a meta data repository rather than to buy one, a separate team is usually charged with the development process. This becomes a sizable sub-project of the overall BI project. Meta Data Repository Development p.320

11. Stage 6 - Deployment Implementation Implementing the new system Training Release Evaluation �Lesson learned� Line In this stage the firm either sell or implement the finished product. Further, the firm measure its effectiveness to determine whether the solution meets, exceeds, or fails to meet the expected return on investment� The steps 15 and 16 are then: Implementation � Training, help desk, maintenance of the BI target database, Scheduling and running ETL batch jobs, monitoring performance and tuning databases� Release Evaluation � benefit from lesson learned from the previous project. Examine missed deadlines, cost overruns and disputes. Process adjustments should be made before the next release begin. Reevaluation of tools, techniques and guidelines that were not used. Adjustment and discardedLine In this stage the firm either sell or implement the finished product. Further, the firm measure its effectiveness to determine whether the solution meets, exceeds, or fails to meet the expected return on investment� The steps 15 and 16 are then: Implementation � Training, help desk, maintenance of the BI target database, Scheduling and running ETL batch jobs, monitoring performance and tuning databases� Release Evaluation � benefit from lesson learned from the previous project. Examine missed deadlines, cost overruns and disputes. Process adjustments should be made before the next release begin. Reevaluation of tools, techniques and guidelines that were not used. Adjustment and discarded

12. Summary of the Model LineLine

13. Practical Use of Business Intelligence Det Norske Veritas Verit4Net The planning The infrastructure The implementation SLA � Support IQM Inside DNV Information flow external and internal GerdGerd

14. GerdGerd

15. Criticism and Benefits (http://www.istart.co.nz/bi.htm) Criticism: �Bullet point book� Implementation and practical use Use of information BI tools are complex and difficult to use Benefits: Access of data Measure BGs Good/Bad customers Customer behavior Track external market trends Fine tuning prices and market policies Track product sales Andreas http://www.istart.co.nz/bi.htm Criticism to BI: - measuring return on investment still presents a challenge - business intelligence tools are complex and difficult to use - In the book there is lack of information on how to implement BI and how to do it in practice. - We think that this book is unfulfilled information, and that it is a �bullet point book�. - Another thing that can be criticized is that there are solutions on how to collect information, but not on how to use or reuse the information present in the systems. Benefits to BI: The benefits may include identifying top customers, product line profitability, demographic trends and fine-tuning of pricing policies, retention of customers and predicting market trends. Benefits A�way to access data in a common format from multiple sources A way to measure business goals by analysing cross-departmental data See who the good, bad and ugly customers are at a glance Track customer behaviour to improve service and relationships Track specific product sales across regions and distributors to improve production and supply Track internal business trends to improve processes Track external market trends to improve competitiveness Fine tune pricing and marketing policies The key to successfully putting business-intelligence tools into the hands of users is co-operation among IT and business managers. Andreas http://www.istart.co.nz/bi.htm Criticism to BI: - measuring return on investment still presents a challenge - business intelligence tools are complex and difficult to use - In the book there is lack of information on how to implement BI and how to do it in practice. - We think that this book is unfulfilled information, and that it is a �bullet point book�. - Another thing that can be criticized is that there are solutions on how to collect information, but not on how to use or reuse the information present in the systems. Benefits to BI: The benefits may include identifying top customers, product line profitability, demographic trends and fine-tuning of pricing policies, retention of customers and predicting market trends. Benefits A�way to access data in a common format from multiple sources A way to measure business goals by analysing cross-departmental data See who the good, bad and ugly customers are at a glance Track customer behaviour to improve service and relationships Track specific product sales across regions and distributors to improve production and supply Track internal business trends to improve processes Track external market trends to improve competitiveness Fine tune pricing and marketing policies The key to successfully putting business-intelligence tools into the hands of users is co-operation among IT and business managers.

16. Discussion/Questions ? AlleAlle