We have a guest lecture this week. The guest lecture was fun. We had people from different industry to share with us their experience on BI/DM and how does this two application help them in their daily operation or marketing.
Most of the information shared by the guest was like a recall of my major project. Since we did data mining for Estee Lauder Companies, thus it is very easy for us to understand the contain and discussion during the guest lecture. Had fun doing the lecture activity quiz, it is just like writing down some of the findings from our major project.
I believe that BI/ DM is very important to any organization and industry. It is very difficult for an organization to grow without BI. BI helps an organization to understand its' customer, coming out more cost effective marketing campaign, reducing churning customer and improving KPIs of the company.
In conclusion, I think I made a right choice of selecting BI as one of my electives. The lecturers and tutors are very experience in this industry. I have benefited a lot from this subject, the lectures help us to build up understanding about Business Intelligence while the lab lessons give us experience on building up a dashboard or scorecard. Personally I enjoy doing the project very much as it build up my confidence in using the software. This is a great subject that I will recommend to my juniors.
Happy learning
Chenyuan
Monday, February 1, 2010
Monday, January 18, 2010
Week 15 Future of Data Warehouse, Data Mining and Data Visualisation
This weeks' lecture is all about trends and future development of the data warehouse. I get an overall picture of the data warehouse development. I am very surprised by the media of storage which is covered in the lecture. According to Ms Chong, the future of data warehousing is not high-performance disk storage, but an array of alternative storage. This involves two forms of storage. Near-line storage involves an automated silo where tape cartridges are handled automatically. Secondary storage is slower and less expensive, such as CD-ROMs and floppy disks.
I think this trend of alternative storage is very reasonable as for data that are placed there once and left alone, so do not need to be updated at high speed. If data gets accessed less often as it ages, it can be moved to secondary storage, giving the resources to the new data and making access to newer data more efficient.
I have read through this article from Corporate Information Factory. It was written by W. H. Inmon. In this article it mention that high performance disk storage plays only a secondary role in the future of data warehousing. The real future of data warehousing is in a storage media collectively known as "alternative storage". This support what Ms Chong have taught us in the lecture.
I have extracted out from the article some reasons that why high-performance disk storage not a choice for data warehouse.
The future of Data Mining is to react more quickly and offer better service, and do it all with fewer people and at a lower cost.
The increase in hardware speed and capacity makes it possible to analyze data sets that were too large just a few years ago. However while the available data exponentially, the industry is looking into automatic procedures for data mining.
Data Mining is also been used in protecting privacy information.
Data visualization has in recent years become an established area of study in academia. Many universities now have faculty members who focus on visualization and a few have excellent programs that serve the needs of many graduate students who produce worthwhile research studies and prototype applications.
Both of them expect that data visualization will continue for the next few years to pursue and mature those trends that have already begun. Dashboards, visual analytics, and even simple graphs will continue to develop and conform to best practices. They also have seen evidence that newer efforts are emerging that will soon develop into full-blown trends.
According to STEPHEN FEW and PERCEPTUAL EDGE, another expression of data visualization that has captured the imagination of many in the business world in recent years is geo-spatial visualization. The popularity of Google Earth and other similar Web services have contributed a great deal to this interest.
Another trend that has made the journey in recent years from the academic research community to commercial software tackles the problem of displaying large sets of quantitative data in the limited space of a screen. The most popular example of this is the treemap.
That's all I like to share for this week
Happy Reading
Cheers
Chenyuan
I think this trend of alternative storage is very reasonable as for data that are placed there once and left alone, so do not need to be updated at high speed. If data gets accessed less often as it ages, it can be moved to secondary storage, giving the resources to the new data and making access to newer data more efficient.I have read through this article from Corporate Information Factory. It was written by W. H. Inmon. In this article it mention that high performance disk storage plays only a secondary role in the future of data warehousing. The real future of data warehousing is in a storage media collectively known as "alternative storage". This support what Ms Chong have taught us in the lecture.
I have extracted out from the article some reasons that why high-performance disk storage not a choice for data warehouse.
- Secondary storage is a form of disk storage but whose disk is slower, significantly less expensive and less cached than high performance storage.
- Data warehouse data is very stable.
- The ability to store far more data on near line and/or secondary storage.
- The rate at which secondary storage and near line storage is getting cheaper is at a faster rate than high performance storage.
The future of Data Mining is to react more quickly and offer better service, and do it all with fewer people and at a lower cost.
The increase in hardware speed and capacity makes it possible to analyze data sets that were too large just a few years ago. However while the available data exponentially, the industry is looking into automatic procedures for data mining.
Data Mining is also been used in protecting privacy information.
- One current intrusion detection technique is misuse detection – scanning for malicious activity patterns known by signatures.
- Another technique is anomaly detection where there is an attempt to identify malicious activity based on deviations from norms.
Data visualization has in recent years become an established area of study in academia. Many universities now have faculty members who focus on visualization and a few have excellent programs that serve the needs of many graduate students who produce worthwhile research studies and prototype applications.
Both of them expect that data visualization will continue for the next few years to pursue and mature those trends that have already begun. Dashboards, visual analytics, and even simple graphs will continue to develop and conform to best practices. They also have seen evidence that newer efforts are emerging that will soon develop into full-blown trends.
According to STEPHEN FEW and PERCEPTUAL EDGE, another expression of data visualization that has captured the imagination of many in the business world in recent years is geo-spatial visualization. The popularity of Google Earth and other similar Web services have contributed a great deal to this interest.
Another trend that has made the journey in recent years from the academic research community to commercial software tackles the problem of displaying large sets of quantitative data in the limited space of a screen. The most popular example of this is the treemap.
That's all I like to share for this week
Happy Reading
Cheers
Chenyuan
Week 14 Implementing Enterprise Business Intelligence Systems
Implementing a BI system is not easy. Before any implementation, organization must first identify what do they want to achieve. Understand what is important for the organization together with data sources and analytical capabilities that the organization have. Finally determine the gap between current resources and the goals that the organization has for BI
BI Program Management Office (BI PMO) has to be created.
The corporate information factory shows the information flow direction inside an organization. Change in management will be one of the cause for the failure of implementing BI system.
Without data quality, reporting and analytical processes will be unreliable and dangerous for decision making
Managing data quality:
Decide how to establish and maintain on-going data quality programs
A organization can either outsource their Business Intelligence Competency Center(BICC) or set up one BICC team in house. According to SAS, a Business Intelligence Competency Center (BICC) is a cross-functional team with a permanent, formal organizational structure. It is owned and staffed by the client and has defined tasks, roles, responsibilities and processes for supporting and promoting the effective use of business intelligence and performance management across the organization.
BICC can be either a permanent structure or virtual team. A permanent structure will be a separate division, the advantage is that will be sharing of experience and knowledge.
A virtual team consisting of staff from different department or companies which require no internal reorganization or shifting of budget. But it lack of communication and alignment between members.
There are three different ways of funding a BICC. Listing it as a cost center. This is run the risks of an under appreciated center as it is not a revenue generating center.
The second method is Internal billing system. It charge users for help given on projects and analysis. The disadvantage of this method is that it can limit the use and growth of BICC.
The third method is to have a subscription-based billing model. It is very difficult to implement as it is very difficult to get agreement upfront. Estimating of usage will be difficult.
According to SAS, big organization are more favour towards the cost center funding where SMB is more like to use the Internal billing system.
Finally before ending for this week's lecture, I like to share this actricle with my friends. It contains a lot of information such as Benefits from having a BICC, Staff members in a BICC and Guidance for setting up a BICC.
Happy Learning
Cheers
Chenyuan
BI Program Management Office (BI PMO) has to be created.
- BI POM will establish project priorities and obtain funding for projects
- Oversee the enforcement of standards, policies and procedures as well as the development of data models and ETL code.
- Identify and create the overarching BI architecture
- Determine how each project will further develop or fit into the existing architecture
The corporate information factory shows the information flow direction inside an organization. Change in management will be one of the cause for the failure of implementing BI system.
Without data quality, reporting and analytical processes will be unreliable and dangerous for decision making
Managing data quality:
- Define what data quality means to your company
- Educate key stakeholders on:
- Data quality theories
- Case studies from other organisations
- Specific problems with your company’s data
- Obtain key stakeholders’ support
Decide how to establish and maintain on-going data quality programs
A organization can either outsource their Business Intelligence Competency Center(BICC) or set up one BICC team in house. According to SAS, a Business Intelligence Competency Center (BICC) is a cross-functional team with a permanent, formal organizational structure. It is owned and staffed by the client and has defined tasks, roles, responsibilities and processes for supporting and promoting the effective use of business intelligence and performance management across the organization.
Your BICC might have some of these functional areas:
- BI Program Management Office: : Defines and monitors implementation of the BI strategy. Responsible for consistent BI deployment, standards, technology assessments, knowledge management, best practices and business analytics expertise.
- Data Stewardship: Data definition, ownership and quality, metadata management, data standards.
- Vendor Management: Vendor evaluation, relationship management, user licenses.
- Information Management: Coordination with the data warehouse team to integrate operational data into the BI environment.
- Information Delivery: Working with the business community to promote proper use of information, data mart design and utilization, analytical analysis, user training and support.
BICC can be either a permanent structure or virtual team. A permanent structure will be a separate division, the advantage is that will be sharing of experience and knowledge.
A virtual team consisting of staff from different department or companies which require no internal reorganization or shifting of budget. But it lack of communication and alignment between members.
There are three different ways of funding a BICC. Listing it as a cost center. This is run the risks of an under appreciated center as it is not a revenue generating center.The second method is Internal billing system. It charge users for help given on projects and analysis. The disadvantage of this method is that it can limit the use and growth of BICC.
The third method is to have a subscription-based billing model. It is very difficult to implement as it is very difficult to get agreement upfront. Estimating of usage will be difficult.
According to SAS, big organization are more favour towards the cost center funding where SMB is more like to use the Internal billing system.
Finally before ending for this week's lecture, I like to share this actricle with my friends. It contains a lot of information such as Benefits from having a BICC, Staff members in a BICC and Guidance for setting up a BICC.
Happy Learning
Cheers
Chenyuan
Sunday, January 17, 2010
Week 13 Text Mining and Web Mining
What we have learnt this week is totally new to my understanding of data mining and business intelligence.
In this week's lecture Ms Chong has introduced text mining to us. Text mining is performing data mining on an unstructured data. It is important for any data analyze to equip with this skill as 80% of organizational information is unstructured textual forms.
Some example of unstructured textual data will be:
During the training stage, user has to create an attribute dictionary. Attribute will be words appear in the data. Only include words that occur in a minimum frequency(Usually use will determine the minimum frequency).
After creation of the attribute dictionary, user has to remove common words which is useless in for data mining (e.g. the, from , of, a)
Some challenges of Textual data mining
Some common textual data mining will be email document and telephone transcripts. Performing textual data mining on this two information will involves more difficult problems.
Some common problems faced during textual data mining will be spelling and grammar errors in the data. (e.g. customer, cust, customar, csmr). Most of the time data analyze has to group all the different words such as customer, cust, customar under one single attribute before doing any analysis.
Other common problems will be Semantic analysis and Syntax analysis.
How do we apply Text Mining to our daily operation?
Text mining can be apply to stop email spam or phishing though analysis of the document content.
Automatic process a message or email and route the message to the most appropriate department.
Identify most common problems from a help center.
Some time organization will receive hundreds of resumes, text mining can help to filter resumes to open positions.
Text mining can also help us to monitoring the website activities and find out user behaviours when browsing the website. The website admin can use the information to improve the website structure and make it more user friendly.
Just like any other data mining, text mining involve of 7KDD steps.
Like to recommend this page to my friends as it explains Typical Applications for Text Mining in detail and the different approaches to Text Mining.
Web Mining is also covered in this lecture
There are three different types of web mining
The above image shows a Web server log file. This is where most of the information is gather for web usage mining. There are referring pages, IP address and date&time in the log file. With this log file user can create a session file which will track down the page viewed by each individual user.
Web server log file is very useful but there are some disadvantages of it. It is sometime difficult in differentiating individual user sessions. The same host addresses may be access by multiple users. The create a more accurate Session file, data analyst can combine referring pages with the host address to identify each individual user. Cookies will be the best choice if it is been allowed to be placed on the users' computer.
I find this case study of web usage mining very interesting.
In this week's lecture Ms Chong has introduced text mining to us. Text mining is performing data mining on an unstructured data. It is important for any data analyze to equip with this skill as 80% of organizational information is unstructured textual forms.
Some example of unstructured textual data will be:
- Remarks of a call centre officer
- Open question from a survey
- Web sites
- Annual reports
During the training stage, user has to create an attribute dictionary. Attribute will be words appear in the data. Only include words that occur in a minimum frequency(Usually use will determine the minimum frequency).After creation of the attribute dictionary, user has to remove common words which is useless in for data mining (e.g. the, from , of, a)
Some challenges of Textual data mining
Some common textual data mining will be email document and telephone transcripts. Performing textual data mining on this two information will involves more difficult problems.
Some common problems faced during textual data mining will be spelling and grammar errors in the data. (e.g. customer, cust, customar, csmr). Most of the time data analyze has to group all the different words such as customer, cust, customar under one single attribute before doing any analysis.
Other common problems will be Semantic analysis and Syntax analysis.
How do we apply Text Mining to our daily operation?
Text mining can be apply to stop email spam or phishing though analysis of the document content.
Automatic process a message or email and route the message to the most appropriate department.
Identify most common problems from a help center.
Some time organization will receive hundreds of resumes, text mining can help to filter resumes to open positions.
Text mining can also help us to monitoring the website activities and find out user behaviours when browsing the website. The website admin can use the information to improve the website structure and make it more user friendly.
Just like any other data mining, text mining involve of 7KDD steps.
Like to recommend this page to my friends as it explains Typical Applications for Text Mining in detail and the different approaches to Text Mining.
Web Mining is also covered in this lecture
There are three different types of web mining
- Web content mining
- Web structure mining
- Web usage mining
The above image shows a Web server log file. This is where most of the information is gather for web usage mining. There are referring pages, IP address and date&time in the log file. With this log file user can create a session file which will track down the page viewed by each individual user.Web server log file is very useful but there are some disadvantages of it. It is sometime difficult in differentiating individual user sessions. The same host addresses may be access by multiple users. The create a more accurate Session file, data analyst can combine referring pages with the host address to identify each individual user. Cookies will be the best choice if it is been allowed to be placed on the users' computer.
I find this case study of web usage mining very interesting.
Saturday, January 16, 2010
Week12 Regression and Neural Networks
This week we have learnt Regression and Neural Networks(NN). Both technique was taught in the year two. This time the topic is more detail.
The lecture covered three different model
Regression analysis is to relate one or more numeric input attributes to a single numeric output attribute. The focus is on the relationship between a dependent variable and one or more independent variables
The lecture covered three different model
- Linear Regression
A straight line graph - Nonlinear Regression
Usually a Curve - Logistic Regression
Categorical data such as y = o or 1
Output of Regression
R2 is the percentage of difference between line and the actual value. Model is more accurate with a higher R2. To increase R2, data analyst can increase the number of attributes.
Adding R2 will always increase R2 value while Adjusted R2 is to adjusts calculation to penalize for number of independent variables.
There are a lot of good examples of Regression Models from hereThe next thing I like to share is Neural Networks
It is computer that will operate like a human brain. The machines possess simultaneous memory storage and works with ambiguous information.
NN can be used for both supervised and unsupervised learning. Only numeric data can be used for NN. The relationships between input and output are not linear. NN is usually used in areas like approval of loan application and fraud prevention.
There two type of NN
Feed Forward NN
This is an example of NN. Life ins Promo Computed output is the predicted value by the model. The difference between the actual value between the predicted value will be is absolute error. Average absolute error should decrease after each iteration.Kohonen Neural Networks
The other type of NN is Kohonen Neural Networks. It is unsupervised mining. Unlike Feed Forward NN there no hidden layer in it. Instances input into the network are assigned to the most appropriate cluster represented by the nodes at the output layer using the input values and connection weights.
You may find this page interesting as there is historical and background about NN and many good examples.
Friday, December 18, 2009
Week 5 & 6 Information Dashboard Design
In this two week's lecture Ms Chong has gone though Dashboard design in detail. I think the two lectures are helpful in doing our project as it provide us with guidelines to effective Dashboard design.
Dashboards are visual display that fits on a single computer screen and display information needed to achieve specific objectives.
There are three different types of Dashboard.
Dashboards for strategic Purpose
This type of dashboard is usually for the top management where real time data is not required.
I like to recommend this article to my friends. Totally agree to the article and find it very detail and contain a lot of information and facts
Dashboard for Analytical Purpose
The type of dashboard should allow drill down and has a more sophisticated display media when compare with Dashboards for strategic Purpose.
Dashboard for operational purposes
Real time information is presented, able to alert user on abnormalies
Visualization is a very powerful way of presenting solutions and findings. Dashboard is one of the popular tool but creating an effective dashboard is not easy. people have short-term memory. Dashboard should not contain too much information as user may not remember if the information is overwhelmed. 3 to 9 chunks of visual at a time and fit everything into one screen.
Having an effective dashboard allow the user to focus on important data and alert user when necessary.
I have summarized the key points for creating an effective dashboard from the two lectures.
Personally I found this example in lecture week 5 very interesting. It demonstrate the importance of good dashboard design.
As you can see by using the appropriate color it make the job of user much easier. In one glance the user is able to get the needed information which is the number of Fives.
Some tips above for effective dashboard creation is going to be useful when it comes to dashboard design. But I think it is not enough, we must understand the different type of display media. I believe it is very important for us to understand when to use which display media and use them appropriately.
The diagram below demonstrate the importance of choosing the correct display media for different information.
There are two different display media in this graph, one is the table and the other is the line chart. Line chart is a better display media for this case. Patterns and trends is easily spotted. Information is very to understand if we present it in a table format, in fact it is almost unreadable and not useful at all.
Some tips from me for choosing the right display media.
That's all I like to share for Information Dashboard Design.
Happy learning
Cheers~
Dashboards are visual display that fits on a single computer screen and display information needed to achieve specific objectives.
There are three different types of Dashboard.
Dashboards for strategic Purpose
This type of dashboard is usually for the top management where real time data is not required.
I like to recommend this article to my friends. Totally agree to the article and find it very detail and contain a lot of information and facts
Dashboard for Analytical Purpose
The type of dashboard should allow drill down and has a more sophisticated display media when compare with Dashboards for strategic Purpose.
Dashboard for operational purposes
Real time information is presented, able to alert user on abnormalies
Visualization is a very powerful way of presenting solutions and findings. Dashboard is one of the popular tool but creating an effective dashboard is not easy. people have short-term memory. Dashboard should not contain too much information as user may not remember if the information is overwhelmed. 3 to 9 chunks of visual at a time and fit everything into one screen.
Having an effective dashboard allow the user to focus on important data and alert user when necessary.
I have summarized the key points for creating an effective dashboard from the two lectures.
- Using appropriate color, emphasis colors for attention grabbing.
- Pay attention on the 6 principles of visual perception.
- Only include absolute needed information(It is a dashboard not a detail report!)
- Condense carefully so that the meaning don't decrease.
- Use visual display mechanisms that can be easily understood.
- Reduce non-data pixels and enhance data pixels
Personally I found this example in lecture week 5 very interesting. It demonstrate the importance of good dashboard design.
As you can see by using the appropriate color it make the job of user much easier. In one glance the user is able to get the needed information which is the number of Fives.Some tips above for effective dashboard creation is going to be useful when it comes to dashboard design. But I think it is not enough, we must understand the different type of display media. I believe it is very important for us to understand when to use which display media and use them appropriately.
The diagram below demonstrate the importance of choosing the correct display media for different information.
There are two different display media in this graph, one is the table and the other is the line chart. Line chart is a better display media for this case. Patterns and trends is easily spotted. Information is very to understand if we present it in a table format, in fact it is almost unreadable and not useful at all.
Some tips from me for choosing the right display media.
- Try to avoid using thermometer as it does not display the maximum and minimum number.
- Bar and Column graphs is used for comparing and usually involve more than one measure.
- Interval Scale for bar and column chart.
- Stacked bar is good for compare the whole data and see information in a little detail.
- Scatter Plots is good for correlation.
That's all I like to share for Information Dashboard Design.
Happy learning
Cheers~
Wednesday, December 16, 2009
Week 4 Data Warehouse and OLAP
This week, there are a lot of things for me to absorb. The lecture is very packed and almost run out of time :)
In this week, I have learned the differences between data warehouse and operational database. Ms Chong also went though the different type of data warehouse schemas and Dimension tables. Different OLAP servers is also covered in the lecture.
Difference between data warehouse and operational database
Data warehouse is made up of both internal and external data. By extracting all the necessary data from different source and perform ETL process will help you to create a data warehouse.
There are three different design of a data warehouse
Star schema architecture is the simplest data warehouse design. It is a data modeling technique used to map multidimensional decision support data into relational database.
You may check out this link for detail information about star schema. This website explain the structure of a Star Schema and different components in it. I think it will be helpful as there are examples given.
Snowflake Schema
Snowflake schema architecture is a more complex variation of a star schema design. The main difference is that dimensional tables in a snowflake schema are normalized, so they have a typical relational database design.
Snowflake schemas are generally used when a dimensional table becomes very big and when a star schema can’t represent the complexity of a data structure
I like to recommend this blog as it has provide both advantage and disadvantage of Snowflake Schema
Constellation Schema
Constellation Schema is made up of two or more Fact table and it is usually used for bottom up approach. Different Fact table are linked by dimension table.
Slowly changing dimensions are dimensions which change over time. There are three type of slowly changing dimensions
Type 1
Type 2
Type 3
An OLAP (Online analytical processing) cube is a data structure that allows fast analysis of data
Relational OLAP (ROLAP)
Hybrid OLAP (HOLAP)
Cheers~
In this week, I have learned the differences between data warehouse and operational database. Ms Chong also went though the different type of data warehouse schemas and Dimension tables. Different OLAP servers is also covered in the lecture.
Difference between data warehouse and operational database
Data warehouse is made up of both internal and external data. By extracting all the necessary data from different source and perform ETL process will help you to create a data warehouse.There are three different design of a data warehouse
- Star Schema
- Snowflake Schema
- Constellation Schema
Star schema architecture is the simplest data warehouse design. It is a data modeling technique used to map multidimensional decision support data into relational database.
You may check out this link for detail information about star schema. This website explain the structure of a Star Schema and different components in it. I think it will be helpful as there are examples given.
Snowflake Schema
Snowflake schema architecture is a more complex variation of a star schema design. The main difference is that dimensional tables in a snowflake schema are normalized, so they have a typical relational database design.Snowflake schemas are generally used when a dimensional table becomes very big and when a star schema can’t represent the complexity of a data structure
I like to recommend this blog as it has provide both advantage and disadvantage of Snowflake Schema
Constellation Schema
Constellation Schema is made up of two or more Fact table and it is usually used for bottom up approach. Different Fact table are linked by dimension table.
Slowly changing dimensions are dimensions which change over time. There are three type of slowly changing dimensionsType 1
- Overwrites the previous dimension information
- Does not track changes
- Usually results in in accurate analysis
Type 2
- Added four more supplementary attributes to track history of a dimension
- This allow the tracking of entire history but require large data storage
Type 3
- Only implement tow more additional attributes
- Tracking only the current and original state of a dimension member
An OLAP (Online analytical processing) cube is a data structure that allows fast analysis of data
Relational OLAP (ROLAP)
- Real time and flexible than cube
- Query response is generally slower
- Low Storage requirement
- Greater scalability
- It can process faster
- Implement for cubes with frequent use and rapid query response
Hybrid OLAP (HOLAP)
- It contain all the advantage of MOLAP and ROLAP but require large volumes of storage space.
Cheers~
Tuesday, December 15, 2009
Week 3 Developing Dashboards
In week 2's lecture, we have gone through Balanced Scored card. Both Balanced Score and Dashboard are a visualization tool, which will alert the user on business performance.
What are the differences between the two of them?
In week 3 Ms Chong has gone though the difference between Dashboard and Balanced Scorecard.
When comparing with Scorecard, Dashboard is more for the use of supervisors and operators where the information is very detail and the usage is more frequent (Daily) mainly for the monitoring of daily operational jobs. Dashboard is more for the usage of managers and the information shown on Dashboard is more summarized.
In this Lecture we have also learn to develop Dashboard
In a simple explanation, six sigma is to improve customer services and reduce costs.
One of the key principles of Six Sigma is DMAIC
The DMAIC project methodology has five phases:
Under Six Sigma there are two project methodologies
Business Process
Business process is a series of related activities that "flow" though an organisation. Problem facing by the functional working are:
Some advantage of process mapping
I like to recommend the article as it provide a step by step guide of creating a Cross-Functional Flowcharts. It is very simple to understand and straight to the point.
I like to share this video on creating Cross-Functional Flowcharts, I think it will be a good recap on what we have learned during lap lesson.
Thats all I want to share for this week.
Cheers~
What are the differences between the two of them?
In week 3 Ms Chong has gone though the difference between Dashboard and Balanced Scorecard.
When comparing with Scorecard, Dashboard is more for the use of supervisors and operators where the information is very detail and the usage is more frequent (Daily) mainly for the monitoring of daily operational jobs. Dashboard is more for the usage of managers and the information shown on Dashboard is more summarized.
In this Lecture we have also learn to develop Dashboard
- Limit dashboards to critical few KPIs
- Get results quickly
- Dashboards are just part of the culture
In a simple explanation, six sigma is to improve customer services and reduce costs.
One of the key principles of Six Sigma is DMAIC
The DMAIC project methodology has five phases:
- Define the problem, the voice of the customer, and the project goals, specifically.
- Measure key aspects of the current process and collect relevant data.
- Analyze the data to investigate and verify cause-and-effect relationships. Determine what the relationships are, and attempt to ensure that all factors have been considered. Seek out root cause of the defect under investigation.
- Improve or optimize the current process based upon data analysis using techniques such as design of experiments, poka yoke or mistake proofing, and standard work to create a new, future state process. Set up pilot runs to establish process capability.
- Control the future state process to ensure that any deviations from target are corrected before they result in defects. Control systems are implemented such as statistical process control, production boards, and visual workplaces and the process is continuously monitored.
Under Six Sigma there are two project methodologies
- DMAIC is used for projects aimed at improving an existing business process.
- DMADV is used for projects aimed at creating new product or process designs
Business Process
Business process is a series of related activities that "flow" though an organisation. Problem facing by the functional working are:
- Bottlenecks
- Repetition
- Bureaucracy
- No ownership
- Management frustration
- Too many steps
- Customer forgotten
- Opportunities for error or even corruption
Some advantage of process mapping
- Increases understanding of the work process
- Provides understanding of resource allocation
- Documents training procedures
- Tracks workflow
- Increases staff awareness
I like to recommend the article as it provide a step by step guide of creating a Cross-Functional Flowcharts. It is very simple to understand and straight to the point.I like to share this video on creating Cross-Functional Flowcharts, I think it will be a good recap on what we have learned during lap lesson.
Thats all I want to share for this week.
Cheers~
Monday, December 14, 2009
Week 2 Business Performance Management
Business Performance Management (BPM) is an umberlla term covering the business methodologies, metrics, processes and systems used to drive the overall performance of an enterprise.
So What is BPM all about?
BPM encompasses a closed-loop set of processes that link strategy to execution in order to optimize business performance, which is achieved by
BPM Closed-Loop Process
Step 1 Strategizes
The first step in BPM Closed-Loop Process is Strategizes
This is to designs a process according to its business objectives and strategy. Common task will be completed
Executes a process in exact accordance with designs.
An organization’s strategic objectives and key metrics should serve as top-down drivers for the allocation of an organization’s tangible and intangible assets
Step 3 Monitoring
Monitors its business activities closely in real-time.
Step 4 Act & Adjust
Improves processes and analyzes feedback on a continuous basis.
I like to remind all that BPM Closed-Loop Process is a continuous process improvement.
In lecture 2, Ms Phyllis Chong went though in detail of the activities under Monitoring
The tool we are going to use is the balance scorecard.
The 4 Perspectives of Balanced Scorecard
Balanced Scorecard (BSC) methodology is a holistic measurement system for aligning strategies with actions based on four perspectives:
The "financial perspective" encourages the identification of a few relevant high-level financial measures. In particular, designers were encouraged to choose measures that helped inform the answer to the question "How do we look to shareholders?"
The "customer perspective" encourages the identification of measures that answer the question "How do customers see us?"
The "internal business perspective" encourages the identification of measures that answer the question "What must we excel at?"
The "innovation and learning perspective" encourages the identification of measures that answer the question "Can we continue to improve and create value?".
You may like to read out more of Balanced scorecard on this article
Balanced Scorecard contain 5 column and 4 perspective. Before creating a Balanced Scorecard, a Corporate Strategy Map must be done.
Corporate Strategy Map
Before creating a Strategy Map and Balanced Scorecard we must fully understand the organization and the internal process.
First we need to develop a strategic foundation which consisting of
PESTEL
A scan of the external macro-environment in which the firm operates can be expressed in terms of the following factors:
Porter's 5 forces
Five Forces Analysis helps the marketer to contrast a competitive environment. It has similarities with other tools for environmental audit, such as PEST analysis, but tends to focus on the single, stand alone, business or SBU (Strategic Business Unit) rather than a single product or range of products. For example, Dell would analyse the market for Business Computers i.e. one of its SBUs.
Go here for a more detail explanation
SWOT
SWOT analysis is a simple framework for generating strategic alternatives from a situation analysis. It is applicable to either the corporate level or the business unit level and frequently appears in marketing plans. SWOT stands for Strengths, Weaknesses, Opportunities, and Threats.
Interested in finding out more? This link will gave you detail explanation
And finally come out with a Strategic Themes
To sum up for this week's lecture. I have learned about BPM and the development of Balanced Scorecard.
Cheers~
So What is BPM all about?
BPM encompasses a closed-loop set of processes that link strategy to execution in order to optimize business performance, which is achieved by
- Strategize
- Plan
- Monitoring
- Act & Adjust
BPM Closed-Loop Process
Step 1 StrategizesThe first step in BPM Closed-Loop Process is Strategizes
This is to designs a process according to its business objectives and strategy. Common task will be completed
- Conduct a current situation analysis
- Determine the planning horizon
- Conduct an environment scan
- Identify critical success factor
- Complete a gap analysis
- Create a strategic vision
- Develop a business
- Identify strategic objectives and goals
Executes a process in exact accordance with designs.
An organization’s strategic objectives and key metrics should serve as top-down drivers for the allocation of an organization’s tangible and intangible assets
Step 3 Monitoring
Monitors its business activities closely in real-time.
Step 4 Act & Adjust
Improves processes and analyzes feedback on a continuous basis.
I like to remind all that BPM Closed-Loop Process is a continuous process improvement.
In lecture 2, Ms Phyllis Chong went though in detail of the activities under Monitoring
The tool we are going to use is the balance scorecard.
The 4 Perspectives of Balanced Scorecard
Balanced Scorecard (BSC) methodology is a holistic measurement system for aligning strategies with actions based on four perspectives:- Financial perspective
- Customer perspective
- Internal (process) perspective
- Learning and Growth perspective
The "financial perspective" encourages the identification of a few relevant high-level financial measures. In particular, designers were encouraged to choose measures that helped inform the answer to the question "How do we look to shareholders?"
The "customer perspective" encourages the identification of measures that answer the question "How do customers see us?"
The "internal business perspective" encourages the identification of measures that answer the question "What must we excel at?"
The "innovation and learning perspective" encourages the identification of measures that answer the question "Can we continue to improve and create value?".
You may like to read out more of Balanced scorecard on this article
Balanced Scorecard contain 5 column and 4 perspective. Before creating a Balanced Scorecard, a Corporate Strategy Map must be done.
Corporate Strategy Map
Before creating a Strategy Map and Balanced Scorecard we must fully understand the organization and the internal process.
First we need to develop a strategic foundation which consisting of
- Mission
- Vision
- Values
PESTEL
A scan of the external macro-environment in which the firm operates can be expressed in terms of the following factors:
- Political
- Economic
- Social
- Technological
Porter's 5 forces
Five Forces Analysis helps the marketer to contrast a competitive environment. It has similarities with other tools for environmental audit, such as PEST analysis, but tends to focus on the single, stand alone, business or SBU (Strategic Business Unit) rather than a single product or range of products. For example, Dell would analyse the market for Business Computers i.e. one of its SBUs.
Go here for a more detail explanation
SWOT
SWOT analysis is a simple framework for generating strategic alternatives from a situation analysis. It is applicable to either the corporate level or the business unit level and frequently appears in marketing plans. SWOT stands for Strengths, Weaknesses, Opportunities, and Threats.
Interested in finding out more? This link will gave you detail explanation
And finally come out with a Strategic Themes
To sum up for this week's lecture. I have learned about BPM and the development of Balanced Scorecard.
Cheers~
Sunday, December 13, 2009
Week 1 INTRODUCTION TO BUSINESS INTELLIGENCE
BI is about connecting data to effective action by drawing reliable conclusion about current conditions and future events.
It is important for us to understand the need of Business Intelligence before studying it.
Managers need information in order to run an effective and profitable company. Good information lets managers know what types of changes will shift things in the company towards a more profitable end products. Today's economy is driven by information. BI helps the manager to get important information and make necessary decisions.
It is good for any BI student to read this article. It gives student an overview of the subject and I think the point is very simple and straight forward thus easy to understand.
Above Image shows the Virtuous Cycle of BI. Results become more accurate after each cycle.
BI require the support from data warehouse. Database will be created from a data warehouse. There are three types of database.
- Star Schema
- Snowflake Schema
- Constellation Schema
The star schema (sometimes referenced as star join schema) is the simplest style of data warehouse schema. The star schema consists of one tables referencing any number of dimension tables.
Snowflake Schema
A snowflake schema is a logical arrangement of tables in a multidimensional database such that the entity relationship diagram resembles a snowflake in shape. Closely related to the star schema, the snowflake schema is represented by centralized fact tables which are connected to multiple dimensions.
From one dimension can be broken down to many dimension
Constellation Schema
It contain more than one fact tables.
Detail of the three database can be found in this article. Personally I like this article as the color and the example clearly explain the linkage and difference between the three database.
Lecture 1 gives me an overall view of BI and here are some of the important tools of BI: Database, OLAP cube, Scorecards and Dashboards.
Subscribe to:
Posts (Atom)