Lecture-38 Case Study: Agri-Data Warehouse Virtual University of Pakistan Data Warehousing Lecture-38 Case Study: Agri-Data Warehouse Ahsan Abdullah Assoc. Prof. & Head Center for Agro-Informatics Research www.nu.edu.pk/cairindex.asp FAST National University of Computers & Emerging Sciences, Islamabad DWH-Ahsan Abdullah

Step-5: Surprise case Graphics Sucking pests Ball Worm Complex SBW: Spotted Ball Worm ABW: Army Ball Worm PBW: Pink Ball Worm Moore’s law on increase in performance of CPUs and decrease in cost has been surpassed by the increase in storage space and decrease in cost. Secondly as I mentioned earlier the data warehouse has the historical data. And one thing that we have learned by using information is that, “the best predictor of the future is the past”. You use historical data, you then use your insight to how the environment is changing. Because of course you cannot use data extracted from the past. You have to have to bring your insight as to how the environment is changing in order to predict the future accurately. So why would you want data warehouse in an organization? First of all a data warehouse gives a total view of an organization. If you look at the operational system the databases in most environments, the databases are designed around different lines of business. Lets say I am a bank for e.g. a bank will typically have current Accounts. They will have another system for leasing, and another system for managing credit cards and another system for every different kind of business they are in. However, no where they have the total view of the environment from the customers perspective. Because the transaction processing systems are typically designed around functional areas, within a business environment. For good decision making you should be able to integrate the data across the organization. So the idea here is to give the total view of the organization especially from a customer’s perspective within the data warehouse. Consider a bank, which is losing customers, for reasons not known. However, one thing is known that the bank is losing business. Therefore, it is important, actually critical to understand which customers have left you and why they have left. This will give you the ability to predict going forward (in future), which customers will leave you. So we are going to talk about this in the course using data mining algorithms, like clustering, classification, regression analysis etc. This being another example of using historical data to predict the future. So I can predict today, which customers will leave me in the nest 3 months before they even leave. There, can be, and there are whole courses on data mining, but we will just have an applied introduction about data mining in this course. If pest population is low, predator population will also be low, because there will be less “food” for predators to live on i.e. pests. DWH-Ahsan Abdullah 48

Step-6: Data Acquisition & Cleansing Graphics Hand filled pest scouting sheet Moore’s law on increase in performance of CPUs and decrease in cost has been surpassed by the increase in storage space and decrease in cost. Secondly as I mentioned earlier the data warehouse has the historical data. And one thing that we have learned by using information is that, “the best predictor of the future is the past”. You use historical data, you then use your insight to how the environment is changing. Because of course you cannot use data extracted from the past. You have to have to bring your insight as to how the environment is changing in order to predict the future accurately. So why would you want data warehouse in an organization? First of all a data warehouse gives a total view of an organization. If you look at the operational system the databases in most environments, the databases are designed around different lines of business. Lets say I am a bank for e.g. a bank will typically have current Accounts. They will have another system for leasing, and another system for managing credit cards and another system for every different kind of business they are in. However, no where they have the total view of the environment from the customers perspective. Because the transaction processing systems are typically designed around functional areas, within a business environment. For good decision making you should be able to integrate the data across the organization. So the idea here is to give the total view of the organization especially from a customer’s perspective within the data warehouse. Consider a bank, which is losing customers, for reasons not known. However, one thing is known that the bank is losing business. Therefore, it is important, actually critical to understand which customers have left you and why they have left. This will give you the ability to predict going forward (in future), which customers will leave you. So we are going to talk about this in the course using data mining algorithms, like clustering, classification, regression analysis etc. This being another example of using historical data to predict the future. So I can predict today, which customers will leave me in the nest 3 months before they even leave. There, can be, and there are whole courses on data mining, but we will just have an applied introduction about data mining in this course. Typed pest scouting sheet DWH-Ahsan Abdullah 48

Step-6: Issues DWH-Ahsan Abdullah 48 Moore’s law on increase in performance of CPUs and decrease in cost has been surpassed by the increase in storage space and decrease in cost. Secondly as I mentioned earlier the data warehouse has the historical data. And one thing that we have learned by using information is that, “the best predictor of the future is the past”. You use historical data, you then use your insight to how the environment is changing. Because of course you cannot use data extracted from the past. You have to have to bring your insight as to how the environment is changing in order to predict the future accurately. So why would you want data warehouse in an organization? First of all a data warehouse gives a total view of an organization. If you look at the operational system the databases in most environments, the databases are designed around different lines of business. Lets say I am a bank for e.g. a bank will typically have current Accounts. They will have another system for leasing, and another system for managing credit cards and another system for every different kind of business they are in. However, no where they have the total view of the environment from the customers perspective. Because the transaction processing systems are typically designed around functional areas, within a business environment. For good decision making you should be able to integrate the data across the organization. So the idea here is to give the total view of the organization especially from a customer’s perspective within the data warehouse. Consider a bank, which is losing customers, for reasons not known. However, one thing is known that the bank is losing business. Therefore, it is important, actually critical to understand which customers have left you and why they have left. This will give you the ability to predict going forward (in future), which customers will leave you. So we are going to talk about this in the course using data mining algorithms, like clustering, classification, regression analysis etc. This being another example of using historical data to predict the future. So I can predict today, which customers will leave me in the nest 3 months before they even leave. There, can be, and there are whole courses on data mining, but we will just have an applied introduction about data mining in this course. DWH-Ahsan Abdullah 48

Step-6: Why the issues? DWH-Ahsan Abdullah 48 Moore’s law on increase in performance of CPUs and decrease in cost has been surpassed by the increase in storage space and decrease in cost. Secondly as I mentioned earlier the data warehouse has the historical data. And one thing that we have learned by using information is that, “the best predictor of the future is the past”. You use historical data, you then use your insight to how the environment is changing. Because of course you cannot use data extracted from the past. You have to have to bring your insight as to how the environment is changing in order to predict the future accurately. So why would you want data warehouse in an organization? First of all a data warehouse gives a total view of an organization. If you look at the operational system the databases in most environments, the databases are designed around different lines of business. Lets say I am a bank for e.g. a bank will typically have current Accounts. They will have another system for leasing, and another system for managing credit cards and another system for every different kind of business they are in. However, no where they have the total view of the environment from the customers perspective. Because the transaction processing systems are typically designed around functional areas, within a business environment. For good decision making you should be able to integrate the data across the organization. So the idea here is to give the total view of the organization especially from a customer’s perspective within the data warehouse. Consider a bank, which is losing customers, for reasons not known. However, one thing is known that the bank is losing business. Therefore, it is important, actually critical to understand which customers have left you and why they have left. This will give you the ability to predict going forward (in future), which customers will leave you. So we are going to talk about this in the course using data mining algorithms, like clustering, classification, regression analysis etc. This being another example of using historical data to predict the future. So I can predict today, which customers will leave me in the nest 3 months before they even leave. There, can be, and there are whole courses on data mining, but we will just have an applied introduction about data mining in this course. DWH-Ahsan Abdullah 48

Step-7: Transform, Transport & Populate Moore’s law on increase in performance of CPUs and decrease in cost has been surpassed by the increase in storage space and decrease in cost. Secondly as I mentioned earlier the data warehouse has the historical data. And one thing that we have learned by using information is that, “the best predictor of the future is the past”. You use historical data, you then use your insight to how the environment is changing. Because of course you cannot use data extracted from the past. You have to have to bring your insight as to how the environment is changing in order to predict the future accurately. So why would you want data warehouse in an organization? First of all a data warehouse gives a total view of an organization. If you look at the operational system the databases in most environments, the databases are designed around different lines of business. Lets say I am a bank for e.g. a bank will typically have current Accounts. They will have another system for leasing, and another system for managing credit cards and another system for every different kind of business they are in. However, no where they have the total view of the environment from the customers perspective. Because the transaction processing systems are typically designed around functional areas, within a business environment. For good decision making you should be able to integrate the data across the organization. So the idea here is to give the total view of the organization especially from a customer’s perspective within the data warehouse. Consider a bank, which is losing customers, for reasons not known. However, one thing is known that the bank is losing business. Therefore, it is important, actually critical to understand which customers have left you and why they have left. This will give you the ability to predict going forward (in future), which customers will leave you. So we are going to talk about this in the course using data mining algorithms, like clustering, classification, regression analysis etc. This being another example of using historical data to predict the future. So I can predict today, which customers will leave me in the nest 3 months before they even leave. There, can be, and there are whole courses on data mining, but we will just have an applied introduction about data mining in this course. DWH-Ahsan Abdullah 48

Motivation For Transformation Graphics Moore’s law on increase in performance of CPUs and decrease in cost has been surpassed by the increase in storage space and decrease in cost. Secondly as I mentioned earlier the data warehouse has the historical data. And one thing that we have learned by using information is that, “the best predictor of the future is the past”. You use historical data, you then use your insight to how the environment is changing. Because of course you cannot use data extracted from the past. You have to have to bring your insight as to how the environment is changing in order to predict the future accurately. So why would you want data warehouse in an organization? First of all a data warehouse gives a total view of an organization. If you look at the operational system the databases in most environments, the databases are designed around different lines of business. Lets say I am a bank for e.g. a bank will typically have current Accounts. They will have another system for leasing, and another system for managing credit cards and another system for every different kind of business they are in. However, no where they have the total view of the environment from the customers perspective. Because the transaction processing systems are typically designed around functional areas, within a business environment. For good decision making you should be able to integrate the data across the organization. So the idea here is to give the total view of the organization especially from a customer’s perspective within the data warehouse. Consider a bank, which is losing customers, for reasons not known. However, one thing is known that the bank is losing business. Therefore, it is important, actually critical to understand which customers have left you and why they have left. This will give you the ability to predict going forward (in future), which customers will leave you. So we are going to talk about this in the course using data mining algorithms, like clustering, classification, regression analysis etc. This being another example of using historical data to predict the future. So I can predict today, which customers will leave me in the nest 3 months before they even leave. There, can be, and there are whole courses on data mining, but we will just have an applied introduction about data mining in this course. DWH-Ahsan Abdullah 48

Step-7: Resolving the issue Graphics Moore’s law on increase in performance of CPUs and decrease in cost has been surpassed by the increase in storage space and decrease in cost. Secondly as I mentioned earlier the data warehouse has the historical data. And one thing that we have learned by using information is that, “the best predictor of the future is the past”. You use historical data, you then use your insight to how the environment is changing. Because of course you cannot use data extracted from the past. You have to have to bring your insight as to how the environment is changing in order to predict the future accurately. So why would you want data warehouse in an organization? First of all a data warehouse gives a total view of an organization. If you look at the operational system the databases in most environments, the databases are designed around different lines of business. Lets say I am a bank for e.g. a bank will typically have current Accounts. They will have another system for leasing, and another system for managing credit cards and another system for every different kind of business they are in. However, no where they have the total view of the environment from the customers perspective. Because the transaction processing systems are typically designed around functional areas, within a business environment. For good decision making you should be able to integrate the data across the organization. So the idea here is to give the total view of the organization especially from a customer’s perspective within the data warehouse. Consider a bank, which is losing customers, for reasons not known. However, one thing is known that the bank is losing business. Therefore, it is important, actually critical to understand which customers have left you and why they have left. This will give you the ability to predict going forward (in future), which customers will leave you. So we are going to talk about this in the course using data mining algorithms, like clustering, classification, regression analysis etc. This being another example of using historical data to predict the future. So I can predict today, which customers will leave me in the nest 3 months before they even leave. There, can be, and there are whole courses on data mining, but we will just have an applied introduction about data mining in this course. DWH-Ahsan Abdullah 48

Step-8: Middleware Connectivity Moore’s law on increase in performance of CPUs and decrease in cost has been surpassed by the increase in storage space and decrease in cost. Secondly as I mentioned earlier the data warehouse has the historical data. And one thing that we have learned by using information is that, “the best predictor of the future is the past”. You use historical data, you then use your insight to how the environment is changing. Because of course you cannot use data extracted from the past. You have to have to bring your insight as to how the environment is changing in order to predict the future accurately. So why would you want data warehouse in an organization? First of all a data warehouse gives a total view of an organization. If you look at the operational system the databases in most environments, the databases are designed around different lines of business. Lets say I am a bank for e.g. a bank will typically have current Accounts. They will have another system for leasing, and another system for managing credit cards and another system for every different kind of business they are in. However, no where they have the total view of the environment from the customers perspective. Because the transaction processing systems are typically designed around functional areas, within a business environment. For good decision making you should be able to integrate the data across the organization. So the idea here is to give the total view of the organization especially from a customer’s perspective within the data warehouse. Consider a bank, which is losing customers, for reasons not known. However, one thing is known that the bank is losing business. Therefore, it is important, actually critical to understand which customers have left you and why they have left. This will give you the ability to predict going forward (in future), which customers will leave you. So we are going to talk about this in the course using data mining algorithms, like clustering, classification, regression analysis etc. This being another example of using historical data to predict the future. So I can predict today, which customers will leave me in the nest 3 months before they even leave. There, can be, and there are whole courses on data mining, but we will just have an applied introduction about data mining in this course. DWH-Ahsan Abdullah 48

Step-9-11: Prototyping, Querying & Reporting SELECT Date_of_Visit, AVG(Predators), …………………………AVG(Dose1+Dose2+Dose3+Dose4) FROM Scouting_Data WHERE Date_of_Visit < #12/31/2001# and predators > 0 GROUP BY Date_of_Visit; Moore’s law on increase in performance of CPUs and decrease in cost has been surpassed by the increase in storage space and decrease in cost. Secondly as I mentioned earlier the data warehouse has the historical data. And one thing that we have learned by using information is that, “the best predictor of the future is the past”. You use historical data, you then use your insight to how the environment is changing. Because of course you cannot use data extracted from the past. You have to have to bring your insight as to how the environment is changing in order to predict the future accurately. So why would you want data warehouse in an organization? First of all a data warehouse gives a total view of an organization. If you look at the operational system the databases in most environments, the databases are designed around different lines of business. Lets say I am a bank for e.g. a bank will typically have current Accounts. They will have another system for leasing, and another system for managing credit cards and another system for every different kind of business they are in. However, no where they have the total view of the environment from the customers perspective. Because the transaction processing systems are typically designed around functional areas, within a business environment. For good decision making you should be able to integrate the data across the organization. So the idea here is to give the total view of the organization especially from a customer’s perspective within the data warehouse. Consider a bank, which is losing customers, for reasons not known. However, one thing is known that the bank is losing business. Therefore, it is important, actually critical to understand which customers have left you and why they have left. This will give you the ability to predict going forward (in future), which customers will leave you. So we are going to talk about this in the course using data mining algorithms, like clustering, classification, regression analysis etc. This being another example of using historical data to predict the future. So I can predict today, which customers will leave me in the nest 3 months before they even leave. There, can be, and there are whole courses on data mining, but we will just have an applied introduction about data mining in this course. Graphics DWH-Ahsan Abdullah 48

Step-12: Deployment & System Management Moore’s law on increase in performance of CPUs and decrease in cost has been surpassed by the increase in storage space and decrease in cost. Secondly as I mentioned earlier the data warehouse has the historical data. And one thing that we have learned by using information is that, “the best predictor of the future is the past”. You use historical data, you then use your insight to how the environment is changing. Because of course you cannot use data extracted from the past. You have to have to bring your insight as to how the environment is changing in order to predict the future accurately. So why would you want data warehouse in an organization? First of all a data warehouse gives a total view of an organization. If you look at the operational system the databases in most environments, the databases are designed around different lines of business. Lets say I am a bank for e.g. a bank will typically have current Accounts. They will have another system for leasing, and another system for managing credit cards and another system for every different kind of business they are in. However, no where they have the total view of the environment from the customers perspective. Because the transaction processing systems are typically designed around functional areas, within a business environment. For good decision making you should be able to integrate the data across the organization. So the idea here is to give the total view of the organization especially from a customer’s perspective within the data warehouse. Consider a bank, which is losing customers, for reasons not known. However, one thing is known that the bank is losing business. Therefore, it is important, actually critical to understand which customers have left you and why they have left. This will give you the ability to predict going forward (in future), which customers will leave you. So we are going to talk about this in the course using data mining algorithms, like clustering, classification, regression analysis etc. This being another example of using historical data to predict the future. So I can predict today, which customers will leave me in the nest 3 months before they even leave. There, can be, and there are whole courses on data mining, but we will just have an applied introduction about data mining in this course. DWH-Ahsan Abdullah 48

Agri-DSS usage: Data Validation Moore’s law on increase in performance of CPUs and decrease in cost has been surpassed by the increase in storage space and decrease in cost. Secondly as I mentioned earlier the data warehouse has the historical data. And one thing that we have learned by using information is that, “the best predictor of the future is the past”. You use historical data, you then use your insight to how the environment is changing. Because of course you cannot use data extracted from the past. You have to have to bring your insight as to how the environment is changing in order to predict the future accurately. So why would you want data warehouse in an organization? First of all a data warehouse gives a total view of an organization. If you look at the operational system the databases in most environments, the databases are designed around different lines of business. Lets say I am a bank for e.g. a bank will typically have current Accounts. They will have another system for leasing, and another system for managing credit cards and another system for every different kind of business they are in. However, no where they have the total view of the environment from the customers perspective. Because the transaction processing systems are typically designed around functional areas, within a business environment. For good decision making you should be able to integrate the data across the organization. So the idea here is to give the total view of the organization especially from a customer’s perspective within the data warehouse. Consider a bank, which is losing customers, for reasons not known. However, one thing is known that the bank is losing business. Therefore, it is important, actually critical to understand which customers have left you and why they have left. This will give you the ability to predict going forward (in future), which customers will leave you. So we are going to talk about this in the course using data mining algorithms, like clustering, classification, regression analysis etc. This being another example of using historical data to predict the future. So I can predict today, which customers will leave me in the nest 3 months before they even leave. There, can be, and there are whole courses on data mining, but we will just have an applied introduction about data mining in this course. DWH-Ahsan Abdullah 48

Agri-DSS usage: Data Validation Graph Moore’s law on increase in performance of CPUs and decrease in cost has been surpassed by the increase in storage space and decrease in cost. Secondly as I mentioned earlier the data warehouse has the historical data. And one thing that we have learned by using information is that, “the best predictor of the future is the past”. You use historical data, you then use your insight to how the environment is changing. Because of course you cannot use data extracted from the past. You have to have to bring your insight as to how the environment is changing in order to predict the future accurately. So why would you want data warehouse in an organization? First of all a data warehouse gives a total view of an organization. If you look at the operational system the databases in most environments, the databases are designed around different lines of business. Lets say I am a bank for e.g. a bank will typically have current Accounts. They will have another system for leasing, and another system for managing credit cards and another system for every different kind of business they are in. However, no where they have the total view of the environment from the customers perspective. Because the transaction processing systems are typically designed around functional areas, within a business environment. For good decision making you should be able to integrate the data across the organization. So the idea here is to give the total view of the organization especially from a customer’s perspective within the data warehouse. Consider a bank, which is losing customers, for reasons not known. However, one thing is known that the bank is losing business. Therefore, it is important, actually critical to understand which customers have left you and why they have left. This will give you the ability to predict going forward (in future), which customers will leave you. So we are going to talk about this in the course using data mining algorithms, like clustering, classification, regression analysis etc. This being another example of using historical data to predict the future. So I can predict today, which customers will leave me in the nest 3 months before they even leave. There, can be, and there are whole courses on data mining, but we will just have an applied introduction about data mining in this course. ALL goes to graphics DWH-Ahsan Abdullah 48

Agri-DSS usage: FAO report Moore’s law on increase in performance of CPUs and decrease in cost has been surpassed by the increase in storage space and decrease in cost. Secondly as I mentioned earlier the data warehouse has the historical data. And one thing that we have learned by using information is that, “the best predictor of the future is the past”. You use historical data, you then use your insight to how the environment is changing. Because of course you cannot use data extracted from the past. You have to have to bring your insight as to how the environment is changing in order to predict the future accurately. So why would you want data warehouse in an organization? First of all a data warehouse gives a total view of an organization. If you look at the operational system the databases in most environments, the databases are designed around different lines of business. Lets say I am a bank for e.g. a bank will typically have current Accounts. They will have another system for leasing, and another system for managing credit cards and another system for every different kind of business they are in. However, no where they have the total view of the environment from the customers perspective. Because the transaction processing systems are typically designed around functional areas, within a business environment. For good decision making you should be able to integrate the data across the organization. So the idea here is to give the total view of the organization especially from a customer’s perspective within the data warehouse. Consider a bank, which is losing customers, for reasons not known. However, one thing is known that the bank is losing business. Therefore, it is important, actually critical to understand which customers have left you and why they have left. This will give you the ability to predict going forward (in future), which customers will leave you. So we are going to talk about this in the course using data mining algorithms, like clustering, classification, regression analysis etc. This being another example of using historical data to predict the future. So I can predict today, which customers will leave me in the nest 3 months before they even leave. There, can be, and there are whole courses on data mining, but we will just have an applied introduction about data mining in this course. DWH-Ahsan Abdullah 48

Graph Using pesticides to increase yield. Graphics Why negative correlation between yield and pesticides? Using pesticides to increase yield. DWH-Ahsan Abdullah

Agri-DSS usage: Spray Dates Moore’s law on increase in performance of CPUs and decrease in cost has been surpassed by the increase in storage space and decrease in cost. Secondly as I mentioned earlier the data warehouse has the historical data. And one thing that we have learned by using information is that, “the best predictor of the future is the past”. You use historical data, you then use your insight to how the environment is changing. Because of course you cannot use data extracted from the past. You have to have to bring your insight as to how the environment is changing in order to predict the future accurately. So why would you want data warehouse in an organization? First of all a data warehouse gives a total view of an organization. If you look at the operational system the databases in most environments, the databases are designed around different lines of business. Lets say I am a bank for e.g. a bank will typically have current Accounts. They will have another system for leasing, and another system for managing credit cards and another system for every different kind of business they are in. However, no where they have the total view of the environment from the customers perspective. Because the transaction processing systems are typically designed around functional areas, within a business environment. For good decision making you should be able to integrate the data across the organization. So the idea here is to give the total view of the organization especially from a customer’s perspective within the data warehouse. Consider a bank, which is losing customers, for reasons not known. However, one thing is known that the bank is losing business. Therefore, it is important, actually critical to understand which customers have left you and why they have left. This will give you the ability to predict going forward (in future), which customers will leave you. So we are going to talk about this in the course using data mining algorithms, like clustering, classification, regression analysis etc. This being another example of using historical data to predict the future. So I can predict today, which customers will leave me in the nest 3 months before they even leave. There, can be, and there are whole courses on data mining, but we will just have an applied introduction about data mining in this course. DWH-Ahsan Abdullah 48

Agri-DSS usage: Spray Dates Graph Moore’s law on increase in performance of CPUs and decrease in cost has been surpassed by the increase in storage space and decrease in cost. Secondly as I mentioned earlier the data warehouse has the historical data. And one thing that we have learned by using information is that, “the best predictor of the future is the past”. You use historical data, you then use your insight to how the environment is changing. Because of course you cannot use data extracted from the past. You have to have to bring your insight as to how the environment is changing in order to predict the future accurately. So why would you want data warehouse in an organization? First of all a data warehouse gives a total view of an organization. If you look at the operational system the databases in most environments, the databases are designed around different lines of business. Lets say I am a bank for e.g. a bank will typically have current Accounts. They will have another system for leasing, and another system for managing credit cards and another system for every different kind of business they are in. However, no where they have the total view of the environment from the customers perspective. Because the transaction processing systems are typically designed around functional areas, within a business environment. For good decision making you should be able to integrate the data across the organization. So the idea here is to give the total view of the organization especially from a customer’s perspective within the data warehouse. Consider a bank, which is losing customers, for reasons not known. However, one thing is known that the bank is losing business. Therefore, it is important, actually critical to understand which customers have left you and why they have left. This will give you the ability to predict going forward (in future), which customers will leave you. So we are going to talk about this in the course using data mining algorithms, like clustering, classification, regression analysis etc. This being another example of using historical data to predict the future. So I can predict today, which customers will leave me in the nest 3 months before they even leave. There, can be, and there are whole courses on data mining, but we will just have an applied introduction about data mining in this course. DWH-Ahsan Abdullah 48

Agri-DSS usage: Explaining Findings Moore’s law on increase in performance of CPUs and decrease in cost has been surpassed by the increase in storage space and decrease in cost. Secondly as I mentioned earlier the data warehouse has the historical data. And one thing that we have learned by using information is that, “the best predictor of the future is the past”. You use historical data, you then use your insight to how the environment is changing. Because of course you cannot use data extracted from the past. You have to have to bring your insight as to how the environment is changing in order to predict the future accurately. So why would you want data warehouse in an organization? First of all a data warehouse gives a total view of an organization. If you look at the operational system the databases in most environments, the databases are designed around different lines of business. Lets say I am a bank for e.g. a bank will typically have current Accounts. They will have another system for leasing, and another system for managing credit cards and another system for every different kind of business they are in. However, no where they have the total view of the environment from the customers perspective. Because the transaction processing systems are typically designed around functional areas, within a business environment. For good decision making you should be able to integrate the data across the organization. So the idea here is to give the total view of the organization especially from a customer’s perspective within the data warehouse. Consider a bank, which is losing customers, for reasons not known. However, one thing is known that the bank is losing business. Therefore, it is important, actually critical to understand which customers have left you and why they have left. This will give you the ability to predict going forward (in future), which customers will leave you. So we are going to talk about this in the course using data mining algorithms, like clustering, classification, regression analysis etc. This being another example of using historical data to predict the future. So I can predict today, which customers will leave me in the nest 3 months before they even leave. There, can be, and there are whole courses on data mining, but we will just have an applied introduction about data mining in this course. DWH-Ahsan Abdullah 48

Agri-DSS usage: Sowing Dates Moore’s law on increase in performance of CPUs and decrease in cost has been surpassed by the increase in storage space and decrease in cost. Secondly as I mentioned earlier the data warehouse has the historical data. And one thing that we have learned by using information is that, “the best predictor of the future is the past”. You use historical data, you then use your insight to how the environment is changing. Because of course you cannot use data extracted from the past. You have to have to bring your insight as to how the environment is changing in order to predict the future accurately. So why would you want data warehouse in an organization? First of all a data warehouse gives a total view of an organization. If you look at the operational system the databases in most environments, the databases are designed around different lines of business. Lets say I am a bank for e.g. a bank will typically have current Accounts. They will have another system for leasing, and another system for managing credit cards and another system for every different kind of business they are in. However, no where they have the total view of the environment from the customers perspective. Because the transaction processing systems are typically designed around functional areas, within a business environment. For good decision making you should be able to integrate the data across the organization. So the idea here is to give the total view of the organization especially from a customer’s perspective within the data warehouse. Consider a bank, which is losing customers, for reasons not known. However, one thing is known that the bank is losing business. Therefore, it is important, actually critical to understand which customers have left you and why they have left. This will give you the ability to predict going forward (in future), which customers will leave you. So we are going to talk about this in the course using data mining algorithms, like clustering, classification, regression analysis etc. This being another example of using historical data to predict the future. So I can predict today, which customers will leave me in the nest 3 months before they even leave. There, can be, and there are whole courses on data mining, but we will just have an applied introduction about data mining in this course. Graphics DWH-Ahsan Abdullah 48

Conclusions & Lessons ETL is a big issue. Each farmer is repeatedly visited There is a skewness in the scouting data. Decision-making goes all the way “down” to the extension level. Moore’s law on increase in performance of CPUs and decrease in cost has been surpassed by the increase in storage space and decrease in cost. Secondly as I mentioned earlier the data warehouse has the historical data. And one thing that we have learned by using information is that, “the best predictor of the future is the past”. You use historical data, you then use your insight to how the environment is changing. Because of course you cannot use data extracted from the past. You have to have to bring your insight as to how the environment is changing in order to predict the future accurately. So why would you want data warehouse in an organization? First of all a data warehouse gives a total view of an organization. If you look at the operational system the databases in most environments, the databases are designed around different lines of business. Lets say I am a bank for e.g. a bank will typically have current Accounts. They will have another system for leasing, and another system for managing credit cards and another system for every different kind of business they are in. However, no where they have the total view of the environment from the customers perspective. Because the transaction processing systems are typically designed around functional areas, within a business environment. For good decision making you should be able to integrate the data across the organization. So the idea here is to give the total view of the organization especially from a customer’s perspective within the data warehouse. Consider a bank, which is losing customers, for reasons not known. However, one thing is known that the bank is losing business. Therefore, it is important, actually critical to understand which customers have left you and why they have left. This will give you the ability to predict going forward (in future), which customers will leave you. So we are going to talk about this in the course using data mining algorithms, like clustering, classification, regression analysis etc. This being another example of using historical data to predict the future. So I can predict today, which customers will leave me in the nest 3 months before they even leave. There, can be, and there are whole courses on data mining, but we will just have an applied introduction about data mining in this course. All goes to graphics DWH-Ahsan Abdullah 48