�ݺ�ߣshows by User: SabirAkhtar

�ݺ�ߣshows by User: SabirAkhtar / http://www.slideshare.net/images/logo.gif �ݺ�ߣshows by User: SabirAkhtar / Sun, 02 Dec 2018 14:55:10 GMT �ݺ�ߣShare feed for �ݺ�ߣshows by User: SabirAkhtar Master Data Management /slideshow/master-data-management-124664443/124664443 masterdatamanagement-181202145510
Data Ownership: Most companies and organizations have this notion that data governance should be taken care of , by the Information Technology department, because IT owns the system which stores the data. The owner of the data is responsible for providing attributes to the data and answerable to any questions regarding data. The people answerable to these kinds of data are generally the ones involved in defining business rules, data cleaning and consolidation.? Data Stewardship:? Data stewards should be favorably those people who are familiar with the data. It is often seen that there is need to deploy several people, to handle and correct data, whereas a single data steward could have done the same job. Since the data being handled involves organizational level data, it is important that there are governance rules for this process.? If there is some certain rule in the data which causes large data volumes to fail, this rule should be fixed while data cleansing. So it is important to take care of the amount of clean data sent to the stewards, since we are not aware of which rules might trigger what amount of data.? Choice of data stewards is again a difficult selection. Data Security:? Although the master data is data on organization level, but there is some confidentiality level linked to it.? Not every employee has the authorization to view its aspects. Security rules can be applied to the data. The various departments in the organization must set different rules to the data they own. They need to grant permissions to these rules , so that the user can view the data. A large company can have data sourced out of many regions. It is to be ensured that they are responsible to correct only their own data.? Data survivorship: There are some guidelines which are set up by data governance. These rules can often change over hthe time according to new data sources being added. The changes made to the data , are communicated to the organization so that data stewards and users can understand the process. So from a data steward's point of view, it is important to apply security rules to the people who are involved in data handling and correction. This is a result of how data governance and data security can be applied while implementing MDM.? ?]]>
Data Ownership: Most companies and organizations have this notion that data governance should be taken care of , by the Information Technology department, because IT owns the system which stores the data. The owner of the data is responsible for providing attributes to the data and answerable to any questions regarding data. The people answerable to these kinds of data are generally the ones involved in defining business rules, data cleaning and consolidation.? Data Stewardship:? Data stewards should be favorably those people who are familiar with the data. It is often seen that there is need to deploy several people, to handle and correct data, whereas a single data steward could have done the same job. Since the data being handled involves organizational level data, it is important that there are governance rules for this process.? If there is some certain rule in the data which causes large data volumes to fail, this rule should be fixed while data cleansing. So it is important to take care of the amount of clean data sent to the stewards, since we are not aware of which rules might trigger what amount of data.? Choice of data stewards is again a difficult selection. Data Security:? Although the master data is data on organization level, but there is some confidentiality level linked to it.? Not every employee has the authorization to view its aspects. Security rules can be applied to the data. The various departments in the organization must set different rules to the data they own. They need to grant permissions to these rules , so that the user can view the data. A large company can have data sourced out of many regions. It is to be ensured that they are responsible to correct only their own data.? Data survivorship: There are some guidelines which are set up by data governance. These rules can often change over hthe time according to new data sources being added. The changes made to the data , are communicated to the organization so that data stewards and users can understand the process. So from a data steward's point of view, it is important to apply security rules to the people who are involved in data handling and correction. This is a result of how data governance and data security can be applied while implementing MDM.? ?]]> Sun, 02 Dec 2018 14:55:10 GMT /slideshow/master-data-management-124664443/124664443 SabirAkhtar@slideshare.net(SabirAkhtar) Master Data Management SabirAkhtar Data Ownership: Most companies and organizations have this notion that data governance should be taken care of , by the Information Technology department, because IT owns the system which stores the data. The owner of the data is responsible for providing attributes to the data and answerable to any questions regarding data. The people answerable to these kinds of data are generally the ones involved in defining business rules, data cleaning and consolidation.? Data Stewardship:? Data stewards should be favorably those people who are familiar with the data. It is often seen that there is need to deploy several people, to handle and correct data, whereas a single data steward could have done the same job. Since the data being handled involves organizational level data, it is important that there are governance rules for this process.? If there is some certain rule in the data which causes large data volumes to fail, this rule should be fixed while data cleansing. So it is important to take care of the amount of clean data sent to the stewards, since we are not aware of which rules might trigger what amount of data.? Choice of data stewards is again a difficult selection. Data Security:? Although the master data is data on organization level, but there is some confidentiality level linked to it.? Not every employee has the authorization to view its aspects. Security rules can be applied to the data. The various departments in the organization must set different rules to the data they own. They need to grant permissions to these rules , so that the user can view the data. A large company can have data sourced out of many regions. It is to be ensured that they are responsible to correct only their own data.? Data survivorship: There are some guidelines which are set up by data governance. These rules can often change over hthe time according to new data sources being added. The changes made to the data , are communicated to the organization so that data stewards and users can understand the process. So from a data steward's point of view, it is important to apply security rules to the people who are involved in data handling and correction. This is a result of how data governance and data security can be applied while implementing MDM.? ? <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/masterdatamanagement-181202145510-thumbnail.jpg?width=120&height=120&fit=bounds" /><br> Data Ownership: Most companies and organizations have this notion that data governance should be taken care of , by the Information Technology department, because IT owns the system which stores the data. The owner of the data is responsible for providing attributes to the data and answerable to any questions regarding data. The people answerable to these kinds of data are generally the ones involved in defining business rules, data cleaning and consolidation.? Data Stewardship:? Data stewards should be favorably those people who are familiar with the data. It is often seen that there is need to deploy several people, to handle and correct data, whereas a single data steward could have done the same job. Since the data being handled involves organizational level data, it is important that there are governance rules for this process.? If there is some certain rule in the data which causes large data volumes to fail, this rule should be fixed while data cleansing. So it is important to take care of the amount of clean data sent to the stewards, since we are not aware of which rules might trigger what amount of data.? Choice of data stewards is again a difficult selection. Data Security:? Although the master data is data on organization level, but there is some confidentiality level linked to it.? Not every employee has the authorization to view its aspects. Security rules can be applied to the data. The various departments in the organization must set different rules to the data they own. They need to grant permissions to these rules , so that the user can view the data. A large company can have data sourced out of many regions. It is to be ensured that they are responsible to correct only their own data.? Data survivorship: There are some guidelines which are set up by data governance. These rules can often change over hthe time according to new data sources being added. The changes made to the data , are communicated to the organization so that data stewards and users can understand the process. So from a data steward's point of view, it is important to apply security rules to the people who are involved in data handling and correction. This is a result of how data governance and data security can be applied while implementing MDM.? ?

Master Data Management from Sabir Akhtar

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0 Big data in retail industry /SabirAkhtar/big-data-in-retail-industry-124663449 bigdatainretailindustry1-181202144310
INTRODUCTION: A lot of people are under the impression that great marketing is an art, but of late, big data has introduced a scientific element to marketing campaigns. Smart marketers are now relying on data more than ever to inform, test, and devise their strategies. And though data and analytics will never replace the creative minds behind the best marketing campaigns, it can definitely provide the marketers with the tools to help perform better. Consumers have 24 hour access to abundant product information which has revolutionized the retail sector. With digital technology becoming ubiquitous, shoppers can make informed decisions using online data and content to discover, compare, and buy products from anywhere and at any time. For brands and retailers, information is also a game-changer. Retail data analytics has the ability to help companies stay at par with the shopping trends by applying customer analytics to uncover, interpret, and act on meaningful data insights. PROBLEM #1: Siloed, Static Customer Views Many retailers still struggle with siloed data – transaction data lives apart from web logs which in turn is separate from CRM data, etc. PROBLEM #2: Time Consuming Vendor & Supply Chain Management Supply chains are already driven by numbers and analytics, but retailers have been slow to embrace the power of realtime analytics and harnessing huge, unstructured data sets. PROBLEM #3: Analysis Based on Historical Data Looking back at shoppers’ past activity often isn’t a good indication of what they will do next. Instead, real-time prediction based of current trends and behaviors from all sources of data is the key. Prediction and Machine Learning in Real Time. PROBLEM #4: Efficiency Although the majority of retailers consider operational efficiencies to be of the utmost importance, less than a third are able to figure out how to achieve them. While “67% of retailers consider overall business operations efficiency to be of high or critical importance, only 27% consider themselves able to manage this well,]]>
INTRODUCTION: A lot of people are under the impression that great marketing is an art, but of late, big data has introduced a scientific element to marketing campaigns. Smart marketers are now relying on data more than ever to inform, test, and devise their strategies. And though data and analytics will never replace the creative minds behind the best marketing campaigns, it can definitely provide the marketers with the tools to help perform better. Consumers have 24 hour access to abundant product information which has revolutionized the retail sector. With digital technology becoming ubiquitous, shoppers can make informed decisions using online data and content to discover, compare, and buy products from anywhere and at any time. For brands and retailers, information is also a game-changer. Retail data analytics has the ability to help companies stay at par with the shopping trends by applying customer analytics to uncover, interpret, and act on meaningful data insights. PROBLEM #1: Siloed, Static Customer Views Many retailers still struggle with siloed data – transaction data lives apart from web logs which in turn is separate from CRM data, etc. PROBLEM #2: Time Consuming Vendor & Supply Chain Management Supply chains are already driven by numbers and analytics, but retailers have been slow to embrace the power of realtime analytics and harnessing huge, unstructured data sets. PROBLEM #3: Analysis Based on Historical Data Looking back at shoppers’ past activity often isn’t a good indication of what they will do next. Instead, real-time prediction based of current trends and behaviors from all sources of data is the key. Prediction and Machine Learning in Real Time. PROBLEM #4: Efficiency Although the majority of retailers consider operational efficiencies to be of the utmost importance, less than a third are able to figure out how to achieve them. While “67% of retailers consider overall business operations efficiency to be of high or critical importance, only 27% consider themselves able to manage this well,]]> Sun, 02 Dec 2018 14:43:09 GMT /SabirAkhtar/big-data-in-retail-industry-124663449 SabirAkhtar@slideshare.net(SabirAkhtar) Big data in retail industry SabirAkhtar INTRODUCTION: A lot of people are under the impression that great marketing is an art, but of late, big data has introduced a scientific element to marketing campaigns. Smart marketers are now relying on data more than ever to inform, test, and devise their strategies. And though data and analytics will never replace the creative minds behind the best marketing campaigns, it can definitely provide the marketers with the tools to help perform better. Consumers have 24 hour access to abundant product information which has revolutionized the retail sector. With digital technology becoming ubiquitous, shoppers can make informed decisions using online data and content to discover, compare, and buy products from anywhere and at any time. For brands and retailers, information is also a game-changer. Retail data analytics has the ability to help companies stay at par with the shopping trends by applying customer analytics to uncover, interpret, and act on meaningful data insights. PROBLEM #1: Siloed, Static Customer Views Many retailers still struggle with siloed data – transaction data lives apart from web logs which in turn is separate from CRM data, etc. PROBLEM #2: Time Consuming Vendor & Supply Chain Management Supply chains are already driven by numbers and analytics, but retailers have been slow to embrace the power of realtime analytics and harnessing huge, unstructured data sets. PROBLEM #3: Analysis Based on Historical Data Looking back at shoppers’ past activity often isn’t a good indication of what they will do next. Instead, real-time prediction based of current trends and behaviors from all sources of data is the key. Prediction and Machine Learning in Real Time. PROBLEM #4: Efficiency Although the majority of retailers consider operational efficiencies to be of the utmost importance, less than a third are able to figure out how to achieve them. While “67% of retailers consider overall business operations efficiency to be of high or critical importance, only 27% consider themselves able to manage this well, <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/bigdatainretailindustry1-181202144310-thumbnail.jpg?width=120&height=120&fit=bounds" /><br> INTRODUCTION: A lot of people are under the impression that great marketing is an art, but of late, big data has introduced a scientific element to marketing campaigns. Smart marketers are now relying on data more than ever to inform, test, and devise their strategies. And though data and analytics will never replace the creative minds behind the best marketing campaigns, it can definitely provide the marketers with the tools to help perform better. Consumers have 24 hour access to abundant product information which has revolutionized the retail sector. With digital technology becoming ubiquitous, shoppers can make informed decisions using online data and content to discover, compare, and buy products from anywhere and at any time. For brands and retailers, information is also a game-changer. Retail data analytics has the ability to help companies stay at par with the shopping trends by applying customer analytics to uncover, interpret, and act on meaningful data insights. PROBLEM #1: Siloed, Static Customer Views Many retailers still struggle with siloed data – transaction data lives apart from web logs which in turn is separate from CRM data, etc. PROBLEM #2: Time Consuming Vendor & Supply Chain Management Supply chains are already driven by numbers and analytics, but retailers have been slow to embrace the power of realtime analytics and harnessing huge, unstructured data sets. PROBLEM #3: Analysis Based on Historical Data Looking back at shoppers’ past activity often isn’t a good indication of what they will do next. Instead, real-time prediction based of current trends and behaviors from all sources of data is the key. Prediction and Machine Learning in Real Time. PROBLEM #4: Efficiency Although the majority of retailers consider operational efficiencies to be of the utmost importance, less than a third are able to figure out how to achieve them. While “67% of retailers consider overall business operations efficiency to be of high or critical importance, only 27% consider themselves able to manage this well,

Big data in retail industry from Sabir Akhtar

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0 https://cdn.slidesharecdn.com/profile-photo-SabirAkhtar-48x48.jpg?cb=1554360364 https://cdn.slidesharecdn.com/ss_thumbnails/masterdatamanagement-181202145510-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/master-data-management-124664443/124664443 Master Data Management https://cdn.slidesharecdn.com/ss_thumbnails/bigdatainretailindustry1-181202144310-thumbnail.jpg?width=320&height=320&fit=bounds SabirAkhtar/big-data-in-retail-industry-124663449 Big data in retail ind...