Business Performance (BPI) Network: Reports

Report

Id: 68
Date
Title: Doing More With Data
Summary: To elevate customer experience, the three critical functions as it relates to customer engagement: marketing, commerce and supply chain, must work together to elevate data strategies.
Author: The CMO Council and BPI Network
Body: As companies seek to elevate customer experience, they increasingly look to data to better engage with customers and identify new opportunities. Yet previous BPI Network and CMO Council research highlights that many executives admit their organizations could do more to transform the data supply chain to allow them to do more with internal data assets. To ignite real change, the three critical functions as it relates to customer engagement: marketing, commerce and supply chain, must work together to elevate data strategies. The report highlights the challenges facing each of these functions when it comes to how data strategies are being challenged and transformed. It also highlights how each individual function aligns, and sometimes clashes, in their perceptions of where and how each function must collaborate around the customer. The research stems from a survey of 165 marketing, commerce and supply chain executives, as well as 12 deep-dive interviews with executives who reveal the ways they are rethinking their engagement strategies through smarter use of data insights. Those executives hail from brands like AT&T, The Body Shop, Samsonite, REI, Ryder, TD Bank, Cabela’s and Nordstrom. Areas of exploration covered in this research include: <ul> <li>The roles each of these functions play when it comes to developing, delivering and continuously improving upon profitable customer engagements</li> <li>The level of cross-functional alignment across all customer experience stakeholders, and ways organizations are ensuring cross-functional participation across the data value chain</li> <li>The types of data sources that functional groups are able to extract important insights from, and those sources of data that are still out of reach</li> <li>The greatest obstacles organizations face as they attempt to realize greater value from internal data assets, and what holds these organizations back from realizing the full potential of data</li> <li>The technology deployment roadmaps organizations are seeking to implement over the next year to improve data utilization across the organization</li> </ul> A free executive summary is available for download. The full report can be purchased for $199. Please follow the links below to download.
Image: IBM-Report-Cover-BPI.png
Url: https://bpinetwork-org.tradepub.com/free/w_bpin74/prgm.cgi?a=1
Created: 2018-07-09 08:06:02
Modified: 2021-11-05 16:42:35

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Related Programs

Id	Title	Subtitle	Description	Date	Created	Modified	Actions
31	Bringing Simplicity To Multi-Cloud Complexity	Advancing Data Connectivity, Logistics and Value	Overview The Business Performance Innovation (BPI) Network, in partnership with DBM Cloud Systems and the CMO Council, is undertaking research into the forces, factors, challenges and requirements driving the need for improved data logistics in today’s multi-cloud world. Global enterprises are seeking to integrate vast repositories of data across multiple public and private clouds and on-premise systems to extract more value, use and return on these assets. In particular, this relates to decision-support analytics, product and service delivery, customer intelligence and personalized engagement, operational improvement, business compliance, and continuity. While multi-cloud and hybrid cloud environments provide major advantages, they also increase the complexity of managing, translating, processing and moving data. Chief Data Officers, infrastructure IT executives and others in the enterprise are now seeking new and innovative ways to gain greater control, visibility, performance and efficiency in the way they manage data to optimize its value. The program will engage data and IT executives from major companies, as well as leading system integrators, consulting firms and solution providers to understand needs, requirements and best practices in optimizing data economics, protection and logistics in today’s multi-cloud, mega-data world. Background The growing importance of the Chief Data Officer underscores widespread recognition of the essential role and value of data and analytics in the modern, agile customer-centric enterprise. This is further compounded by the significant cost, challenge and complexity of unifying, migrating, replicating, protecting, storing and governing data “ponds, lakes and dams” in today’s multi-cloud and hybrid cloud computing environments. Data has become one of the chief engines of value creation. Its role in improving analytical decision making, enhancing our ability to understand, reach and engage customers, and increasing the value of both existing and new products and services has made data a highly valued currency and asset base for enterprises operating digitally across data borders, ramparts and fences. Fueled by an explosive proliferation of new data sources—from mobile computing, social media and rich media applications and services, to the advent of the Internet of Things, which will generate some 50 million connected sensors by the end of 2020—the amount and variety of data being stored is growing exponentially. According Seagate Technology, the amount of data generated worldwide will increase to 163 zettabytes by 2025 from 12 zettabytes in 2015. In today’s multi-cloud, mega-data world, better Data Logistics and Performance Management (DLPM) has become essential to competing in the Data Economy. According to 451 Research, some 80 percent of companies are now utilizing two or more clouds. Three-quarters of companies say they are pursuing a hybrid/multi-cloud strategy. In this highly distributed compute environment, the requirements to understand where data resides across multiple public clouds and on-premise systems and cost-effectively store, protect and move data to its optimal location becomes critical. With the coming rise of edge computing and micro-data centers designed to bring data and applications closer to users to reduce latency and thereby improve application and workload performance, data management and logistics become even more complex. The edge is creating even more data, with increased data volumes having to be transmitted across networks and between clouds. Object-based data storage is now the dominant storage architecture globally, particularly in the cloud, and will be used to store 85 percent of the world’s data by 2025. Developed initially to handle the explosive growth of unstructured data, such as video, images, web pages, log files and IoT data, object storage saves data along with associated metadata in blocks as objects that can contain multiple information formats. It has a variety of advantages over file and block storage, including massive scalability, greater flexibility via metadata for analytics, better governance, faster retrieval, and improved cost efficiencies. In addition, continued technical advances are improving both the performance and the applicability of object storage for new use cases. A major challenge, however, is that the object-based storage systems used my various cloud providers are all different, making data synchronization and translation a challenge. The Data Logistics Mandate In this new world, Chief Data Officers and CIOs must optimize how they manage vast volumes of data across multiple clouds and data environments through better data logistics. There are more data silos, greater distribution of data, vast increases in volume, and a growing demand to understand, protect and exploit data for business growth and optimization while holding down costs. Among those logistical challenges are the need to keep track of where data resides, and cost-effectively synchronize, replicate and move data to optimize value, efficiency, security and governance based on cost differences, processing and analytics capabilities, performance advantages, and regulatory requirements. Organizations need to be able to be able to identify what data needs to be replicated and effectively transmitted and synchronized across different clouds and on-premise locations to improve the availability, security and value of data. For example, organizations increasingly need to move data to specific service providers to conduct analytics or take advantage of specific vendor technologies, lower processing and storage costs, or meet geographic requirements. Particularly when data replication and synchronization is a continuous activity, organizations need to automate this process based on established rules and policies to improve cost efficiency, eliminate errors and reduce management complexity. For object storage data, the capacity to fully utilize metadata to identify location, type and value is now a critical aspect of data logistics, as is the ability to transfer data with a minimum amount of performance degradation and data corruption. New solutions that can handle replication outside of the data path are delivering improved visibility, performance and data quality. Advancing data logistics is a major imperative for today’s data-driven organizations and their chief data officers. The mandate to improve data availability, performance, value and cost-efficiency will be a critical competitive differentiator in a multi-cloud, mega-data world. To get involved in this initiative, please reach out to Dave Murray.	2020-08-12	2020-08-11 21:56:11	2020-08-14 18:49:19	View Edit Delete

Title

Subtitle

Description

Date

Created

Modified

Actions

Bringing Simplicity To Multi-Cloud Complexity

Advancing Data Connectivity, Logistics and Value

Overview

The Business Performance Innovation (BPI) Network, in partnership with DBM Cloud Systems and the CMO Council, is undertaking research into the forces, factors, challenges and requirements driving the need for improved data logistics in today’s multi-cloud world. Global enterprises are seeking to integrate vast repositories of data across multiple public and private clouds and on-premise systems to extract more value, use and return on these assets. In particular, this relates to decision-support analytics, product and service delivery, customer intelligence and personalized engagement, operational improvement, business compliance, and continuity.

While multi-cloud and hybrid cloud environments provide major advantages, they also increase the complexity of managing, translating, processing and moving data. Chief Data Officers, infrastructure IT executives and others in the enterprise are now seeking new and innovative ways to gain greater control, visibility, performance and efficiency in the way they manage data to optimize its value.

The program will engage data and IT executives from major companies, as well as leading system integrators, consulting firms and solution providers to understand needs, requirements and best practices in optimizing data economics, protection and logistics in today’s multi-cloud, mega-data world.

Background

The growing importance of the Chief Data Officer underscores widespread recognition of the essential role and value of data and analytics in the modern, agile customer-centric enterprise. This is further compounded by the significant cost, challenge and complexity of unifying, migrating, replicating, protecting, storing and governing data “ponds, lakes and dams” in today’s multi-cloud and hybrid cloud computing environments.

Data has become one of the chief engines of value creation. Its role in improving analytical decision making, enhancing our ability to understand, reach and engage customers, and increasing the value of both existing and new products and services has made data a highly valued currency and asset base for enterprises operating digitally across data borders, ramparts and fences.

Fueled by an explosive proliferation of new data sources—from mobile computing, social media and rich media applications and services, to the advent of the Internet of Things, which will generate some 50 million connected sensors by the end of 2020—the amount and variety of data being stored is growing exponentially. According Seagate Technology, the amount of data generated worldwide will increase to 163 zettabytes by 2025 from 12 zettabytes in 2015.

In today’s multi-cloud, mega-data world, better Data Logistics and Performance Management (DLPM) has become essential to competing in the Data Economy. According to 451 Research, some 80 percent of companies are now utilizing two or more clouds. Three-quarters of companies say they are pursuing a hybrid/multi-cloud strategy.

In this highly distributed compute environment, the requirements to understand where data resides across multiple public clouds and on-premise systems and cost-effectively store, protect and move data to its optimal location becomes critical. With the coming rise of edge computing and micro-data centers designed to bring data and applications closer to users to reduce latency and thereby improve application and workload performance, data management and logistics become even more complex. The edge is creating even more data, with increased data volumes having to be transmitted across networks and between clouds.

Object-based data storage is now the dominant storage architecture globally, particularly in the cloud, and will be used to store 85 percent of the world’s data by 2025. Developed initially to handle the explosive growth of unstructured data, such as video, images, web pages, log files and IoT data, object storage saves data along with associated metadata in blocks as objects that can contain multiple information formats.

It has a variety of advantages over file and block storage, including massive scalability, greater flexibility via metadata for analytics, better governance, faster retrieval, and improved cost efficiencies. In addition, continued technical advances are improving both the performance and the applicability of object storage for new use cases. A major challenge, however, is that the object-based storage systems used my various cloud providers are all different, making data synchronization and translation a challenge.

The Data Logistics Mandate

In this new world, Chief Data Officers and CIOs must optimize how they manage vast volumes of data across multiple clouds and data environments through better data logistics. There are more data silos, greater distribution of data, vast increases in volume, and a growing demand to understand, protect and exploit data for business growth and optimization while holding down costs.

Among those logistical challenges are the need to keep track of where data resides, and cost-effectively synchronize, replicate and move data to optimize value, efficiency, security and governance based on cost differences, processing and analytics capabilities, performance advantages, and regulatory requirements.

Organizations need to be able to be able to identify what data needs to be replicated and effectively transmitted and synchronized across different clouds and on-premise locations to improve the availability, security and value of data.

For example, organizations increasingly need to move data to specific service providers to conduct analytics or take advantage of specific vendor technologies, lower processing and storage costs, or meet geographic requirements. Particularly when data replication and synchronization is a continuous activity, organizations need to automate this process based on established rules and policies to improve cost efficiency, eliminate errors and reduce management complexity.

For object storage data, the capacity to fully utilize metadata to identify location, type and value is now a critical aspect of data logistics, as is the ability to transfer data with a minimum amount of performance degradation and data corruption. New solutions that can handle replication outside of the data path are delivering improved visibility, performance and data quality.

Advancing data logistics is a major imperative for today’s data-driven organizations and their chief data officers. The mandate to improve data availability, performance, value and cost-efficiency will be a critical competitive differentiator in a multi-cloud, mega-data world.

To get involved in this initiative, please reach out to Dave Murray.

2020-08-12

2020-08-11 21:56:11

2020-08-14 18:49:19

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 authorThe CMO Council and BPI Network
 bodyAs companies seek to elevate customer experience, they increasingly look to data to better engage with customers and identify new opportunities. Yet previous BPI Network and CMO Council research highlights that many executives admit their organizations could do more to transform the data supply chain to allow them to do more with internal data assets. To ignite real change, the three critical functions as it relates to customer engagement: marketing, commerce and supply chain, must work together to elevate data strategies. The report highlights the challenges facing each of these functions when it comes to how data strategies are being challenged and transformed. It also highlights how each individual function aligns, and sometimes clashes, in their perceptions of where and how each function must collaborate around the customer. The research stems from a survey of 165 marketing, commerce and supply chain executives, as well as 12 deep-dive interviews with executives who reveal the ways they are rethinking their engagement strategies through smarter use of data insights. Those executives hail from brands like AT&T, The Body Shop, Samsonite, REI, Ryder, TD Bank, Cabela’s and Nordstrom. Areas of exploration covered in this research include: <ul> <li>The roles each of these functions play when it comes to developing, delivering and continuously improving upon profitable customer engagements</li> <li>The level of cross-functional alignment across all customer experience stakeholders, and ways organizations are ensuring cross-functional participation across the data value chain</li> <li>The types of data sources that functional groups are able to extract important insights from, and those sources of data that are still out of reach</li> <li>The greatest obstacles organizations face as they attempt to realize greater value from internal data assets, and what holds these organizations back from realizing the full potential of data</li> <li>The technology deployment roadmaps organizations are seeking to implement over the next year to improve data utilization across the organization</li> </ul> A free executive summary is available for download. The full report can be purchased for 9. Please follow the links below to download.
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Chief Data Officers, infrastructure IT executives and others in the enterprise are now seeking new and innovative ways to gain greater control, visibility, performance and efficiency in the way they manage data to optimize its value. The program will engage data and IT executives from major companies, as well as leading system integrators, consulting firms and solution providers to understand needs, requirements and best practices in optimizing data economics, protection and logistics in today’s multi-cloud, mega-data world. Background The growing importance of the Chief Data Officer underscores widespread recognition of the essential role and value of data and analytics in the modern, agile customer-centric enterprise. This is further compounded by the significant cost, challenge and complexity of unifying, migrating, replicating, protecting, storing and governing data “ponds, lakes and dams” in today’s multi-cloud and hybrid cloud computing environments. Data has become one of the chief engines of value creation. Its role in improving analytical decision making, enhancing our ability to understand, reach and engage customers, and increasing the value of both existing and new products and services has made data a highly valued currency and asset base for enterprises operating digitally across data borders, ramparts and fences. Fueled by an explosive proliferation of new data sources—from mobile computing, social media and rich media applications and services, to the advent of the Internet of Things, which will generate some 50 million connected sensors by the end of 2020—the amount and variety of data being stored is growing exponentially. According Seagate Technology, the amount of data generated worldwide will increase to 163 zettabytes by 2025 from 12 zettabytes in 2015. In today’s multi-cloud, mega-data world, better Data Logistics and Performance Management (DLPM) has become essential to competing in the Data Economy. According to 451 Research, some 80 percent of companies are now utilizing two or more clouds. Three-quarters of companies say they are pursuing a hybrid/multi-cloud strategy. In this highly distributed compute environment, the requirements to understand where data resides across multiple public clouds and on-premise systems and cost-effectively store, protect and move data to its optimal location becomes critical. With the coming rise of edge computing and micro-data centers designed to bring data and applications closer to users to reduce latency and thereby improve application and workload performance, data management and logistics become even more complex. The edge is creating even more data, with increased data volumes having to be transmitted across networks and between clouds. Object-based data storage is now the dominant storage architecture globally, particularly in the cloud, and will be used to store 85 percent of the world’s data by 2025. 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There are more data silos, greater distribution of data, vast increases in volume, and a growing demand to understand, protect and exploit data for business growth and optimization while holding down costs. Among those logistical challenges are the need to keep track of where data resides, and cost-effectively synchronize, replicate and move data to optimize value, efficiency, security and governance based on cost differences, processing and analytics capabilities, performance advantages, and regulatory requirements. Organizations need to be able to be able to identify what data needs to be replicated and effectively transmitted and synchronized across different clouds and on-premise locations to improve the availability, security and value of data. For example, organizations increasingly need to move data to specific service providers to conduct analytics or take advantage of specific vendor technologies, lower processing and storage costs, or meet geographic requirements. Particularly when data replication and synchronization is a continuous activity, organizations need to automate this process based on established rules and policies to improve cost efficiency, eliminate errors and reduce management complexity. For object storage data, the capacity to fully utilize metadata to identify location, type and value is now a critical aspect of data logistics, as is the ability to transfer data with a minimum amount of performance degradation and data corruption. New solutions that can handle replication outside of the data path are delivering improved visibility, performance and data quality. Advancing data logistics is a major imperative for today’s data-driven organizations and their chief data officers. The mandate to improve data availability, performance, value and cost-efficiency will be a critical competitive differentiator in a multi-cloud, mega-data world. 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According to 451 Research, some 80 percent of companies are now utilizing two or more clouds. Three-quarters of companies say they are pursuing a hybrid/multi-cloud strategy. In this highly distributed compute environment, the requirements to understand where data resides across multiple public clouds and on-premise systems and cost-effectively store, protect and move data to its optimal location becomes critical. With the coming rise of edge computing and micro-data centers designed to bring data and applications closer to users to reduce latency and thereby improve application and workload performance, data management and logistics become even more complex. The edge is creating even more data, with increased data volumes having to be transmitted across networks and between clouds. Object-based data storage is now the dominant storage architecture globally, particularly in the cloud, and will be used to store 85 percent of the world’s data by 2025. Developed initially to handle the explosive growth of unstructured data, such as video, images, web pages, log files and IoT data, object storage saves data along with associated metadata in blocks as objects that can contain multiple information formats. It has a variety of advantages over file and block storage, including massive scalability, greater flexibility via metadata for analytics, better governance, faster retrieval, and improved cost efficiencies. In addition, continued technical advances are improving both the performance and the applicability of object storage for new use cases. A major challenge, however, is that the object-based storage systems used my various cloud providers are all different, making data synchronization and translation a challenge. The Data Logistics Mandate In this new world, Chief Data Officers and CIOs must optimize how they manage vast volumes of data across multiple clouds and data environments through better data logistics. There are more data silos, greater distribution of data, vast increases in volume, and a growing demand to understand, protect and exploit data for business growth and optimization while holding down costs. Among those logistical challenges are the need to keep track of where data resides, and cost-effectively synchronize, replicate and move data to optimize value, efficiency, security and governance based on cost differences, processing and analytics capabilities, performance advantages, and regulatory requirements. Organizations need to be able to be able to identify what data needs to be replicated and effectively transmitted and synchronized across different clouds and on-premise locations to improve the availability, security and value of data. For example, organizations increasingly need to move data to specific service providers to conduct analytics or take advantage of specific vendor technologies, lower processing and storage costs, or meet geographic requirements. 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Chief Data Officers, infrastructure IT executives and others in the enterprise are now seeking new and innovative ways to gain greater control, visibility, performance and efficiency in the way they manage data to optimize its value. The program will engage data and IT executives from major companies, as well as leading system integrators, consulting firms and solution providers to understand needs, requirements and best practices in optimizing data economics, protection and logistics in today’s multi-cloud, mega-data world. Background The growing importance of the Chief Data Officer underscores widespread recognition of the essential role and value of data and analytics in the modern, agile customer-centric enterprise. This is further compounded by the significant cost, challenge and complexity of unifying, migrating, replicating, protecting, storing and governing data “ponds, lakes and dams” in today’s multi-cloud and hybrid cloud computing environments. 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According to 451 Research, some 80 percent of companies are now utilizing two or more clouds. Three-quarters of companies say they are pursuing a hybrid/multi-cloud strategy. In this highly distributed compute environment, the requirements to understand where data resides across multiple public clouds and on-premise systems and cost-effectively store, protect and move data to its optimal location becomes critical. With the coming rise of edge computing and micro-data centers designed to bring data and applications closer to users to reduce latency and thereby improve application and workload performance, data management and logistics become even more complex. The edge is creating even more data, with increased data volumes having to be transmitted across networks and between clouds. Object-based data storage is now the dominant storage architecture globally, particularly in the cloud, and will be used to store 85 percent of the world’s data by 2025. Developed initially to handle the explosive growth of unstructured data, such as video, images, web pages, log files and IoT data, object storage saves data along with associated metadata in blocks as objects that can contain multiple information formats. It has a variety of advantages over file and block storage, including massive scalability, greater flexibility via metadata for analytics, better governance, faster retrieval, and improved cost efficiencies. In addition, continued technical advances are improving both the performance and the applicability of object storage for new use cases. A major challenge, however, is that the object-based storage systems used my various cloud providers are all different, making data synchronization and translation a challenge. The Data Logistics Mandate In this new world, Chief Data Officers and CIOs must optimize how they manage vast volumes of data across multiple clouds and data environments through better data logistics. There are more data silos, greater distribution of data, vast increases in volume, and a growing demand to understand, protect and exploit data for business growth and optimization while holding down costs. Among those logistical challenges are the need to keep track of where data resides, and cost-effectively synchronize, replicate and move data to optimize value, efficiency, security and governance based on cost differences, processing and analytics capabilities, performance advantages, and regulatory requirements. Organizations need to be able to be able to identify what data needs to be replicated and effectively transmitted and synchronized across different clouds and on-premise locations to improve the availability, security and value of data. For example, organizations increasingly need to move data to specific service providers to conduct analytics or take advantage of specific vendor technologies, lower processing and storage costs, or meet geographic requirements. 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Chief Data Officers, infrastructure IT executives and others in the enterprise are now seeking new and innovative ways to gain greater control, visibility, performance and efficiency in the way they manage data to optimize its value. The program will engage data and IT executives from major companies, as well as leading system integrators, consulting firms and solution providers to understand needs, requirements and best practices in optimizing data economics, protection and logistics in today’s multi-cloud, mega-data world. Background The growing importance of the Chief Data Officer underscores widespread recognition of the essential role and value of data and analytics in the modern, agile customer-centric enterprise. This is further compounded by the significant cost, challenge and complexity of unifying, migrating, replicating, protecting, storing and governing data “ponds, lakes and dams” in today’s multi-cloud and hybrid cloud computing environments. Data has become one of the chief engines of value creation. Its role in improving analytical decision making, enhancing our ability to understand, reach and engage customers, and increasing the value of both existing and new products and services has made data a highly valued currency and asset base for enterprises operating digitally across data borders, ramparts and fences. Fueled by an explosive proliferation of new data sources—from mobile computing, social media and rich media applications and services, to the advent of the Internet of Things, which will generate some 50 million connected sensors by the end of 2020—the amount and variety of data being stored is growing exponentially. According Seagate Technology, the amount of data generated worldwide will increase to 163 zettabytes by 2025 from 12 zettabytes in 2015. In today’s multi-cloud, mega-data world, better Data Logistics and Performance Management (DLPM) has become essential to competing in the Data Economy. According to 451 Research, some 80 percent of companies are now utilizing two or more clouds. Three-quarters of companies say they are pursuing a hybrid/multi-cloud strategy. In this highly distributed compute environment, the requirements to understand where data resides across multiple public clouds and on-premise systems and cost-effectively store, protect and move data to its optimal location becomes critical. With the coming rise of edge computing and micro-data centers designed to bring data and applications closer to users to reduce latency and thereby improve application and workload performance, data management and logistics become even more complex. The edge is creating even more data, with increased data volumes having to be transmitted across networks and between clouds. Object-based data storage is now the dominant storage architecture globally, particularly in the cloud, and will be used to store 85 percent of the world’s data by 2025. Developed initially to handle the explosive growth of unstructured data, such as video, images, web pages, log files and IoT data, object storage saves data along with associated metadata in blocks as objects that can contain multiple information formats. It has a variety of advantages over file and block storage, including massive scalability, greater flexibility via metadata for analytics, better governance, faster retrieval, and improved cost efficiencies. In addition, continued technical advances are improving both the performance and the applicability of object storage for new use cases. A major challenge, however, is that the object-based storage systems used my various cloud providers are all different, making data synchronization and translation a challenge. The Data Logistics Mandate In this new world, Chief Data Officers and CIOs must optimize how they manage vast volumes of data across multiple clouds and data environments through better data logistics. There are more data silos, greater distribution of data, vast increases in volume, and a growing demand to understand, protect and exploit data for business growth and optimization while holding down costs. Among those logistical challenges are the need to keep track of where data resides, and cost-effectively synchronize, replicate and move data to optimize value, efficiency, security and governance based on cost differences, processing and analytics capabilities, performance advantages, and regulatory requirements. Organizations need to be able to be able to identify what data needs to be replicated and effectively transmitted and synchronized across different clouds and on-premise locations to improve the availability, security and value of data. For example, organizations increasingly need to move data to specific service providers to conduct analytics or take advantage of specific vendor technologies, lower processing and storage costs, or meet geographic requirements. 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Chief Data Officers, infrastructure IT executives and others in the enterprise are now seeking new and innovative ways to gain greater control, visibility, performance and efficiency in the way they manage data to optimize its value. The program will engage data and IT executives from major companies, as well as leading system integrators, consulting firms and solution providers to understand needs, requirements and best practices in optimizing data economics, protection and logistics in today’s multi-cloud, mega-data world. Background The growing importance of the Chief Data Officer underscores widespread recognition of the essential role and value of data and analytics in the modern, agile customer-centric enterprise. This is further compounded by the significant cost, challenge and complexity of unifying, migrating, replicating, protecting, storing and governing data “ponds, lakes and dams” in today’s multi-cloud and hybrid cloud computing environments. Data has become one of the chief engines of value creation. Its role in improving analytical decision making, enhancing our ability to understand, reach and engage customers, and increasing the value of both existing and new products and services has made data a highly valued currency and asset base for enterprises operating digitally across data borders, ramparts and fences. Fueled by an explosive proliferation of new data sources—from mobile computing, social media and rich media applications and services, to the advent of the Internet of Things, which will generate some 50 million connected sensors by the end of 2020—the amount and variety of data being stored is growing exponentially. According Seagate Technology, the amount of data generated worldwide will increase to 163 zettabytes by 2025 from 12 zettabytes in 2015. In today’s multi-cloud, mega-data world, better Data Logistics and Performance Management (DLPM) has become essential to competing in the Data Economy. According to 451 Research, some 80 percent of companies are now utilizing two or more clouds. Three-quarters of companies say they are pursuing a hybrid/multi-cloud strategy. In this highly distributed compute environment, the requirements to understand where data resides across multiple public clouds and on-premise systems and cost-effectively store, protect and move data to its optimal location becomes critical. With the coming rise of edge computing and micro-data centers designed to bring data and applications closer to users to reduce latency and thereby improve application and workload performance, data management and logistics become even more complex. The edge is creating even more data, with increased data volumes having to be transmitted across networks and between clouds. Object-based data storage is now the dominant storage architecture globally, particularly in the cloud, and will be used to store 85 percent of the world’s data by 2025. Developed initially to handle the explosive growth of unstructured data, such as video, images, web pages, log files and IoT data, object storage saves data along with associated metadata in blocks as objects that can contain multiple information formats. It has a variety of advantages over file and block storage, including massive scalability, greater flexibility via metadata for analytics, better governance, faster retrieval, and improved cost efficiencies. In addition, continued technical advances are improving both the performance and the applicability of object storage for new use cases. A major challenge, however, is that the object-based storage systems used my various cloud providers are all different, making data synchronization and translation a challenge. The Data Logistics Mandate In this new world, Chief Data Officers and CIOs must optimize how they manage vast volumes of data across multiple clouds and data environments through better data logistics. There are more data silos, greater distribution of data, vast increases in volume, and a growing demand to understand, protect and exploit data for business growth and optimization while holding down costs. Among those logistical challenges are the need to keep track of where data resides, and cost-effectively synchronize, replicate and move data to optimize value, efficiency, security and governance based on cost differences, processing and analytics capabilities, performance advantages, and regulatory requirements. Organizations need to be able to be able to identify what data needs to be replicated and effectively transmitted and synchronized across different clouds and on-premise locations to improve the availability, security and value of data. For example, organizations increasingly need to move data to specific service providers to conduct analytics or take advantage of specific vendor technologies, lower processing and storage costs, or meet geographic requirements. Particularly when data replication and synchronization is a continuous activity, organizations need to automate this process based on established rules and policies to improve cost efficiency, eliminate errors and reduce management complexity. For object storage data, the capacity to fully utilize metadata to identify location, type and value is now a critical aspect of data logistics, as is the ability to transfer data with a minimum amount of performance degradation and data corruption. New solutions that can handle replication outside of the data path are delivering improved visibility, performance and data quality. Advancing data logistics is a major imperative for today’s data-driven organizations and their chief data officers. The mandate to improve data availability, performance, value and cost-efficiency will be a critical competitive differentiator in a multi-cloud, mega-data world. 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Server Software	Apache
Server Name	bpinetwork.org
Server Addr	173.231.198.134
Server Port	443
Remote Addr	52.14.93.7
Document Root	/home/bpinorg/public_html
Request Scheme	https
Context Prefix
Context Document Root	/home/bpinorg/public_html
Server Admin	webmaster@bpinetwork.org
Script Filename	/home/bpinorg/public_html/app/webroot/index.php
Remote Port	58716
Redirect Url	/app/webroot/reports/view/68
Gateway Interface	CGI/1.1
Server Protocol	HTTP/2.0
Request Method	GET
Query String
Request Uri	/reports/view/68
Script Name	/app/webroot/index.php
Php Self	/app/webroot/index.php
Request Time Float	1745927971.6007
Request Time	1745927971
Argc	0

====

Include
+
Included Files

Include Paths
- 0/home/bpinorg/public_html/lib
- 2/opt/cpanel/ea-php56/root/usr/share/pear
- 3-> /home/bpinorg/public_html/lib/Cake/
Included Files
- core
  Behavior
  0CORE/Model/Behavior/ContainableBehavior.php
  Cache
  0CORE/Cache/Cache.php
  1CORE/Cache/Engine/FileEngine.php
  2CORE/Cache/CacheEngine.php
  Component
  0CORE/Controller/Component/SessionComponent.php
  1CORE/Controller/Component/AuthComponent.php
  2CORE/Controller/Component/RequestHandlerComponent.php
  Config
  0CORE/Config/routes.php
  1CORE/Config/config.php
  Controller
  0CORE/Controller/Controller.php
  1CORE/Controller/ComponentCollection.php
  2CORE/Controller/Component.php
  Datasource
  0CORE/Model/Datasource/Database/Mysql.php
  1CORE/Model/Datasource/DboSource.php
  2CORE/Model/Datasource/DataSource.php
  3CORE/Model/Datasource/CakeSession.php
  Error
  0CORE/Error/exceptions.php
  1CORE/Error/ErrorHandler.php
  I18n
  0CORE/I18n/I18n.php
  1CORE/I18n/L10n.php
  Log
  0CORE/Log/CakeLog.php
  1CORE/Log/LogEngineCollection.php
  2CORE/Log/Engine/FileLog.php
  3CORE/Log/Engine/BaseLog.php
  4CORE/Log/CakeLogInterface.php
  Model
  0CORE/Model/Model.php
  1CORE/Model/BehaviorCollection.php
  2CORE/Model/ConnectionManager.php
  3CORE/Model/ModelBehavior.php
  Network
  0CORE/Network/CakeRequest.php
  1CORE/Network/CakeResponse.php
  Other
  0CORE/bootstrap.php
  1CORE/basics.php
  2CORE/Core/App.php
  3CORE/Core/Configure.php
  4CORE/Core/CakePlugin.php
  5CORE/Event/CakeEventListener.php
  6CORE/Event/CakeEvent.php
  7CORE/Event/CakeEventManager.php
  8CORE/Core/Object.php
  Routing
  0CORE/Routing/Dispatcher.php
  1CORE/Routing/Filter/AssetDispatcher.php
  2CORE/Routing/DispatcherFilter.php
  3CORE/Routing/Filter/CacheDispatcher.php
  4CORE/Routing/Router.php
  5CORE/Routing/Route/CakeRoute.php
  6CORE/Routing/Route/RedirectRoute.php
  7CORE/Routing/Route/PluginShortRoute.php
  Utility
  0CORE/Utility/Hash.php
  1CORE/Utility/ObjectCollection.php
  2CORE/Utility/Debugger.php
  3CORE/Utility/String.php
  4CORE/Utility/ClassRegistry.php
  5CORE/Utility/Inflector.php
  6CORE/Utility/Folder.php
  View
  0CORE/View/HelperCollection.php
- app
  Component
  0APP/Controller/Component/PasswordComponent.php
  Config
  0APP/Config/core.php
  1APP/Config/bootstrap.php
  2APP/Config/routes.php
  3APP/Config/database.php
  Controller
  0APP/Controller/ReportsController.php
  1APP/Controller/AppController.php
  Model
  0APP/Model/Menu.php
  1APP/Model/AppModel.php
  2APP/Model/User.php
  3APP/Model/Article.php
  4APP/Model/Event.php
  5APP/Model/Report.php
  6APP/Model/Leader.php
  7APP/Model/Program.php
  8APP/Model/Tag.php
  9APP/Model/Brainwafe.php
  10APP/Model/Tracking.php
  11APP/Model/Download.php
  12APP/Model/MediaCoverage.php
  Other
  0APP/webroot/index.php
  1APP/Lib/AppError.php
  2APP/Vendor/ChromePhp/ChromePhp.php
  3APP/Lib/Captcha/Captcha.php
- plugins
  CsvView
  Component
  0CsvView/Controller/Component/CsvViewComponent.php
  DebugKit
  Component
  0DebugKit/Controller/Component/ToolbarComponent.php
  Plugin
  0DebugKit/Lib/DebugMemory.php
  1DebugKit/Lib/Panel/HistoryPanel.php
  2DebugKit/Lib/DebugPanel.php
  3DebugKit/Lib/Panel/SessionPanel.php
  4DebugKit/Lib/Panel/RequestPanel.php
  5DebugKit/Lib/Panel/SqlLogPanel.php
  6DebugKit/Lib/Panel/TimerPanel.php
  7DebugKit/Lib/Panel/LogPanel.php
  8DebugKit/Lib/Log/Engine/DebugKitLog.php
  9DebugKit/Lib/Panel/VariablesPanel.php
  10DebugKit/Lib/Panel/EnvironmentPanel.php
  11DebugKit/Lib/Panel/IncludePanel.php
  12DebugKit/Lib/DebugTimer.php
  LessCompiler
  Component
  0LessCompiler/Controller/Component/LessComponent.php
  Plugin
  0LessCompiler/Lib/LessCompiler.php
  1LessCompiler/Vendor/lessc.php
  Minify
  Config
  0Minify/Config/routes.php
  Opauth
  Config
  0Opauth/Config/bootstrap.php
  1Opauth/Config/routes.php
====

Business Performance (BPI) Network

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Request History

Session

Request

Cake Params

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Query string

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Sql Logs

default

Total Time: 5 ms Total Queries: 28 queries

Query Explain:

Memory

Timers

Logs

View Variables

App Constants

CakePHP Constants

PHP Environment

Included Files

Include Paths

Included Files

Total Time: 5 ms
Total Queries: 28 queries