In the power distribution sub-sector, which is presently the weakest link in the power supply chain, Cost-effective Technology interventions can improve the operational and financial performance of the entire power sector in India
This document discusses the importance of managing data effectively and having clean, accurate data. It notes that dirty or unmanaged data can become unreliable very quickly as errors accumulate. It provides examples of what constitutes dirty data like duplicates, missing or inaccurate information. The document recommends having processes and standards for entering data to maintain data quality. These include reconciling donations, using batch numbers, and regular audits to identify dirty data issues. The overall message is that non-profits must treat data as a valuable asset and implement strategies to organize, maintain and protect their data.
The document discusses implementing a single view of the customer (SVC) using IBM Infosphere (formerly Websphere Customer Center). It provides an overview of the product's features such as a flexible data model, pre-defined services, and integration with data quality tools. A phased approach to MDM implementation is proposed starting with a customer profile data mart and expanding to a customer data integration hub and full synchronization of master data across systems.
Third-Party Risk Management: Implementing a StrategyNICSA
Two Part Series: Part I of II
Third-Party Risk Management: Implementing a Strategy
Sleep Better at Night: Learn techniques to manage risks associated with third-party relationships.
The document outlines several upcoming workshops hosted by CCG, an analytics consulting firm, including:
- An Analytics in a Day workshop focusing on Synapse on March 16th and April 20th.
- An Introduction to Machine Learning workshop on March 23rd.
- A Data Modernization workshop on March 30th.
- A Data Governance workshop with CCG and Profisee on May 4th focusing on leveraging MDM within data governance.
More details and registration information can be found on ccganalytics.com/events. The document encourages following CCG on LinkedIn for event updates.
Data Quality: A Raising Data Warehousing ConcernAmin Chowdhury
Characteristics of Data Warehouse
Benefits of a data warehouse
Designing of Data Warehouse
Extract, Transform, Load (ETL)
Data Quality
Classification Of Data Quality Issues
Causes Of Data Quality
Impact of Data Quality Issues
Cost of Poor Data Quality
Confidence and Satisfaction-based impacts
Impact on Productivity
Risk and Compliance impacts
Why Data Quality Influences?
Causes of Data Quality Problems
How to deal: Missing Data
Data Corruption
Data: Out of Range error
Techniques of Data Quality Control
Data warehousing security
Data-Ed Webinar: Data Quality EngineeringDATAVERSITY
Organizations must realize what it means to utilize data quality management in support of business strategy. This webinar will illustrate how organizations with chronic business challenges often can trace the root of the problem to poor data quality. Showing how data quality should be engineered provides a useful framework in which to develop an effective approach. This in turn allows organizations to more quickly identify business problems as well as data problems caused by structural issues versus practice-oriented defects and prevent these from re-occurring.
Takeaways:
Understanding foundational data quality concepts based on the DAMA DMBOK
Utilizing data quality engineering in support of business strategy
Data Quality guiding principles & best practices
Steps for improving data quality at your organization
On this slides, we tried to give an overview of advanced Data quality management (ADQM). To understand about DQ why important, and all those steps of DQ management.
The document describes EMC's experiences with environmental data analytics projects. It discusses EMC setting up India's first environmental data management system for CPCB in 1986. This included air and water data management and analysis. The document also outlines other projects EMC has worked on, including an online environmental monitoring system for Egypt, analysis of Ganga river water quality data from sensors, and a corporate sustainability report for an Indian company. The presentation emphasizes that environmental data is large, irregular, fuzzy and from diverse sources, requiring advanced analytics to generate meaningful insights and reports.
This document discusses the importance of managing data effectively and having clean, accurate data. It notes that dirty or unmanaged data can become unreliable very quickly as errors accumulate. It provides examples of what constitutes dirty data like duplicates, missing or inaccurate information. The document recommends having processes and standards for entering data to maintain data quality. These include reconciling donations, using batch numbers, and regular audits to identify dirty data issues. The overall message is that non-profits must treat data as a valuable asset and implement strategies to organize, maintain and protect their data.
The document discusses implementing a single view of the customer (SVC) using IBM Infosphere (formerly Websphere Customer Center). It provides an overview of the product's features such as a flexible data model, pre-defined services, and integration with data quality tools. A phased approach to MDM implementation is proposed starting with a customer profile data mart and expanding to a customer data integration hub and full synchronization of master data across systems.
Third-Party Risk Management: Implementing a StrategyNICSA
Two Part Series: Part I of II
Third-Party Risk Management: Implementing a Strategy
Sleep Better at Night: Learn techniques to manage risks associated with third-party relationships.
The document outlines several upcoming workshops hosted by CCG, an analytics consulting firm, including:
- An Analytics in a Day workshop focusing on Synapse on March 16th and April 20th.
- An Introduction to Machine Learning workshop on March 23rd.
- A Data Modernization workshop on March 30th.
- A Data Governance workshop with CCG and Profisee on May 4th focusing on leveraging MDM within data governance.
More details and registration information can be found on ccganalytics.com/events. The document encourages following CCG on LinkedIn for event updates.
Data Quality: A Raising Data Warehousing ConcernAmin Chowdhury
Characteristics of Data Warehouse
Benefits of a data warehouse
Designing of Data Warehouse
Extract, Transform, Load (ETL)
Data Quality
Classification Of Data Quality Issues
Causes Of Data Quality
Impact of Data Quality Issues
Cost of Poor Data Quality
Confidence and Satisfaction-based impacts
Impact on Productivity
Risk and Compliance impacts
Why Data Quality Influences?
Causes of Data Quality Problems
How to deal: Missing Data
Data Corruption
Data: Out of Range error
Techniques of Data Quality Control
Data warehousing security
Data-Ed Webinar: Data Quality EngineeringDATAVERSITY
Organizations must realize what it means to utilize data quality management in support of business strategy. This webinar will illustrate how organizations with chronic business challenges often can trace the root of the problem to poor data quality. Showing how data quality should be engineered provides a useful framework in which to develop an effective approach. This in turn allows organizations to more quickly identify business problems as well as data problems caused by structural issues versus practice-oriented defects and prevent these from re-occurring.
Takeaways:
Understanding foundational data quality concepts based on the DAMA DMBOK
Utilizing data quality engineering in support of business strategy
Data Quality guiding principles & best practices
Steps for improving data quality at your organization
On this slides, we tried to give an overview of advanced Data quality management (ADQM). To understand about DQ why important, and all those steps of DQ management.
The document describes EMC's experiences with environmental data analytics projects. It discusses EMC setting up India's first environmental data management system for CPCB in 1986. This included air and water data management and analysis. The document also outlines other projects EMC has worked on, including an online environmental monitoring system for Egypt, analysis of Ganga river water quality data from sensors, and a corporate sustainability report for an Indian company. The presentation emphasizes that environmental data is large, irregular, fuzzy and from diverse sources, requiring advanced analytics to generate meaningful insights and reports.
Slides: Data Monetization — Demonstrating Quantifiable Financial Benefits fro...DATAVERSITY
The document introduces a new cloud-based data monetization platform called YourDataConnect focused on helping Chief Data Officers. It notes that 68% of Fortune 1000 companies have a CDO but they struggle to measure ROI on data management spending. YourDataConnect is a SaaS platform that can help CDOs quantify the financial benefits of data across revenue growth, cost reduction, and risk mitigation through an automated dashboard. It allows for data valuation, continuous ROI measurement, data sharing in a marketplace, and regulatory compliance tracking.
Data Quality Management: Cleaner Data, Better Reportingaccenture
This document discusses Accenture's regulatory reporting framework and offerings around data quality management. It provides an overview of Accenture's high-performance financial reporting framework, which aims to consolidate frameworks, processes, and technology to create efficiencies across reporting functions. It also summarizes Accenture's regulatory reporting offerings, including data quality management, capability design, target operating models, and regulatory reporting vendor implementation support. Finally, it covers key aspects of data quality management, such as issue classification, management processes, governance structures, root cause analysis, and issue prioritization. The goal is to help financial institutions improve data quality, reporting accuracy and efficiency.
This document discusses how geographic information systems (GIS) can be used to plan and site solar energy projects. It describes Eolfi, a company that develops solar and wind projects. GIS tools like spatial analysis and elevation data are used to identify optimal locations based on slope, aspect, shadows, environmental constraints, and grid connectivity. The GIS model enables evaluation of potential sites and informs siting decisions to reduce costs and environmental impacts. In conclusion, the document states that GIS provides a practical and reliable way to map suitable areas and make more efficient siting decisions.
This document discusses data quality and provides facts about the high costs of poor data quality to businesses and the US economy. It defines data quality as ensuring data is "fit for purpose" by measuring it against its intended uses and dimensions of quality. The document outlines best practices for measuring data quality including profiling data to understand metadata and trends, using statistical process control, master data management to create standardized "gold records", and implementing a data governance program to centrally manage data quality.
DAS Slides: Data Quality Best PracticesDATAVERSITY
Tackling Data Quality problems requires more than a series of tactical, one-off improvement projects. By their nature, many Data Quality problems extend across and often beyond an organization. Addressing these issues requires a holistic architectural approach combining people, process, and technology. Join Nigel Turner and Donna Burbank as they provide practical ways to control Data Quality issues in your organization.
Chapter 12: Data Quality ManagementAhmed Alorage
This document discusses data quality management (DQM). It covers DQM concepts and activities, including developing data quality awareness, defining data quality requirements, profiling and assessing data quality, and defining metrics. The key DQM approach is the Deming cycle of planning, deploying, monitoring, and acting to continuously improve data quality. Data quality requirements are identified by reviewing business policies and rules to understand dimensions like accuracy, completeness, consistency and more.
This document discusses the BCBS 239 regulatory requirements for risk data aggregation and risk reporting. It outlines the key components of BCBS 239 including risk governance, infrastructure, data aggregation, and reporting. It also describes a risk data self-assessment diagnostic study that banks should conduct to evaluate their risk operating model, processes, data usage, and infrastructure in order to identify gaps and develop projects to address deficiencies to comply with BCBS 239. Finally, it presents a proposed unified risk data model and architecture to integrate risk data across different risk types and business units.
This document discusses data quality and data profiling. It begins by describing problems with data like duplication, inconsistency, and incompleteness. Good data is a valuable asset while bad data can harm a business. Data quality is assessed based on dimensions like accuracy, consistency, completeness, and timeliness. Data profiling statistically examines data to understand issues before development begins. It helps assess data quality and catch problems early. Common analyses include analyzing null values, keys, formats, and more. Data profiling is conducted using SQL or profiling tools during requirements, modeling, and ETL design.
This presentation was part of the IDS Webinar on Data Governance. It gives a brief overview of the history on Data Governance, describes how governing data has to be further developed in the era of business and data ecosystems, and outlines the contribution of the International Data Spaces Association on the topic.
A Day in the Life of an Enterprise Architect (Role Play Exercise) 2016Daljit Banger
During Nov 2016 the BCS EA SIG ran a session entitled "Enterprise Architecture Practitioners Day / Hackathon" in London - These are my slides for my sesion at the event.
Real-World DG Webinar: A Data Governance Framework for Success DATAVERSITY
A Data Governance Framework must include best practices, a practical set of roles & responsibilities for Data Governance built specifically for your organization, a plan for communicating with the entire organization and an action plan for applying governance in effective and measurable ways.
Join Bob Seiner for this Real-World Data Governance webinar as he discusses how to stay practical and work within the culture of your organization to develop and deliver a Data Governance Framework to meet your specifications and the business’ expectations.
This session will focus on:
Defining a Non-Invasive Operating Model of Roles & Responsibilities
Clearly Stating the Difference between Executive, Strategic, Tactical, Operational & Supporting Roles
Defining Data Stewards, Data Stewardship and How to Steward the Data
Recognizing & Identifying People into Roles Rather than Handing them to People as New Responsibilities
Leveraging the Framework to Implement a Successful Data Governance Program
Tackling Data Quality problems requires more than a series of tactical, one-off improvement projects. By their nature, many Data Quality problems extend across and often beyond an organization. Addressing these issues requires a holistic architectural approach combining people, process, and technology. Join Nigel Turner and Donna Burbank as they provide practical ways to control Data Quality issues in your organization.
This document discusses the importance of data quality and data governance. It states that poor data quality can lead to wrong decisions, bad reputation, and wasted money. It then provides examples of different dimensions of data quality like accuracy, completeness, currency, and uniqueness. It also discusses methods and tools for ensuring data quality, such as validation, data merging, and minimizing human errors. Finally, it defines data governance as a set of policies and standards to maintain data quality and provides examples of data governance team missions and a sample data quality scorecard.
Cluster analysis is an unsupervised machine learning technique that groups similar data objects into clusters. It finds internal structures within unlabeled data by partitioning it into groups based on similarity. Some key applications of cluster analysis include market segmentation, document classification, and identifying subtypes of diseases. The quality of clusters depends on both the similarity measure used and how well objects are grouped within each cluster versus across clusters.
Datasaturday Pordenone Azure Purview Erwin de KreukErwin de Kreuk
Azure Purview is Microsoft's solution for unified data governance. It includes three main components:
1. The Purview Data Map automates metadata scanning and lineage identification across hybrid data stores and applies over 100 classifiers and Microsoft sensitivity labels.
2. The Purview Data Catalog enables effortless discovery through semantic search and a business glossary, and shows data lineage with sources, owners, and transformations.
3. Purview Insights provides reports on assets, scans, the glossary, classification, and sensitive data labeling to give visibility into data usage across the estate.
The document discusses technical vulnerability management and outlines the key steps in the NIST Risk Management Framework that include vulnerability analysis. It also covers establishing an effective Patch and Vulnerability Group to monitor for vulnerabilities, prioritize remediation, and deploy patches. Finally, it provides examples of different types of vulnerability analysis tools including network scanners, host scanners, and web application scanners.
Building a Data Strategy – Practical Steps for Aligning with Business GoalsDATAVERSITY
Developing a Data Strategy for your organization can seem like a daunting task – but it’s worth the effort. Getting your Data Strategy right can provide significant value, as data drives many of the key initiatives in today’s marketplace – from digital transformation, to marketing, to customer centricity, to population health, and more. This webinar will help demystify Data Strategy and its relationship to Data Architecture and will provide concrete, practical ways to get started.
The document discusses data governance at OMES. It defines data governance as an active, cross-organizational framework for securely sharing data, analyzing data across divisions, collaborating with stakeholders, and improving data quality. The mission of OMES's data governance program is to proactively define and align data rules, provide ongoing protection and services to data stakeholders, and identify and resolve data issues. Data governance supports strategic business goals by ensuring business needs drive information needs and technical needs. It is a business function that directly supports the agency's strategic goals.
Duke Energy implemented a smart grid project in Ohio with the objectives of improving reliability, reducing costs, and enabling greater customer access to energy use data. The project invested $100 million to install over 140,000 smart meters and distribution automation equipment, benefiting both customers and utilities. Customers gained near real-time energy use data and more accurate billing while utilities saw decreased outage times, reduced system losses and improved data for planning.
This document discusses using stream computing approaches to better analyze large amounts of smart meter data from power grids. It proposes moving away from centralized data processing models towards more distributed event processing models. This would allow utilities to create real-time insights from operational data and improve demand response management. The document also explores using cloud platforms and complex event processing techniques to more efficiently handle smart meter data streams in real-time at large scales.
Slides: Data Monetization — Demonstrating Quantifiable Financial Benefits fro...DATAVERSITY
The document introduces a new cloud-based data monetization platform called YourDataConnect focused on helping Chief Data Officers. It notes that 68% of Fortune 1000 companies have a CDO but they struggle to measure ROI on data management spending. YourDataConnect is a SaaS platform that can help CDOs quantify the financial benefits of data across revenue growth, cost reduction, and risk mitigation through an automated dashboard. It allows for data valuation, continuous ROI measurement, data sharing in a marketplace, and regulatory compliance tracking.
Data Quality Management: Cleaner Data, Better Reportingaccenture
This document discusses Accenture's regulatory reporting framework and offerings around data quality management. It provides an overview of Accenture's high-performance financial reporting framework, which aims to consolidate frameworks, processes, and technology to create efficiencies across reporting functions. It also summarizes Accenture's regulatory reporting offerings, including data quality management, capability design, target operating models, and regulatory reporting vendor implementation support. Finally, it covers key aspects of data quality management, such as issue classification, management processes, governance structures, root cause analysis, and issue prioritization. The goal is to help financial institutions improve data quality, reporting accuracy and efficiency.
This document discusses how geographic information systems (GIS) can be used to plan and site solar energy projects. It describes Eolfi, a company that develops solar and wind projects. GIS tools like spatial analysis and elevation data are used to identify optimal locations based on slope, aspect, shadows, environmental constraints, and grid connectivity. The GIS model enables evaluation of potential sites and informs siting decisions to reduce costs and environmental impacts. In conclusion, the document states that GIS provides a practical and reliable way to map suitable areas and make more efficient siting decisions.
This document discusses data quality and provides facts about the high costs of poor data quality to businesses and the US economy. It defines data quality as ensuring data is "fit for purpose" by measuring it against its intended uses and dimensions of quality. The document outlines best practices for measuring data quality including profiling data to understand metadata and trends, using statistical process control, master data management to create standardized "gold records", and implementing a data governance program to centrally manage data quality.
DAS Slides: Data Quality Best PracticesDATAVERSITY
Tackling Data Quality problems requires more than a series of tactical, one-off improvement projects. By their nature, many Data Quality problems extend across and often beyond an organization. Addressing these issues requires a holistic architectural approach combining people, process, and technology. Join Nigel Turner and Donna Burbank as they provide practical ways to control Data Quality issues in your organization.
Chapter 12: Data Quality ManagementAhmed Alorage
This document discusses data quality management (DQM). It covers DQM concepts and activities, including developing data quality awareness, defining data quality requirements, profiling and assessing data quality, and defining metrics. The key DQM approach is the Deming cycle of planning, deploying, monitoring, and acting to continuously improve data quality. Data quality requirements are identified by reviewing business policies and rules to understand dimensions like accuracy, completeness, consistency and more.
This document discusses the BCBS 239 regulatory requirements for risk data aggregation and risk reporting. It outlines the key components of BCBS 239 including risk governance, infrastructure, data aggregation, and reporting. It also describes a risk data self-assessment diagnostic study that banks should conduct to evaluate their risk operating model, processes, data usage, and infrastructure in order to identify gaps and develop projects to address deficiencies to comply with BCBS 239. Finally, it presents a proposed unified risk data model and architecture to integrate risk data across different risk types and business units.
This document discusses data quality and data profiling. It begins by describing problems with data like duplication, inconsistency, and incompleteness. Good data is a valuable asset while bad data can harm a business. Data quality is assessed based on dimensions like accuracy, consistency, completeness, and timeliness. Data profiling statistically examines data to understand issues before development begins. It helps assess data quality and catch problems early. Common analyses include analyzing null values, keys, formats, and more. Data profiling is conducted using SQL or profiling tools during requirements, modeling, and ETL design.
This presentation was part of the IDS Webinar on Data Governance. It gives a brief overview of the history on Data Governance, describes how governing data has to be further developed in the era of business and data ecosystems, and outlines the contribution of the International Data Spaces Association on the topic.
A Day in the Life of an Enterprise Architect (Role Play Exercise) 2016Daljit Banger
During Nov 2016 the BCS EA SIG ran a session entitled "Enterprise Architecture Practitioners Day / Hackathon" in London - These are my slides for my sesion at the event.
Real-World DG Webinar: A Data Governance Framework for Success DATAVERSITY
A Data Governance Framework must include best practices, a practical set of roles & responsibilities for Data Governance built specifically for your organization, a plan for communicating with the entire organization and an action plan for applying governance in effective and measurable ways.
Join Bob Seiner for this Real-World Data Governance webinar as he discusses how to stay practical and work within the culture of your organization to develop and deliver a Data Governance Framework to meet your specifications and the business’ expectations.
This session will focus on:
Defining a Non-Invasive Operating Model of Roles & Responsibilities
Clearly Stating the Difference between Executive, Strategic, Tactical, Operational & Supporting Roles
Defining Data Stewards, Data Stewardship and How to Steward the Data
Recognizing & Identifying People into Roles Rather than Handing them to People as New Responsibilities
Leveraging the Framework to Implement a Successful Data Governance Program
Tackling Data Quality problems requires more than a series of tactical, one-off improvement projects. By their nature, many Data Quality problems extend across and often beyond an organization. Addressing these issues requires a holistic architectural approach combining people, process, and technology. Join Nigel Turner and Donna Burbank as they provide practical ways to control Data Quality issues in your organization.
This document discusses the importance of data quality and data governance. It states that poor data quality can lead to wrong decisions, bad reputation, and wasted money. It then provides examples of different dimensions of data quality like accuracy, completeness, currency, and uniqueness. It also discusses methods and tools for ensuring data quality, such as validation, data merging, and minimizing human errors. Finally, it defines data governance as a set of policies and standards to maintain data quality and provides examples of data governance team missions and a sample data quality scorecard.
Cluster analysis is an unsupervised machine learning technique that groups similar data objects into clusters. It finds internal structures within unlabeled data by partitioning it into groups based on similarity. Some key applications of cluster analysis include market segmentation, document classification, and identifying subtypes of diseases. The quality of clusters depends on both the similarity measure used and how well objects are grouped within each cluster versus across clusters.
Datasaturday Pordenone Azure Purview Erwin de KreukErwin de Kreuk
Azure Purview is Microsoft's solution for unified data governance. It includes three main components:
1. The Purview Data Map automates metadata scanning and lineage identification across hybrid data stores and applies over 100 classifiers and Microsoft sensitivity labels.
2. The Purview Data Catalog enables effortless discovery through semantic search and a business glossary, and shows data lineage with sources, owners, and transformations.
3. Purview Insights provides reports on assets, scans, the glossary, classification, and sensitive data labeling to give visibility into data usage across the estate.
The document discusses technical vulnerability management and outlines the key steps in the NIST Risk Management Framework that include vulnerability analysis. It also covers establishing an effective Patch and Vulnerability Group to monitor for vulnerabilities, prioritize remediation, and deploy patches. Finally, it provides examples of different types of vulnerability analysis tools including network scanners, host scanners, and web application scanners.
Building a Data Strategy – Practical Steps for Aligning with Business GoalsDATAVERSITY
Developing a Data Strategy for your organization can seem like a daunting task – but it’s worth the effort. Getting your Data Strategy right can provide significant value, as data drives many of the key initiatives in today’s marketplace – from digital transformation, to marketing, to customer centricity, to population health, and more. This webinar will help demystify Data Strategy and its relationship to Data Architecture and will provide concrete, practical ways to get started.
The document discusses data governance at OMES. It defines data governance as an active, cross-organizational framework for securely sharing data, analyzing data across divisions, collaborating with stakeholders, and improving data quality. The mission of OMES's data governance program is to proactively define and align data rules, provide ongoing protection and services to data stakeholders, and identify and resolve data issues. Data governance supports strategic business goals by ensuring business needs drive information needs and technical needs. It is a business function that directly supports the agency's strategic goals.
Duke Energy implemented a smart grid project in Ohio with the objectives of improving reliability, reducing costs, and enabling greater customer access to energy use data. The project invested $100 million to install over 140,000 smart meters and distribution automation equipment, benefiting both customers and utilities. Customers gained near real-time energy use data and more accurate billing while utilities saw decreased outage times, reduced system losses and improved data for planning.
This document discusses using stream computing approaches to better analyze large amounts of smart meter data from power grids. It proposes moving away from centralized data processing models towards more distributed event processing models. This would allow utilities to create real-time insights from operational data and improve demand response management. The document also explores using cloud platforms and complex event processing techniques to more efficiently handle smart meter data streams in real-time at large scales.
An intelligent water network asset management solution provides three key benefits: 1) city-wide visibility of water network assets to improve operational planning and prioritization; 2) superior predictive and preventive maintenance by analyzing data to proactively maintain assets; and 3) consumption analysis and dashboards to provide intuitive views of water usage and allow better planning.
The document discusses the implementation of the Restructured Accelerated Power Development and Reforms Program (R-APDRP) in Rajasthan, India. Key points:
- R-APDRP aims to establish reliable baseline data and adopt IT in energy accounting to reduce losses before distribution strengthening projects.
- It has two parts - Part A focuses on IT applications for energy auditing and consumer services. Part B covers network renovation.
- The Discoms of Rajasthan have taken steps like forming implementation committees and appointing an IT consultant to timely execute the scheme and avail grants.
- Benefits of R-APDRP include increased consumer satisfaction, transparency, reduced out
Service based / modeled IT operations demands that Infrastructure needs are catered to with minimal disruptions and loss of user experience. Demand and capacity management for a critical cog in IT / service design to ensure that the service / infrastructure is fully available to users through its lifecycle
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Smart distribution system the need of automation & it application in powe...SoumyaRanjanDas13
This document discusses automation technologies in power distribution systems. It describes several key automation systems including SCADA for monitoring equipment, substation automation, distribution management systems, outage management systems, advanced metering infrastructure, and geographical information systems. It also discusses smart meters and remote control switches as devices used in automation. The document provides details on the features and functions of these various automation components for improving reliability, efficiency, and safety in power distribution networks.
This document provides an overview of smart grid deployment in the United States, including smart meter infrastructure and benefits. It discusses the status of smart meter deployments across the country, with 46 million smart meters installed so far and a goal of 65 million by 2015. Nearly 75% of smart meters have been installed in 10 states that have driven adoption through policies, incentives and experience. Smart meters provide benefits like remote meter reading, outage detection, and voltage management. The document also defines smart meters and meter systems, outlining the evolution from automated meter reading to advanced metering infrastructure. Key benefits for utilities include reduced costs from limited truck rolls and improved outage management. Metering operations play an important role in smart grid projects.
Smart Grids:Enterprise GIS For Distribution Loss Reduction in Electric Utilit...HIMADRI BANERJI
1. The document discusses implementing an Enterprise GIS system for two power distribution companies in Delhi, BRPL and BYPL, to help reduce distribution losses and improve customer service.
2. Key goals of implementing GIS include reducing outage times, stopping power theft, improving asset management, and achieving a zero fatality safety rate.
3. The implementation plan includes developing GIS data models, capturing network and customer data digitally, integrating GIS with other systems like SAP and SCADA, and providing network analysis tools.
4. Estimates show the project has a payback period of less than 1 year and will generate over $400 million in additional revenue over 3 years with returns of 138%, making
The document describes IBM's Intelligent Operations Center software, which provides integrated data visualization, real-time collaboration and analytics tools to help city agencies improve operational efficiency, anticipate and respond better to problems, and enhance public services without increasing costs. The Intelligent Operations Center integrates information from different city systems and departments, provides executive dashboards and reports, supports emergency response standard operating procedures, and facilitates citizen collaboration and social media analytics. It aims to give cities a unified operational picture and help agencies better coordinate resources and responses.
Presentation of Inetrproject for Water treatment sectors, Interproject is the member of Association of Industrial Automation in Ukraine, they have big experience in Waste Water sectors. This presentation is an overview of their solutions and references for mentioned sectors.
Mr. Paul Chang's presentation at QITCOM 2011QITCOM
QITCOM 2011
Presentation:
City Operations Centre for Managing City
Presenter:
Mr. Paul Chang - Business Development Executive for Emerging Markets, IBM
Defining Pace of Urban Development: E-Governance in ULB's and PWD's.Omkar Parishwad
The rapid development of cities has been concerned with the delivery of services in an organized, planned manner. The urban sector in India, is struggling to make effective use of Information and Communication Technology to further deployment of resources for information retrieval, decision making, ongoing management, service delivery and outreach. All evidence points to the obvious benefits of the use of ICT; environmental and economic sustainability and general livability. This vision of egovernance involves ICT applications to mitigate the impacts of rapid urbanization. With E-Government systems revolutions befalling urban India due to various policy level interventions by the government; swiftness in development has been ascertained. The present paper investigates Urban Development that has brought e-Governance applications catering to the Government relating to Infrastructure Sector, amongst others; thus affecting environmental, social and economic structure significantly. The study further finds the scope of progress and affected areas for development, encouraged by certain e-Government solutions. The research helps us arrive at a line of action and necessary initiatives for successful implementation of ICT based solutions in Infrastructure industry. It also allows a peek into future scenario of improvements and deliberations in India in consideration with the scenario of developing countries.
This document provides an executive summary on the transformation of utility asset management due to new technologies like smart meters, sensors, cloud computing, predictive analytics, and the internet of things. It discusses how these technologies have created new sources of data that utilities must now integrate and analyze in real-time to improve asset management. The future of asset management will rely more on data-driven decision making using descriptive, predictive, prescriptive and adaptive analytics. This will allow utilities to move from reactive to proactive maintenance to improve reliability and reduce costs.
Automation of a state electric transmission and distribution department in indiaHcl Brand
HCL automated the power distribution system of a utility in North India serving over 40 lakh consumers. It prepared a baseline data system with consumer indexing, GIS mapping and automatic meter reading. It also implemented applications for meter reading, billing, asset management and consumer grievances. This is helping reduce transmission losses, increase commercial viability and consumer satisfaction.
EPC Solutions LLP, which is into Large Scale Infra Projects (Metro, Airports, Stadiums & Other Mega Projects), Energy Solutions (EPC Solutions for Transmission & Distributed System upto 765kV, Contour & Route Survey, Soil Investigating etc.), Solar (EPC), Structure Supply - For Infra, Energy T&D & Solar Segment, MEP Services, SEZ & Other Consultancy Services, BIM Services (Upto LOD 500) & Geographical Information System, IT Services (Web Development, Software Solutions & Manpower Solutions).
The document discusses how in-memory database systems can support the data processing needs of the Smart Grid 2.0 by enabling real-time analytics and decision making. It describes how the Smart Grid requires real-time monitoring and control of energy generation, distribution and usage. In-memory databases are designed to handle the large volumes of real-time data generated by the Smart Grid and support use cases like demand response, forecasting, and real-time pricing that require immediate analysis of smart meter and other operational data. The document provides examples of Smart Grid applications that could benefit from the real-time analytics capabilities of in-memory database systems.
The document discusses the use of geographic information systems (GIS) in managing smart grid technology for power distribution utilities. It describes how GIS can be used to map distribution assets, monitor power supply, and improve commercial and customer services functions. The document also outlines some of the key components of GIS, including software, data, and infrastructure. Finally, it discusses how GIS will play a critical role in enabling smart grid technologies by facilitating an easily updatable and accessible database to support reliable power supply, efficient billing and collections, comprehensive energy auditing, and theft detection.
Revue de presse IoT / Data du 26/03/2017Romain Bochet
Sommaire :
- From the Edge To the Enterprise
- The Internet of Energy: Smart Sockets
- Google's big data calculates US rooftop solar potential
- Energy management: Oracle Utilities launches smart grid and IoT device management solution in the cloud
- Are vehicles the mobile sensor beds of the future?
This document provides an overview of how information technology is being used to improve operations in the power sector. It discusses how IT can increase business process efficiency, capacity building, metering and billing accuracy, and customer satisfaction. The document then examines specific challenges around network architecture standards and the case study of KPCL in India. KPCL has established a satellite-based communication network and utilizes MPLS for services like video conferencing. The document also explores how geographic information systems (GIS) can be used to map infrastructure and improve decision making. Finally, it discusses security requirements around availability, confidentiality, integrity and authentication for power sector communication networks.
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2. Intelligent management of Power Transmission and
distribution
2
It requires extensive use of ICT based systems for effective management of Power
transmission and distribution network Assets
Achieve smarter city imperatives with real time geospatial
enabled analytics for effective Citizen services
Complete Asset information and
better accountability
Real time operational data for
informed decision making
Single Unified view of entire
Operations
SmartCities are expected to provide world class services to its Citizens by effective and
efficient operations of its Power supply network assets, reducing the power leakages &
un-planned Outages, and improving operational performance.
3. Role of ICTs in Power Transmission & Distribution
3
Apart from the increasing gap between demand and supply, the main challenge being faced by
Indian power sector is the huge Transmission and Distribution (T&D) losses. The national average
Aggregate Technical and Commercial losses (AT&C) were reported to around 27.15% in 2009-2010
[CEA Energy Generation Report 2010-11]
In the distribution sub-sector, which is presently the weakest link in the power supply chain, Cost-
effective ICT interventions can improve the operational and financial performance of the entire
power sector. An important enabler is Government of India’s flagship program R-APDRP
Key ICT interventions in power distribution sub-sector that need immediate attention are:
Advanced Metering Infrastructure [AMI],
Advanced Distribution Operation including Distribution Automation and advanced controls,
Asset management,
Improved interfaces and decision support,
Regulatory Information Management system,
Service Delivery Mechanism
Source: ‘Smart(er) Metering: An Enabler of Transformation’ by Dr. Rahul Tongia, Tech. Advisor,
Smart Grid Task Force, Govt. of India [ Feb. 10, 2015]
4. Priority areas for ICT Interventions
4
Major components
of power
distribution
Opportunities for ICT sector Impact /Benefits
AMI • Smart Meters
• Consumer portals
• Home Area Network (HAN)
• Meter Data Acquisition and Data Management
System
• Customer service application and operational
gateway applications
• Assist revenue management
• Empower consumers
• Reliable supply
Asset management • Developing central database of information of
Assets / equipment
• GIS mapping of power line and power system
equipment
• Management Information System(MIS)
• Resource optimization
• Inventory Management
• Process streamlining
• Locating leakage and checking
• conductor theft
• Quick restoring the services post
• damage/ outage
Service Delivery
Mechanism
• Enabling different modes of billing (e-billing, m-
billing) and collection
• Fault Monitoring System
• SMS based alerts about services (outages,
maintenance, billing etc.)
• Empower customers
• Streamline the service delivery
As per the NASSCOM report, the following are the areas which need immediate ICT interventions
Source: Report ‘Sustainable Tomorrow: Harnessing ICT Potential’ by Business Council for Sustainable
Development (TERI-BCSD) and National Association of Software & Services Companies (NASSCOM).
5. Role of Ministry of Power, GOI - Integrated Power
Development Scheme [IPDS]
5
Source : http://apdrp.gov.in/Form_IPDS/Additional_Guidelines_Regarding_Towns
The Union Cabinet chaired by the Prime Minister, Shri Narendra Modi, launched "Integrated Power
Development Scheme" (IPDS) with the objectives of:
• strengthen of sub transmission and distribution system,
• metering of distribution transformers / feeders / consumers in the urban areas, and
• IT enablement of distribution sector [as per CCEA target laid under R-APDRP]
Source: APDRP Order http://www.apdrp.gov.in/IPDS_Order_Guidelines/IPDS_OM.pdf
The component of IT enablement approved by CCEA in June, 2013 in the form of RAPDRP for 12th and
13th Plans will get subsumed in this scheme and CCEA-approved scheme outlay of Rs.44,011 crore
including a budgetary support of Rs. 22,727 crore will be carried over to the new scheme of IPDS.
• The process of sanction of projects shall commence from December 2014
• After sanction of projects, contracts for execution of projects are to be awarded by States
Discoms / Power Departments.
• The projects shall be completed within 24 months from date of award.
6. How State Power companies can take up ICTs
6
State Government owned power companies can leverage the IPDS scheme to implement ICTs and
make the power transmission more efficient
Coverage of Urban Areas:
Monitoring Committee approved include the following towns also in addition to the statutory towns
as urban area to be covered under IPDS:
• District head quaters (particularly in NE States) if covered under urban category.
• Towns covered under urban category as per Census 2011.
• Towns notified by State Govt.(as urban / Municipal area).
[Additional Criteria for Towns Approved in 2nd Monitoring Committee on 19-Feb-2015]
[Inclusion of additional towns beyond Statutory towns for coverage under urban area for sanction of project under
IPDS]
7. Integrated Power Development Scheme
7
Scope of ICT under this scheme
Source: APDRP Order http://www.apdrp.gov.in/IPDS_Order_Guidelines/IPDS_OM.pdf
8. Need for an Intelligent Operations Management
System for Power Transmission & Distribution
Smart Operations Management system
enables the Power Distribution
companies to overcome challenges with:
Transmission Assets
(Transformers, Capacitors,
Poles, etc) location & condition,
Preventive Maintenance of
assets,
Equipment Theft and
Real time monitor and control
of Field force operations.
8
9. Intelligent Transmission Monitoring System
Asset Management
Field Force / Work
Management
Inventory &
Procurement
Decision Support
Geographic
Information
System
9
Asset Usage Monitoring
Field force & Work Management
GIS/GPS based Inventory tracking
Real time monitoring of assets
Key Tenets of Intelligent Transmission Management System
10. GIS based Tracking of Transmission Assets
10
The Integrated Asset Management solution extends the capabilities of the Asset Management applications by
adding maps to Asset, Location, Work Order & Service Request applications.
Integrated solution that is linked to GIS and Asset related information is displayed on the Maps
Spatial Analysis of asset
deployment
Visualization of consumption
Data
Better Decision Making
Assets and Resource
Optimization
No Asset Theft
Asset
Identification on
Maps
Asset Health &
Usage Monitor
Location based
Outage
management
Service
Assurance &
Theft Detection
Service order
Management
GIS based
Asset
Management
11. Integrated Distribution Operations solution for smart
cities
11
Value proposition
Empowers Cities towards effective information
sharing through spatially enabled workflow
Geospatial enabled e-Governance solution to
deliver government services and workflows
12. Continuous Monitoring of Asset Usage & condition
12
Key Benefits
Real time data for monitor
and control
Timely triggers for predictive
maintenance, Work Orders
Reduces data discrepancy
Reduces loss/lack of crucial
data
Remote Terminal
Unit (RTU): it’s a
device which
converts the
sensor signals to
data signals
RTU
Supervisory
Station
EAM
refers to the servers
responsible for
communications
between RTU’s &
the Human-Machine
Interface
software solution
which will handle
the alerts raised
thro SCADA system
till the point of
resolution.
Real time operational details from Transmission Asset Monitoring application provides information for
Preventive Maintenance Schedules and Condition based Predictive Maintenance defined on critical
assets
13. Intelligent Outage Management
13
Outage
Mgmt
System
GIS
CIS
EAM
SCADA
Leveraging GIS maps, the system diagnose the outage for location, cause, extent of
outage, its priority, affected customers, expected restoration time
The outage management system helps in identifying the location of
fault (on the GIS Maps) and its causes resulting in improved response
time for dispatching crews and improving the service restoration time.
Outage Management system integrates with Geographic Information
system (GIS) Enterprise Asset Management (EAM), Customer
Information System (CIS), and SCADA
The historical data enables measurement of key performance metrics
like Outage Response duration, work back log, frequency
An integrated Outage Management system to manage different types
of works and service requests in one application environment. Outage
Management System helps to manage outages effectively and
efficiently.
14. Outage Management Solution…ctd
14
Network elements
affected by the break
down of selected
element are highlighted
in yellow color
Selected network
element highlighted in
RED color
15. Smart Field Force Management
15
Our Integrated Asset Management contains the Field
Force Management capabilities:
Field Force is a combination of labor and tools defined to
perform a work.
Field Force Management helps in identification of right
resources for the right work
Identify and Map Right Skills and Qualifications for a work
Work allocation based on current location
View ongoing work assignments and priorities
Asset Condition Assessment Solutions
Damage Assessment Solutions
Live field assistance for supporting the crew at work
Flexible interface capabilities for work assignment notifications in real
time or near real time based on requirement
Field Navigatio
Enterprise GIS Repository
Extract
Update
Asset Condition
Assessment
Vegetation
Management Field Mapping/
compliance
Surveys
Download
Interne
t
It enables real time location tracking of crews and dispatch crews to work and manage Work order
16. Smart Field Force Management – GIS based
16
Add Retail Dealer
to the Route
Planner
Add Warehouse
to the Route
Planner
Add New Retail
Dealer
Get the Route on the
map
Change the
position of
the
waypoint
along the
route
Delete
button,
meant to
delete the
way point
18. A bird’s eye view of proposed Intelligent Power
Distribution Management solution landscape
18
Power Transmission Company
Work Management Material Management
Material
Requests
Item
Master
Material
Planning
Inventory Receipts
Issues /
Returns
Transfers
Assets Maintenance
Asset Management
GIS
Locations
Move/Swap
Revenue
O&M
Non
Revenue
Field Force Mgmt
Work Plan
Work order
Dispatch
Route
Plan
Service
Requests
GL
Project
Costing
Projects
Asset
Accounting O&M Budgeting
Purchase Inventory
Service
Orders
Billing
Payments Collections
Customer Service
19. Comprehensive Functional Landscape
19
EnterpriseServiceBus
PROCESS AND
FUNCTIONS
Work Management
Asset
Management
Material Management
Asset Life Cycle
Asset Specifications
Meters
Asset Maintenance History
Move Swap
Asset and Locations Hierarchy
Locations
Receive and
Setup
Dispose
Track
Move
Embedded GIS
Service Address
Spatial Enablement
Availability and Valuation
Issue, Transfer and Returns
Storerooms and Reordering
Items, Tools and Inventory
Procurement
Material and Purchase
Requests
Contracts, RFQ and PO
Receiving
Invoicing
Project Request
Assign Planner
Scope, Schedule & Budget
Preliminary Design
Siting & Permitting
Land Acquisition
Final Design
Specifications
Material Requirements
Procurement (LL)
Site Preparation
Construction
Commissioning
Close Out
Financial
Management
Cost
Management
Chart of
AccountsCondition Monitoring
Service Requests
Work Order Tracking Job Plans
Safety Plans
Failure Codes
SchedulingWork Assignments
Work Dispatch
Work Flow and
Notifications
Electronic Routing
and Approval
Labor Reporting
Preventive Maintenance
Corrective Maintenance
Failure Reporting
Quick Reporting