The documents discuss the importance of data warehouses and decision support systems for large organizations. A data warehouse integrates data from multiple sources to provide a comprehensive view of information. A decision support system transforms this data into actionable knowledge through analysis and discussion. The documents outline best practices for designing such a system, including determining user needs, establishing governance, and ensuring collaboration between data and IT experts. Finally, they discuss steps for building the framework, such as conceptual agreement, staffing, and funding, noting it is a multidimensional process.
ELDP Capital Planning White Paper_20130130Carlos Rivero
This document provides recommendations for building tools to improve capital planning within the Department of Commerce (DOC). It finds that effective capital asset management requires integrated processes, tools, and human engagement across DOC bureaus. It recommends: 1) Creating processes and tools to support enterprise-wide asset management, 2) Developing, testing, and implementing a capital asset portal and database, and 3) Training and engaging DOC bureaus in using these new tools. The document outlines these recommendations and next steps in detail to streamline DOC's capital planning and ensure transparent access to asset data.
This document is the annual Global Venture Capital and Private Equity Country Attractiveness Index for 2009/2010 published by Alexander Groh and Heinrich Liechtenstein. It measures and ranks countries based on their attractiveness for venture capital and private equity investments. The index analyzes over 300 data points for many countries and calculates scores based on factors like market size, economic activity, capital market development, taxation, and legal protections. It finds the United States to be the most attractive country, followed by Canada, the UK, Israel, and India. The report also includes analysis of VC/PE activity and conditions in different global regions.
Executive skills reference by bhawani nandan prasadBhawani N Prasad
This document provides an overview of executive skills reference topics including aligning executive strategy, managing and transforming professional service firms, managing family businesses for generational success, leading growth through customer centricity, building a global enterprise, driving growth through innovation, improving corporate performance and profitability, leadership and corporate accountability, and maximizing leadership potential. Each topic contains 3-7 subtopics that provide guidance on strategic issues executives may face.
This document is the 2011 annual report of the Global Venture Capital and Private Equity Country Attractiveness Index. It introduces the index, which measures the attractiveness of countries for venture capital and private equity investors. The report covers 80 countries, more than the first edition. It describes how the index is constructed based on quantitative data about markets, tax/regulation, investment opportunities and human/social factors. Country rankings and profiles are provided. Analyses show the index successfully tracks real investment activity and returns. Guest articles also examine the relationship between fund size and performance.
State of Georgia Annual state it report 2012 finalState of Georgia
The document provides an annual report on the state of Georgia's information technology for fiscal year 2012. It discusses the state's $1 billion annual investment in IT and the ongoing transformation of the state's IT systems and services through the Georgia Enterprise Technology Services program. The report covers topics such as the current IT portfolio, industry trends, examples of how technology supports the governor's goals, IT governance, security, and strategy. It aims to provide state leaders with information to make informed decisions about IT investments.
RBC C1 Working Group - NAIC Risk-Based Capital Factor - Proposal August 3, 2015kerrynelson
This document proposes new risk-based capital factors for fixed income securities in the NAIC's Life Risk-Based Capital formula. It recommends increasing the number of rating categories from 6 to 14 to better differentiate risk. The proposed factors are based on projected losses over 10 years at a 92nd percentile confidence level. It details the methodology used to develop the factors, including assumptions around default rates, recovery rates, economic scenarios, representative portfolios, and more. Appendices provide additional details on the modeling approach, assumptions, validation process, and reconciliation of the new factors.
ELDP Capital Planning White Paper_20130130Carlos Rivero
This document provides recommendations for building tools to improve capital planning within the Department of Commerce (DOC). It finds that effective capital asset management requires integrated processes, tools, and human engagement across DOC bureaus. It recommends: 1) Creating processes and tools to support enterprise-wide asset management, 2) Developing, testing, and implementing a capital asset portal and database, and 3) Training and engaging DOC bureaus in using these new tools. The document outlines these recommendations and next steps in detail to streamline DOC's capital planning and ensure transparent access to asset data.
This document is the annual Global Venture Capital and Private Equity Country Attractiveness Index for 2009/2010 published by Alexander Groh and Heinrich Liechtenstein. It measures and ranks countries based on their attractiveness for venture capital and private equity investments. The index analyzes over 300 data points for many countries and calculates scores based on factors like market size, economic activity, capital market development, taxation, and legal protections. It finds the United States to be the most attractive country, followed by Canada, the UK, Israel, and India. The report also includes analysis of VC/PE activity and conditions in different global regions.
Executive skills reference by bhawani nandan prasadBhawani N Prasad
This document provides an overview of executive skills reference topics including aligning executive strategy, managing and transforming professional service firms, managing family businesses for generational success, leading growth through customer centricity, building a global enterprise, driving growth through innovation, improving corporate performance and profitability, leadership and corporate accountability, and maximizing leadership potential. Each topic contains 3-7 subtopics that provide guidance on strategic issues executives may face.
This document is the 2011 annual report of the Global Venture Capital and Private Equity Country Attractiveness Index. It introduces the index, which measures the attractiveness of countries for venture capital and private equity investors. The report covers 80 countries, more than the first edition. It describes how the index is constructed based on quantitative data about markets, tax/regulation, investment opportunities and human/social factors. Country rankings and profiles are provided. Analyses show the index successfully tracks real investment activity and returns. Guest articles also examine the relationship between fund size and performance.
State of Georgia Annual state it report 2012 finalState of Georgia
The document provides an annual report on the state of Georgia's information technology for fiscal year 2012. It discusses the state's $1 billion annual investment in IT and the ongoing transformation of the state's IT systems and services through the Georgia Enterprise Technology Services program. The report covers topics such as the current IT portfolio, industry trends, examples of how technology supports the governor's goals, IT governance, security, and strategy. It aims to provide state leaders with information to make informed decisions about IT investments.
RBC C1 Working Group - NAIC Risk-Based Capital Factor - Proposal August 3, 2015kerrynelson
This document proposes new risk-based capital factors for fixed income securities in the NAIC's Life Risk-Based Capital formula. It recommends increasing the number of rating categories from 6 to 14 to better differentiate risk. The proposed factors are based on projected losses over 10 years at a 92nd percentile confidence level. It details the methodology used to develop the factors, including assumptions around default rates, recovery rates, economic scenarios, representative portfolios, and more. Appendices provide additional details on the modeling approach, assumptions, validation process, and reconciliation of the new factors.
Why is it so hard to set up a Google Mobile listing?Dev Bhatia
Why is it so hard to set up a Google Mobile listing? There are some real barriers which prevent small business owners from setting up their mobile listings. At mTrax, we find that about 95% of the small businesses we talk to are ready to do it, but haven't yet. Here's why.
[1] Scientific research provides welfare benefits to society when funding is increased and knowledge is effectively communicated. [2] The Spanish national health system guarantees universal coverage at a low cost per capita compared to countries like the US, and is ranked among the best in the world. [3] The system has strategic value by ensuring workforce health, generating wealth, and providing high-quality jobs that support other industries like biotechnology and tourism.
Prezentace Richarda Fridricha o reputačních systémech na WebExpo Prague 2011. Více naleznete na http://webexpo.cz/praha2011/prednaska/reputacni-systemy/
Jaroslav Šnajdr: Getting a Business Collaboration Service Into Cloud: A Case ...WebExpo
"Want to build a complete service product in cloud? Come and learn what it takes."
More at http://webexpo.net/prague2013/talk/getting-business-collaboration-service-into-cloud/
The Solar Future DE - Xiaofeng Peng "What is the grid parity vision of the le...Paul van der Linden
The document is a disclaimer and presentation from LDK Group providing an overview and update about the company in June 2010. It summarizes that LDK is a leading global solar energy company that has grown rapidly since being founded in 2005. It is vertically integrated across the solar supply chain from polysilicon production to wafer, cell, and module manufacturing. The presentation discusses LDK's mission, vision, strengths, large scale manufacturing capacity, technology advancements, and growth strategies.
This document discusses the changing relationship between boards of directors and chief information officers (CIOs) in light of rapid technological changes. It argues that boards need to develop greater capabilities and understanding of technology to effectively oversee companies in today's digital business environment. The document explores how different technologies may shape the role of the CIO and notes a need for boards to work with CIOs to develop strategic alignment between technology, organizational goals, and innovation initiatives. It also examines some structural barriers that can prevent CIOs from being successful in their roles.
The document discusses business intelligence tools and how they can be used in marketing from a retail industry perspective. It provides an overview of various BI tools like reporting, dashboards, OLAP, data mining and how they are used. It also discusses how metrics like marketing scorecards, customer lifetime value and sensitivity analysis can help marketing. Finally, it outlines how BI tools can help with key performance metrics, complex analysis, predictive modeling and decision making for the retail industry.
Big Data, Little Data, and Everything in Betweenxband
This white paper discusses how IBM SPSS solutions help organizations analyze both big data and smaller datasets to provide analytics to diverse users. It notes that while many organizations claim to have big data, analytics needs vary widely depending on the user and department. The paper advocates providing a unified analytics platform that can scale from small to large datasets and meet the needs of users with different skill levels. It also discusses trends toward predictive analytics and giving more users access to modeling tools to support data-driven decision making across organizations.
This course provides an overview of data analytics and business intelligence. It teaches students how to analyze and tell stories with data, which is an in-demand skill as data collection increases. The course covers topics such as SQL, data modeling, Power BI, data warehousing, and big data to prepare students for careers as data analysts. Upon completing the hands-on training, students will feel confident to begin work in the industry analyzing, extracting, transforming, and loading data according to business requirements.
Architecting a-big-data-platform-for-analytics 24606569Kun Le
This document discusses the growth of big data and the need for businesses to analyze new and complex data sources. It describes how data has become more varied in type, larger in volume, and generated faster. It also outlines different types of big data analytics workloads and technology options for building an end-to-end big data analytics platform. Finally, it provides an example of IBM's solution for analyzing both data in motion and at rest across the entire big data analytics lifecycle.
Data Science & BI Salary & Skills ReportPaul Buzby
The document is a report on data science and business intelligence skills and salaries based on a large survey. Some of the key findings from the report include:
- Small and medium enterprises pay inexperienced data scientists and analysts higher starting salaries than large enterprises. Finance also offers high pay for those just starting out.
- Data architect is a highly valuable role, especially in fast-paced industries like media and entertainment where building business-critical solutions is important.
- While consulting has many data professionals with over 20 years of experience, education/academia and research attract less experienced data scientists despite not being the highest paying industries.
This document discusses how business intelligence tools can help the retail industry. It describes various BI tools like reporting, forecasting, dashboards, and data mining. It explains how these tools can help with marketing metrics like the marketing scorecard, customer lifetime value calculation, and sensitivity analysis. Finally, it outlines how BI supports retail businesses with key performance reporting, complex analysis, predictive modeling, and informed decision making.
The emergent recognition of the value of analytics clashes with the rampant growth of the volume of
both structured and unstructured data. Competitive organizations are evolving by adopting strategies
and methods for integrating business intelligence and analysis in a way that supplements the spectrum
of decisions that are made on a day-to-day and sometimes even moment-to-moment basis. Individuals overwhelmed with data may succumb to analysis paralysis, but delivering trustworthy actionable
intelligence to the right people when they need it short-circuits analysis paralysis and encourages
rational and confident decisions.
This document discusses IT financial management (ITFM) and the need for transparency and effective governance. It outlines the information needs of key ITFM stakeholders like IT management, business leaders, and the CFO office. Common flawed ITFM practices that create problems are identified, such as differing accounting methods and a lack of business accountability for IT investments. The document proposes seven best practice principles for ITFM, including enabling transparency through measuring labor/asset usage and forecasting. It describes how HP software and services can help implement these principles to improve IT investment decisions, financial accounting, and resource allocation.
District Office of Info and KM - Proposed - by Joel Magnussen - 2004Peter Stinson
The document discusses the potential benefits of improved information sharing and knowledge management. It envisions a future where everyone within an organization has access to all relevant information whenever needed. This would allow for better decision-making, more efficient responses to issues, and continuous learning from past experiences and events. The document outlines several initiatives underway to build an integrated information framework with these goals.
IoT intelligence: Attitudes towards big data and advanced analyticsAbhishek Sood
According to the extensive market study by Dresner Advisory Services, fewer than 15% of respondents consider IoT a “critical” business opportunity, but about 53% will say it’s at least “somewhat important.”
And yet, hype continues to build around IoT’s potential to drive innovation in the way people work, connect, and interact. To understand this IoT phenomenon, this study delves into topics like:
Perceptions of IoT by region and industry
Top drivers of analytics and BI
The potential impact of IoT and cloud on BI
Predictive analytics and big data
And more
Read on to find out more about how IoT advocate vs. skeptic perceptions of smart technology stack up, and learn how IoT could impact analytics and BI.
This document outlines an agenda and presentation on business intelligence (BI) given by Stanislava Tropcheva and Ani Vasileva. The presentation introduces BI and discusses data visualization, BI architecture, demonstrations of BI tools, the Gartner Magic Quadrant for BI platforms, and encourages participants to try using BI tools themselves. It poses questions to help explain key BI concepts and get participants more engaged in understanding how BI can transform raw data into useful information to power decision making.
Why is it so hard to set up a Google Mobile listing?Dev Bhatia
Why is it so hard to set up a Google Mobile listing? There are some real barriers which prevent small business owners from setting up their mobile listings. At mTrax, we find that about 95% of the small businesses we talk to are ready to do it, but haven't yet. Here's why.
[1] Scientific research provides welfare benefits to society when funding is increased and knowledge is effectively communicated. [2] The Spanish national health system guarantees universal coverage at a low cost per capita compared to countries like the US, and is ranked among the best in the world. [3] The system has strategic value by ensuring workforce health, generating wealth, and providing high-quality jobs that support other industries like biotechnology and tourism.
Prezentace Richarda Fridricha o reputačních systémech na WebExpo Prague 2011. Více naleznete na http://webexpo.cz/praha2011/prednaska/reputacni-systemy/
Jaroslav Šnajdr: Getting a Business Collaboration Service Into Cloud: A Case ...WebExpo
"Want to build a complete service product in cloud? Come and learn what it takes."
More at http://webexpo.net/prague2013/talk/getting-business-collaboration-service-into-cloud/
The Solar Future DE - Xiaofeng Peng "What is the grid parity vision of the le...Paul van der Linden
The document is a disclaimer and presentation from LDK Group providing an overview and update about the company in June 2010. It summarizes that LDK is a leading global solar energy company that has grown rapidly since being founded in 2005. It is vertically integrated across the solar supply chain from polysilicon production to wafer, cell, and module manufacturing. The presentation discusses LDK's mission, vision, strengths, large scale manufacturing capacity, technology advancements, and growth strategies.
This document discusses the changing relationship between boards of directors and chief information officers (CIOs) in light of rapid technological changes. It argues that boards need to develop greater capabilities and understanding of technology to effectively oversee companies in today's digital business environment. The document explores how different technologies may shape the role of the CIO and notes a need for boards to work with CIOs to develop strategic alignment between technology, organizational goals, and innovation initiatives. It also examines some structural barriers that can prevent CIOs from being successful in their roles.
The document discusses business intelligence tools and how they can be used in marketing from a retail industry perspective. It provides an overview of various BI tools like reporting, dashboards, OLAP, data mining and how they are used. It also discusses how metrics like marketing scorecards, customer lifetime value and sensitivity analysis can help marketing. Finally, it outlines how BI tools can help with key performance metrics, complex analysis, predictive modeling and decision making for the retail industry.
Big Data, Little Data, and Everything in Betweenxband
This white paper discusses how IBM SPSS solutions help organizations analyze both big data and smaller datasets to provide analytics to diverse users. It notes that while many organizations claim to have big data, analytics needs vary widely depending on the user and department. The paper advocates providing a unified analytics platform that can scale from small to large datasets and meet the needs of users with different skill levels. It also discusses trends toward predictive analytics and giving more users access to modeling tools to support data-driven decision making across organizations.
This course provides an overview of data analytics and business intelligence. It teaches students how to analyze and tell stories with data, which is an in-demand skill as data collection increases. The course covers topics such as SQL, data modeling, Power BI, data warehousing, and big data to prepare students for careers as data analysts. Upon completing the hands-on training, students will feel confident to begin work in the industry analyzing, extracting, transforming, and loading data according to business requirements.
Architecting a-big-data-platform-for-analytics 24606569Kun Le
This document discusses the growth of big data and the need for businesses to analyze new and complex data sources. It describes how data has become more varied in type, larger in volume, and generated faster. It also outlines different types of big data analytics workloads and technology options for building an end-to-end big data analytics platform. Finally, it provides an example of IBM's solution for analyzing both data in motion and at rest across the entire big data analytics lifecycle.
Data Science & BI Salary & Skills ReportPaul Buzby
The document is a report on data science and business intelligence skills and salaries based on a large survey. Some of the key findings from the report include:
- Small and medium enterprises pay inexperienced data scientists and analysts higher starting salaries than large enterprises. Finance also offers high pay for those just starting out.
- Data architect is a highly valuable role, especially in fast-paced industries like media and entertainment where building business-critical solutions is important.
- While consulting has many data professionals with over 20 years of experience, education/academia and research attract less experienced data scientists despite not being the highest paying industries.
This document discusses how business intelligence tools can help the retail industry. It describes various BI tools like reporting, forecasting, dashboards, and data mining. It explains how these tools can help with marketing metrics like the marketing scorecard, customer lifetime value calculation, and sensitivity analysis. Finally, it outlines how BI supports retail businesses with key performance reporting, complex analysis, predictive modeling, and informed decision making.
The emergent recognition of the value of analytics clashes with the rampant growth of the volume of
both structured and unstructured data. Competitive organizations are evolving by adopting strategies
and methods for integrating business intelligence and analysis in a way that supplements the spectrum
of decisions that are made on a day-to-day and sometimes even moment-to-moment basis. Individuals overwhelmed with data may succumb to analysis paralysis, but delivering trustworthy actionable
intelligence to the right people when they need it short-circuits analysis paralysis and encourages
rational and confident decisions.
This document discusses IT financial management (ITFM) and the need for transparency and effective governance. It outlines the information needs of key ITFM stakeholders like IT management, business leaders, and the CFO office. Common flawed ITFM practices that create problems are identified, such as differing accounting methods and a lack of business accountability for IT investments. The document proposes seven best practice principles for ITFM, including enabling transparency through measuring labor/asset usage and forecasting. It describes how HP software and services can help implement these principles to improve IT investment decisions, financial accounting, and resource allocation.
District Office of Info and KM - Proposed - by Joel Magnussen - 2004Peter Stinson
The document discusses the potential benefits of improved information sharing and knowledge management. It envisions a future where everyone within an organization has access to all relevant information whenever needed. This would allow for better decision-making, more efficient responses to issues, and continuous learning from past experiences and events. The document outlines several initiatives underway to build an integrated information framework with these goals.
IoT intelligence: Attitudes towards big data and advanced analyticsAbhishek Sood
According to the extensive market study by Dresner Advisory Services, fewer than 15% of respondents consider IoT a “critical” business opportunity, but about 53% will say it’s at least “somewhat important.”
And yet, hype continues to build around IoT’s potential to drive innovation in the way people work, connect, and interact. To understand this IoT phenomenon, this study delves into topics like:
Perceptions of IoT by region and industry
Top drivers of analytics and BI
The potential impact of IoT and cloud on BI
Predictive analytics and big data
And more
Read on to find out more about how IoT advocate vs. skeptic perceptions of smart technology stack up, and learn how IoT could impact analytics and BI.
This document outlines an agenda and presentation on business intelligence (BI) given by Stanislava Tropcheva and Ani Vasileva. The presentation introduces BI and discusses data visualization, BI architecture, demonstrations of BI tools, the Gartner Magic Quadrant for BI platforms, and encourages participants to try using BI tools themselves. It poses questions to help explain key BI concepts and get participants more engaged in understanding how BI can transform raw data into useful information to power decision making.
The document is a seminar report on business intelligence submitted by Gayatri Padhi. It discusses various topics related to business intelligence including its definition, components, issues, future, reasons for using it, and benefits. The report provides definitions of business intelligence from various experts and describes its key components such as data warehousing, data marts, OLAP, analytics and dashboards. It also discusses factors influencing business intelligence and how to design and implement effective BI systems.
Business intelligence systems help organizations make better decisions by analyzing large amounts of data and presenting the information in a usable way. The document discusses decision support systems and business intelligence, how they help organizational decision making, and reviews several sources that describe the relationship between decision making and business intelligence from different perspectives. Decision support systems use data and models to help managers solve problems, while business intelligence encompasses broader technologies and processes for gathering and analyzing various types of data to support decision making.
This document provides an overview of careers in big data and business intelligence. It discusses the differences between data scientists and business analysts, including their roles, skills, tools used, and career paths. While both fields are growing, data scientists focus more on technical work like coding and algorithms, while business analysts communicate between business and IT and help make data-driven decisions. The document suggests considering personal interests and skills when choosing between these two options.
Everyone's talking about big data – getting our arms around it and putting it to work for us. This paper summarizes a panel discussion at the 2012 SAS Financial Services Executive Summit where industry leaders shared their ideas about big data and what their organizations are doing with it. Aditya Bhasin from Bank of America talked about how to extract more value from the data you already have, even if it's just a fraction of what's out there. Robert Kirkpatrick, who leads the UN Global Pulse initiative, talked about how data can help us better understand global economies and human welfare. Charles Thomas, a market research and analytics executive at USAA, described how his company is navigating the shift to more real-time and predictive analysis. Request the full whitepaper at: http://www.sas.com/reg/wp/corp/50060?&utm_source=NAFCUServices&utm_medium=landingpage&utm_campaign=SASwhitepaper82912. More info at: www.nafcu.org/sas
This document discusses metrics that can be used to assess the effectiveness of business analytics initiatives. It summarizes the results of a benchmark study that evaluated organizations on 8 metrics: productivity, governance, timeliness, ROI, accuracy, effectiveness, empowerment and maturity. The study found that on average organizations scored highest in governance and lowest in effectiveness. Certain industries tended to score higher or lower on different metrics. The document recommends evaluating an organization across 64 questions related to the 8 metrics in order to identify strengths and opportunities for improvement.
The document discusses database performance optimization and management. It notes that performance issues often lead to finger pointing between teams. It then outlines the many demands on DBA time and how focusing only on reactive problem solving puts the business in constant reaction mode without understanding trends. The document advocates for a proactive performance intelligence approach that collects wait time event data, analyzes it using business intelligence techniques, and provides experienced DBAs to interpret the data and make recommendations to improve performance. This helps eliminate finger pointing and allows DBAs to focus on other tasks.
The document discusses database performance optimization and management. It notes that performance issues often lead to finger pointing between teams. It then outlines the many demands on DBA time and how focusing only on reactive problem solving puts the business in constant reaction mode without understanding trends. The document advocates using a performance intelligence approach that focuses on wait time event analysis over traditional metrics. This approach collects wait time data in a repository to enable slicing and dicing the data similarly to business intelligence analysis. It provides context on other system activity. Senior DBAs can then interpret the data, identify bottlenecks, and make recommendations to optimize performance.
An MLE (Managed Learning Environment) is a digital platform that supports teaching and learning through various online tools and content. It consists of modules like an LMS, eportfolio, blogs, and repositories. The document discusses setting up a non-centralized single sign-on system across different MLE tools to allow for easier access and integration of resources for students, teachers and parents. Several schools have already implemented this approach successfully.
The document provides an overview of managed learning environments (MLEs) in New Zealand schools. It discusses key components of MLEs like learning management systems, eportfolios, and student management systems. It also addresses integration of these systems with student records, attendance tracking, and online assessment. The presenter aims to help schools choose and implement appropriate online tools and discusses support available from the Ministry of Education.
IAM for Schools: Presentation to ICT Services Working GroupPaul Seiler
This document discusses identity and access management (IAM) solutions for schools and education systems. It proposes a non-centralized approach where identity data remains at the source, with single sign-on authentication allowing access to multiple learning modules and digital tools. Over 100 schools are now using interconnected identity providers to automate provisioning and access for teachers, students, and parents across learning management systems, e-portfolios, libraries and other services without needing separate usernames and passwords. While technical implementation poses minimal risks, successful adoption requires schools to establish appropriate identity and access policies and help students understand responsible digital identity management.
The document discusses identity and access management (IAM) in education, specifically in New Zealand schools. It proposes a modular, standards-based approach to developing and using a managed learning environment (MLE) that keeps student identity data at the source and allows for single sign-on access across different education applications and services. Over 40 schools are already using this approach through independent identity providers, with growing commercial offerings and connected services. While there are some risks, the technical challenges are considered the easy part, with policy and risk management more important issues for schools.
Attendance management, learning management and eportfoliosPaul Seiler
This document discusses attendance management systems and the use of electronic attendance reporting (eAR) in New Zealand schools. It provides details on eAR implementations, benefits for schools, students, caregivers and the Ministry of Education. Key points include increased communication between schools and caregivers, faster identification of truancy patterns, improved attendance rates and student engagement, and a reduction in unjustified absences nationally. Challenges to eAR adoption like lack of teacher buy-in and infrastructure issues are also examined. Definitions of attendance codes and reporting metrics are provided.
Pass the baton: How to run a faster racePaul Seiler
This document discusses the development of reusable and portable educational content in New Zealand schools. It outlines efforts to break dependencies between content and learning management systems by developing content that can be easily shared and reused across different platforms. A conceptual model was created based on workflow analysis. Wikis were identified as a way to enable authoring, sharing and ensuring interoperability of content. Three work streams were proposed to cover these areas as well as interoperability with multiple learning environments. Challenges around policies, politics and perceptions of open educational resources were also discussed.
The document outlines New Zealand's managed learning environment (MLE) initiative and provides guidance for schools on participating. It describes the MLE as a collection of digital tools and content to support learning. It encourages schools to join relevant groups and lists key work areas like attendance management, online systems, e-portfolios, and reusable content. The presenter offers to answer questions and help schools integrate various education technologies and digital resources.
This document explores managed learning environments (MLEs) and how schools can use them. It discusses using a learning management system (LMS) like KnowledgeNET as a central hub that connects various components of an MLE, including student management systems, eportfolios, digital content repositories, and parent portals. It recommends exploring the community resources available through groups like the MLE Reference Group and connections that KnowledgeNET can make to other systems and content. The goal is to help schools better understand and utilize MLEs to support student learning.
The document discusses options for managed learning environments (MLEs) that are educationally relevant, open, modular, standards-based, and sustainable. It outlines some core components or modules of MLEs, including student management systems, content stores, learning management systems, parent portals, and identity and access management. It then provides examples of both open source and free software options that schools could use for these different components, such as Moodle, Mahara, OpenOffice, and SimpleSAMLphp. It raises questions about implementing open MLE solutions in education.
The document discusses various topics related to the managed learning environment (MLE) in New Zealand schools, including:
1. Market shares of different student management systems (SMS) and learning management systems (LMS) from 2005-2010.
2. Usage statistics for early notification systems, e-portfolios, attendance management systems, and online systems/LMS/parent portals.
3. Developments in areas like student record transfer, integration/interoperability, reusable learning content, and identity/access management.
4. Soliciting feedback from readers on priority issues and how the SMS Services team can provide more assistance to schools.
The document discusses student record transfer (SRT), which allows schools to electronically transfer student records between school administration systems. It provides background on SRT, including that the need for such a system was first documented in 2001. It outlines key parts of SRT including a phase one rollout involving 30 schools. Learnings from phase one showed that SRT works when schools alter processes to utilize the new technology. The future may involve adding more assessment data and integrating additional school administration vendors into SRT.
This presentation provides an overview of New Zealand's managed learning environment (MLE). The MLE aims to develop an educationally relevant, open, modular, standards-based, and sustainable approach to digital tools and content for schools. Key components of the MLE include student management systems, learning management systems, eportfolios, reusable learning objects, and single sign-on identity management. The presentation outlines several work strands, including attendance management, system integration, online platforms, and eportfolios. It encourages participation and collaboration to improve the MLE for enhancing student learning.
The document provides an overview of New Zealand's managed learning environment (MLE) initiative, including:
1) The MLE aims to provide a standards-based approach to developing and using online learning tools and resources for schools.
2) Key aspects of the MLE include integrating various online systems, e-portfolios, reusable learning content, and identity/access management.
3) Stakeholders are working on initiatives related to online learning management systems, parent portals, e-portfolios, and assessment.
Ministry support for moodle (and mahara)Paul Seiler
1. The document discusses New Zealand Ministry of Education's support for learning management systems (LMS) like Moodle and digital portfolios like Mahara.
2. It outlines initiatives to integrate student management systems (SMS) with LMS and parent portals to provide online access to student information and learning resources.
3. The Ministry is providing financial assistance to schools and selecting preferred LMS providers through an open tender process while standards and technologies are developed to connect different systems.
The document discusses several topics related to data sharing and integration between student management systems (SMS) and learning management systems (LMS):
1. 110 KAMAR schools have an LMS and 103 could link the SMS and LMS to automate data transfer and improve data accuracy and timeliness.
2. Assistance is available to help schools link their SMS (KAMAR) and LMS, including a daily batch data transfer process and support for setting it up.
3. Over 53,000 student records were transferred between schools horizontally and over 136,000 changes were recorded overall as students moved between schools. A secure student record transfer system helps with this process.
Skybuffer AI: Advanced Conversational and Generative AI Solution on SAP Busin...Tatiana Kojar
Skybuffer AI, built on the robust SAP Business Technology Platform (SAP BTP), is the latest and most advanced version of our AI development, reaffirming our commitment to delivering top-tier AI solutions. Skybuffer AI harnesses all the innovative capabilities of the SAP BTP in the AI domain, from Conversational AI to cutting-edge Generative AI and Retrieval-Augmented Generation (RAG). It also helps SAP customers safeguard their investments into SAP Conversational AI and ensure a seamless, one-click transition to SAP Business AI.
With Skybuffer AI, various AI models can be integrated into a single communication channel such as Microsoft Teams. This integration empowers business users with insights drawn from SAP backend systems, enterprise documents, and the expansive knowledge of Generative AI. And the best part of it is that it is all managed through our intuitive no-code Action Server interface, requiring no extensive coding knowledge and making the advanced AI accessible to more users.
Taking AI to the Next Level in Manufacturing.pdfssuserfac0301
Read Taking AI to the Next Level in Manufacturing to gain insights on AI adoption in the manufacturing industry, such as:
1. How quickly AI is being implemented in manufacturing.
2. Which barriers stand in the way of AI adoption.
3. How data quality and governance form the backbone of AI.
4. Organizational processes and structures that may inhibit effective AI adoption.
6. Ideas and approaches to help build your organization's AI strategy.
Main news related to the CCS TSI 2023 (2023/1695)Jakub Marek
An English 🇬🇧 translation of a presentation to the speech I gave about the main changes brought by CCS TSI 2023 at the biggest Czech conference on Communications and signalling systems on Railways, which was held in Clarion Hotel Olomouc from 7th to 9th November 2023 (konferenceszt.cz). Attended by around 500 participants and 200 on-line followers.
The original Czech 🇨🇿 version of the presentation can be found here: https://www.slideshare.net/slideshow/hlavni-novinky-souvisejici-s-ccs-tsi-2023-2023-1695/269688092 .
The videorecording (in Czech) from the presentation is available here: https://youtu.be/WzjJWm4IyPk?si=SImb06tuXGb30BEH .
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
Trusted Execution Environment for Decentralized Process MiningLucaBarbaro3
Presentation of the paper "Trusted Execution Environment for Decentralized Process Mining" given during the CAiSE 2024 Conference in Cyprus on June 7, 2024.
Monitoring and Managing Anomaly Detection on OpenShift.pdfTosin Akinosho
Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
1. Introduction to Anomaly Detection
- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
3. What is ArgoCD?
- Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices.
4. Deployment Using ArgoCD for Edge Devices
- Step-by-step guide on deploying anomaly detection models on edge devices using ArgoCD.
5. Introduction to Apache Kafka and S3
- Explore Apache Kafka for real-time data streaming and Amazon S3 for scalable storage solutions.
6. Viewing Kafka Messages in the Data Lake
- Learn how to view and analyze Kafka messages stored in a data lake for better insights.
7. What is Prometheus?
- Get to know Prometheus, an open-source monitoring and alerting toolkit, and its application in monitoring edge devices.
8. Monitoring Application Metrics with Prometheus
- Detailed instructions on setting up Prometheus to monitor the performance and health of your anomaly detection system.
9. What is Camel K?
- Introduction to Camel K, a lightweight integration framework built on Apache Camel, designed for Kubernetes.
10. Configuring Camel K Integrations for Data Pipelines
- Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow.
11. What is a Jupyter Notebook?
- Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text.
12. Jupyter Notebooks with Code Examples
- Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.
This presentation provides valuable insights into effective cost-saving techniques on AWS. Learn how to optimize your AWS resources by rightsizing, increasing elasticity, picking the right storage class, and choosing the best pricing model. Additionally, discover essential governance mechanisms to ensure continuous cost efficiency. Whether you are new to AWS or an experienced user, this presentation provides clear and practical tips to help you reduce your cloud costs and get the most out of your budget.
Nunit vs XUnit vs MSTest Differences Between These Unit Testing Frameworks.pdfflufftailshop
When it comes to unit testing in the .NET ecosystem, developers have a wide range of options available. Among the most popular choices are NUnit, XUnit, and MSTest. These unit testing frameworks provide essential tools and features to help ensure the quality and reliability of code. However, understanding the differences between these frameworks is crucial for selecting the most suitable one for your projects.
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slackshyamraj55
Discover the seamless integration of RPA (Robotic Process Automation), COMPOSER, and APM with AWS IDP enhanced with Slack notifications. Explore how these technologies converge to streamline workflows, optimize performance, and ensure secure access, all while leveraging the power of AWS IDP and real-time communication via Slack notifications.
Best 20 SEO Techniques To Improve Website Visibility In SERPPixlogix Infotech
Boost your website's visibility with proven SEO techniques! Our latest blog dives into essential strategies to enhance your online presence, increase traffic, and rank higher on search engines. From keyword optimization to quality content creation, learn how to make your site stand out in the crowded digital landscape. Discover actionable tips and expert insights to elevate your SEO game.
A Comprehensive Guide to DeFi Development Services in 2024Intelisync
DeFi represents a paradigm shift in the financial industry. Instead of relying on traditional, centralized institutions like banks, DeFi leverages blockchain technology to create a decentralized network of financial services. This means that financial transactions can occur directly between parties, without intermediaries, using smart contracts on platforms like Ethereum.
In 2024, we are witnessing an explosion of new DeFi projects and protocols, each pushing the boundaries of what’s possible in finance.
In summary, DeFi in 2024 is not just a trend; it’s a revolution that democratizes finance, enhances security and transparency, and fosters continuous innovation. As we proceed through this presentation, we'll explore the various components and services of DeFi in detail, shedding light on how they are transforming the financial landscape.
At Intelisync, we specialize in providing comprehensive DeFi development services tailored to meet the unique needs of our clients. From smart contract development to dApp creation and security audits, we ensure that your DeFi project is built with innovation, security, and scalability in mind. Trust Intelisync to guide you through the intricate landscape of decentralized finance and unlock the full potential of blockchain technology.
Ready to take your DeFi project to the next level? Partner with Intelisync for expert DeFi development services today!
GraphRAG for Life Science to increase LLM accuracyTomaz Bratanic
GraphRAG for life science domain, where you retriever information from biomedical knowledge graphs using LLMs to increase the accuracy and performance of generated answers
Fueling AI with Great Data with Airbyte WebinarZilliz
This talk will focus on how to collect data from a variety of sources, leveraging this data for RAG and other GenAI use cases, and finally charting your course to productionalization.
Dive into the realm of operating systems (OS) with Pravash Chandra Das, a seasoned Digital Forensic Analyst, as your guide. 🚀 This comprehensive presentation illuminates the core concepts, types, and evolution of OS, essential for understanding modern computing landscapes.
Beginning with the foundational definition, Das clarifies the pivotal role of OS as system software orchestrating hardware resources, software applications, and user interactions. Through succinct descriptions, he delineates the diverse types of OS, from single-user, single-task environments like early MS-DOS iterations, to multi-user, multi-tasking systems exemplified by modern Linux distributions.
Crucial components like the kernel and shell are dissected, highlighting their indispensable functions in resource management and user interface interaction. Das elucidates how the kernel acts as the central nervous system, orchestrating process scheduling, memory allocation, and device management. Meanwhile, the shell serves as the gateway for user commands, bridging the gap between human input and machine execution. 💻
The narrative then shifts to a captivating exploration of prominent desktop OSs, Windows, macOS, and Linux. Windows, with its globally ubiquitous presence and user-friendly interface, emerges as a cornerstone in personal computing history. macOS, lauded for its sleek design and seamless integration with Apple's ecosystem, stands as a beacon of stability and creativity. Linux, an open-source marvel, offers unparalleled flexibility and security, revolutionizing the computing landscape. 🖥️
Moving to the realm of mobile devices, Das unravels the dominance of Android and iOS. Android's open-source ethos fosters a vibrant ecosystem of customization and innovation, while iOS boasts a seamless user experience and robust security infrastructure. Meanwhile, discontinued platforms like Symbian and Palm OS evoke nostalgia for their pioneering roles in the smartphone revolution.
The journey concludes with a reflection on the ever-evolving landscape of OS, underscored by the emergence of real-time operating systems (RTOS) and the persistent quest for innovation and efficiency. As technology continues to shape our world, understanding the foundations and evolution of operating systems remains paramount. Join Pravash Chandra Das on this illuminating journey through the heart of computing. 🌟
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdfMalak Abu Hammad
Discover how MongoDB Atlas and vector search technology can revolutionize your application's search capabilities. This comprehensive presentation covers:
* What is Vector Search?
* Importance and benefits of vector search
* Practical use cases across various industries
* Step-by-step implementation guide
* Live demos with code snippets
* Enhancing LLM capabilities with vector search
* Best practices and optimization strategies
Perfect for developers, AI enthusiasts, and tech leaders. Learn how to leverage MongoDB Atlas to deliver highly relevant, context-aware search results, transforming your data retrieval process. Stay ahead in tech innovation and maximize the potential of your applications.
#MongoDB #VectorSearch #AI #SemanticSearch #TechInnovation #DataScience #LLM #MachineLearning #SearchTechnology
Ocean lotus Threat actors project by John Sitima 2024 (1).pptxSitimaJohn
Ocean Lotus cyber threat actors represent a sophisticated, persistent, and politically motivated group that poses a significant risk to organizations and individuals in the Southeast Asian region. Their continuous evolution and adaptability underscore the need for robust cybersecurity measures and international cooperation to identify and mitigate the threats posed by such advanced persistent threat groups.
4. How important is a DSS?
Imagine the CEO of a large enterprise with:
53,800 employees
5. How important is a DSS?
Imagine the CEO of a large enterprise with:
53,800 employees
2,600 branch offices
6. How important is a DSS?
Imagine the CEO of a large enterprise with:
53,800 employees
2,600 branch offices
$6.4 billion annual budget
7. How important is a DSS?
Imagine the CEO of a large enterprise with:
53,800 employees
2,600 branch offices
$6.4 billion annual budget
758,000 customers
8. How important is a DSS?
Imagine the CEO of a large enterprise with:
53,800 employees
2,600 branch offices
$6.4 billion annual budget
758,000 customers
What strategic information might
this CEO expect to be available?
9. How important is a DSS?
Imagine the CEO of a large enterprise with:
53,800 employees
2,600 branch offices
$6.4 billion annual budget
758,000 customers
What strategic information might
this CEO expect to be available?
Would an 18 month delay in
finding out how many employees
left the company be OK?
10. How important is a DSS?
Imagine the CEO of a large enterprise with:
53,800 employees
2,600 branch offices
$6.4 billion annual budget
758,000 customers
What strategic information might
this CEO expect to be available?
Finding out how a customer
performed on an evaluation -
six months later?
11. How important is a DSS?
Imagine the CEO of a large enterprise with:
53,800 employees
2,600 branch offices
$6.4 billion annual budget
758,000 customers
What strategic information might
this CEO expect to be available?
Not knowing the location or the
age of the technology in your
branch offices?
12. How important is a DSS?
Imagine the CEO of a large enterprise with:
53,800 employees
2,600 branch offices
$6.4 billion annual budget
758,000 customers
What strategic information might
this CEO expect to be available?
We find ourselves in the
Information Age with an aging
information system
13. What are the needs of the
educational community?
16. Current Education Data Sets
Performance Infrastructure
Personal
Student
Finance Foreign
Promotes narrow decisions based on information
extracted from one or two functional data-sets
(finance and assessment)
17. Current Education Data Sets
Performance Infrastructure
Personal
Student
Finance Foreign
Redundant data entry is common
Disconnected data increases resources needed
Collections become costly and inefficient
18. Educational Data Warehousing
Performance
TID (10 digit) Student Personnel
NCLB math TID (10 digit) Admin Unit No.
Teacher SS# ........ Supply
NCLB read
LEA Number Teacher SS# IHE Unit No,
........
........ Teacher Assign ........
AP score
Stud gender ........ SS#
PSAT math
Stu Grade lvl Type of Cert ........
........
Stu FTE ........ IHE Endorsement
........
ACT enroll ........ Cert Exp Date
Admin Unit No.
Finance
LEA Number
School
........
Infrastructure Per Pupil
Admin Unit No. Total Rev
Data Partnerships
........ ........
Technology Avg Salary
Crime/Safety
Foreign Data
Gov Data Operating Bdgt
Admin Unit No.
........ ........
Admin Unit No. ........
........
........ Employment
Bld Age
Live Births NCES
........
GPS system University
Title I
Num Arrests
Cong Dist
19. Educational Data Warehousing
Performance
TID (10 digit) Student Personnel
NCLB math a location Admin Unit No. Supply
that:
TID (10 digit)
Teacher SS#
NCLB read ........
Integrates information from . . . . . . . .
........ Teacher SS# disparate
LEA Number
........
Teacher Assign
IHE Unit No,
AP score
systems into a total view of Cert . . . . . . . .
PSAT math . . . and a common
Stud gender
Stu Grade lvl
Type
..... SS#
........
foundation for understanding student
........ Stu FTE
........
........
Cert Exp Date
IHE Endorsement
ACT enroll
performance and school improvement
Admin Unit No.
Finance
LEA Number
School
........
Infrastructure Per Pupil
Admin Unit No. Total Rev
Data Partnerships
........ ........
Technology
Crime/Safety and Foreign Data Avg Salary
Operating Bdgt
Gov Data Admin Unit No.
Provides for Admin Unit No. set. .of. .definitions
........
........
a common . . . . ........
Becomes the Live .Births.
Bld Age sole . source Employment
... . .
of reusable data
NCES
........
Improves timeliness and utility of reports
Title I
GPS system University
Num Arrests
Cong Dist
21. What is a Data Warehouse?
DW is not just storage but the
tools to query, analyze and
present information on the web.
22. What is a Data Warehouse?
DWs have many definitions
- with these similarities:
Subject oriented - gives information about a person instead
of operations.
Integrated - a variety of sources are merged into a whole.
Non-volatile - provides users with a consistent picture over
specified time periods.
Robust architecture - that allows concurrent access by a
multiple number of users with frequent queries.
Quality data - valid and reliable data that promotes
confidence in DW and forms the nucleus of information
used by the educational community.
23. What is a Data Warehouse?
DWs have many definitions
- with these similarities:
Subject oriented - gives information about a person instead
of operations.
Integrated - a variety of sources are merged into a whole.
Non-volatile - provides users with a consistent picture over
specified time periods.
Robust architecture - that allows concurrent access by a
multiple number of users with frequent queries.
Quality data - valid and reliable data that promotes
confidence in DW and forms the nucleus of information
used by the educational community.
24. What is a Decision
Support System?
DSS is a process used by the
educational community (with support
of the data warehouse) that
transforms data into a knowledgebase
that will support decision-making.
25. DSS starts with a: What is a Decision
Problem + Support System?
administration
=
Data
26. DSS starts with a: What is a Decision
Problem + Support System?
administration
=
Data + dissemination =
Information
27. DSS starts with a: What is a Decision
Problem + Support System?
administration
=
Data + dissemination =
Information
+ social
discussion =
Knowledge
28. DSS starts with a: What is a Decision
Problem + Support System?
administration
=
Data + dissemination =
Policy/Action Information
+ social
discussion =
+ community
Knowledge
response =
29. DSS starts with a: What is a Decision
Problem + Support System?
administration
=
Data + dissemination =
Policy/Action Information
+ wisdom to ask a more + social
complex question =
discussion =
+ community
Knowledge
response =
30. 12 Steps to Creating the DSS
These steps are a
combination of buying and
building that depend on
time and money
31. 12 Steps to Creating the DSS
Education Community
Involvement
These steps are a
combination of buying and
building that depend on
time and money
32. 12 Steps to Creating the DSS
Education Community
Involvement
Conceptual Agreement
DRA/DBA Staffing
Meta Data
DRA
Security/Confidentiality
Unique ID#
Edit check/ETL Time from
Operation to
Dup Res
These steps are a Analysis
BI tool DBA
combination of buying and
building that depend on
time and money Data
Warehouse
Data Mining
Information
Democracy
Training
Decision Support System
33. 12 Steps to Creating the DSS
Education Community
Involvement
Conceptual Agreement
DRA/DBA Staffing
Meta Data
DRA
Security/Confidentiality
Unique ID#
Edit check/ETL Time from
Operation to
Dup Res
Analysis
BI tool DBA
Looks linear - is
multidimensional
Data
Warehouse
Data Mining
Information
Democracy
Training
Decision Support System
34. 12 Steps to Creating the DSS
Education Community
Involvement
Conceptual Agreement
DRA/DBA Staffing
Meta Data
DRA
Security/Confidentiality
Unique ID#
Edit check/ETL Time from
Operation to
Dup Res
Analysis
BI tool DBA
Factor in the
fatigue-fizzle function Data
Warehouse
Data Mining
Information
Democracy
Training
Decision Support System
35. 12 Steps to Creating the DSS
Education Community
Involvement
Conceptual Agreement
DRA/DBA Staffing
Meta Data
DRA
Security/Confidentiality
Unique ID#
Edit check/ETL Time from
Operation to
Dup Res
Analysis
BI tool DBA
Factor in the
fatigue-fizzle function Data
Warehouse
Information
Escape velocity Data Mining
Democracy
Training
Decision Support System
40. Building the Framework
for the DW and DSS
Must have conceptual agreement on the:
Design of support system
41. Building the Framework
for the DW and DSS
Must have conceptual agreement on the:
Design of support system
Policy to protect data
42. Building the Framework
for the DW and DSS
Must have conceptual agreement on the:
Design of support system
Policy to protect data
Advisory committee(s)
43. Building the Framework
for the DW and DSS
Must have conceptual agreement on the:
Design of support system
Policy to protect data
Advisory committee(s)
Buy or build and
44. Building the Framework
for the DW and DSS
Must have conceptual agreement on the:
Design of support system
Policy to protect data
Advisory committee(s)
Buy or build and
Funding
45. Building the Framework
for the DW and DSS
Must have conceptual agreement on the:
Design of support system
Policy to protect data
Advisory committee(s)
Buy or build and
Funding
47. DSS Design: Best Practice
Who? Whom? Where? With? What?
School Codes
When?
Data Warehouse
48. DSS Design: Best Practice
Who? Whom? Where? With? What?
Decision Decision
Support Support
Users Tools
Used for: Used how:
Operation Data mining
Management School Codes Analysis
Policy Makers Ad-hoc query
Instruction Off-line
Research
When? manipulation
Data Warehouse
Foreign data
i.e. Employment, Higher Ed
49. DSS Design: Best Practice
Data Democracy
Web Interface
Who? Whom? Where? With? What?
Decision Decision
Support Support
Users Tools
Used for: Used how:
Operation Data mining
Management School Codes Analysis
Policy Makers Ad-hoc query
Instruction Off-line
Research
When? manipulation
Data Warehouse
Foreign data
i.e. Employment, Higher Ed
50. DSS Design: Best Practice
Data Democracy
Web Interface
Who? Whom? Where? With? What?
Decision Decision
Support Support
Users Tools
Used for: Used how:
Operation Data mining
Management School Codes Analysis
Policy Makers Ad-hoc query
Instruction Off-line
Research
When? manipulation
Data Warehouse
Foreign data
i.e. Employment, Higher Ed
h y As professionals, we need to make informed
W decisions, anticipate their impact on
education and design appropriate policy.
55. Steering Committee
Oversight to design of the DSS
Local district policy concerns
Meta Data modification
Standard reports, and
56. Steering Committee
Oversight to design of the DSS
Local district policy concerns
Meta Data modification
Standard reports, and
Long term funding
57.
58. Cost Savings?
(OCIO-USED)
Warehouse/DSS
initiative
Current costs
(paper and mail)
Break
even
2001 2002 2003 2004 2005 2006
59. Cost Savings?
(OCIO-USED)
Warehouse/DSS
initiative
Current costs
(paper and mail)
Break
even
2001 2002 2003 2004 2005 2006
“We spend a lot of resources on an existing
data edifice that isn’t very useful”
64. Partnership on Both
Sides of the
Keyboard
DRA: modifies and DBA: technical
enforces standards implementation
that sustain the of the data
DSS environment - warehouse
chairs data environment -
managers group chairs IT group
65.
66. DRA and DBA Collaboration
User
requirements
Feature
expectation (DRA) Critical
Divergence
IT Development
Cycle (DBA)
18 Mo 30 Mo
Time
67. DRA and DBA Collaboration
User
requirements
Feature
expectation (DRA) Critical
Divergence
IT Development
Cycle (DBA)
18 Mo 30 Mo
Time
68. DRA and DBA Collaboration
User
requirements
Feature
expectation (DRA) Critical
Divergence
IT Development
Cycle (DBA)
18 Mo 30 Mo
Time
69. DRA and DBA Collaboration
User
requirements
Feature
expectation (DRA) Critical
Divergence
IT Development
Cycle (DBA)
18 Mo 30 Mo
Time
Outcome of building the DW within time frame:
Data Warehouse will run 12-15 years - whereas
Current apps last 6-7 years (with patches)
73. Meta Data
Data about the Database
in the Data Warehouse
to define Meta Data Pupil
Personnel
break task into
Student
Meta Data
Human
Manual
logical support Personnel Resources
groups
Meta Data
Manual
Finance
Meta Data promotes the -
Finance
Meta Data Office
Manual
• common understanding by users
• data interchange with other agencies Test
Performance
Meta Data Company
Manual
School
Facilities
Meta Data
Manual
Manager
74.
75. Meta Data Online Manuals
Student
Performance
Personnel
Finance
School
Infrastructure
76. Meta Data Online Manuals
Student
Performance
Personnel
Finance
School
Infrastructure
Employment
Higher Education
77. Meta Data Online Manuals
Name of Field
Student
Field Number
Technical Information
Number of characters: (length) SIF name:
Blanks: (not accepted, null) XML tag: < >
Performance Field type: (alpha, numeric, character)
Record position: (35-39)
Warehouse name:R/Ecode
Warehouse type: VCAR
Progam Information
Code format:
Personnel
Definition:
Finance Elements (variables):
School
Date Information
Submission: Effective: Reporting Period:
Infrastructure Revised: Discontinued: ?
Edits
Employment
Error traps: Fatal Error:
Cross field edits: Warning:
Historical Information
Higher Education
Form number replaced: Used for:
Statutory requirement: Report number:
80. Protection is both
sides of the keyboard
System Security (DBA)
Identification (confident of who)
Authentication (confident of source)
Authorization (grant access rights)
Access control (user profiling)
Administration (security procedures)
Auditing (monitoring and detection)
81. Protection is both
sides of the keyboard
Confidentiality (DRA)
Established FERPA policy
Unique NSN w/check sum
Statistical disclosure (<6)
System Security (DBA) Human subject review policy
Purge and destruction
Identification (confident of who) Set levels of access & audit
Authentication (confident of source)
Authorization (grant access rights)
Access control (user profiling)
Administration (security procedures)
Auditing (monitoring and detection)
84. Test Identification Number:
Production
Record Warehouse
layout layout
First name TID (10 digit)
Last name First name
Date of Birth Last name
Gender Date of Birth
……… Gender
FTE ………
……… FTE
Grade ………
Race/Ethnic Grade
……… Race/Ethnic
……… ………
………
85. Test Identification Number:
Production
Record Warehouse
layout layout TID rules:
• Only assigned to one student (is unique).
First name TID (10 digit) • Number and name can be confirmed as
Last name First name being correct (verified via check sum).
Date of Birth Last name • Meets criteria as an identifier (is valid).
Gender Date of Birth • Has no intrinsic meaning (is nominal).
……… Gender • Can be substituted for a student’s name
FTE ……… (is not personally identifiable).
……… FTE • Permanent over the life-cycle of the
Grade ……… student (0-21 for special education).
Race/Ethnic Grade • Is returned and used by all local
……… Race/Ethnic education agencies (is ubiquitous).
……… ……… • Issued only by the SEA (is restricted).
……… • Accessible by selected SEA employees
only (is confidential).
86. Test Identification Number:
Problems
10 digit
Check Sum
First name
Last name
Constant Date of Birth
Gender
………
FTE
………
Variables Grade
Race/Ethnic
………
………
ID#
Admin Unit #
87. Test Identification Number:
Problems
10 digit
Check Sum
First name First name
Last name Last name
Constant Date of Birth
Moves
Date of Birth
Gender Gender
……… ………
FTE FTE
……… ………
Variables
Variables Grade
Race/Ethnic change
Grade
Race/Ethnic
……… ………
……… ………
ID# ID#
Admin Unit # Admin Unit #
88. Test Identification Number:
Problems
10 digit
Check Sum
First name First name
Last name Last name Need other constant:
Constant Date of Birth
Moves
Date of Birth Date of Immunization
Gender Gender Place of Birth
……… ………
Birth Cert Number
FTE FTE
……… ………
Variables
Variables Grade
Race/Ethnic change
Grade
Race/Ethnic
……… ………
……… ………
ID# ID#
Admin Unit # Admin Unit #
89. 42 states use a unique
student identifier (DQC)
How constructed How issued
(NCES) (NCES)
ISD
Combination (1)
of fields (5)
Soc Sec
Number (8) LEA (9) SEA (20)
SSN plus
algorithm (1) Other (9)
Random School (2)
number (8) Other
(4)
90. Crossing over from aggregate
to single record
Data reliability
and validity
Aggregate
collection
Time
91. Crossing over from aggregate
to single record
Data reliability
and validity
Single record
collection
Aggregate
collection
Time
92. Crossing over from aggregate
to single record
Data reliability
and validity
Single record
collection
Aggregate
collection
Time
93. Crossing over from aggregate
to single record
Data reliability
and validity
Single record
collection
Aggregate
collection
Time
97. Reports using Disaggregated Data
10 10
5 5
District A District B
10 20 10 20
10 10
Individual
reading scores
5 5 Four districts are
District C District D very different
10 20 10 20
101. Quality Data
Reasons for poor quality of data:
Absence of definitions
Unclear definitions
Lack of human resources
Inconsistent collections cycles (not ongoing)
Insufficient time
Inadequate training on entry and data traps
Lack of data integration
Fear of 'punishment' (look bad syndrome)
102. Quality Data
The key elements that improve the quality of what is being collected
include:
• Consistency. Data fields must have a standardized definition
so that each entity can be collected from each district in a
systematic manner.
• Timeliness. There is no efficiency in gathering statewide data
that reflects a one-time need or an unusual piece of
information. Do a survey.
• Reliability. The data set should reflect a dependable
measurement of every entity from one collection cycle to
another (i.e., data has accuracy regardless of who enters it.)
• Validity. A data element must reflect a logical and
meaningful description of an entity and should not be
subject to interpretation (i.e., data has utility to answer the
question being asked.)
105. Thresholds and Assigning ID numbers
True False
Match is true - are
the same student
(assign same ID#)
Match
Pat ! Smith! M! 1/19/60
Pat! T! Smith! M! 1/19/60
Non-
match
106. Thresholds and Assigning ID numbers
True False
Match is true - are
the same student
(assign same ID#)
Match
Pat ! Smith! M! 1/19/60
Pat! T! Smith! M! 1/19/60
Non-match is true - are
different students
Non-
(assign different ID#s)
match
Pat ! Smith! F! 1/19/60
Pat! T! Smith! ! 1/19/61
Patrick ! Smith ! M! 1/19/60
107. Thresholds and Assigning ID numbers
True False
Match is true - are Match is false - are
the same student different students
(assign same ID#) (assign same ID#)
Match
Pat ! Smith! M! 1/19/60 Patricia! Smith! F! 1/19/60
Pat! T! Smith! M! 1/19/60 Pat! ! Smith! ! 1/19/60
Non-match is true - are
different students
Non-
(assign different ID#s)
match
Pat ! Smith! F! 1/19/60
Pat! T! Smith! ! 1/19/61
Patrick ! Smith ! M! 1/19/60
108. Thresholds and Assigning ID numbers
True False
Match is true - are Match is false - are
the same student different students
(assign same ID#) (assign same ID#)
Match
Pat ! Smith! M! 1/19/60 Patricia! Smith! F! 1/19/60
Pat! T! Smith! M! 1/19/60 Pat! ! Smith! ! 1/19/60
Non-match is true - are Non-match is false -
different students are the same student
Non-
(assign different ID#s) (assign different ID#s)
match
Pat ! Smith! F! 1/19/60 Pat ! Smith! M! 1/19/60
Pat! T! Smith! ! 1/19/61 Patrick! Smith! ! 1/19/60
Patrick ! Smith ! M! 1/19/60 Pat ! ! Smyth! M! 1/19/60
109. Thresholds and Assigning ID numbers
True False
Match is true - are Match is false - are
the same student different students
(assign same ID#) (assign same ID#)
Match
Pat ! Smith! M! 1/19/60 Patricia! Smith! F! 1/19/60
Pat! T! Smith! M! 1/19/60 Pat! ! Smith! ! 1/19/60
Non-match is true - are Non-match is false -
different students are the same student
Non-
(assign different ID#s) (assign different ID#s)
match
Pat ! Smith! F! 1/19/60 Pat ! Smith! M! 1/19/60
Pat! T! Smith! ! 1/19/61 Patrick! Smith! ! 1/19/60
Patrick ! Smith ! M! 1/19/60 Pat ! ! Smyth! M! 1/19/60
Error Creep
118. Benefits of DW:
Reduction of paper forms
Savings from data duplication
Best use of technology
Sole source of reusable data
Common set of definitions
Integrated environment of core data
Breaks cycle of low quality data
Answers that took months take days
Reports that took days take minutes
119. Data Democracy for the
Educational Community
Ad-hoc
Reports
Pre-
defined
Simple - Query Sophisticated
one time - ongoing
120. Data Democracy for the
Educational Community
Ad-hoc
Leg
isla rs
tive Re searche
Ai d
es
Finance
Officers
Reports
rs
ito
A ud
General
Public Reporters
Pre-
defined
Simple - Query Sophisticated
one time - ongoing
121. Data Democracy for the
Educational Community
Ad-hoc
Leg
isla rs
tive Re searche
Finance
Ai d
es
u ll
Officers P
As system is
Reports
rs
ito used one will
A ud
find a need to
store data not
being captured
sh
General
Reporters
Pre- P u
Public
defined
Simple - Query Sophisticated
one time - ongoing
123. Push example:
one time - pre defined
School report card
• School Size: small vs. large schools
• Spending: percent of budget on staff salary
• Safety: rate of expulsions and degree of crime
• Technology: ratio of pc's to students & connectivity
• Class Size: teacher-student ratio, average size
• Staff Turnover: rate and attendance
• Advanced Placement: number passing test
• Test Scores: gaps in State performance test
• College Acceptance Rate: percent taking ACT, PSAT
• Graduation/Dropout Rates: number taking GED
• Satisfaction: teachers, parents and students
125. Pull example:
ongoing - Ad hoc
Significant Usable
The largest class size in high school is the 9th grade Not
No
really
126. Pull example:
ongoing - Ad hoc
Significant Usable
The largest class size in high school is the 9th grade Not
No
really
Some 9th grades have a disproportionate number
Possibly No
of Hispanics
127. Pull example:
ongoing - Ad hoc
Significant Usable
The largest class size in high school is the 9th grade Not
No
really
Some 9th grades have a disproportionate number
Possibly No
of Hispanics
Many female Hispanics in the 9th grade are
Possibly Yes
retained due to poor science skills
128. Pull example:
ongoing - Ad hoc
Significant Usable
The largest class size in high school is the 9th grade Not
No
really
Some 9th grades have a disproportionate number
Possibly No
of Hispanics
Many female Hispanics in the 9th grade are
Possibly Yes
retained due to poor science skills
Hispanics in the 8th grade had fewer computers in
science classrooms and more teachers who do not Yes Yes
have a teaching major in science
129. The DW Backbone:
The Sole Authority for the Educational Community
NCLB School
Accreditation
Crime/
Safety
Quality
Workforce
AYP State Report
Card
Title II
(IHE)
IDEA
Fiscal
Trends
130. The DW Backbone:
The Sole Authority for the Educational Community
NCLB School
Accreditation
Crime/
Safety
Quality
Workforce
AYP State Report
Card
Title II
(IHE)
IDEA
Fiscal
Trends
134. Data re-construction
Undirected and exploratory
knowledge discovery
Sequencing: order of
patterns or groups
135. Data re-construction
Undirected and exploratory
knowledge discovery
Framing: using past
data to predict trend
Sequencing: order of
patterns or groups
136. Data re-construction
Undirected and exploratory
knowledge discovery
Framing: using past
data to predict trend
Sequencing: order of
patterns or groups
Clustering: assembling
unforeseen groups
137. Data re-construction
Undirected and exploratory
knowledge discovery
Framing: using past
data to predict trend
Sequencing: order of
patterns or groups
Clustering: assembling
unforeseen groups
Drilling:
interactive
discovery
139. Multidimensional Single Parent Homes
Ad-hoc Analysis Live
Births
Student
Technology
Infrastructure Millages
Passed
Performance
140. Multidimensional Single Parent Homes
Ad-hoc Analysis Live
Births
Ethnic change
Student and growth by
enrollment
Technology
Infrastructure Millages
Passed
Performance
141. Multidimensional Single Parent Homes
Ad-hoc Analysis Live
Births
Ethnic change
Student and growth by
enrollment
Technology
Infrastructure Millages
Performance Passed
by gender
by PCs
Performance
142. Multidimensional Single Parent Homes
Ad-hoc Analysis Live
Births
Ethnic change
Student and growth by
enrollment
Technology
Infrastructure Millages
Performance Passed
by gender
by PCs
Trends and Projections
Performance
143. Multidimensional Single Parent Homes
Ad-hoc Analysis Live
Births
Ethnic change
Student and growth by
enrollment
Technology
Infrastructure Millages
Performance Passed
by gender
by PCs
Trends and Projections
Performance Similar districts
that passed bonds
by month
over past 3 yrs
by ethnicity
by building
by grade
145. The ultimate goal of training is to have
everyone who touches the data at every
level know what is expected of them, so
that the data that are submitted will be
the valid and reliable.
147. Training
Training must also include detailed
procedures, for example:
Who gets notified when an error
is discovered and how is the notification done?
148. Training
Training must also include detailed
procedures, for example:
Who gets notified when an error
is discovered and how is the notification done?
What is the procedure for making corrections of data within
an agency (i.e., who actually makes them and retransmits
the new error-free data)?
149. Training
Training must also include detailed
procedures, for example:
Who gets notified when an error
is discovered and how is the notification done?
What is the procedure for making corrections of data within
an agency (i.e., who actually makes them and retransmits
the new error-free data)?
Who reviews, verifies or signs off on the cleaned data?
150. Training
Training must also include detailed
procedures, for example:
Who gets notified when an error
is discovered and how is the notification done?
What is the procedure for making corrections of data within
an agency (i.e., who actually makes them and retransmits
the new error-free data)?
Who reviews, verifies or signs off on the cleaned data?
Who provides technical assistance to the end user?
151. Training
Training must also include detailed
procedures, for example:
Who gets notified when an error
is discovered and how is the notification done?
What is the procedure for making corrections of data within
an agency (i.e., who actually makes them and retransmits
the new error-free data)?
Who reviews, verifies or signs off on the cleaned data?
Who provides technical assistance to the end user?
What is the procedure to ensure a new copy of the data is
retained for auditing?
152. Training
Training must also include detailed
procedures, for example:
Who gets notified when an error
is discovered and how is the notification done?
What is the procedure for making corrections of data within
an agency (i.e., who actually makes them and retransmits
the new error-free data)?
Who reviews, verifies or signs off on the cleaned data?
Who provides technical assistance to the end user?
What is the procedure to ensure a new copy of the data is
retained for auditing?
Who receives confirmation that the file has been received as
specified?
153. Training
Training must also include detailed
procedures, for example:
Who gets notified when an error
is discovered and how is the notification done?
What is the procedure for making corrections of data within
an agency (i.e., who actually makes them and retransmits
the new error-free data)?
Who reviews, verifies or signs off on the cleaned data?
Who provides technical assistance to the end user?
What is the procedure to ensure a new copy of the data is
retained for auditing?
Who receives confirmation that the file has been received as
specified?
Who secures the data and maintains confidentiality?
154. Reallocation of Resources
Have
Have multiple
collections -
use once
disregard
Data collection, Analysis Reporting Decision
error checks, support and
and clean-up shared data
155. Reallocation of Resources
Have Want
Have multiple
collections -
use once
disregard
Data collection, Analysis Reporting Decision
error checks, support and
and clean-up shared data
Staff training - shifts from front to back end
157. Step #12
The DSS
Providing access to critical information for
driving, managing, tracking, and measuring
institutional policies and goals.
12 S
S
teps
DS
158. The first decision of the
DSS is to make a decision
Transactional Cyclical
159. The first decision of the
DSS is to make a decision
Transactional Cyclical
Realtime Points in time
Day to day operations Historical
Updates daily/weekly Updates quarterly
7X24 6X18
Read/write Read only
Short term data retention Long-term (longitudinal)
Mission critical queries Strategic-analytical queries
More open access paths More restricted access
Standardized reports Adhoc reports
Server based Warehouse technology
160. DSS: Helps Anticipate Issues
Problem
Anticipation
Policy Policy
Repercussion Forecasting
Problem
Reaction
161. DSS: Helps Anticipate Issues
Problem
Anticipation
Policy Policy
Repercussion Forecasting
Current
Problem
Reaction
162. DSS: Helps Anticipate Issues
Problem
Anticipation
Need
to be
Policy Policy
Repercussion Forecasting
Problem
Reaction
163. DSS: Helps Anticipate Issues
Problem
Anticipation Cannot
anticipate
with only
Need ‘required’
to be data
Policy Policy
Repercussion Forecasting
Problem
Reaction
165. Help Anticipate
Impact of Policy:
Class Size
Achievement Issues - Does reducing the class size below
grade 4 improve achievement on the state performance test?
166. Help Anticipate
Impact of Policy:
Class Size
Achievement Issues - Does reducing the class size below
grade 4 improve achievement on the state performance test?
Teacher Supply/Demand Issues - Will the current production of
teachers by IHEs meet the increased need for staff at the LEAs?
167. Help Anticipate
Impact of Policy:
Class Size
Achievement Issues - Does reducing the class size below
grade 4 improve achievement on the state performance test?
Teacher Supply/Demand Issues - Will the current production of
teachers by IHEs meet the increased need for staff at the LEAs?
Fiscal Issues - Using average salary, will there be sufficient
funds for LEAs to hire additional staff?
168. Help Anticipate
Impact of Policy:
Class Size
Achievement Issues - Does reducing the class size below
grade 4 improve achievement on the state performance test?
Teacher Supply/Demand Issues - Will the current production of
teachers by IHEs meet the increased need for staff at the LEAs?
Fiscal Issues - Using average salary, will there be sufficient
funds for LEAs to hire additional staff?
Infrastructure Issues - Do buildings have the space for
additional classrooms?
169. Help Anticipate
Impact of Policy:
Class Size
Achievement Issues - Does reducing the class size below
grade 4 improve achievement on the state performance test?
Teacher Supply/Demand Issues - Will the current production of
teachers by IHEs meet the increased need for staff at the LEAs?
Fiscal Issues - Using average salary, will there be sufficient
funds for LEAs to hire additional staff?
Infrastructure Issues - Do buildings have the space for
additional classrooms?
Trend Issues - Will improved achievement impact employment,
graduation or adult life roles?
171. Impact on State Standards
Efficiency of System
Inputs Process Outputs
172. Impact on State Standards
Efficiency of System
Inputs Process Outputs
Input issues:
fiscal resources
teacher supply
building structure
technology
poverty
173. Impact on State Standards
Efficiency of System
Inputs Process Outputs
Input issues:
fiscal resources
teacher supply
building structure
technology Process issues:
poverty crime and safety
prof development
attendance
teacher experience
student performance
174. Impact on State Standards
Efficiency of System
Inputs Process Outputs
Input issues: Output issues:
fiscal resources college entrance
teacher supply graduate numbers
building structure retention rates
technology employment
Process issues:
poverty crime and safety
prof development
attendance
teacher experience
student performance
175. Impact on State Standards
Effectiveness of System
Efficiency of System
Inputs Process Outputs Outcomes
Input issues: Output issues:
fiscal resources college entrance
teacher supply graduate numbers
building structure retention rates Outcome issues:
employment works with others
technology Process issues: acquires information
poverty crime and safety understands inter-relationships
prof development allocates resources
attendance works w/variety of tech
teacher experience
student performance Impact Policy
176. Impact on State Standards
Effectiveness of System
Efficiency of System
Inputs Process Outputs Outcomes
Output issues:
college entrance
graduate numbers
retention rates Outcome issues:
pa ct employment works with others
im acquires information
i
ot
ll n ith o nly understands inter-relationships
allocates resources
W yw ata works w/variety of tech
lic re d’ d
p o ui
req Impact Policy
‘
177. pa ct
im
i
ot
ll n ith o nly
W yw ata
lic re d’ d
p o ui
‘ req
178. Finding the Balance
Required Desired
Data Data
Social Integration
Mandatory Vocational Orientation
Measurement in volume Use of Time
(amounts, avg., ranks, percents) Daily Living Skills
Realistic Mobility
Use of Environmental Ques
179. Finding the Balance
Required Desired
Data Data
Social Integration
Mandatory Vocational Orientation
Measurement in volume Use of Time
(amounts, avg., ranks, percents) Daily Living Skills
Realistic Mobility
Use of Environmental Ques
The DSS must help policy makers find a
comfortable balance between
acceptable risks and benefits.
180. Helps in Data Discovery
Input Process Output Outcomes
Issues Issues Issues Issues
General Public
Parents
Teachers
Standards moves from
Support Staff
efficiency to effectiveness
Admin/Boards
State
Legislators
Others
181. One Last Time
Web Front End
District Users
Upload data formats Public Portal Access
Correct duplicate data Predefined Report Cards
FERPA requests Limited queries
Dashboard/Scorecard
182. One Last Time
Web Front End
District Users
Security Upload data formats Public Portal Access
Correct duplicate data Predefined Report Cards
FERPA requests Limited queries
Dashboard/Scorecard
File ETL:
Developer Applications
• Student
• Assessment
• Finance
• Professional
Student IDs
Match & Merge
Check Sum
Audit (FERPA)
Error reports
183. One Last Time
Web Front End
District Users
Security Upload data formats Public Portal Access
Correct duplicate data Predefined Report Cards
FERPA requests Limited queries
Dashboard/Scorecard
File ETL:
Developer Applications
• Student Reliable/
• Assessment Valid
• Finance
• Professional
Student IDs
Match & Merge
Check Sum WAREHOUSE
Audit (FERPA)
GPS
Error reports
School Meta
Codes Data
184. One Last Time
Web Front End
District Users
Security Upload data formats Public Portal Access
Correct duplicate data Predefined Report Cards
FERPA requests Limited queries
Dashboard/Scorecard
File ETL:
Developer Applications
• Student Reliable/
• Assessment Valid Data Mart
• Finance
• Professional
Student IDs
Match & Merge
Check Sum WAREHOUSE
Audit (FERPA)
GPS DoE Users
Error reports Generate Report Card
School Meta Federal: EDEN, NCLB, IDEA
Codes Data
Skopus
Issue Assessment IDs
186. Current problem:
data rich and information poor
Data
Silos
Department
187. Current problem:
data rich and information poor
Data Gap:
Silos
Lack of confidence
No trust in system
Have a low ROI
Department Educational
Community
188. Solution
Data
Democracy
Data
Warehouse Secure
Scalable
Flexible
Finance
Apply information
Personnel Meta Data Scho ol and facilitate
Meta Data
decision-making
Manual Meta Data P
ent erfor
Stu d ta Manual Manual m
Meta ance
D a Dat
Meta al Manu a
Manu al
Department Educational
Community
189. Solution
Data
Democracy
Data
Warehouse Secure
Scalable
Flexible
Finance
Apply information
Personnel Meta Data Scho ol and facilitate
Meta Data
decision-making
Manual Meta Data P
ent erfor
Stu d ta Manual Manual m
Meta ance
D a Dat
Meta al Manu a
Manu al
Department Educational
Community