The document discusses operationalizing data governance through data quality control. It describes linking business value drivers to data expectations and objectives through data policies, processes, and oversight. It also discusses assessing data quality, defining rules and measures, monitoring data, and integrating quality reporting into the governance framework. The goal is to ensure data accuracy, completeness, and consistency to maximize business value.
Connexica formed in 2006 and realized search technology could be harnessed to build an analytics solution for healthcare organizations. Their solution, CXAIR, integrates disparate data sources into a single search-powered platform that can scale to billions of records and be used by anyone. CXAIR accelerates reporting capabilities, joins previously siloed data to analyze structured and unstructured data, and integrates with major systems for fast implementation. It also ensures compliance with governance standards and improves data quality for accurate operational decisions.
Running a business requires accurate data to improve operations and implement predictive analytics initiatives. However, poor data quality causes 40% of business initiatives to fail in achieving their goals and can lower labor productivity by up to 20%. A Prolifics Data Assessment can help identify necessary remediation steps to avoid interruptions, engage in an effective approach to data quality improvement, and leverage analytics with confidence by understanding data quality issues and risks.
The document discusses Vocera Communications' investor presentation from June 2018. It provides forward-looking statements regarding the market shifting toward Vocera, accelerating operating leverage, and potential future products and services. It also notes large market opportunities in the US and globally, recent platform wins validating their strategy, and a software platform that could expand into areas like voice assistants and real-time location systems. Financial highlights show growing revenue and adjusted EBITDA margins.
Evaluating How Blockchain Can Transform the Pharmaceutical and Healthcare Ind...Kate Barlow
- Collaborating with 17 companies across the industry
- Understanding the landscape in the pharma and healthcare settings
- Exploring the areas where Blockchain could be used
- Presenting two detailed use cases (a. Smart Contract: Vendor and Site
- Oversight; b. Distributed Asset Ledger: Patient Data Access/Transparency) to support future development and implementation for proof of concept.
The document discusses State Street's data management solution that acts as a data refinery to help organizations deal with large amounts of data from numerous sources in different formats. It offers a hosted data-as-a-service platform called DataGX that provides a complete view of investments from a single source. State Street also provides full data outsourcing services using DataGX to help clients optimize data, improve accuracy and integrity, and better manage risk. The solution provides capabilities for performance measurement, attribution analysis, compliance monitoring, and risk analytics.
The survey polled over 50 health systems to understand AI/ML adoption, challenges, and outlook. Key findings include:
- AI/ML adoption is higher among large health systems (> $1B revenue), with 71% of systems over $4B having adopted it
- Lack of clear use cases and ROI, skills shortage, and technology selection are top challenges for CIOs
- Clinical performance and operational improvements are top priority domains for seeing ROI from AI/ML
- While few health systems have large dedicated AI/ML teams now, 75% of large systems plan significant team growth in 3 years
Privacera Databricks CCPA Webinar Feb 2020Privacera
Privacera’s Vice President of Product Management Srikanth Venkat and Nauman Fakhar, Director, ISV Solutions at Databricks, survey the current and future data privacy landscape, what it means for enterprises like yours, and what you can do to ensure compliance. The webinar includes an in-depth CCPA compliance demonstration on Azure Databricks with Privacera, based on Apache Ranger.
Learn more at www.privacera.com
Connexica formed in 2006 and realized search technology could be harnessed to build an analytics solution for healthcare organizations. Their solution, CXAIR, integrates disparate data sources into a single search-powered platform that can scale to billions of records and be used by anyone. CXAIR accelerates reporting capabilities, joins previously siloed data to analyze structured and unstructured data, and integrates with major systems for fast implementation. It also ensures compliance with governance standards and improves data quality for accurate operational decisions.
Running a business requires accurate data to improve operations and implement predictive analytics initiatives. However, poor data quality causes 40% of business initiatives to fail in achieving their goals and can lower labor productivity by up to 20%. A Prolifics Data Assessment can help identify necessary remediation steps to avoid interruptions, engage in an effective approach to data quality improvement, and leverage analytics with confidence by understanding data quality issues and risks.
The document discusses Vocera Communications' investor presentation from June 2018. It provides forward-looking statements regarding the market shifting toward Vocera, accelerating operating leverage, and potential future products and services. It also notes large market opportunities in the US and globally, recent platform wins validating their strategy, and a software platform that could expand into areas like voice assistants and real-time location systems. Financial highlights show growing revenue and adjusted EBITDA margins.
Evaluating How Blockchain Can Transform the Pharmaceutical and Healthcare Ind...Kate Barlow
- Collaborating with 17 companies across the industry
- Understanding the landscape in the pharma and healthcare settings
- Exploring the areas where Blockchain could be used
- Presenting two detailed use cases (a. Smart Contract: Vendor and Site
- Oversight; b. Distributed Asset Ledger: Patient Data Access/Transparency) to support future development and implementation for proof of concept.
The document discusses State Street's data management solution that acts as a data refinery to help organizations deal with large amounts of data from numerous sources in different formats. It offers a hosted data-as-a-service platform called DataGX that provides a complete view of investments from a single source. State Street also provides full data outsourcing services using DataGX to help clients optimize data, improve accuracy and integrity, and better manage risk. The solution provides capabilities for performance measurement, attribution analysis, compliance monitoring, and risk analytics.
The survey polled over 50 health systems to understand AI/ML adoption, challenges, and outlook. Key findings include:
- AI/ML adoption is higher among large health systems (> $1B revenue), with 71% of systems over $4B having adopted it
- Lack of clear use cases and ROI, skills shortage, and technology selection are top challenges for CIOs
- Clinical performance and operational improvements are top priority domains for seeing ROI from AI/ML
- While few health systems have large dedicated AI/ML teams now, 75% of large systems plan significant team growth in 3 years
Privacera Databricks CCPA Webinar Feb 2020Privacera
Privacera’s Vice President of Product Management Srikanth Venkat and Nauman Fakhar, Director, ISV Solutions at Databricks, survey the current and future data privacy landscape, what it means for enterprises like yours, and what you can do to ensure compliance. The webinar includes an in-depth CCPA compliance demonstration on Azure Databricks with Privacera, based on Apache Ranger.
Learn more at www.privacera.com
Geek Sync | 5 Best Practices for Operationalizing Data GovernanceIDERA Software
You can watch the replay of this webinar in the IDERA Resource Center: http://ow.ly/gac350A4srt.
The combination of increased regulations impacting data management, growing demand for accessibility to data assets for analytical purposes, and expansion of information management platforms beyond the traditional corporate boundaries means that ensuring data quality and usability is becoming more and more complex. Data governance programs are intended to establish oversight and control over enterprise information management processes and procedures. In a data governance program, defined data policies are intended to establish data quality assurance.One of the biggest challenges, though, is transforming corporate data policies into processes and procedures for ensuring policy compliance.
Join IDERA and David Loshin as he looks at the relationships between business policies and regulations, data policies, and five best practices to take advantage of tools and technologies to operationalize data governance. Attendees will learn about requirements analysis, data quality, business glossaries, data standards, data lineage, collaborative metadata, and data catalogs to support data governance.
About David: David Loshin is the president of Knowledge Integrity, Inc. and is a globally recognized thought leader and expert consultant in the areas of analytics, big data, data governance, data quality, master data management, and business intelligence. Along with consulting on numerous data management projects over the past 20 years, David is also a prolific author regarding data management best practices, including “Big Data Analytics: From Strategic Planning to Enterprise Integration with Tools, Techniques, NoSQL, and Graph,” the second edition of “Business Intelligence – The Savvy Manager’s Guide,” as well as other books and articles on data quality, master data management, big data, and data governance. David is a frequent invited speaker at conferences, web seminars, and sponsored web sites and TechTarget channels, and is the Program Director of the Master of Information Management program at the College of Information Studies at the University of Maryland.
How do you assess the quality and reliability of data sources in data analysi...Soumodeep Nanee Kundu
**Assessing the Quality and Reliability of Data Sources in Data Analysis**
Data is often referred to as the lifeblood of data analysis. It forms the foundation upon which decisions are made, insights are drawn, and actions are taken. However, not all data is created equal. The quality and reliability of data sources are paramount to the success of data analysis efforts. In this essay, we will explore the intricate process of assessing data quality and reliability, touching on the methods, considerations, and best practices to ensure the data used in the analysis is trustworthy and fit for purpose.
Fuel your Data-Driven Ambitions with Data GovernancePedro Martins
The document discusses the importance of data governance and provides an overview of how to implement an effective data governance program. It recommends obtaining executive sponsorship, aligning objectives to business initiatives, prioritizing initiatives, getting frameworks ready, and socializing the program. The document outlines data governance building blocks, including assessing maturity, developing a master plan, selecting tools, and establishing an organizational framework. It also discusses preparing an organization for success with data governance.
The document discusses performance measurement and strategic information management. It outlines key concepts like using consistent data to evaluate processes and meet business objectives. It also discusses benefits like understanding customers, assessing progress, and reducing costs. The document provides guidelines for developing an effective performance measurement system, including linking measures to key business drivers and strategies and ensuring measures support different organizational levels.
A Business-first Approach to Building Data Governance ProgramsPrecisely
Traditional data governance initiatives fail by focusing too heavily on policies, compliance, and enforcement, which quickly lose business interest and support. This leaves data management and governance leaders having to continually make the case for data governance to secure business adoption. In this presentation, we share a lean, business-first data governance approach that connects key initiatives to governance capabilities and quickly delivers business value for the long-term.
Automating and Orchestrating Processes and Decisions Across the EnterpriseDenis Gagné
PRESENTED BY
Carl Lehmann – Principal Analyst, 451 Research
Denis Gagne – CEO and CTO, Trisotech
DESCRIPTION
When business processes must execute complex decisions across the enterprise, most process automation platforms and rules management engines fall short. While competent in rules-based process modeling and automation they’re unable to model, automate and orchestrate complex decision-making processes, for example, in areas such as in clinical contexts, insurance risk management, and structured financial services, among others.
451 Research is tracking an emerging class of automation and orchestration technology that is becoming competent in both.
Join us to explore the industry trends driving the need for joint process and decision automation.
The benefits derived from a unified approach to both.
The technical apparatus needed to automate and orchestrate process and decision models.
This document discusses implementing a non-invasive enterprise data governance program. It begins by outlining some common data challenges around data quality, variety, and volume. It then proposes formalizing existing informal governance by putting structure around current practices to improve data risk management, quality, and coordination. The solution involves taking a non-invasive approach and not spending a lot of money. Several frameworks and models are presented for implementing an effective yet lightweight data governance program, including an Enterprise Information Management framework and an Enterprise Data Strategy and Design framework.
Empowering Your Business with Advanced Data Analytics ServicesCorotsystems
Corot Systems offers advanced data analytics services to empower businesses with valuable insights and data-driven strategies. Our team of experts utilizes cutting-edge techniques in predictive modeling, data visualization, and analysis to unlock the full potential of your data. Transform your business with our comprehensive data analytics solutions.
Data research services serve as a cornerstone for informed decision-making, strategic planning, and innovation in organizations across various industries.
A brief introduction to Data Quality rule development and implementation covering:
- What are Data Quality Rules.
- Examples of Data Quality Rules.
- What are the benefits of rules.
- How can I create my own rules?
- What alternate approaches are there to building my own rules?
The presentation also includes a very brief overview of our Data Quality Rule services. For more information on this please contact us.
Predictive analytics are increasingly a must-have competitive tool. A well-defined workflow and effective decision modeling approach ensures that the right predictive analytic models get built and deployed.
Business project success means connecting the dots between strategy, mission success, programmatic, and technical performance management using units of measure meaningful to the decision makers, to deliver Needed Value for planned budget on planned date.
Data Governance: From speed dating to lifelong partnershipPrecisely
Data Governance, like a relationship, requires a strong foundation and commitment to make it work. This presentation explores the main reasons why Data Governance initiatives fail and the key components that are necessary to build a sustainable program with long-term engagement within your organisation. Learn about the Data Integrity Framework, data governance tools, and how to establish a structured decision tree to drive prioritisation.
5 Signs Your Privacy Management Program is Not Working for YouTrustArc
GDPR, CCPA, and other privacy regulations have forced companies over the last five years to focus on building out a privacy management program regardless of their size or maturity. Privacy management can range from ad hoc decentralized spreadsheets to fully- optimized, technology- backed solutions, depending on the resources and support provided.
Whether you pulled together the bare minimum compliance requirements or built out an end-to-end privacy management program, the goal is to provide your internal stakeholders actionable insights to make strategic data-driven decisions.
Join this webinar to learn the five signs that signal your privacy management program isn’t built to last and find out how you can get on the road to recovery.
Key takeaways:
- The five signs that signal your privacy management program isn’t built to last
- What a privacy management program should include to provide actionable insights to make strategic data-driven decisions
This document discusses using data analytics to address fraud risk. It summarizes the COSO framework for fraud risk assessment and Cressey's fraud triangle. It provides examples of using data analytics to test anti-fraud controls in areas like accounts payable, payroll, travel and entertainment, and journal entries. It also describes a methodology for data extraction, import, and analysis using Excel and Access. Finally, it discusses a joint effort with Rutgers to develop an exception prioritization framework using a suspicious scoring model.
The document discusses IT service management (ITSM). It defines ITSM as a process-based approach to aligning IT services with organizational needs. ITSM is performed through people, processes, products, and partners. The document outlines some key benefits of ITSM, such as improved quality and productivity. It also discusses various ITSM frameworks and criteria for successful ITSM implementation, noting the importance of change management and business alignment.
Predictive Analytics to Discover Risk.
Organizations are seeking new ways to transform their rapidly growing data into insight that mitigates risks and unlocks new opportunities. However, using the traditional reporting tools to look for unusual patterns in large data sets is like finding a needle in a haystack.
The problem is not the resources, the personnel, or the data. It’s that many organizations simply don’t have the advanced analytics required to arrange the data, identify suspicious patterns and weaknesses; at least not fast enough. There’s too much data and not enough analytics!
We need a better way of knowing what the information means — of interpreting the data to discover an unknown business risk or opportunity as it happens or, even better, anticipate the next one. For most organizations, reducing transaction errors and misuse continues to be one of the largest untapped opportunities to manage costs, improve top-line revenue recognition, and ensure compliance with policies.
Join SafePaaS CEO Adil Khan as he discusses how to discover patterns in all types of structured and unstructured enterprise data, and use this insight to improve bottom line, significantly reduce cash leakage and post-audit recovery costs, improve revenue recognition timing, safeguard the integrity of financial statements, reduce the cost of internal and external audits, increase visibility into controls environment and mitigate exposure to fraud.
This document discusses dimensions of data quality and data quality assessments. It defines key aspects of data quality like validity, reliability, precision, and integrity. It explains why data quality is important for evidence-based programming and accountability. The document outlines the components of a data quality assessment, including assessing the data collection process, data management systems, and verifying reported data. It provides tools to assess monitoring and evaluation structures, indicator definitions, data collection forms, data management processes, and capacity. The goal of a data quality assessment is to determine the quality of data captured by a monitoring and evaluation system.
7. emc isilon hdfs enterprise storage for hadoopTaldor Group
This document discusses using EMC Isilon scale-out NAS storage for Hadoop. It provides an overview of HDFS architecture challenges, how Isilon addresses these challenges through its scale-out NAS architecture and native HDFS support. It describes how Isilon allows HDFS metadata and data to be hosted on its clustered storage, providing enterprise-level data protection, efficiency, scalability and protocol support for Hadoop deployments.
The document discusses big data and how it differs from traditional IT approaches. It defines big data using the four V's - volume, velocity, variety, and variability. Technologies used for big data like Hadoop, MapReduce, and NoSQL databases are outlined. Differences between big data infrastructure and traditional IT infrastructure and BI are explored. Examples of how Orbitz and the DoD use big data are provided. The business value of big data analytics is discussed as enabling new types of analysis and insights not previously possible.
Geek Sync | 5 Best Practices for Operationalizing Data GovernanceIDERA Software
You can watch the replay of this webinar in the IDERA Resource Center: http://ow.ly/gac350A4srt.
The combination of increased regulations impacting data management, growing demand for accessibility to data assets for analytical purposes, and expansion of information management platforms beyond the traditional corporate boundaries means that ensuring data quality and usability is becoming more and more complex. Data governance programs are intended to establish oversight and control over enterprise information management processes and procedures. In a data governance program, defined data policies are intended to establish data quality assurance.One of the biggest challenges, though, is transforming corporate data policies into processes and procedures for ensuring policy compliance.
Join IDERA and David Loshin as he looks at the relationships between business policies and regulations, data policies, and five best practices to take advantage of tools and technologies to operationalize data governance. Attendees will learn about requirements analysis, data quality, business glossaries, data standards, data lineage, collaborative metadata, and data catalogs to support data governance.
About David: David Loshin is the president of Knowledge Integrity, Inc. and is a globally recognized thought leader and expert consultant in the areas of analytics, big data, data governance, data quality, master data management, and business intelligence. Along with consulting on numerous data management projects over the past 20 years, David is also a prolific author regarding data management best practices, including “Big Data Analytics: From Strategic Planning to Enterprise Integration with Tools, Techniques, NoSQL, and Graph,” the second edition of “Business Intelligence – The Savvy Manager’s Guide,” as well as other books and articles on data quality, master data management, big data, and data governance. David is a frequent invited speaker at conferences, web seminars, and sponsored web sites and TechTarget channels, and is the Program Director of the Master of Information Management program at the College of Information Studies at the University of Maryland.
How do you assess the quality and reliability of data sources in data analysi...Soumodeep Nanee Kundu
**Assessing the Quality and Reliability of Data Sources in Data Analysis**
Data is often referred to as the lifeblood of data analysis. It forms the foundation upon which decisions are made, insights are drawn, and actions are taken. However, not all data is created equal. The quality and reliability of data sources are paramount to the success of data analysis efforts. In this essay, we will explore the intricate process of assessing data quality and reliability, touching on the methods, considerations, and best practices to ensure the data used in the analysis is trustworthy and fit for purpose.
Fuel your Data-Driven Ambitions with Data GovernancePedro Martins
The document discusses the importance of data governance and provides an overview of how to implement an effective data governance program. It recommends obtaining executive sponsorship, aligning objectives to business initiatives, prioritizing initiatives, getting frameworks ready, and socializing the program. The document outlines data governance building blocks, including assessing maturity, developing a master plan, selecting tools, and establishing an organizational framework. It also discusses preparing an organization for success with data governance.
The document discusses performance measurement and strategic information management. It outlines key concepts like using consistent data to evaluate processes and meet business objectives. It also discusses benefits like understanding customers, assessing progress, and reducing costs. The document provides guidelines for developing an effective performance measurement system, including linking measures to key business drivers and strategies and ensuring measures support different organizational levels.
A Business-first Approach to Building Data Governance ProgramsPrecisely
Traditional data governance initiatives fail by focusing too heavily on policies, compliance, and enforcement, which quickly lose business interest and support. This leaves data management and governance leaders having to continually make the case for data governance to secure business adoption. In this presentation, we share a lean, business-first data governance approach that connects key initiatives to governance capabilities and quickly delivers business value for the long-term.
Automating and Orchestrating Processes and Decisions Across the EnterpriseDenis Gagné
PRESENTED BY
Carl Lehmann – Principal Analyst, 451 Research
Denis Gagne – CEO and CTO, Trisotech
DESCRIPTION
When business processes must execute complex decisions across the enterprise, most process automation platforms and rules management engines fall short. While competent in rules-based process modeling and automation they’re unable to model, automate and orchestrate complex decision-making processes, for example, in areas such as in clinical contexts, insurance risk management, and structured financial services, among others.
451 Research is tracking an emerging class of automation and orchestration technology that is becoming competent in both.
Join us to explore the industry trends driving the need for joint process and decision automation.
The benefits derived from a unified approach to both.
The technical apparatus needed to automate and orchestrate process and decision models.
This document discusses implementing a non-invasive enterprise data governance program. It begins by outlining some common data challenges around data quality, variety, and volume. It then proposes formalizing existing informal governance by putting structure around current practices to improve data risk management, quality, and coordination. The solution involves taking a non-invasive approach and not spending a lot of money. Several frameworks and models are presented for implementing an effective yet lightweight data governance program, including an Enterprise Information Management framework and an Enterprise Data Strategy and Design framework.
Empowering Your Business with Advanced Data Analytics ServicesCorotsystems
Corot Systems offers advanced data analytics services to empower businesses with valuable insights and data-driven strategies. Our team of experts utilizes cutting-edge techniques in predictive modeling, data visualization, and analysis to unlock the full potential of your data. Transform your business with our comprehensive data analytics solutions.
Data research services serve as a cornerstone for informed decision-making, strategic planning, and innovation in organizations across various industries.
A brief introduction to Data Quality rule development and implementation covering:
- What are Data Quality Rules.
- Examples of Data Quality Rules.
- What are the benefits of rules.
- How can I create my own rules?
- What alternate approaches are there to building my own rules?
The presentation also includes a very brief overview of our Data Quality Rule services. For more information on this please contact us.
Predictive analytics are increasingly a must-have competitive tool. A well-defined workflow and effective decision modeling approach ensures that the right predictive analytic models get built and deployed.
Business project success means connecting the dots between strategy, mission success, programmatic, and technical performance management using units of measure meaningful to the decision makers, to deliver Needed Value for planned budget on planned date.
Data Governance: From speed dating to lifelong partnershipPrecisely
Data Governance, like a relationship, requires a strong foundation and commitment to make it work. This presentation explores the main reasons why Data Governance initiatives fail and the key components that are necessary to build a sustainable program with long-term engagement within your organisation. Learn about the Data Integrity Framework, data governance tools, and how to establish a structured decision tree to drive prioritisation.
5 Signs Your Privacy Management Program is Not Working for YouTrustArc
GDPR, CCPA, and other privacy regulations have forced companies over the last five years to focus on building out a privacy management program regardless of their size or maturity. Privacy management can range from ad hoc decentralized spreadsheets to fully- optimized, technology- backed solutions, depending on the resources and support provided.
Whether you pulled together the bare minimum compliance requirements or built out an end-to-end privacy management program, the goal is to provide your internal stakeholders actionable insights to make strategic data-driven decisions.
Join this webinar to learn the five signs that signal your privacy management program isn’t built to last and find out how you can get on the road to recovery.
Key takeaways:
- The five signs that signal your privacy management program isn’t built to last
- What a privacy management program should include to provide actionable insights to make strategic data-driven decisions
This document discusses using data analytics to address fraud risk. It summarizes the COSO framework for fraud risk assessment and Cressey's fraud triangle. It provides examples of using data analytics to test anti-fraud controls in areas like accounts payable, payroll, travel and entertainment, and journal entries. It also describes a methodology for data extraction, import, and analysis using Excel and Access. Finally, it discusses a joint effort with Rutgers to develop an exception prioritization framework using a suspicious scoring model.
The document discusses IT service management (ITSM). It defines ITSM as a process-based approach to aligning IT services with organizational needs. ITSM is performed through people, processes, products, and partners. The document outlines some key benefits of ITSM, such as improved quality and productivity. It also discusses various ITSM frameworks and criteria for successful ITSM implementation, noting the importance of change management and business alignment.
Predictive Analytics to Discover Risk.
Organizations are seeking new ways to transform their rapidly growing data into insight that mitigates risks and unlocks new opportunities. However, using the traditional reporting tools to look for unusual patterns in large data sets is like finding a needle in a haystack.
The problem is not the resources, the personnel, or the data. It’s that many organizations simply don’t have the advanced analytics required to arrange the data, identify suspicious patterns and weaknesses; at least not fast enough. There’s too much data and not enough analytics!
We need a better way of knowing what the information means — of interpreting the data to discover an unknown business risk or opportunity as it happens or, even better, anticipate the next one. For most organizations, reducing transaction errors and misuse continues to be one of the largest untapped opportunities to manage costs, improve top-line revenue recognition, and ensure compliance with policies.
Join SafePaaS CEO Adil Khan as he discusses how to discover patterns in all types of structured and unstructured enterprise data, and use this insight to improve bottom line, significantly reduce cash leakage and post-audit recovery costs, improve revenue recognition timing, safeguard the integrity of financial statements, reduce the cost of internal and external audits, increase visibility into controls environment and mitigate exposure to fraud.
This document discusses dimensions of data quality and data quality assessments. It defines key aspects of data quality like validity, reliability, precision, and integrity. It explains why data quality is important for evidence-based programming and accountability. The document outlines the components of a data quality assessment, including assessing the data collection process, data management systems, and verifying reported data. It provides tools to assess monitoring and evaluation structures, indicator definitions, data collection forms, data management processes, and capacity. The goal of a data quality assessment is to determine the quality of data captured by a monitoring and evaluation system.
Similar to Loshin operationalizingdatagovernance (20)
7. emc isilon hdfs enterprise storage for hadoopTaldor Group
This document discusses using EMC Isilon scale-out NAS storage for Hadoop. It provides an overview of HDFS architecture challenges, how Isilon addresses these challenges through its scale-out NAS architecture and native HDFS support. It describes how Isilon allows HDFS metadata and data to be hosted on its clustered storage, providing enterprise-level data protection, efficiency, scalability and protocol support for Hadoop deployments.
The document discusses big data and how it differs from traditional IT approaches. It defines big data using the four V's - volume, velocity, variety, and variability. Technologies used for big data like Hadoop, MapReduce, and NoSQL databases are outlined. Differences between big data infrastructure and traditional IT infrastructure and BI are explored. Examples of how Orbitz and the DoD use big data are provided. The business value of big data analytics is discussed as enabling new types of analysis and insights not previously possible.
This document discusses big data and Hadoop. It provides an overview of Hadoop, including what it is, how it works, and its core components like HDFS and MapReduce. It also discusses what Hadoop is good for, such as processing large datasets, and what it is not as good for, like low-latency queries or transactional systems. Finally, it covers some best practices for implementing Hadoop, such as infrastructure design and performance considerations.
3. ami big data hadoop on ucs seminar may 2013Taldor Group
Cisco has partnered with leading software providers to offer a comprehensive infrastructure and management solution for big data. This includes tested and validated reference architectures, joint engineering labs, solution bundles, and technical collateral. Cisco's UCS is the exclusive hardware reference platform and offers benefits like unified management, unified fabric, and seamless data and management integration.
Pivotal introduces its new Pivotal HD platform for big data analytics. Pivotal HD integrates Hadoop, HBase, Pig, Hive and other big data tools into an enterprise-grade distribution. It also includes tools like Command Center for job and cluster monitoring and HAWQ for SQL queries on Hadoop. Pivotal positions Pivotal HD as addressing pain points with Hadoop like usability, manageability and performance in order to make big data analytics mission-critical for enterprises.
This document discusses personalization using big data and collaborative filtering techniques. It describes how collaborative filtering works by analyzing user preferences for items to predict other items a user may like. Several collaborative filtering algorithms are presented, including item-based algorithms that recommend similar items to those a user has rated highly. Matrix math and Apache Mahout are introduced as ways to implement collaborative filtering at scale using Hadoop. Real-world examples of using these techniques for recommendations are also briefly mentioned.
פיני מנדל תובנות עסקיות מיישומי HadoopTaldor Group
This document discusses how Hadoop can be used for batch processing, parallel execution, and storing large amounts of data. It then lists five common uses of Hadoop: recommendation engines, ad targeting, large scale business intelligence, text processing, and data sandboxes. For each use, it provides brief explanations of how Hadoop is applied.
Hadoop is an open-source software framework for distributed storage and processing of large datasets across clusters of commodity servers. It was designed to scale up from single servers to thousands of machines, with very large datasets measured in multiple petabytes. Hadoop allows for the distributed processing of large data sets across clusters of computers using simple programming models and is suitable for applications that require distributed computing across commodity hardware.
Taldor data quality einat shimoni - stkiTaldor Group
The document discusses trends in data management and governance. It notes that data quality is decreasing each year while data sources and types are increasing. Many organizations still view data as a byproduct rather than an asset. While regulatory requirements are driving some data cleansing efforts, master data management is still immature in Israel. As business intelligence becomes easier to use, effective data management and governance are becoming more difficult. The role of the business user is growing, while IT will focus more on data quality, access, and channels to analytics tools. Most organizations have a long way to go to reach a mature level of data governance.
2013 04 irm mdmdg - jon asprey 4 most asked dg questions v 1 3Taldor Group
This document provides guidance on addressing the four most common questions around implementing an effective data governance program. It recommends starting by understanding business processes and identifying "hidden factories" of data issues. Standards and rules should be defined through dialogue with stakeholders. Engaging the business requires building a business case by quantifying impacts and communicating successes. Demonstrating value involves measuring improvements in key business metrics from risk reduction, cost savings, or increased efficiency.