The document discusses Rapid Mobile Phone-based Surveys (RAMP) for evidence-based emergency response. RAMP uses mobile phones to conduct surveys more efficiently during emergencies by reducing time and costs while improving quality assurance. It was initially developed for malaria surveys but is being expanded to other areas. RAMP surveys can provide timely preliminary analysis and reports to inform decision making. Key benefits include timeliness of results, cost-effectiveness, and building local evaluation capacity.
'Learning from disaster' study launch presentationALNAP
This presentation outlines the main findings of 'Learning from disaster'. This ALNAP study explores how national disaster management authorities and other state actors learn and improve their humanitarian response activities with a view to identifying current practice, challenges that impeded learning and improvement and ways in which collaboration with others has assisted in overcoming these.
The document discusses RAMP (Rapid Mobile Phone-based Surveys), a mobile data collection methodology developed by the IFRC to conduct surveys more efficiently during emergencies. RAMP uses mobile phones and web-based software to reduce the time and costs of surveys while improving quality. It allows for real-time monitoring and analysis of data. The presentation provides an overview of how RAMP works and its potential benefits for emergency contexts like rapid needs assessments, distribution monitoring, and disease surveillance. Challenges include connectivity issues and RAMP's limitations for very long questionnaires.
InfoSphere Streams toolkits :Real-Time Analytics on Data in MotionAvadhoot Patwardhan
InfoSphere Streams comes standard with several real-time analytic toolkits to help provide quicker time to value. These include telecommunications event data, time series, text, messaging, database, geospatial, and more. Many of these toolkits are part of the InfoSphere Streams Open Source Project.
iMicrobe and iVirus: Extending the iPlant cyberinfrastructure from plants to ...Bonnie Hurwitz
The document discusses extending the iPlant cyberinfrastructure to support microbes in addition to plants. It provides an overview of iPlant, including its funding from NSF, collaborations, resources like data storage and computing platforms, and applications for analysis. Future plans are outlined to build tools and streamline workflows for metagenomics and enable high-throughput computing for microbial data.
Democratizing Data Science: Balancing Flexibility and Usability for Scientifi...PerkinElmer Informatics
The document discusses Democratizing Data Science by balancing flexibility and usability for scientific applications. It describes the challenges of scientific data including large volumes of varied and complex data from many sources. It outlines a platform called Signals Apps that aims to bridge the gap between raw data and results by providing a flexible yet usable system. Signals Apps includes a variety of domain-specific apps, customizable app creation, workflows, automatic data handling, and visualization capabilities to simplify complex analysis.
Big Data & Analytics for Government - Case StudiesJohn Palfreyman
This presentation explains the future challenges that Governments face, and illustrates how Big Data & Analytics technologies can help address these challenges. Four case studies - based on recent customer projects - are used to show the value that the innovative application of these technologies can bring.
This document discusses how connected data platforms can help companies unlock value from all their data. It provides examples of how Hortonworks has helped customers renovate their data architectures to better capture, store and analyze both data at rest and data in motion. Specific examples are given of how Merck and Symantec have used Hortonworks platforms to innovate in vaccine yield optimization and cyber security threat detection, gaining significant business benefits.
You Want to Mobilize Your Workforce… Now What?Vortex Connect
The six steps to ensuring a successful mobilization of your workforce. Neil Grunberg, the VP of Client Services for Vortex Connect manages the Client Engagement team and he is tasked to make every Vortex customer successful and happy with their mobile deployments. Neil has over a decade of workforce management experience and he will highlight the successes and pitfalls of a mobile deployment. From managing and selecting the right devices to support, to determining what systems you want to prioritize, mobile device management (MDM), how to select the right solution and how to deploy, how to build a business case and ROI, lastly addressing change management and user acceptance of managing a new workflow processes.
'Learning from disaster' study launch presentationALNAP
This presentation outlines the main findings of 'Learning from disaster'. This ALNAP study explores how national disaster management authorities and other state actors learn and improve their humanitarian response activities with a view to identifying current practice, challenges that impeded learning and improvement and ways in which collaboration with others has assisted in overcoming these.
The document discusses RAMP (Rapid Mobile Phone-based Surveys), a mobile data collection methodology developed by the IFRC to conduct surveys more efficiently during emergencies. RAMP uses mobile phones and web-based software to reduce the time and costs of surveys while improving quality. It allows for real-time monitoring and analysis of data. The presentation provides an overview of how RAMP works and its potential benefits for emergency contexts like rapid needs assessments, distribution monitoring, and disease surveillance. Challenges include connectivity issues and RAMP's limitations for very long questionnaires.
InfoSphere Streams toolkits :Real-Time Analytics on Data in MotionAvadhoot Patwardhan
InfoSphere Streams comes standard with several real-time analytic toolkits to help provide quicker time to value. These include telecommunications event data, time series, text, messaging, database, geospatial, and more. Many of these toolkits are part of the InfoSphere Streams Open Source Project.
iMicrobe and iVirus: Extending the iPlant cyberinfrastructure from plants to ...Bonnie Hurwitz
The document discusses extending the iPlant cyberinfrastructure to support microbes in addition to plants. It provides an overview of iPlant, including its funding from NSF, collaborations, resources like data storage and computing platforms, and applications for analysis. Future plans are outlined to build tools and streamline workflows for metagenomics and enable high-throughput computing for microbial data.
Democratizing Data Science: Balancing Flexibility and Usability for Scientifi...PerkinElmer Informatics
The document discusses Democratizing Data Science by balancing flexibility and usability for scientific applications. It describes the challenges of scientific data including large volumes of varied and complex data from many sources. It outlines a platform called Signals Apps that aims to bridge the gap between raw data and results by providing a flexible yet usable system. Signals Apps includes a variety of domain-specific apps, customizable app creation, workflows, automatic data handling, and visualization capabilities to simplify complex analysis.
Big Data & Analytics for Government - Case StudiesJohn Palfreyman
This presentation explains the future challenges that Governments face, and illustrates how Big Data & Analytics technologies can help address these challenges. Four case studies - based on recent customer projects - are used to show the value that the innovative application of these technologies can bring.
This document discusses how connected data platforms can help companies unlock value from all their data. It provides examples of how Hortonworks has helped customers renovate their data architectures to better capture, store and analyze both data at rest and data in motion. Specific examples are given of how Merck and Symantec have used Hortonworks platforms to innovate in vaccine yield optimization and cyber security threat detection, gaining significant business benefits.
You Want to Mobilize Your Workforce… Now What?Vortex Connect
The six steps to ensuring a successful mobilization of your workforce. Neil Grunberg, the VP of Client Services for Vortex Connect manages the Client Engagement team and he is tasked to make every Vortex customer successful and happy with their mobile deployments. Neil has over a decade of workforce management experience and he will highlight the successes and pitfalls of a mobile deployment. From managing and selecting the right devices to support, to determining what systems you want to prioritize, mobile device management (MDM), how to select the right solution and how to deploy, how to build a business case and ROI, lastly addressing change management and user acceptance of managing a new workflow processes.
You Want to Mobilize Your Workforce… Now What?cwongsala
The six steps to ensuring a successful mobilization of your workforce. Neil Grunberg, the VP of Client Services for Vortex Connect manages the Client Engagement team and he is tasked to make every Vortex customer successful and happy with their mobile deployments. Neil has over a decade of workforce management experience and he will highlight the successes and pitfalls of a mobile deployment. From managing and selecting the right devices to support, to determining what systems you want to prioritize, mobile device management (MDM), how to select the right solution and how to deploy, how to build a business case and ROI, lastly addressing change management and user acceptance of managing a new workflow processes.
Offline Surveys: Seamlessly Collect Data AnywhereNestForms
In the dynamic landscape of data collection, where connectivity isn't always guaranteed, conducting offline surveys has become a crucial aspect of efficient field research. Whether you're working in remote areas, event venues with limited internet access, or simply want to eliminate the dependency on a stable network, having a robust offline survey application can make a significant difference.
Real Time Business Platform by Ivan Novick from PivotalVMware Tanzu Korea
This document discusses Pivotal's real time business platform for maximizing the value of data investments. It recommends identifying business problems with high ROI potential, then focusing data solutions on high-speed ingestion, consolidation, real-time queries, and analytics to drive real-time insights. The platform combines Gemfire for fast transactions with Greenplum for analytics. Use cases discussed include predictive maintenance, fraud detection, and recommendation engines. The platform provides a complete solution from data capture and analytics to application integration.
HPC traditionally handles data at rest. The acquisition of streaming data presents a different set of challenges that, at scale, can be difficult to tackle. The approach to building data ingestion infrastructure at ARC-TS involves treating every service as a swappable building block. With this pluggable design using Docker containers you are free to choose which component is best. We will use an example use case to show how data is being generated, ingested, and how each component in the stack can be replaced.
The document discusses a mobile application called SmatForms that allows for malaria surveillance in remote areas. It describes features of the app that allow health workers to collect field data, submit it to the cloud for analysis, and receive responses from experts. The app aims to improve on existing paper-based surveillance systems by streamlining data collection and transmission through a mobile interface. Health workers can use the app to track and manage tasks, submit various types of reports, and access maps to support their work.
Getting Started with Splunk Breakout SessionSplunk
Splunk is a software company that provides software for searching, monitoring, and analyzing machine-generated big data via a web-style interface. The document discusses why organizations use Splunk, provides an overview of the company and its products, describes how Splunk works and how to get started with it. It also advertises Splunk's upcoming user conference to provide training, certification, and opportunities to learn from customers and partners about using Splunk.
The Critical Role of Spatial Data in Today's Data EcosystemSafe Software
In today's data-driven landscape, integrating spatial data is becoming increasingly crucial for organizations aiming to harness the full potential of their data. Spatial data offers unique insights based on location, making it a fundamental component for addressing various challenges across different sectors, including urban planning, environmental sustainability, public health, and logistics.
Our webinar delves into the indispensable role of spatial data in data management and analysis. We'll showcase how omitting spatial data from your data strategy not only weakens your data infrastructure, but also limits the depth of your insights. Through real-world case studies, we'll highlight the transformative impact of spatial data, demonstrating its ability to uncover complex patterns, trends, and relationships.
Join us for this introductory-level webinar as we explore the critical importance of spatial data integration in driving strategic decision-making processes. By the end of the webinar, you'll gain a renewed perspective on how spatial data is essential for confronting and overcoming challenges across various domains.
Building Intelligent Applications, Experimental ML with Uber’s Data Science W...Databricks
In this talk, we will explore how Uber enables rapid experimentation of machine learning models and optimization algorithms through the Uber’s Data Science Workbench (DSW). DSW covers a series of stages in data scientists’ workflow including data exploration, feature engineering, machine learning model training, testing and production deployment. DSW provides interactive notebooks for multiple languages with on-demand resource allocation and share their works through community features.
It also has support for notebooks and intelligent applications backed by spark job servers. Deep learning applications based on TensorFlow and Torch can be brought into DSW smoothly where resources management is taken care of by the system. The environment in DSW is customizable where users can bring their own libraries and frameworks. Moreover, DSW provides support for Shiny and Python dashboards as well as many other in-house visualization and mapping tools.
In the second part of this talk, we will explore the use cases where custom machine learning models developed in DSW are productionized within the platform. Uber applies Machine learning extensively to solve some hard problems. Some use cases include calculating the right prices for rides in over 600 cities and applying NLP technologies to customer feedbacks to offer safe rides and reduce support costs. We will look at various options evaluated for productionizing custom models (server based and serverless). We will also look at how DSW integrates into the larger Uber’s ML ecosystem, e.g. model/feature stores and other ML tools, to realize the vision of a complete ML platform for Uber.
Uber - Building Intelligent Applications, Experimental ML with Uber’s Data Sc...Karthik Murugesan
This document summarizes Uber's data science workbench (DSW), which provides scalable infrastructure, tools, customization, and support for Uber's large data science community. The DSW allows data scientists to access internal data sources and compute engines through Jupyter notebooks or RStudio IDEs in a secure, hosted environment. It helps standardize workflows and facilitates collaboration, publishing of results, and model deployment to production. The DSW integrates with Uber's Spark and machine learning systems to enable large-scale data exploration, parallelized model training, and evaluation at Uber's massive scale. It has supported a wide range of use cases across safety, risk, recommendations, and operations.
This is a re-boot of a presentation originally given on the potential role of cloud infrastructure in healthcare delivery from eHealth Canada 2012.
Key concepts are the drivers of change in healthcare, how hospitals can protect themselves when using of cloud, the potential use of enterprise content management as part of healthcare delivery and the current models that we are seeing in Canada and the US.
This document provides an overview of artificial intelligence, machine learning, cloud computing and their applications. It discusses key concepts like different machine learning algorithms including neural networks, deep learning and natural language processing. It also highlights various use cases of AI in different industries like automotive, banking, supply chain etc. and the impact of AI on businesses. Finally, it covers cloud computing delivery and deployment models as well as best practices for data management in the cloud.
The document discusses challenges with data collection in developing areas and how the Open Data Kit (ODK) aims to address them. ODK is an open-source suite of tools that allows for modular, customizable and scalable mobile data collection using Android devices. It includes tools like ODK Collect for collecting data, ODK Aggregate for storing and exporting data, and ODK Build for generating forms. The document provides examples of organizations successfully using ODK to improve data collection and management.
This document summarizes a presentation on mobile electronic health record (EHR) trends. The presentation covers:
1. Phasing EHR functionality to mobile platforms and formulating a development strategy.
2. Building the right team with considerations for user experience and security challenges.
3. Representative case studies of mobilizing EHRs and mobile chronic disease management solutions.
4. A question and answer section.
This document discusses mobile testing trends, challenges, and innovations. It covers the proliferation of devices, accelerated release cycles, and increased workload for test teams as trends. Unique challenges include understanding mobile strategies, transitioning traditional test teams to support mobile, and supporting more testing permutations efficiently. Innovations discussed include using emulators and simulators in testing labs and when each is appropriate. The document also provides an overview of different types of mobile technologies, solutions, configurations, and testing like connectivity, gestures, interruptions, and more.
The Essentials of Mobile App Performance Testing and MonitoringCorrelsense
Complexity across mobile carriers, locations and operating systems has made building mobile apps and monitoring their end user performance time consuming and expensive. The importance of testing mobile apps on iOS, Android and Windows Phone is increasing as more users embrace these devices. Join Correlsense and uTest for an online seminar which will teach you the steps to successful mobile application testing and performance management. We will discuss:
- The proliferation of mobile devices and the technical challenges they bring to end user experience monitoring
- Ways to prepare mobile applications for peak usage periods with the right load and performance testing techniques
- Tips and techniques for gaining visibility into the performance of mobile applications with the right monitoring tools
We will conclude with a discussion of the Correlsense and uTest solutions.
Presentation given by Appistry's Vice President of Product Strategy, Sultan Meghi at the World Genome Data Analysis Summit. Meghi presented about the big data challenges facing labs as they strive to manage the flow of genetic data from sequencer to the clinic.
The document discusses several key ICT trends worldwide:
1. Open innovation and crowdsourcing, where companies collaborate with outside partners and the public to generate ideas. Examples given include a juice company collaborating on new flavors and an open smartphone platform.
2. Reality sensing, which analyzes existing data like mobile phone and sensor data to measure events in real-time. Examples include traffic monitoring and flu tracking.
3. Empowering energy efficiency, with consumers and businesses seeking more sustainable energy solutions. Examples include devices that generate power from human motion and smart homes that monitor energy usage.
4. Human interface trends like emotions in robots, brain-wave controlled devices, and new semantic search engines
This document discusses Interac Association/Acxsys Corporation and how Josh Diakun uses Splunk. It provides the following information:
- Interac Association was formed in 1984 and operates the Inter-Member Network, providing services like Interac Cash and Debit. Acxsys Corporation was founded in 1996 and provides management services to Interac Association.
- Before Splunk, Josh faced challenges like slow incident response, a lack of single visibility across infrastructure, and stress due to unclear root causes. Splunk helped address these by consolidating logs and providing faster investigations.
- Josh built several apps with Splunk like ones for enterprise storage, security analysis, and application monitoring to help various business units with control
Data drives innovation in the life sciences. Collaborative teams in biomedical research, pharmacology, academia, government and national laboratories need to quickly and efficiently exchange and process vast amounts of data. New research technologies – in particular, next-generation genomic sequencing – create tens of gigabytes of data for each experimental run. Supporting the movement of these huge data sets, Aspera software provides breakthrough high-speed file transfer across the globe for projects which serve up vast public databases for the study of human genomic variation. Scientific users enjoy familiar Unix-style interfaces, embeddable APIs and user-friendly web and desktop GUIs.
From best practice to best fit: changing to a more flexible approach to human...ALNAP
This presentation by ALNAP's Director John Mitchell outlines four models of aid which respond to different circumstances and different needs in order to explain some of the performance challenges of the humanitarian system. Presented in Bern on the 13th of May 2015 to the Swiss Agency for Development and Cooperation
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In the dynamic landscape of data collection, where connectivity isn't always guaranteed, conducting offline surveys has become a crucial aspect of efficient field research. Whether you're working in remote areas, event venues with limited internet access, or simply want to eliminate the dependency on a stable network, having a robust offline survey application can make a significant difference.
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HPC traditionally handles data at rest. The acquisition of streaming data presents a different set of challenges that, at scale, can be difficult to tackle. The approach to building data ingestion infrastructure at ARC-TS involves treating every service as a swappable building block. With this pluggable design using Docker containers you are free to choose which component is best. We will use an example use case to show how data is being generated, ingested, and how each component in the stack can be replaced.
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Splunk is a software company that provides software for searching, monitoring, and analyzing machine-generated big data via a web-style interface. The document discusses why organizations use Splunk, provides an overview of the company and its products, describes how Splunk works and how to get started with it. It also advertises Splunk's upcoming user conference to provide training, certification, and opportunities to learn from customers and partners about using Splunk.
The Critical Role of Spatial Data in Today's Data EcosystemSafe Software
In today's data-driven landscape, integrating spatial data is becoming increasingly crucial for organizations aiming to harness the full potential of their data. Spatial data offers unique insights based on location, making it a fundamental component for addressing various challenges across different sectors, including urban planning, environmental sustainability, public health, and logistics.
Our webinar delves into the indispensable role of spatial data in data management and analysis. We'll showcase how omitting spatial data from your data strategy not only weakens your data infrastructure, but also limits the depth of your insights. Through real-world case studies, we'll highlight the transformative impact of spatial data, demonstrating its ability to uncover complex patterns, trends, and relationships.
Join us for this introductory-level webinar as we explore the critical importance of spatial data integration in driving strategic decision-making processes. By the end of the webinar, you'll gain a renewed perspective on how spatial data is essential for confronting and overcoming challenges across various domains.
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In this talk, we will explore how Uber enables rapid experimentation of machine learning models and optimization algorithms through the Uber’s Data Science Workbench (DSW). DSW covers a series of stages in data scientists’ workflow including data exploration, feature engineering, machine learning model training, testing and production deployment. DSW provides interactive notebooks for multiple languages with on-demand resource allocation and share their works through community features.
It also has support for notebooks and intelligent applications backed by spark job servers. Deep learning applications based on TensorFlow and Torch can be brought into DSW smoothly where resources management is taken care of by the system. The environment in DSW is customizable where users can bring their own libraries and frameworks. Moreover, DSW provides support for Shiny and Python dashboards as well as many other in-house visualization and mapping tools.
In the second part of this talk, we will explore the use cases where custom machine learning models developed in DSW are productionized within the platform. Uber applies Machine learning extensively to solve some hard problems. Some use cases include calculating the right prices for rides in over 600 cities and applying NLP technologies to customer feedbacks to offer safe rides and reduce support costs. We will look at various options evaluated for productionizing custom models (server based and serverless). We will also look at how DSW integrates into the larger Uber’s ML ecosystem, e.g. model/feature stores and other ML tools, to realize the vision of a complete ML platform for Uber.
Uber - Building Intelligent Applications, Experimental ML with Uber’s Data Sc...Karthik Murugesan
This document summarizes Uber's data science workbench (DSW), which provides scalable infrastructure, tools, customization, and support for Uber's large data science community. The DSW allows data scientists to access internal data sources and compute engines through Jupyter notebooks or RStudio IDEs in a secure, hosted environment. It helps standardize workflows and facilitates collaboration, publishing of results, and model deployment to production. The DSW integrates with Uber's Spark and machine learning systems to enable large-scale data exploration, parallelized model training, and evaluation at Uber's massive scale. It has supported a wide range of use cases across safety, risk, recommendations, and operations.
This is a re-boot of a presentation originally given on the potential role of cloud infrastructure in healthcare delivery from eHealth Canada 2012.
Key concepts are the drivers of change in healthcare, how hospitals can protect themselves when using of cloud, the potential use of enterprise content management as part of healthcare delivery and the current models that we are seeing in Canada and the US.
This document provides an overview of artificial intelligence, machine learning, cloud computing and their applications. It discusses key concepts like different machine learning algorithms including neural networks, deep learning and natural language processing. It also highlights various use cases of AI in different industries like automotive, banking, supply chain etc. and the impact of AI on businesses. Finally, it covers cloud computing delivery and deployment models as well as best practices for data management in the cloud.
The document discusses challenges with data collection in developing areas and how the Open Data Kit (ODK) aims to address them. ODK is an open-source suite of tools that allows for modular, customizable and scalable mobile data collection using Android devices. It includes tools like ODK Collect for collecting data, ODK Aggregate for storing and exporting data, and ODK Build for generating forms. The document provides examples of organizations successfully using ODK to improve data collection and management.
This document summarizes a presentation on mobile electronic health record (EHR) trends. The presentation covers:
1. Phasing EHR functionality to mobile platforms and formulating a development strategy.
2. Building the right team with considerations for user experience and security challenges.
3. Representative case studies of mobilizing EHRs and mobile chronic disease management solutions.
4. A question and answer section.
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Complexity across mobile carriers, locations and operating systems has made building mobile apps and monitoring their end user performance time consuming and expensive. The importance of testing mobile apps on iOS, Android and Windows Phone is increasing as more users embrace these devices. Join Correlsense and uTest for an online seminar which will teach you the steps to successful mobile application testing and performance management. We will discuss:
- The proliferation of mobile devices and the technical challenges they bring to end user experience monitoring
- Ways to prepare mobile applications for peak usage periods with the right load and performance testing techniques
- Tips and techniques for gaining visibility into the performance of mobile applications with the right monitoring tools
We will conclude with a discussion of the Correlsense and uTest solutions.
Presentation given by Appistry's Vice President of Product Strategy, Sultan Meghi at the World Genome Data Analysis Summit. Meghi presented about the big data challenges facing labs as they strive to manage the flow of genetic data from sequencer to the clinic.
The document discusses several key ICT trends worldwide:
1. Open innovation and crowdsourcing, where companies collaborate with outside partners and the public to generate ideas. Examples given include a juice company collaborating on new flavors and an open smartphone platform.
2. Reality sensing, which analyzes existing data like mobile phone and sensor data to measure events in real-time. Examples include traffic monitoring and flu tracking.
3. Empowering energy efficiency, with consumers and businesses seeking more sustainable energy solutions. Examples include devices that generate power from human motion and smart homes that monitor energy usage.
4. Human interface trends like emotions in robots, brain-wave controlled devices, and new semantic search engines
This document discusses Interac Association/Acxsys Corporation and how Josh Diakun uses Splunk. It provides the following information:
- Interac Association was formed in 1984 and operates the Inter-Member Network, providing services like Interac Cash and Debit. Acxsys Corporation was founded in 1996 and provides management services to Interac Association.
- Before Splunk, Josh faced challenges like slow incident response, a lack of single visibility across infrastructure, and stress due to unclear root causes. Splunk helped address these by consolidating logs and providing faster investigations.
- Josh built several apps with Splunk like ones for enterprise storage, security analysis, and application monitoring to help various business units with control
Data drives innovation in the life sciences. Collaborative teams in biomedical research, pharmacology, academia, government and national laboratories need to quickly and efficiently exchange and process vast amounts of data. New research technologies – in particular, next-generation genomic sequencing – create tens of gigabytes of data for each experimental run. Supporting the movement of these huge data sets, Aspera software provides breakthrough high-speed file transfer across the globe for projects which serve up vast public databases for the study of human genomic variation. Scientific users enjoy familiar Unix-style interfaces, embeddable APIs and user-friendly web and desktop GUIs.
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Comisión Nacional de Prevención de Riesgos y Atención de EmergenciasALNAP
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Rapid mobile phone based surveys (Scott Chaplowe, IFRC)
1. Rapid Mobile Phone-based Surveys (RAMP)
for Evidence-based Emergency Response
ALNAP 28th Annual Meeting,
5-7 March 2013, Washington, D.C.
Scott Chaplowe, Senior M&E Officer, IFRC
Rose Donna, Director, Datadyne.org
Jason Peat, Senior Officer Public Health, IFRC
Amanda Mcclelland, Emergency Health Officer, IFRC
Joel Selanikio, CEO DataDyne Group
Mac Otten
www.ifrc.org
Saving lives, changing minds.
2. Presentation Overview
Application of mobile technology (RAMP) to address specific
challenges in data collection during emergency operations.
1) Introduce RAMP
2) How RAMP works
3) Emergency contexts
4) Key considerations
www.ifrc.org
Saving lives, changing minds.
3. What is RAMP?
RAMP (Rapid Mobile Phone-based Surveys) is a survey
methodology utilizing mobile phones to help RCRC National
Societies, governments, NGOs and other partners efficiently
conduct quality surveys that:
Reduced time
Reduced cost
Improved quality assurance
Limited external technical assistance
www.ifrc.org
Saving lives, changing minds.
4. RAMP Background (www.ifrc.org/ramp)
1. Developed by IFRC in partnership with WHO, CDC, and
other partners.
2. Initial focus = malaria program household surveys
Four pilots in Africa 2011-2012 (Kenya, Namibia and Nigeria),
3. Refine and developed trio of user guides:
1. Designing a RAMP survey
2. Implementing a RAMP survey
3. Training a RAMP survey team
4. Scale-up to other program areas – increase survey
functionality – use of SMS
www.ifrc.org
Saving lives, changing minds.
5. RAMP takes advantage of 2 technologies
1. Mobile phone to collect data
(Low-cost, standard mobile phones, as well as Android,
Symbian, Blackberry, SMS, and iPhone)
2. Web-based software
application
Enables mobile phones
to become a data
collection platform
www.ifrc.org
Saving lives, changing minds.
6. How does RAMP work?
2. Data collection
on phone
1. Develop survey on
website
3. Transmit
data
4. Collate/analyze
5. Data Reports
data on computer
www.ifrc.org
Saving lives, changing minds.
7. Connectivity
Internet Internet
Required Not Required
• Create/edit surveys • Collect data
• View/export data
• Create reports
Can be cellular, wifi, cable
www.ifrc.org
Saving lives, changing minds.
8. Data monitoring and analysis
Preliminary analysis available
before data collection is
complete
www.ifrc.org
Saving lives, changing minds.
10. Digital Data Collection – Changing the way we work
The “old” The “new”
Paper questionnaires filled out in Mobile and internet-based
the field technologies used to reduce time
for data collection to reporting
Data entered into a computer at
a central location Enables rapid reporting of results,
decision making, and action
Data analysis and reporting often
takes months to complete Empowers local ownership of
evaluation and research
Local capacity is often under-
utilized and there is a
dependence on external experts
www.ifrc.org
Saving lives, changing minds.
11. Anything that can be put on a form
Vaccination coverage
Surveillance
Supply chain management
Household surveys
Clinic surveys
Supervisory checklists
www.ifrc.org
Saving lives, changing minds.
12. RAMP Potential in Emergencies?
Beginning to explore the potential of RAMP in emergency context:
Site assessment – needs, damage
Community assessment – needs, damage
Beneficiary registration
Distribution of emergency (and non-emergency) items
Baseline/endline data collection (monitoring and impact study)
Repeated surveys to track time trends for key indicators
Beneficiary communication – (broadcast Terra)
Beneficiary/community monitoring
Disaster preparedness – EWS monitoring
www.ifrc.org
Saving lives, changing minds.
13. SMS Disease Surveillance Systems
Piloting in community based disease surveillance
Sierra Leone – 400 community volunteers distributing ORS.
Referred only 5% of cases of AWD they saw in community = only
5% of cases were potentially recorded in normal MoH system.
RAMP allows real time communication and data gathering
suitable for this context.
Problems with integration and harmonization of data between
community and MoH.
But SMS proved real time information to assist program
prioritization in outbreak scenarios.
www.ifrc.org
Saving lives, changing minds.
14. SMS Considerations
Simplified questions rather than full surveys
Coding syntax with 2 to 7 key variables as best practice
Quantity of messages handled depend on networks, and whether
staggered or simultaneous reporting.
Paper form can be used to facilitate data entry to SMS
Quality assurance auto feedback
Reminder SMS to field person to report data at a set time
Thank you SMS to confirm receipt of data.
Ability to send airtime to the mobile account if someone reports from
a common central account.
www.ifrc.org
Saving lives, changing minds.
16. Benefits – decision making
Data rapidly available for
decision-making
Maintain data control
Scalable for studies of varying
sizes
Shared, electronic database to compare across contexts and with
partners to build a body of evidence related to impact
www.ifrc.org
Saving lives, changing minds.
17. Benefits - management
Cost effective
Do not have to reinvent the wheel –
Adaptable RAMP toolkit
Consultants not required
No software licensing or subscriptions
Multiple languages (depending on
program)
Export data for custom analysis using
any statistical analysis package
Additional SMART phone features
www.ifrc.org
Saving lives, changing minds.
18. Benefits - management
Online library of survey
forms
Collect and aggregate data
form multiple areas and
partners
Ease of creating and
changing analyses/reports
Efficient reporting and
dissemination
www.ifrc.org
Saving lives, changing minds.
19. Benefits - Fieldworkers
• Build local capacity for M&E
• Standard and familiar mobile
phones
• No more paper to collect,
transport or return
• Automated data submission
(assuming network)
www.ifrc.org
Saving lives, changing minds.
20. Benefits - Quality Assurance
Immediate QA:
Real-time error analysis and field
correction
Utilize skip patterns, custom logic and
validation
Remote QA:
Enables monitoring of survey team work rate, productivity and quality
Monitor times/location of data collection (time/date data stamps)
Provide feedback remotely
Efficient data management reduces “paper” mistakes
Easier to back-up forms/data
Reduced error of repetitive data entry and re-entry
Easier to change and update forms
www.ifrc.org
Saving lives, changing minds.
21. Reality Check!
Not suitable for very long questionnaires
No “magic bullet” –work is still in the details!
Things to improve – i.e. offline form generation
Technology is a moving target – (hardware and software)
Challenges resource development/training
(But also means improvements and reduced costs)
www.ifrc.org
Saving lives, changing minds.
22. Questions to Consider
What applications do you see for mobile data collection
in the humanitarian sector?
What has worked well?
What hasn’t worked well?
www.ifrc.org
Saving lives, changing minds.
23. www.ifrc.org/ramp
Package of field-friendly User Guides:
1. Volume 1: Designing a RAMP survey: technical considerations
2. Volume 2: Implementing a RAMP survey: practical field guide
3. Volume 3: Training a RAMP survey team: guide for trainers
Living archive of additional resources:
Example database and STATA files for data cleaning and analysis of a
sample malaria survey
Latest up-to-date malaria questionnaires and STATA files for data
cleaning and analysis
Country reports and results bulletins, information, useful links
www.ifrc.org
Saving lives, changing minds.
25. The following slides are extra and can be
referred to if needed, (but unlikely).
www.ifrc.org
Saving lives, changing minds.
26. Cost of a IFRC RAMP HH survey for
Malaria programming (average)
Description Cost (US $)
Training (4 or 5 days) including two facilitators 10,623
Field survey, including transportation, daily allowances
12,415
and accommodation
Mobile phones, accessories and air time 3,806
Survey administration 2,243
Total in-country expenditure (US $) US $ 29,087
www.ifrc.org
Saving lives, changing minds.
27. When might a RAMP survey be suitable? Flexibility
Items that can be adjusted Comments
Adjust precision ±10%, 5%, 3%, etc.
Adjust indicator type (denominator of - Person all ages
indicator) including mixtures of indicator - Children <5 years old
types - Pregnant women
- Households
- Schools
Adjust number of domains - 1 domain with 30 clusters
- 2 domains with 30 clusters to
compare statistically
- 10 domains with 30 clusters each to
compare
Adjust overall sample size - 200 to 5000 households
www.ifrc.org
Saving lives, changing minds.
28. How is the RAMP method different from MIS/DHS?
MIS/DHS RAMP
Complex design, uses external consultants Simple design, external consultants not
to design survey needed
Listing of all households is done in all Divides clusters into manageable-sized
clusters; expensive, often taking several segments using standard survey methods;
days in each cluster takes <1/2 day
Simple random sampling of households Simple random sampling of households
(from the cluster list) (from the final segment list)
Real-time data cleaning not possible Real-time data cleaning during the survey
Real-time data analysis not possible; results Real-time data analysis and results/draft
take several months report finished within 3 days of last
interview
Data analysis done by third-party Organization performs analysis, building
consultants capacity and maintaining control of data
www.ifrc.org
Saving lives, changing minds.
30. RAMP
Based on standard survey sampling methodology
Web-based platform for survey design, data storage,
analysis, reporting and data export.
Field-based data entry through mobile phone application.
Questionnaires downloaded to standard mobile phones
Web-based dataset that can export “real-time” for rapid
analysis and reporting
www.ifrc.org
Saving lives, changing minds.
Editor's Notes
To decrease dramatically the time and effort needed to complete data collectionEnables timely reportingResults are rapidly available for decision-making: emergency & development programming.
Epi Info is public domain statistical software for epidemiology developed by Centers for Disease Control and Prevention (CDC) in Atlanta, Georgia (USA).The mobile phone software used for RAMP is EpiSurveyor, created by the not-for-profit organization Datadyne.MIS= Malaria Indicator SurveyRBM= Roll Back MalariaThe Red Cross National Societies at headquarters and branch levels played a leading role in the surveys, and Red Cross volunteers were recruited and trained to collect the data in the field survey. There are many public health problems in Africa that could have been chosen to pilot the surveys. However, malaria was selected to test the RAMP tools. The pilot surveys in Africa established conclusively that National Societies can be a core partner in leading a RAMP survey, with community-based volunteers able to collect data using mobile phones, and the results being available within days of the last interviews in the survey. Lessons learnt from the pilot surveys have been used to refine the RAMP survey methodology and tools, and to provide sample materials in the RAMP toolkit.
Smart phones increasingly as cheaper
RAMP deceases the time between data collection and the production of the survey resultsThe results can be available within days of the last interviews
Traditionally, the paper questionnaires used in the field are sent to a central location where the data are entered into a computer.
Quality assurance: SMS program can automatically feedback on mistakes, i.e. type “I” instead of “1” or “O” instead of “0” automatically generates a correction request to sender. You cant do any of the three last points with RAMP yet !!
Trees!
Reduced time = more timely decision making and action. Real-time dataset exported for rapid analysis and reporting purposesMore timely with changes/adjustments to survey tool
Reduced monetary & environmental costs Paper usage, data entry, transportation and associated costs (i.e. change a form)Additional SMART phone features i.e. GPS, pictures, videoMobile phones are widely-available and understood technology, (jumps digital divides in developing countries).
Paper and data entry
Not suitable for very long questionnaires with a large quantity of skip patternsNo “magic bullet” – the work is still in the details Survey design, enumerator training, data collection and analysis, and effective reporting and dissemination.Things to improve – i.e. offline form generation (i.e. on long airline flights)
- Examples might include: surveys to estimate the percentage of households that were visited by community-based volunteers to discuss the care and repair of mosquito netssurveys to estimate the percentage of households that are receiving clean watersurveys to estimate the percentage of six year old female children that are attending school
MIS=Malaria Indicator SurveyDHS=Demographic and Health Survey
To decrease dramatically the time and effort needed to complete data collectionEnables timely reportingResults are rapidly available for decision-making: emergency & development programming.