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It is the basics of what is Big Data and What are the challenges for Big Data Analysis. This slides are for beginners of computer engineering....Hope this will be somewhat helpful!!!
Learn the concept of Data Science by uniting Statistics, mathematics, computer science and domain knowledge and information science to extract structured and unstructured data’s.
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3) Legal And Contractual Documents
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- Classification of legal, contractual and medical documents
- Automated monitoring of government regulations
- Achieving Regulatory Compliance leveraging large Text Data
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5. Data Collection Evolution
1086
Great survey in
England
1937
Unemployment
survey US census
Late 1980s
PDAs & “pocket
computers”
Mid 1990s
Teleform
2007
Rise of smartphones
Future??
7. What is mobile
data collection?
Targeted gathering of structured information using
mobile phones, tablets or other devices using a
software application
9. Government of South Africa
National Development
Plan 2030
“The goal is to have universal access to
quality early childhood development with
a strong nutrition and educational focus.”
The NDP also emphasizes the need to invest in training early
childhood development practitioners.
10. Other national
policy documents
Children’s Act No. 38 of 2005
National Plan of Action for Children in South Africa: 2012–2017
South African National Curriculum Framework for Children from
Birth to Four
Curriculum Assessment Policy for Grades R–12 (CAPS): Grade R-3
National Integrated Early Childhood Development Policy
Policy on Minimum Requirements for Programmes Leading to
Qualifications in Higher Education for Practitioners and Educators
in Early Childhood Care and Education
11. No data on number of registered/funded ECD centres and
programmes
No data on how many children access ECD services
No data on quality of ECD services
Current Challenges
No administrative data system for ECD similar
to those used by health and education
12. Why national ECD
Monitoring System?
When we have scarce resources, we need data to
ensure coverage AND appropriate resource targeting
14. Khulisa is assisting the South African Department of
Basic Education and UNICEF to develop a national ECD
M&E System
15. Rationale
Standardise ECD indicators across provinces
Target implementation for DBE and DSD
Make district and provincial reporting easier
Integrate M&E data into existing frameworks
and policies
20. Data is submitted in real time
to a central online database
Managers can see:
Pace of data collection
Coverage
What data is being submitted
Location
Allows for real time data quality assurance
22. Leads to cleaner data as responses can have
pre-determined limits (e.g. only enter a number)
and automated skip patterns
(e.g. only ask the next question if response was yes)
24. Access data faster with less effort
Reduce cost (minimal printing; no data entry costs)
No need for double entry of forms
Data aggregated automatically
Save time and money
25. A 2011 World Bank study found that mobile data
collection reduced the average interview cost by
approximately 70%
Schuster & Brito, in Satterlee et. al 2015
26. Make data more user friendly
Create demand for data
Data visualisation
30. Our approach
Use Open Data Kit (ODK) on Android tablets/smart phones
to collect information
Spend one hour in each school or ECD centre
31. Open Data Kit
ODK is a free and open-source set of
tools which help researchers author,
field, and manage mobile data
collection
It allows data collection using mobile
devices and data submission to an
online server, even without an
Internet connection or mobile carrier
service at the time of data collection
32. Process
Data collectors pull forms
from cloud repository
called ODK Aggregate
Use ODK Build to convert
paper questionnaires to
e-forms or e-surveys
Install ODK Collect on
phones or tablets for
data collectors
33. Consider
No need for internet connection during data
collection, but will need internet to upload form
Mobile data collection works well for quantitative
data, but not as well for qualitative data
ODK only works on Android devices, so need to
consider if method is fit for your purposes
40. Benefit
Get standard routine data faster linked to
images, GIS and crucial indicators
With routine data, the DBE and provinces can
make better decisions and allocate resources
more effectively
41. Lessons Learned
While overall enthusiasm, success requires:
Mobile devices that meet requirements
Access to internet (data or Wi-Fi)
Build capacity of ECD officials to collect data
43. Goal
Government receives
and manages ECD
M&E data
Government and
stakeholders use data
for decision making
This results in
improved ECD
implementation
Overall, there is
improved transparency
and accountability
44. Further reading
• http://www.betterevaluation.org/en/evaluation-options/mobile_data_collection
• https://opendatakit.org/
• Latif Jameel A. (2013). Mobile-Based Technology for Monitoring and Evaluation. Poverty
Action Lab, Institute for Financial Management and Research, Regional Centers for
Learning on Evaluation and Results, Fieldata, Innovations for Poverty Action. Available
from: https://www.theclearinitiative.org/resources/mobile-based-technology-for-
monitoring-and-evaluation
• Satterlee, E., McCullough, L., Dawson, M. and Cheung, K. (2015). Paper-to-mobile data
collection: a manual. Available from:
https://www.fhi360.org/sites/default/files/media/documents/Paper_to_Mobile_Data_Collec
tion_Manual_1.0.pdf
45. Want to try it out?
Visit us at our Data Café!
11:30 – 13:00pm Friday
27 October 2017
Tau Meeting Room, Hilton Sandton
Mobile phones are more accessible than ever before!
1086 – great survey in England
1937 – unemployment survey US census bureau
Late 1980s – PDAs and “pocket computers”
Mid 1990s – using teleform to collect data
2007 – to date – rise of smartphones as we know today and apps for data collection
Future??
Mobile data collection activities have existed in various forms for thousands of years (the Egyptians were famously data-centric in managing their empire).
“great survey” ordered in 1086 in England by King William the Conqueror, which sent data collectors all over the country to find out how many people and livestock there were (among other “indicators) – and which resulted in the “Domesday Book” of data.
1937 unemployment survey US census bureau (during great depression: In the early 20th century people like Jerzy Neyman helped to develop the statistics, and selection and sampling methods that formed part of the much-improved survey methods of the 20th century – and this brought about a “big paper” revolution in the amount and quality of data collected
Note: Pen and paper considered “mobile”, we’re really talking about mobile electronic data collection using mobile phones, SMS, and other technologies that were only invented, or made widely available, in the last two decades or so.
3. TeleForm is a highly intelligent data capture system designed to reduce data entry and manual processes associated with paper based forms projects. The software offers unrivalled flexibility in capturing handwritten data from paper and seamlessly incorporate the data output into secure, automated business applications. Through the scan and verification interface, forms and associated documents are classified, recognized, verified and transferred into reliable, accurate, searchable and highly structured electronic data critical to business processes.
We’ve used mobile data collection in South Africa, Malawi, Kenya, Uganda, Tanzania, Ethiopia and India
Data reliability (will we get the same data, when collected again?)
Data validity (Are we measuring what we say we are measuring?)
Data integrity (Is the data free of manipulation?)
Data accuracy/precision (Is the data measuring the “indicator” accurately?)
Data timeliness (Are you getting the data in time?)
Data security/confidentiality (Loss of data / loss of privacy)
For example, when we were collecting data in Uganda, our colleague who was managing the data from our office here in Johannesburg was able to pick up that a fieldworker was collecting data way faster than others. As a result, he was able to tell he had just been sitting under a tree and fabricating data. This led us to take prompt action and terminate his contract and replace him with another fieldworker.
This also leads to eliminating errors in data capturing and data entering (as we remove the human element - no data entry reduces error)
GIS, audio, photos, time it took to collect data, date and time of data collection, etc
Leads back to the “timeliness of data”. For instance, with the DHIS 1 system used by the department of health, there is a delay from the time data is collected to the time it is actually seen and acted upon. Mobile data collection can accelerate this process! Ultimately, we want the relevant stakeholders to make decisions based on the data collected, and they can’t do that if the data isn’t available when it is needed (e.g. when it’s time for budget decisions, etc)
1. Through removing paper surveys, can make data collection process less tedious. At the end of the day, data collection needs to be easy!
2. Through visualising data through dashboards, can ensure that the data is actually used by those who can act upon it and ultimately create a demand for data.
Before considering using mobile data collection, need to see if it fits your data collection needs.
Officials saw how variety of data could assist in their decision-making and resource allocation