#maketechinclusive– sdgs and statistics for csos · §huge storage with high availability and...

#MakeTechInclusive – SDGs and Statistics for CSOs

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Tech Innovation for SDGs

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UNTIL: is a platform for…

Citizens’ engagement around technology innovation

All actors of the tech innovation ecosystem to collaborate and co-create

Building local digital technology solutions for global SDG reach

…and #MakeTechInclusive

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UNTIL: Philosophy

When technology succeeds in solving a problem in our lives, it becomes invisible. The ticking of a clock so constant as to fade into the background.

Too often, we begin to take this transformative force for granted.

But technology is not, by nature, an inclusive phenomenon. Its availability in every context can not be assumed. And as the clock ticks on unnoticed, lives are lost.

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UNTIL: Philosophy

The right combination of capabilities and conditions must be created to give technology a more humane dimension, and allow the benefits it can confer to be enjoyed more widely.

The right connections must be made, at the correct moment.

This is our role at UNTIL. Until technology includes all.

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UNTIL: Focus on Women Entrepreneurship, Social inclusion, Digital ID

First South Asia Lab in India

UNTIL LABSL O C A T I O N S , S T A T U S & T H E M A T I C A R E A S

Eco-tourism, smart cities

(smart transportation), and

ethical fashion

M A L A Y S I A

O P E R A T I O N A L

Peace & mediation,

education, health and

circular economy

F I N L A N D


Education, health, industry

and agriculture

E G Y P T


Women entrepreneurship,

gender equality, health, smart

cities and quality education

I N D I A - H A R Y A N A


Under Discussions: Germany,

Rwanda, UAE, Thailand,

Canada, Mauritius,

Kazakhstan, Italy, Iceland,

Spain

N E W L O C A T I O N S

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Technology for data collection and analysis

Challenges in Data collection, analysis and monitoring:

§ Huge data to be collected§ Unskilled staff as data collector§ Huge storage with high availability and security is required§ Data authentication proof missing§ Monitoring is not real time§ Data retrieval takes very long

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New technologies for data collection:

§ Mobile data collection – bite size surveys using mobile phones, tablets, or laptops for data collection.

§ Web based survey – Google forms with Google analytics § Crowdsourcing and crowdseeding - are real-time data collection methods that involve different

technologies. Information is directly obtained from technology users who volunteer their own data (crowdsourcing), or from trained informants in the field (crowdseeding).

§ Speech data - are being collected smarter, hands-free speech systems. Example - Alexa for people with disabilities

§ Geospatial technology – using global positioning systems (GPS) and geographical information systems (GIS)

§ Remote sensing – Opium poppy survey

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Big data

It’s not the amount of data that’s important. It’s what organizations do with the data that matters. Big data can be analyzed for insights that lead to better decisions and strategic business moves.

§ Huge data to be collected§ Large volume of data makes it difficult to collate and analyse§ Huge storage with high availability and security is required§ Data retrieval takes very long

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Mobile technology for data collection:

§ Less time is spent collecting and entering data§ Errors are significantly reduced§ High quality data – entry limits§ Allow real-time photos, videos, geo-coordinates with time stamp§ High level of security added – authentication§ Minimal or no data loss§ Faster validation and results

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Self learning data collections:

§ Machine learning - to make our system learn based on data captured and then use it further for data categorization and analysis.

§ Self learning chat bots

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Latest storage and availability solutions:

§ Cloud for online storage – AWS, Azure, Google cloud, etc.§ Own online servers for real-time storage – company data centre§ Offline storage for uploading it later – stored in the device memory

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Technology for data collection and analysisNew Technologies of analytics:§ Self-service BI tools, such as Tableau, Qlik Sense, Power BI, and Domo,

managers can obtain current business information in graphical form on demand.

§ Early big data systems were mostly deployed on premises, particularly in large organizations that collected, organized and analyzed massive amounts of data. But cloud platform vendors, such as Amazon Web Services (AWS) and Microsoft, have made it easier to set up and manage Hadoop clusters in the cloud, as have Hadoop suppliers such as Cloudera-Hortonworks, which supports the distribution of the big data framework on the AWS and Microsoft Azure clouds. Users can now spin up clusters in the cloud, run them for as long as they need and then take them offline with usage-based pricing that doesn't require ongoing software licenses.

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Data retrieval:

§ Tagging enabled data collection to be done, if data retrieval is to be made easier.

§ If analytics to be run or graphical representation is required, make questions quantitative, not qualitative.

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Technology for data security

Data Security:

§ Why – sensitive, expansive, irrecoverable, time consuming, etc.§ Confidentiality, availability, and integrity of data§ Levels – collection, storage, analysis, archival and during access

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Data retrieval:

§ Tagging enabled data collection to be done, if data retrieval is to be made easier.

§ If analytics to be run or graphical representation is required, make questions quantitative, not qualitative.

Tech Innovation for Member States

Thank you

Office of Information and Communications Technology (OICT)United Nations

Websites: until.un.org / unite.un.org / www.un.org

Twitter: @UNTILabs

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#maketechinclusive– sdgs and statistics for csos · §huge storage with high availability and...

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