DATA & ANALYTI CS 4 Developm ent Driving development results through big (& small) data integration...

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DATA & ANALYTICS 4 Development Driving development results through big (& small) data integration and analytics. #data4dev 1

Transcript of DATA & ANALYTI CS 4 Developm ent Driving development results through big (& small) data integration...

Page 1: DATA & ANALYTI CS 4 Developm ent Driving development results through big (& small) data integration and analytics. #data4dev 1.

DATA & ANALYTICS 4DevelopmentDriving development results through big (& small) data integration and analytics. #data4dev

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Page 2: DATA & ANALYTI CS 4 Developm ent Driving development results through big (& small) data integration and analytics. #data4dev 1.

“at a time when our need for policy agility has never been greater, our

traditional 20th-century tools for tracking international development

simply cannot keep up”

– UN Secretary-General Ban Ki-moon, 2011

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➡ How will development projects impact increasingly complex issues like climate change?

➡ How can programs keep up with rapidly changing social and economic circumstances?

➡ How can interventions be more inclusive of diverse groups given disparate impacts of the same intervention?

➡ How can we treat beneficiaries more like customers who have a choice in the services that they use?

➡ Increased availability of data, and increased uptake of technology within host countries

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Why are we talking now?

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Data & Analytics 4 Dev- What isn’t advanced analytics

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Word Clouds

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Counting ThigsB

abie

s D

eliv

ere

d

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Exclusively analyzing social

media

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Data & Analytics 4 Dev- Power of data & analytics

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Data is a simplified representation of aspects of the world.

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What is transactional Data

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Institution

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What is Big Data

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Institu-tion

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Successful Analytics ProcessLogistics/supply

chain

Mobile DataSatellite ImageryStatistical Offices Social

OperationsBeneficiary

EngagementsFinance

Complete view of field of operations

Predictive Modeling

Organizational Data

Data Enhancements

TechnicalInfrastructure

Advanced Analytics

Tools

Better Decisions

Data Exploration Tools Reporting Custom Analysis

Tactical Decisions Accountability Strategic Decisions

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➡ Having data is the first step in the analytics process. The more quality data that is captured by the organization, the more precise the models can be, and the more policy and programs can be improved. Data can come from internal sources, government, mobile providers and internet “big data”.

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GATHERING OPERATIONAL DATA

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Gathering Data• Input data from via mobile apps, and collect data from mobile/cellular services. Record data from sensors in field locations

• Mine data from social networks such as twitter and Facebook

• Track data from public sources such as financial markets, utilities, government agencies

• Combining all of the above data for a specific geographic area at any given time showing opportunity for impacts of projects as well as contextual data

Example: Tools to Improve Monitoring and Evaluation

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CASE STUDY - CAMDEN HEALTHThe Camden Coalition of Healthcare Providers achieved higher

patient outcomes and lower costs with the help of data analytics.

➡ Integrated patient records, hospital statistics and outside datasets

➡ Analyzed patients outcomes and creating predictive models to estimate future outcomes

➡ Used data and models to identify patients where treatment outcomes can be improved (receiving poor care)

➡ Identified patients who are “super-utilizers” and enrolled them in prevention programs (that are better for patients and more cost effective)

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CASE STUDY - PRICE TRACKING➡Premise is a start up that uses datasets, mobile data collectors,

and automated algorithms to accurately track the price of commodities and inflation on a frequent basis across developing markets.

➡ Premise collects data through an artificial intelligent algorithm gathering price information and through employees collecting data information on the ground.

➡ Their data is updated much more rapidly and frequently than official CPI data or industry pricing data. Allowing decisions to be made with much more updated data.

➡ Users can track multiple indicators across multiple countries through their web platform and API. They also make available the dataset in CSVs form to their clients for further analysis.

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➡ Once the organization has data, it must be made useful for policy formation and program planning.

➡ Three steps can be taken to make the data more useful:

➡ integrating different data sets into one to facilitate easier and more comprehensive comparisons,

➡ building algorithms that analyze (and learn) from the data allowing simple operations to be automated, and

➡ creating data visualization and dashboards that allow staff to make better tactical decisions

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MAKING DATA USEFUL

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Making Data Useful• Creating a dashboard

showing indicators of poverty and indicators of project implementation

• Interactive graphics allowing users to create charts comparing intervention groups to control groups, between men and women, between rural and urban areas to see discrepancies

• Track the indicators and inequality over time

• This will allow program officers to be sensitive of the most poor or the most vulnerable groups and the impact of their project on them

Example: Showing Impact of Poverty Reduction Projects

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CASE STUDY - Crowdsourcing➡Humanitarian OpenStreetMap Team create data rich maps to help

organizations in places like West Africa responding to the Ebola outbreak.

➡ They create maps through satellite images and physical surveyance, and make them open source therefore available to everyone. This is especially useful in places not mapped well by Google, Bing etc.

➡ They gather data about the area prior to any crisis from open sources or by collaborating with partners working in the area.

➡ During crisis they build specialised maps, for example mapping suspected and reported Ebola cases. This also allows for offline road navigation by health providers to reach these areas.

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➡ Building on datasets to create sophisticated models to aid program design and project decision making. There are many tools including:

➡ Using available data to model areas without data

➡ Predicting behavior of clients, including different sub-populations, so that programs can be responsive

➡ Identifying signals from big data that indicate shifts in social/economic trends

➡ Testing the efficacy of interventions through experiments on a representative sample to optimize overall impact

➡ Conduct rapid experiments on projects to inform program implementation for maximum efficacy

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INSIGHTS FROM DATA

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Insights From Data• Conduct an experiment to test the

persuasion of a public health intervention• Create models to identify impactful targets

based on the experiment• Examine the differential impact on sub-

populations• Optimize communications and outreach to

avoid populations with a negative impact and focus on populations with strong positive effects

• Monitor implementation through small scale rapid experiments

Example: Optimize Public Health Promotion Interventions

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CASE STUDY - TRANSPORT➡ IBM using mobile phone data provided by Orange built a

program to optimize transportation called AllAboard for Abidjan, Cote d’Ivoire.

➡ Large mobile phone data sets of location information were used, and compared with current ridership on public transport routes.

➡ Based on origin and destination data new routes were suggested.

➡ Using optimization models, suggestions were made on how to increase ridership and reduce traffic time by reforming routes.

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CASE STUDY - MALALA FUNDThe new international NGO founded by Malala

Yousafzai is embarking on a data driven approach.

➡ Data Collection from various sources on girls education and international development

➡ Building logical data architecture to be able to capture and quickly analyse large and unified datasets

➡ Informing policy, advocacy and programming through historical and up to date data and analytics

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CASE STUDY - FUNDRAISINGWe helped a large charity that operates in the US and

internationally to increase fundraising through:

➡ Combined internal membership data with datasets available externally and matching the unique IDs

➡ Narrowed a list of 1.5 million donors to the to 190,000 most valuable targets. Their next mail campaign with a more targeted focus performed 32% higher in donations.

➡ Dynamically updated telemarketing model to suggest higher or lower asks on fundraising calls, optimising caller’s time and amounts contributed.

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➡ BlueLabs is an analytics and technology company founded by senior members of the Obama for America analytics team, dedicated to using data science techniques to further social good.

➡ We have partnered with organizations to improve health service delivery, promote human rights, expand technological capacity, and optimise resource mobilization efforts.

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WE ARE BLUELABS

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[email protected]

@blue_labs#data4dev