Data Analytics in WASH sector

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Figure 1: Goal-6 in Sustainable Development Goals



World population that will face water shortage by 2030

people lack access to safe drinking water

Targets under Goal-6

But to understand, visualise and track these targets, and subsequently ensuring policy actions requires development of a database of relevant indicators and an analysis of data collected from various sources, both at macro as well as micro levels.

The Role of Data in Tracking the Progress in Goal 6

If one has to visualise the changing trends in achieving SDG 6, track developments and filter relevant information to design effective policy, it has to begin with the collection of relevant information and data.

Various organisation around the globe invest in data collection and collation, including member states of UN. While there are many data sources available that are curated by International and Regional bodies, the following have been picked for their technological relevance, expansive area coverage and continuing reliability. Most of them are a result of combined effort and the data is available for nation states, organisation, academia as well as individual researchers.

Figure 2: An illustration how data can be used to visualize, tracking, and policy creation for achieving SDG targets

Data source

Wash Point Data

  • Data is collected by a community or a group of people for a region who take multiple inputs.
  • Information on salinity, bacterial content, location, water depth, state of machine or instrument installed etc is collected.
  • This is then used by local authorities as well as national governments to devise solutions, update existing plans, train people and take short term actions.


Safely Managed 55%

Basic 26%

Unimproved 10%

Limited 6%

Surface Water 4%


Safely Managed 85%

Basic 10%

Unimproved 2%

Limited 2%

Surface Water 1%

Figure 3: Water point data informing about global drinking water service coverage

Data Source: JMP Report 2017 – Progress on Drinking Water, Sanitation and Hygiene

Aqueduct Data by WRI (World Resource Institute)

  • The Aqueduct project by World Resource Institute collects data from various places and develops water stress and water resource availability simulation models for organisations and businesses.
  • You can find data on water quality, ecosystem vulnerability, water variability, public awareness on water issues etc.
  • The project assesses water risk across the globe and aids businesses to strategize their water resource plan.

Figure 4: Ground Water Stress in North-West India

Data Source:

World Bank Data

  • The World Bank Data is an extensive repository covering various topics including water and sanitation.
  • While there is a visualization tool online that depicts the data, the same can be downloaded and representations like the one to the right can be made.

Figure 5: Tracking of global coverage using safe managed sanitation services (in % of population)

Data Source:

Other useful sources of data

1. Nasa’s GRACE (Gravity Recovery and Climate Experiment)

GRACE picks up minute changes in the gravitational field caused by ice sheet movement, movement of water on the surface and in aquifers, and movement of surface earth mass. Gravity anomaly maps generated by GRACE are 1000 times more accurate and have significantly improved the accuracy and coverage of groundwater distribution globally thus revealing critically affected aquifers and drought prone areas.


2. Aquastat Data

The Food and Agriculture Organisation of The United Nations developed the AQUASTAT database that allows queries by selecting various variables from the ‘Data Dictionary’ along with region for an adjustable time span.


3. Our World Data

This site presents interesting data visualisation on various topics aligned with all SDGs and conveniently has the data sources attached to the visualisations.


Overview of data analysis under Goal-6


Figure 6: Percentage of population with safe access to improved water sources (% shown in the fig are for ‘improved water source’)

Data Source:

In the recent JMP (Joint Monitoring Programme) Report, a new statistic was developed based on the definition of Safely Managed Drinking Water. This definition encompasses previously excluded criteria like availability on premises, chemical contamination etc. While incomplete in terms of coverage around the globe due to lack of data, the report still developed a schematic by approximating missing data based on rational assumptions derived from past data.

Safely Managed 71%

Basic 17%

Unimproved 6%

Limited 4%

Surface Water 2%

Figure 7: Proportional distribution of types of water source access for world

Data Source: JMP Report & Wash Data


According to the data available there are still around 890 million people practicing open defecation and about 2.3 million people still lacking the basic sanitation services. India along with many African countries is lagging with not even 50% coverage of basic sanitation services. Open defecation is seen declining in SDG countries except in sub Saharan Africa but, yet the pace needs to be improved to achieve the target.

World 68%

USA 100%

Brazil 86%

China 75%

Indonesia 68%

India 44%

% shown are for population with access to basic sanitation

Figure 10: Access to Basic Sanitation Services
Data Source:

  • Population with access to basic sanitation

  • Population without access to basic sanitation


Poonam Shah and Haritha Songola are currently pursuing their master’s degree in Climate Change and Sustainability Studies. Their research work focuses around Food Security and Climate Change, Feasibility of Carbon Budget Scenarios and Sustainable Growth.

Special thanks to Dr Kamal Kumar Murari for his invaluable inputs. He is Assistant Professor in School of Habitat Studies, TISS and works on issues related to Climate change, Agriculture and Water resources.

Manvirender Singh Rawat is founder of Klaymatrix. He has worked in a wide array of projects in development sector and is constantly trying to use his data science expertise in this field.

Concept & Visualization:

This blog takes a selected set of data for illustration purpose. Authors do not claim the authenticity of these datasets.


One Comment

  1. Manvirender Singh Rawat August 16, 2018 at 4:19 am - Reply

    Our next article realted to “Data analytics in social sector” is available now at:

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