Implications of Mashup Technology for Enterprises

Below is more from Information Management’s Webcast, “The Last Mile: Data Visualization in a Mashed-Up”. This Webcast was hosted by Eric Kavanagh and included BI consultants William Laurent and Malcolm Chisholm, and InetSoft's Product Manager Byron Igoe.

Eric Kavanagh (EK): Our first official guest is Byron Igoe from InetSoft. Byron, welcome to DM Radio.

Byron Igoe (BI): Hi. Thanks, Eric.

EK: Let’s talk about this enterprise implication. Large organizations have lots of rules and regulations, and there are reasons for all that stuff. We hear all about data governance these days and obviously it’s a big deal, especially for public companies because they have to report on this stuff, and the stock market goes up and down depending on what people hear about things so it’s important. But what do you think are some critical implications of mashup technology with respect to the sort of protocols or processes of large enterprises?

BI: Sure, well, speaking to the point that you made earlier, about not seeing mashups used as heavily in large enterprises, I think probably one of the big reasons for that is inertia of the old paradigm. It used to be the case that companies would always focus on ETL and data marts and data warehouses. There was a huge concern about cleanliness and security rules and with the new mashup paradigm, a lot of people are thinking “oh, all the old rules are broken.”

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I really don’t think that is the case. If you do it right, you can really straddle the two worlds and have IT providing the access to the enterprise sources with the appropriate levels of security and governance in place, and still allow a whole self-service layer for the users to really fend for themselves and take ad hoc querying to the next level.

EK: You see, what you just described, it seems to me is an ideal way to go because IT still needs to have that cortically important role of making sure the right data gets to the right people at the right time but I think your point is very well taken, about inertia related to the old way of doing things, and I have to think that part of the reason why you have inertia, quite frankly, is just because these environments are so brittle, in a way, and the people who are managing this stuff have a lot of pressure on them to hit certain batch windows, to make certain this data gets refreshed, that data gets refreshed. So I think that’s probably one of the key reasons why you have that inertia. Do you think that’s a fair assessment?

BI: Eric, I agree 100%, and the great irony is doing data mashups right can actually alleviate a lot of those problems.

EK: Right.

What Data Does a Water and Sanitation NGO Mashup for Analytical Dashboards?

A water and sanitation NGO would typically mash up a variety of datasets to create comprehensive analytical dashboards. These datasets are crucial for monitoring progress, assessing impact, optimizing resources, and ensuring compliance with environmental and regulatory standards. The data could come from various sources and cover different aspects of the NGO's work, including operational performance, water quality, financial tracking, and community outreach.

Here's a breakdown of the types of data that might be mashed up:

1. Water Quality Data

  • Water Source Data: Details about the sources (e.g., wells, rivers, rainwater collection systems) and their locations.
  • Water Testing Results: Data on the chemical, biological, and physical parameters (e.g., pH, turbidity, contaminants like E. coli, nitrates).
  • Real-time Monitoring: Data from IoT sensors that continuously monitor water quality to detect any potential issues like pollution or contamination.
  • Historical Data: Long-term water quality data to analyze trends and impacts of interventions.

2. Sanitation Infrastructure Data

  • Facility Locations: Geographic locations of sanitation infrastructure like latrines, septic tanks, and sewage systems.
  • Maintenance Records: Data on the maintenance schedules and repairs for sanitation facilities.
  • Utilization Metrics: Information on how frequently sanitation facilities are used, which helps in capacity planning and identifying over/underutilization.

3. Health and Hygiene Data

  • Disease Outbreaks: Data on waterborne diseases like cholera, typhoid, or diarrhea, collected from local health authorities or healthcare partners.
  • Health Impact Data: Surveys or reports showing the impact of water and sanitation interventions on community health, particularly reductions in disease rates.
  • Behavior Change Metrics: Data from hygiene education campaigns, such as handwashing practices, and their effectiveness in promoting better sanitation.

4. Demographic and Population Data

  • Population Density: Data on population distribution in project areas to assess the reach of water and sanitation services.
  • Vulnerable Populations: Data identifying vulnerable groups (e.g., children, elderly, displaced persons) and mapping areas where they reside.
  • Community Surveys: Feedback from the community on service delivery, water accessibility, and sanitation infrastructure use.

5. Geospatial Data

  • Mapping: Geographic Information Systems (GIS) data showing water sources, distribution networks, and sanitation infrastructure in relation to population centers.
  • Topographical Data: Information about the terrain, which is important for infrastructure planning and water catchment management.
  • Environmental Data: Climate data like rainfall patterns, which can affect water availability and influence decisions on infrastructure placement.

6. Operational and Logistical Data

  • Supply Chain: Data on the procurement and delivery of equipment, materials, and chemicals necessary for water purification or sanitation.
  • Project Management Data: Information on project timelines, milestones, and resource allocation.
  • Asset Management: Data on the condition and performance of water pumps, filtration systems, and other critical assets.
  • Workforce Data: Information about personnel, including engineers, health workers, and field staff, along with their performance and training.

7. Financial Data

  • Budget Tracking: Data on how funds are allocated across various water and sanitation projects.
  • Funding Sources: Information on grants, donations, and government subsidies, and how those funds are being used.
  • Cost-Benefit Analysis: Data showing the cost of interventions versus their impact, helping to prioritize more efficient and effective solutions.
  • Revenue Generation: In some cases, NGOs may have a cost-recovery mechanism (e.g., user fees for water) that would need tracking.

8. Partner and Stakeholder Data

  • Government Collaboration: Data on partnerships with local or national governments, such as permits, regulations, and policy compliance.
  • Community Partners: Information about local organizations, community leaders, and volunteers who help implement and maintain water and sanitation projects.
  • Donor Requirements: Data related to donor reporting, such as Key Performance Indicators (KPIs) or specific metrics that need to be reported for accountability.

9. Environmental Impact Data

  • Waste Management: Data on how waste, particularly sewage and greywater, is treated, recycled, or disposed of.
  • Sustainability Metrics: Measures of the long-term environmental impact of water and sanitation projects, such as groundwater depletion, reforestation efforts, or carbon footprint.
  • Water Usage: Data on how much water is being used by households and agriculture, which helps ensure sustainability.

10. Community Engagement and Feedback

  • Satisfaction Surveys: Data on how satisfied the community is with access to water and sanitation services.
  • Complaint Tracking: Records of issues raised by the community and how they were resolved.
  • Engagement Metrics: Data on community participation in meetings, training sessions, and decision-making processes related to water and sanitation projects.

How This Data Is Used in Analytical Dashboards:

  1. Performance Monitoring: Dashboards track the efficiency of water distribution systems, downtime of sanitation facilities, and overall service delivery.

  2. Impact Assessment: Health and hygiene data, when combined with water quality and population data, helps assess the NGO's impact on reducing disease.

  3. Resource Optimization: Financial and operational data allows NGOs to optimize resource allocation, ensuring that the most critical areas receive attention.

  4. Predictive Analytics: Using historical and environmental data, the NGO can forecast water shortages, infrastructure failures, or disease outbreaks, allowing proactive intervention.

  5. Reporting and Accountability: Combining donor and partner data with project outcomes ensures transparency and accountability, essential for maintaining funding and partnerships.

This data mashup provides a comprehensive, real-time picture that helps NGOs manage their operations effectively while ensuring they achieve their mission of providing clean water and sanitation to communities.

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