Today we have seen an unprecedented rise in efforts to harness data-driven decision support systems in organizations across the world. However, in the social sector organizations, especially in the developing world, these possibilities are yet to emerge. Akshaya Patra – one of India’s most renowned NGOs – is leading the curve towards data-driven decision making.
Here is what Mr. Vinay Kumar, the Head of Operations and Enterprise Resource Planning, Akshaya Patra Bangalore, had to say on Akshaya Patra’s evolving approaches for managing data and decision-making.
Akshaya Patra serves food to 1.4 million school children daily across 24 locations in 10 states. As anticipated, they generate a lot of data by operating at this scale.
Five years ago, when they first felt the need for systematic data management, they opted for an ERP (Enterprise Resource Planning) system. Now with the rapid increase in data collection and consequently swelling databases, they are contemplating ways to utilize data to generate intelligent insights.
They have three main categories of data sources:
- Operations data: The operations segment consists of data on kitchen raw materials, fuel and human resources.
- Logistics data: The logistics sector includes delivery and supply chain data, with a recent addition of GPS data from select locations.
- Financial data: This includes data related to donations and expenditures.
All the three data segments have different sizes, life and review cycles. While the first two data sets — operations and logistics — are regularly monitored by the procurement team, the level of analysis is mostly limited to generating exception reports. The financial data, on the other hand, undergoes rigorous analysis periodically.
Operations and logistics data are captured from all the centers through the ERP system. There are regional variations due to differences in infrastructure and technology practices. Nevertheless they manage to collect data from all the centers with a maximum delay of one day.
They also use some Excel templates to overcome limitations of the ERP system.
Recently, they have started collecting GPS data from the vehicles with a goal to optimize fuel and other logistics costs.
In their experience, data collection has been improved over the last five years, except for certain important pockets of data like field data, which is yet to be captured.
What they do with the data currently
They admit that they have not been able to analyze the vast amount of data they have. However, moving forward, they are sensitive to the new possibilities which could emerge from better analysis of data.
They have already identified an umbrella requirement for streamlining their data analytics work. With a plausible hypothesis that the data which they collect but do not analyze holds valuable insights for better decision-making, they are trying to adopt new data analytics solutions to mine smart insights from data. They understand that currently they do not know how to analyze this data, and thus their plan is to engage data experts to solve this problem.
Driving better decision making with data
While they currently use data for exception reports, they are confident that, with proper analytics support, they can optimize all levels of decision-making in the organization.
Once the data analysis process starts getting streamlined, they believe they can develop a sense of more nuanced aspects of data quality and contextual data questions like venders’ quality check data, employee productivity data, market data on raw materials, seasonal variations, etc.
The need for a sustained ecosystem
Being a major nonprofit organization, their perspective on the future of the overall emerging social data ecosystem is, “There has to be a sustained demand for data from the regulators – in this case, the government – for organizations to systematize their processes and supply data.” In connection with the food safety standards law, they think the law should implemented strictly and, moving forward, it will be tough for organizations to operate in unsystematic ways.
Akshaya Patra’s data system gives us a promising perspective on how the data analytics movement in the Indian social sector can be perceived beyond the fundamental aspect of “transparency” to streamline organizational processes and services. The whole can be greater than the sum of its parts only through interactivity of components at different levels. While organic or ecological interactivities may be distant dreams, the story does suggest the emergence of a certain dialogue between data collection and potential knowledge production.
This is a part of our Data Ecosystem series, an effort to highlight organizations and nonprofits leading the curve towards data-driven decision making.
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