Exploring the value of adding a data layer to cooperatives: Notes from the second field visit to Megha Mandli

By Sushmitha Viswanathan

Introduction

Aapti Institute has been interested in the potential of data cooperatives as a model for data stewardship. To explore the viability of converting an existing cooperative into a data cooperative by adding a data layer, we have been working with Megha Mandli, a women’s farmer cooperative in Gujarat. Our first visit to Megha Mandli involved a scoping of the key issues faced by the cooperative and its members as well as their main priorities. Through this we learned that a lack of access to credit is hampering the cooperative’s ability to scale. Based on these insights, we formed a hypothesis that adding a data layer on Megha will facilitate access to credit from digital financial service providers. This in turn is based on the premise that the aggregation of personal, farm and financial data of all member-farmers could demonstrate credit-worthiness. To test the viability of our hypothesis, we made a visit to Megha. The agenda of this visit was to gain an understanding of Megha’s current data collection practices and to identify the kinds of data the members are comfortable in sharing. Insights from this visit are crucial to structuring co-design workshops to build the data layer for Megha.

Insights from the field

The members explained that the inability to access credit affects membership, drives up operational costs by negatively impacting logistics, lowers opportunities for members, affects their ability to purchase quality inputs and hinders engagements with experts. Therefore, they saw value in our hypothesis and were eager to understand how pooled data can facilitate access to credit.

It is relevant to note that the concept of data collection is not alien to Megha. The cooperative is presently gathering data such as name, village, mobile number, output, farm size, Aadhaar, farming input needs, input taken and bank details of the Aagewan. Data is collected in a register and is then entered into an excel sheet. While the frequency of such data collection remains unclear, we are aware that the data collection is mostly facilitated by the Aagewans, who are the community leaders for one or two villages.

Megha has fostered a culture of actively exchanging information and tips on farming through the cooperative’s WhatsApp group. We also found out that the WhatsApp group is used to connect members with ladies who could be potential members.

To identify the kinds of data the members are open to sharing, we circulated a questionnaire consisting of a list of illustrative data points. The 20 members who were present at the meeting were required to indicate whether or not they would be comfortable sharing data on the points listed.

We observed the following from the responses:

1. Financial data: Farmers are reluctant to share financial data as they believe that if their financial status came out in the open, government benefits such as ration cards would be revoked. Several members also did not want to disclose Aadhar details due to its linkage with their bank accounts.
2. Personal data: Members were generally open to sharing their personal data. Several members, however, were hesitant to share their Aadhaar details and health records.
3. Farm data: Members were willing to share data related to farm size and information, input quantity and the kinds of crops they harvested. However only around 50% of the members were open to sharing details of their crop yield and price paid at harvest. We believe that their reluctance stems from the fact that the cooperative presently has limited engagement in the output side. It is also relevant to note that the majority of the members present indicated that they are unwilling to share insurance details and details relating to their farmer certificate.

Identifying opportunities for delivering value to Megha

Gaining an understanding of the issues faced by the members of Megha was instrumental to identifying how exactly adding a data layer would help the members. Although our hypothesis revolves around access to credit, the member-farmers were forthcoming in identifying other routes through which pooled data could deliver value to them. These routes fall into the following categories:

1. Knowledge creation and information sharing
2. Insights for planning future action
3. Demonstration of state of affairs

1. Facilitating access

• Credit: Based on the challenges highlighted by the members of Megha, we gathered that pooling data to obtain finances is crucial to facilitate the following:
  - Purchasing more quality inputs
  - Purchasing more quality inputs
  - Purchasing sufficient inputs to meet the demands of all member-farmers
  - Making up-front payments to farmers for their outputs
  - Cover high transportation costs for procurement of output
  - Investing in infrastructure to store output locally
  - Attracting new members
• Insurance: Most farmers have no crop insurance, primarily due to lack of awareness and documentation. Lack of insurance has prevented them from claiming compensation for crop loss from the government. The members believe that collecting and pooling data on loss could be beneficial to getting compensation in the future. It is also worth exploring how data can help with proper documentation to register for crop insurance schemes.
• Government schemes: The members expressed that pooling data is likely to help Megha and its individual farmers claim benefits under government schemes.

2. Building trust:

Concerns on sharing data mainly spring from the lack of trust in the data aggregation process and the digital environment, in general. We believe that once the members begin to realise the benefits of capitalising on their aggregated data, they are likely to place more trust in the process of data pooling, which in turn would encourage them to collect more data. We believe that the trust which is inherent in the existing cooperative structure can be leveraged to create an environment of trust surrounding data.

3. Creating linkages

• Suppliers: Members expressed that data could simplify the process of identifying and selecting potential suppliers. Presently, the members are sometimes unable to procure quality inputs. They believed that pooling data could make it easier to connect with suppliers and enhance bargaining power with respect to prices and quality. They envision that using data, Megha Mandli will eventually become a one-stop platform to procure high quality inputs.
• Market: Members hope that data could link Megha to customers, similar to what is being done in Ucchal district. In the future, it could also facilitate their entry into new markets.
• Members: Megha has been unable to attract members due to low initial investment. They are positive that enhanced visibility of Megha’s activities due to digitization and data collection is likely to attract new members to join the cooperative.

4. Empowering members to make decisions on data and its use:

We expect that adding a data layer over Megha through a co-design process will empower the member-farmers in two ways:

• Members are empowered to make decisions on the purposes of data collection and the use of their data
• Aggregated data will empower members to make business and farming decisions that are in line with their economic and social goals

5. Enhancing efficiency

• Resource management and business planning: The members believe that pooling and sharing data can help them save time by reducing transaction costs and information asymmetry, track prices (input and output), and ease logistical hurdles. They also hope to use data to make business decisions and identify focus areas for expansion.
• Farming insights: Season-based insights on requirement and use of inputs such as seeds and fertilisers is another avenue for using data. Personalised insights based on the crop grown, to maximise yield is also an opportunity worth exploring.

6. Building knowledge

• Expert engagements: The members of Megha are keen about being empowered with the knowledge required to improve farming practices. They expressed their interest in using data to identify and locate experts who can help with evaluating seed quality.
• Knowledge repository: The data cooperative model could facilitate the creation of a repository for knowledge on farming, which is currently being done through WhatsApp. This can also be done for traditional knowledge on the cultivation and preservation of crops as it would not only benefit other members, but also help in preserving this knowledge for the future generations.

Conclusion

Although there is plenty of potential in adding a data layer around Megha, a few challenges have to be addressed for our endeavour to materialise. As evidenced by the responses to our questionnaire, trust continues to be a major hurdle. Since the farmer-members are uncomfortable with sharing financial data, it is pertinent to identify digital financial service providers who are likely to grant credit using alternative data. Since data cooperatives are founded on the values of trust and mutual help, they are effective channels to negotiate with DFSPs on behalf of the member-farmers. Furthermore, since our proposition rests on the premise that the loan will be disbursed to the cooperative, and not individual members, the scope of individual members obtaining personal loans using their data is likely to be limited.

An added dilemma is the need to incentivise data collection. Since data collection and sharing is a time-consuming process which could negatively impact the daily income of a farmer, incentives to collect data need to be identified.

Despite these challenges, our visit has impressed upon us that the member-farmers are not only enthusiastic about adding a data layer around Megha, but are aware about the exact avenues in which data can be used. To move ahead with the co-designing process to build the data layer, it will be essential for us to build greater trust with the members around data sharing and also identify pathways to incentivise data collection.

With these insights in mind, our immediate goals are as follows:

• Identify data flows and appropriate consent models through the first co-design workshop
• Identify DFSPs which will issue credit using alternative data
• Ideate on what incentives to collect and share data should look like
• Explore partnerships with grassroot organisations working on digitization in the project site.