Evolución del movimiento

Late-cycle evaluation activities packaged the mined data into more robust and meaningful narratives by adding expert forms of knowledge to the data. Whereas previous activities involved filtering, editing data in, or cutting data out, these activities involved adding expertise to the data, bundling in value‐adding knowledges, using knowledge to package data, rather than data to make new knowledge, as in DIKW assumptions. Four kinds of expertise were bundled in: technology and data management; technical development discourse; scientific methods; and marketing. Chandan was also strategically aware of the lack of expertise in the wider sector.

In terms of technology and data management, Leonard stressed Imagine's strategic aims when talking about how impact data helped the philanthropy to create categories of aid effectiveness. These categories framed the design of the evaluation MIS. According to Leonard, this design process involved a “roadmap,” evaluation “templates” and “poverty alleviation verticals”.26

“Yes, that’s the long-term roadmap, … so what we would like to achieve is to create a template for each of the different verticals, like one of them being poverty alleviation or you know that sort of thing, and do other templates for other verticals so … for the NGOs we would like to standardize the data collection procedures and make it easier to get quality data … and it is in those templates where we can, so we can bounce ideas off you [Author], so we can get better templates, create it and so on …” (Leonard)

Signposted here was one of Imagine’s reasons for collaborating on this research case, to secure advice for further developing their MIS designs. For example, bouncing ideas off the author helped in standardizing evaluation templates and defining verticals to structure the MIS design. With Leonard explaining how the MIS would be marketed to other NGOs and agencies in the sector, “for a small cost”, this bundling of impact data within technology expertise and resources shows how farmer data travelled not just to UK funding decision-makers in reports, but also how it entered other strategic considerations. Farmer data fed into impact narratives and distinct business strategies, prominently the MIS.

Alongside digital know-how, the partners demonstrated development expertise too. Development expertise was evident in terms used by the managers, in conversations, reports and on the philanthropy’s website. For example, Leonard talked of “poverty alleviation”, Chandan of “capacity building” and “integrated development”. Phrases used in reports included “scaling up projects” and “providing enablers” for other NGOs. On their website, Imagine stressed their commitment to “integrated development”, “technological solutions”, and supporting local NGOs in terms of “efficiency”, “productivity”, “ideas”, “methods” and “impacts”. The partners used their development expertise to further package impact data as part of “social and technological solutions”, “lasting developmental results”, and “transformative impacts”. Such terminology is a standard component in contemporary development discourse. Further excerpts from Imagine’s website are given below.

“This approach addresses the social and economic challenges of a developing society by establishing a multi-level partnership between non-profit organizations, civil societies, social entrepreneurs and government bodies. It seeks to mobilize systemic transformations by integrating government policies to expedited financial, social and technological solutions and create an enabling environment to foster innovations, forge partnerships and build networks.”

“Imagine catalyzes innovation, not only by providing funds but, by extending organizational support to enhance the efficiency and productivity of NGOs with new ideas, methods and models that can achieve lasting developmental results. It is also important that these innovative approaches should have a direct and transformative impact on the marginalized sections of the community and have the potential to scale up significantly in the region so that they can be replicated elsewhere.” (Imagine website, Programs page, 21/2/16)

Together with technical development discourse and data/technology expertise, the partners also made sure that scientific vocabulary and methods were featured in their impact knowledge products. Scientific discourse is evident in the term a “model district” mentioned above, or references to “solid evidence on the ground,” having “our analysis in place”, and a “hydro‐ electric” engineering metaphor, used by Chandan.

The model district phrase was used to compare villages and use the exemplary ones to highlight where positive results and impacts had been found. These results depended on having good

data and evidence. The hydro-electric metaphor was deployed by Chandan frequently to explain how the partners’ work was a dependable, scientific and mechanical process to produce favourable impacts. Scientific methods are also represented by the “model district” phrase and constituted by the collection of digital data on farmer livelihoods and organized in spreadsheets and cells for measurement purposes, as discussed in the previous sections. In such ways, scientific methods and measurement rationales are added during the construction of impact messages and narratives. Chandan explains:

“We have taken the district as a model district. We want to showcase this, that the work has changed the district vis‐à‐vis the other neighbouring districts. We need to present it to prospective funders, at fundraising events, so this is a kind of pitch that we are trying to do on the basis of solid evidence on the ground.” (Chandan) “And we understand that if you put an x in, we expect about 10x or 15x at the other end. I also use, I often use, the analogy of hydro-electrics, so if you put x pressure at one end, the other end should give you about 10x or 15, or some multiple of x pressure at the other end.” (Chandan)

In these ways, scientific techniques, methods and discourses were incorporated into impact evaluation messages and narratives. As insertion of such explicit scientific vocabulary was less evident earlier in the evaluation cycle, it can be considered as important for bundling-in, value- adding, as part of late cycle knowledge packaging to boost impact message legitimacy.

Marketing expertise and phrases were also bundled into the partners’ late-cycle impact activities. Rural India and Imagine’s evaluation performance delivered impact messages able to support marketing activities. Impact was an issue at the level of specific projects, but also at the wider level of impact as an organisational capacity. Project impacts and processes were combined with various organisational expertise to form the wider narrative on impact produced by Rural India and Imagine within the Indian aid sector.

Marketing terms and phrases used by the partners in conference calls, reports, and website pages included: “leveraging expect 10× or 15× at the other end”, “our marketing story”. “our pitch”, “a strong pitch”, “put together data in a nice document”, “to go fundraising”, “to showcase impacts”, “leverage resources”, and “the need to ask others to pitch in, but for others to pitch in we need a strong marketing story”.

This marketing language appears in one sense a mundane and professional part of business. Yet it is this normal, professional bundling of marketing concepts into the partners’ notions of, understanding of, and evaluation of impact that is itself significant.

Finally, it is worth noting that the bundling-in of expertise in digital technology and data, development, scientific techniques, and marketing was accompanied by a dismissal of the capabilities of other NGOs in the Indian aid sector. Other NGO were “slow”, “incompetent”

in digital processes such as data management, “inarticulate”, and in need of “smarter” workflows.

“NGOs in India, most of them, the level … they are not very competent with technology, or smarter ways of capturing data … bad data logistics cause project delays.” (Leonard)

“Now generally, ask any NGO in India, most NGOs in India, almost all of them, are well meaning, honest, with high level of individuals, but if you ask them what they are doing, the chance is that probably they don’t know, they are not articulate.” (Chandan)

To summarise, there were numerous activities involved in late-cycle processing or packaging of data into impact messages, such as bundling expertise in with the legitimate data captured and bifurcated earlier. Through the evaluation cycle, data was incrementally processed, packaged, and made into strategic knowledge. Packaging and bundling activities included:

1. processing or packaging data to make it into legitimate impact knowledge;

2. adding data management or technology know-how to impact data to make impact knowledge e.g. MIS templates and verticals;

3. adding development expertise to impact data to make impact knowledge; 4. adding scientific expertise to impact data to make impact knowledge; 5. adding marketing expertise to impact data to make impact knowledge; and 6. downplaying other NGOs ways of making / understanding impact.