What to expect from data week?

This article is authored by Nalin Agarwal, founding partner, CCF.

Does India need another climate week? Maybe yes and maybe no. A climate week that is impactful is always welcome. But in those initiatives, we do need deeper conversations that work towards solutions that are data backed, tools that go deeper and systems that shape how climate problems are understood and solved.

Data

The climate crisis is a physical problem, but many of the solutions to that problem are gradually becoming a data problem as well. Glacier melts, flood prediction, climate trends, energy needs, and even renewable energy grid management--all hinge on our capability to deal with large amounts of data.

However, the challenge is that those working towards solving these pertinent problems don't always collaborate. The climate crisis specialists understand the issues on the ground, while big data and AI specialists develop increasingly sophisticated technologies. Each holds a piece of the puzzle to the whole problem.

Bengaluru as a city has positioned itself as the technology and data capital of the country--spending decades building expertise in technology and data--it isn't called the Silicon Valley of India for nothing. That makes a conversation on climate and data a natural fit for the city. The real question is how the capability sitting in Bengaluru's talent pool gets applied to problems that affect the planet and people's lives.

Climate Data Week 2026 (CDW 2026) in Bengaluru was designed to go beyond one climate domain. Managing climate change today touches energy, transport, agriculture, industry, urban planning, and waste management. Each of these sectors generates enormous amounts of data.

CDW 2026's conversations, this year, centered on three broad themes: Decarbonisation, adaptation and resilience, and circularity. Decarbonisation covers efforts to cut emissions across energy, transport, and industry. Adaptation and resilience focus on helping communities and infrastructure withstand and adjust to climate impacts. Circularity is about how resources are used--reducing waste and extending the life of materials.

These fields are usually treated as separate conversations in several forums, when they are intrinsically connected. Precise data helps cities identify flood-prone areas and plan infrastructure accordingly. Accurate forecasting makes it easier to integrate renewable energy into the power grid. For farmers, timely data supports smarter decisions on irrigation and fertilisation as weather patterns shift. CDW 2026 addressed conversation across these intersections.

There is no shortage of talk about AI and climate and its correlation. The more useful question is what AI can actually do to serve the climate.

At the level of specific tasks, that breaks down into five functions: Detection, prediction, optimisation, simulation and discovery. Detection identifies faults or abnormalities in an electricity network. Prediction forecasts future energy demand. Optimisation determines the ideal mix of wind, solar and storage to meet a city's power needs at the lowest cost. Simulation lets us test scenarios before committing to them. Discovery is about finding new patterns or possibilities, like identifying new materials for batteries or solar cells.

Zoom out from each of these individual tasks and the relationship between AI and climate actually spans four distinct conversations, each asking a different question:

• AI for climate solutions — What can AI do for climate tech? Applications embedded in products, operations and assets that accelerate decarbonisation, circularity, adaptation and resilience across sectors. This is where the five functions above tend to live.
• Sustainable AI — How can AI run on cleaner resources? Powering AI infrastructure with clean energy, including data centres operating as flexible grid loads, efficient use of water, adopting circular material, and reducing lifecycle emissions across hardware supply chains.
• Efficient AI — How can AI do more with less? Improving the energy and compute efficiency of AI through better algorithms, models, software, hardware and chip design. Gains should be measured against absolute resource demand, since efficiency can also drive greater total usage — a more efficient model that gets used ten times more isn't necessarily a climate win.
• AI for climate intelligence — How can AI improve how we understand and govern climate? AI as a knowledge and decision layer: measurement, forecasting, MRV, risk assessment and planning that inform climate action by governments, financiers and communities, often beyond any single product.

Data Week in Bengaluru brought AI not only to fore of climate conversation but into focus on where the technology is useful--to help answer questions or move an idea from conceptualisation to reality.

The third question is what should come out of these conversations. A founder, a researcher, a corporate leader, and a policymaker could walk into the same room with very different needs.

A founder may be looking for technical talent or a new use case. A researcher may need a partner to test an idea outside the lab. A company may be looking for a technology it can deploy. A policymaker may be looking for better evidence to inform a decision.

Bringing these groups together can create connections that might otherwise take far longer to form. Success shouldn't be measured by a collection of interesting panels--it should look like a new problem statement, a research collaboration, a startup finding an implementation partner, a company identifying a technology worth testing, or a policymaker encountering a new approach to an old challenge.

There's a fair question buried in all this: Do we really need another conference? If Data Week is just another event where people listen to panels and leave with the same questions they arrived with, its value is limited. Its purpose should be to create new avenues for collaboration - to let different stakeholders visualise the same problems from differently, work towards addressing them. More data, on its own, doesn't lead to better climate action. Data needs context, infrastructure, diversity, and interpretation. It needs people who understand the problem and can turn information into decisions.

This conversation matters especially for the Global South. Much of the current discourse on AI and climate centres on technologies built in other markets. But countries in the Global South have their own climate problems, their own datasets, and their own infrastructure constraints and deployment realities.

The region shouldn't be treated only as a market for technologies developed elsewhere. There's a real opportunity to build and test solutions around local conditions--and in doing so, develop approaches that could prove useful well beyond the region.

The success of Data Week shouldn't be measured by how many conversations happened during the week. It should be measured by what continues after it ends: A researcher finding an implementation partner, a founder discovering a new use case, a company identifying a technology worth testing, an investor spotting an emerging area, a policymaker finding evidence that reshapes how a problem is approached.

The larger goal is to move the AI-and-climate conversation beyond isolated pilots and toward real collaboration, testing, and deployment. For the Global South, the opportunity is even bigger: Not simply adopting technologies built elsewhere, but building, testing, and deploying solutions rooted in the realities of this region--and contributing that work back to the global climate-tech conversation.

A good Data Week should leave behind new collaborations between people previously working in silos, bring new pathways between research and implementation and new opportunities to turn useful technology into climate action.

(The views expressed are personal)

This article is authored by Nalin Agarwal, founding partner, CCF.