How to useThe data Studios

Every Data Studio package is built the same way: a set of case studies, dashboards, data assets, and step-by-step how-to guides, organized so you can start wherever fits your project and your team’s capacity. You do not need to work through every tier, and you do not need a GIS background to get value from a package. Here is how to find your starting point.
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Step 1: Start with the question, not the data

Before opening a package, get specific about what you are actually trying to answer. Is this an early warning question, a resource allocation question, an accountability question? What decisions could satellite data support, and who is making them? The more concrete this is up front, the faster you will find the right entry point, and the easier it will be to know when you have enough evidence to act.

Step 2: pick your tier

Each Data Studio package is organized into three tiers of technical complexity. Pick the one that matches your team’s current capacity, not the one that sounds most impressive.

Beginner: Dashboards, No GIS or coding required. This tier is built for practitioners exploring the landscape for the first time. You will get oriented on where the relevant data lives, browse ready-made dashboards built by trusted partners, and learn how to read and communicate what you are seeing in plain terms. This is the right starting point if your team has never worked with satellite data before, or if you need an answer quickly and a ready-made tool already covers your use case.

Intermediate: Analyzing satellite indicators, Basic GIS experience needed. This tier walks through extracting time series data yourself, calculating anomalies against historical baselines, and formatting your findings as evidence for established reporting frameworks. This is the right starting point if your team can run basic GIS analysis but does not have coding or machine learning capacity.

Advanced: Building integrated systems, Pipeline development and machine learning experience needed. This tier covers designing cloud-native pipelines, training detection or classification models, and publishing results to shared dashboards for the wider community. This is the right starting point if your team is building an operational, reproducible system rather than answering a one-time question.
NASA Maps Hurricane Dorians Damage to the Bahamas

Step 3: Use the case study as your model

Each package includes real case studies at every tier, from a flood-damage estimate built entirely from existing dashboards, to a famine determination built on satellite indicator analysis in an area with no ground access, to a country-scale infrastructure damage assessment built on machine learning. These are not just examples. They are templates. If your situation resembles one of them, you can follow the same workflow.

Step 4: Know what else is in the package

Beyond the how-to guides, every package includes:

Data assets: the specific satellite datasets relevant to that use case, with access instructions

Dashboards: ready-to-use tools built by trusted partners, no setup required

Case studies: real examples of the workflow in action, organized by complexity

A Decision-Making Guide companion covering cost expectations, the expertise you will need, points of contact for continued support, and the ethical and practical limits of the data
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Step 5: Know the limits before you start

Every package is built around the same set of considerations, because they apply no matter which use case you are working on:

Local knowledge and ground truth matter. Satellite data shows correlates, not the thing itself. Local context is what turns a correlate into a defensible conclusion.

Communicate uncertainty honestly. A risk map or damage assessment is only useful if the person receiving it understands its confidence level and its limitations.

Look for existing tools before building new ones. Data Studios are meant to complement and fill gaps in tools that already exist, not duplicate them.

Partner where your team’s expertise runs out. No single person or team has every skill a workflow requires, from remote sensing to coding to domain expertise. Interdisciplinary partnerships are built into how these packages are meant to be used.

Step 6: Contribute back

The Data Studios are community-built. If you develop a method, a workflow, or a case study that could help someone else facing a similar challenge, you can contribute it to an existing package or propose a new one. We are actively working with partners on use cases we do not yet have coverage for, including drought monitoring.

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