Design & Data - Part 1: Reading the World Through Open Signals

Everywhere you look, signals are humming. Salaries rising and falling. Satellites passing silently overhead. Code repositories pulsing with commits. Pages on Wikipedia being opened, closed, and reopened.

Most of the time, these signals flicker past without notice. They don’t wait for us to pay attention; they move whether we engage or not. But when you start to frame them, the noise sharpens into meaning. Satellite images show farmland quietly dissolving into warehouse roofs, landscapes reshaped before the zoning boards even meet. Search traffic reveals that excitement around new technologies doesn’t crest at the fanfare of launch, but months later, when hype has cooled and adoption takes hold. Weather records uncover the moments when grids really break, counterintuitively, in damp, breathless afternoons when nothing moves.

The signals are already here, moving with or without us. Left untouched, they dissolve back into noise. But when you reach in and pull on a thread, you may realize you've unearthed an opportunity, or a story worth telling. Some stories become propaganda, others become policy, but all shape how the world is understood. To work with data is to take part in that shaping, to find the threads worth pulling, and to frame them in ways that reveal rather than obscure. There’s a literacy hidden in the noise. Learn it, and you read the world more directly, less at the mercy of those who would tell the story for you.

Once you’ve chosen what to notice and how to frame it, the challenge becomes navigating the maze of available data sources. Scarcity isn’t the bottleneck here, but abundance is.

Most of what follows comes from sources in plain sight: government statistics, public APIs, open repositories. What makes them meaningful is the framing you choose to give them. Each example shows how an ordinary dataset can reveal patterns, surface tensions, or expose dynamics that would otherwise slip past.

The Bureau of Labor Statistics (BLS) has quietly tracked salaries across professions for decades. Pulling just a few of those lines onto a chart shows how design fields stack up against the national median, and how newer roles like data science have surged into view. What looks like raw tables in a government database becomes a time-series narrative of growth, competition, and changing demand.

Data Scientists accelerate in salary growth. Web and Digital Interface Designers keep a steady lead above the national median. The median itself drifts almost flat, more backdrop than signal. The pattern isn’t surprising, but it is grounding. Laid side by side, the numbers turn vague impressions into something you can point to, measure, and carry into a conversation.

BLS makes its wage data public, updated year after year and free to pull. The tables may look dry on their own, but they’re structured, consistent, and built for use. A dataset like this doesn’t require permission or a paywall; it’s sitting there, waiting for someone to draw it into their work. Access is as simple as downloading a file or calling an endpoint:

Wage data shows how roles are valued in the market, tracing the rise of one profession against another, the ebb and flow of demand over time. But that’s only one side of the picture. To understand who can participate in the market at all, you need another lens. Before skills can be rewarded, there has to be a line of connection, a way in. That foundation, the infrastructure itself, comes into view in the World Bank’s open datasets.

Before wages can rise or fall, before the idea of a career is even on the table, there has to be connection. A means of exchange. From my vantage, I take for granted that I can open a laptop, connect instantly, and find my way into the marketplace. But step back to a global view, and access is anything but uniform.

The World Bank tracks indicators like internet penetration and broadband availability across countries, year after year. On a map, the patterns leap out: dense clusters of high connectivity in one region, broad gray zones in another. To a designer, entrepreneur, or policymaker, these disparities can be powerful signals of opportunity. A gray zone may mean a market still waiting to be served. A rapidly darkening shade may mean a surge in digital participation that’s only just beginning to register in the economy.

What looks like a table of percentages turns into a living map of where ideas can spread, where communities can grow, and where the next wave of digital economies might take hold.

World Bank data is useful when the question is bigger than one country or one sector. It’s where you look if you want to know

• How quickly is broadband access spreading in Southeast Asia compared to Sub-Saharan Africa?
• Where is mobile adoption outpacing fixed-line infrastructure, and what does that mean for commerce?
• Which regions are closing connectivity gaps fastest, and which ones are stalled?
• How does digital access track against education, health, or income in the same places?

Questions like these turn into strategy. They reveal where markets may be about to shift, where investment is likely to flow, and where new users might come online.

The dataset is open and straightforward to work with. The World Development Indicators API serves results in a simple JSON structure:

Visualized as a choropleth map with react-simple-maps and a d3-scale color ramp, those answers stop being abstract and become something you can see directly.

When connection reaches everyone, the story shifts. Networks give way to economies, and the next pattern emerges: the movement of trust. You can see it in what people hold onto when confidence falters, and what they release when it returns.

Inflation is the quiet tide that lifts and sinks all other stories. It defines the baseline against which everything else moves. When you overlay the U.S. Consumer Price Index (CPI) with the price of BTC over the past five years, you see two kinds of trust in motion.

CPI’s line rises with familiar steadiness; gradual, bureaucratic, slow. It moves like paperwork, measured, reported, and rarely questioned. The erosion of value hides in that steadiness, distributed so thinly across months that most people don’t notice until the baseline has shifted.

Bitcoin’s line moves differently. It arcs and breaks in violent cycles, spiking at the edges of global anxiety, falling as calm returns. Where CPI reflects managed confidence, Bitcoin reflects raw sentiment, an unfiltered record of collective mood swings. In one chart, you see policy; in the other, emotion.

Each, in its way, is a mirror of trust. CPI is institutional trust made procedural. Bitcoin is distrust made liquid. Belief unmediated by governments, banks, or schedules. Both lines tell the same story in different languages: what people cling to when stability feels uncertain.

Viewed together, the two lines form a kind of emotional topography; the friction between stability and speculation, between managed systems and parallel ones. One moves through policy, the other through pulse.

For anyone visualizing financial data, the goal isn’t to show wealth but to show belief over time: how people assign value, withdraw it, and return when the story changes. The patterns show how trust migrates from one system to another.

Trust migrates quietly. You can see it in what people build, the skills they chase, the futures they’re still willing to bet on.

LinkedIn’s workforce data captures that process in real time, millions of small edits, tiny acts of adaptation that, together, show how the world is recalibrating its skills.

Each profile update is a kind of vote: a signal of what someone thinks will matter tomorrow. Aggregated at scale, those signals reveal patterns no legislation can produce. When visualized, the rise of AI-adjacent skills—data storytelling, cloud computing, cybersecurity, generative AI prompting—forms a map of collective adaptation.

Pulling skill frequency data or top-growing job titles from the LinkedIn Workforce Report transforms static lists into momentum maps. Pair it with BLS wage data and you can see not just what pays, but what’s emerging.

For anyone designing learning experiences or career products, this open-source pulse is invaluable: it turns speculation into signal.

Skills signal where people are moving. Capital signals where belief follows.

To see what ideas are being funded, you look at filings, not headlines.

Form D is a simple filing with the SEC. Any company that raises money from private investors has to submit one, a short notice with the amount offered, the amount sold, and the sector it falls under. There’s no pitch deck, no marketing cycle, no narrative shaping. Just the facts of the raise.

Every one of those filings marks a private investment: a quiet disclosure that a new idea has found backers willing to fund it. In aggregate, they form a real-time ledger of what the market considers worth building. Scraped through EDGAR’s open API, the data becomes a map of intent, less glossy than press releases, but far more honest.

The pattern is striking even before you drill down. “Other Real Estate” and “Other Technology” dwarf nearly every other category in planned capital. But when you look at sold versus planned amounts, a different picture emerges:

• Climate tech and energy: high volume of offerings actually closed, suggesting real momentum.
• Advanced manufacturing & infrastructure: strong raises, slower conversion, hinting at long timelines and heavy lift.
• AI/ML and biotech: meaningful allocations, but large portions still left unsold — signals of ambition outrunning certainty.
• Oil & Gas: raises remain significant, but the slow close rate shows a sector in negotiation with its own future.

Read this way, Form D filings can be read as a sort of stress map. They show where belief crystallizes quickly and where it stalls. They show which ideas attract instant checks and which require convincing. They show the difference between a pitch with traction and a pitch with hope.

Data, in its raw form, is indifferent. It hums beneath everything we build.

The act of design—of framing—turns that hum into something legible, sometimes beautiful, and sometimes urgent.

The five datasets in this piece aren’t a taxonomy or a toolkit. They’re reminders. Each shows that public data is both a record of what has already happened, as well as a preview of what’s gathering force right now.

The challenge here is to see more clearly: to use what’s already open, connect the signals, and tell the stories that make the world intelligible again.

In the noise, there’s literacy. In the patterns, there’s power.

And in every open dataset, there’s a living story waiting to be told.