My grandmother, or Nana as I lovingly called her, never owned a laptop. She never touched a spreadsheet, and the phrase “SQL database” would have drawn a blank stare. Yet, as I navigate the complex world of machine learning, automation, and predictive analytics today, I realize something profound: my grandmother was a data analyst, perhaps the most intuitive and effective one I’ve ever known.
She operated without a single digital tool, yet she could look at a handful of beans, glance at the sky, and listen to the faint sounds from the market three streets over, and tell you with uncanny precision how much to cook, how much to save, or when to sell. Her household wasn’t just run; it was optimized with a kind of contextual intelligence that no algorithm could replicate. Her predictions weren’t based on models, but on memory, acute senses, and decades of lived experience. It’s a strange realization, but one that continues to shape how I view data and its true purpose.
Her Dashboards Were Her Senses & Her Context
Growing up, I watched her continuously gather information, processing it in real-time. These weren’t idle observations; they were her real-world data points, informing every decision.
“The price of tomatoes has gone up,” she’d murmur, “that means the rains were late, and the harvest will be small this year.” This wasn’t just commentary; it was market trend analysis, connecting a single variable (tomato price) to a root cause (weather patterns) and predicting future scarcity.
Or, “Our neighbour is visiting twice this week. A wedding is surely coming. We need to buy rice now before prices surge.” She was performing social signal analysis, correlating a seemingly benign interaction with a significant economic event, and recommending a proactive purchasing strategy.
The Power of Real-Time Observation
The hens were quieter than usual? Rain was surely coming. Better not hang the clothes outside. She didn’t label it “signal analysis” or “trend forecasting.” She called it “living with your eyes open.” But make no mistake: she was reading patterns, identifying variables, and adjusting her decisions in real-time. Every glance, every sound, every conversation was a data input, feeding a constantly updating mental model of her world. This deep, localized contextual awareness is something many modern data systems strive for but often miss.
Her Tools Were Conversations, Not Code
In our modern world, we lean on APIs, elaborate dashboards, and interconnected systems for data collection. My grandmother relied on something far more organic and robust: conversation. Morning walks weren’t just for exercise; they were her daily data collection rounds.
She’d greet the pepper seller, exchanging quick updates about the harvest. She’d chat with the butcher about supply chain issues, disguised as friendly banter. She observed who had their shop open early, who was late, and who seemed to be stocking up. She built and maintained a sprawling, robust human data network long before anyone conceived of social graphs or LinkedIn.
A Human Data Network Before Social Media
When it came time to make decisions – whether planning meals, preparing for guests, or saving money – she did what any good analyst does: she triangulated stories, filtered out the noise, and looked for truth in the patterns. Her “data points” were the subtle shifts in tone from a vendor, the unsaid anxieties in a neighbor’s voice, the collective wisdom gleaned from a community.
I distinctly remember being sent to the market as a child, a list in one hand, coins clutched in the other. Nana would give me the exact price for each item, often down to the last cent. If I returned with change even a fraction short, she didn’t hesitate. She’d take the list, march back to the market, and demand her balance, not with anger, but with data-backed confidence. Everyone knew I was her granddaughter, and they knew better than to mess with her numbers.
The Original Price Tracker
Fast forward ten years, and she still had that same market list, folded and tucked into an old notebook. It was annotated. Adjusted. Tracked. It wasn’t just a sentimental keepsake; it was her living dataset, a meticulous record of economic patterns, seasonal shifts, and vendor behaviors. She didn’t call it a dataset; she called it “price tracking.” It was a testament to the fact that fundamental data collection, analysis, and record-keeping have always been part of human ingenuity.
Probabilities, Not Certainties – The Art of Intuitive Risk Management
Nothing in Nana’s world was exact, but it didn’t need to be. Her insights offered probabilities, not certainties, and that was more than enough to navigate life efficiently.
When she’d say, “We may not have visitors today, but let me cook a little extra just in case,” she wasn’t making a random guess. She was calculating risk tolerance, weighing the small cost of extra ingredients against the potential embarrassment of not being able to feed unexpected guests. It was a classic risk-reward assessment, executed instinctively.
When she insisted on buying firewood weeks before the harmattan (the dry, dusty trade wind) fully arrived, she was modelling seasonal behavior and anticipating future demand. She understood that prices would rise once the cold fully set in, and securing resources early was a financially sound decision. These weren’t random guesses; they were hypothesis-driven decisions, backed by lived data, time-tested, deeply local, and constantly updated.
Beyond Confidence Intervals: Lived Data and Feedback Loops
She may not have had confidence intervals or p-values, but she had confidence earned through direct, immediate feedback loops. When her predictions were right, it reinforced her model. When they were wrong, she absorbed the lesson, adjusted her mental algorithm, and moved forward. This iterative process of learning and adapting is at the heart of any successful data strategy, whether human or machine-driven.
What Modern Data Science Could Learn from Nana
We live in an age awash with dashboards, real-time metrics, and machine learning models predicting everything from customer churn to flu outbreaks. Yet, in our relentless pursuit of more data, we often overlook the profound power of knowing our environment deeply, of listening before calculating, and of contextual intelligence that simply can’t be scraped from the web or purchased as a dataset.
My grandmother’s approach reminds me that data analysis is not always digital. It’s fundamentally human first. It teaches me that truly good analysts don’t just crunch numbers; they understand people. They can read silence. They know when the data looks perfectly fine on a screen, but something in their gut still feels off. They trust their tools, yes, but they also trust their gut, their experience, and their intuition — elements that are often dismissed in purely quantitative approaches.
The Human Element in the Age of AI
Now, as someone who works with machine learning and artificial intelligence every day, I often find myself asking: What would my grandmother think of predictive analytics? Would she implicitly trust a dashboard to tell her how much to spend? Would she let an AI model determine exactly when to plant or sell? My best guess? Maybe. But only if it proved itself.
She wouldn’t care about accuracy scores or ROC curves. She would want to know: “Has it ever been wrong?” “Does it truly understand this land, these people, these rhythms?” “Can it explain itself in a way that makes practical sense?” And if it couldn’t, she’d toss it out. Because at the end of the day, tools are only as good as the people using them and the profound wisdom they bring to the table.
The Legacy of a Non-Technical Analyst
Data science isn’t new; it’s just newly named. For centuries, in markets, in kitchens, on farms, and in parenting, humans have always been data-driven. We just called it experience, wisdom, or common sense.
So, here’s to the hidden analysts among us: the grandmothers, the market merchants, the teachers, and the farmers who read patterns, made predictions, adjusted strategies, and, most importantly, handed down the very instincts we now strive to model in complex code. My algorithms are trained on vast datasets. But I was trained by her.
