Data is a series of observations, measurements or events that describe the world we live in. From the temperature of your body, the number of hairs on your head; or more interestingly, the percentage of grey hairs on your head over time! These are all data points which we can measure, record and analyse. Almost anything you can observe can be measured in a qualitative or a quantitative way.
The need to understand data is not recent. It’s a skill that’s been at the core of our existence for millennia. Understanding the climate, weather, crop growth, amount of grain in storage and the number of mouths to feed all required some form of data collection, storage, analysis and forecasting; perhaps at a smaller scale than modern times. As our societies became far more complex, we could no longer keep track of our observations using memory alone.
Datasets became larger and needed a more permanent and reliable system of record. Writing became one of the first ways to store, pass on and provide a far more reliable way to store data. It would be a shame if the person responsible for keeping track of land and cattle ownership for a village was eaten by a bear!
Stone tablets, wall paintings, stories written on parchment were the earliest ancestors of our modern high performance databases.
But why do we collect data? We’ve never really collected data just to stare at it, we do however, call upon the collected data to make decisions in the future. In modern times with the variety and volume of data collection, we need to transform the raw data by weeding out what we think is unnecessary in order to tease out the story it, only then can we call what we have as information. The transformations and the step to tease out insights is the role of modern day data professionals such as myself.
In the last paragraph I touched on data volume; it refers to the sheer amount of data organisation are collecting nowadays. We call this ‘Big Data’. As our computational capabilities have improved exponentially over the decades, so has our appetite for data collection, storage and analysis. Data has become the competitive advantage for organisations to better serve customer needs; and ultimately a revenue driver. For example, Facebook has a vested interest in collecting as many data points as possible on their users to build a more complete digital model of them. This enables them to allows their customers, who are advertisers (yes, advertisers are their customers, not users!) to target the individual users with a higher degree of granularity.
With the large datasets at our fingertips, we can perform more advanced analytics to even build a model of what the future might hold based on past events; enter Machine Learning. I’ll dive into Big Data and Machine Learning in future posts.
Until next time – I would like all of you to just notice the touch points of data in your life. Next time you’re on social media or online shopping, think about:
- What am I clicking on or looking at that can be used give advertise something to me later?
- The next time a computer recommends you a book to read, a movie to watch or even a generates a playlist on Spotify (the modern day romantic mix tape) think about the data it might have used to understand your taste!
I implore all of you to make that conscious effort to understand where an algorithm might be telling you what to click, buy or look at. Data is wonderful and has provided us with many new technologies and insights of the world, but a data professional it’s also my duty to help everyone understand the nature of data from multiple angles.