Data is the quintessential buzzword of the twenty-first century. It has attracted elusive and endearing sobriquets. For instance, according to British mathematician and entrepreneur Clive Humby, it is the “new oil” and has the potential to make or break economies. Multinational firms like Facebook spend hefty amounts on storing it. Amid this hullabaloo, how can a high-schooler make sense of data?
In a bid to answer this question, we must take our focus away from seemingly heavyweight terms like big data, data mining and extraction, extensive servers, data warehouses etc. and go back to the basics. Data, in its rawest form is information collected through information. Think about the television you have at your house. If you record the amount of time you watched, you magically have your first datapoint!
There can be many more trivial but meaningful examples. The amount of water our households consume, on average, can be easily recorded; the number is pivotal to how municipalities plan it out for the entire city. Think about averages of other variables like electricity consumption, food requirements, education expenses etc. and you’ll be able to decode your annual budget and how it has changed over the years.
Thus, the zeroth rule is to understand that data is omnipresent.
Indeed, it is everywhere but it might not be in our interest to accumulate all of them. Does it matter that a typical Irish spends fourteen minutes between hitting the snooze for the first time and finally waking up? Or that an average Bangalorean spends 243 hr/year in traffic? To most of us, it doesn’t. And that brings us to the next tenet: we undertake labour to collect what’s essential.
This data can now be collected in various forms. In school, you must have learnt about using neatly-drawn tables to record observations from an experiment. This was largely the norm until spreadsheets became ubiquitously available, credit to MS Excel. For companies, the revolution had begun a few decades before this happened, with the advent of computational power and interconnectivity in the United States.
Put yourself in the shoes of a store owner. To keep track of your sales and purchases, you may start maintaining a sheet. Keeping all the information in a single sheet may soon run its course and you might feel the need to maintain two sheets – one for procurements and the other for sold items. Soon, you may perceive that it is better to store customer data separately. In a similar vein, you might have one more sheet to preserve employee data. So on and so forth, you might end up with a dozen individual sheets, within a workbook. This is what you can call a database.
Think of a database as a collection of individual tables, simplistically. The sizes for these can grow at very fast rates, which force us to opt for hardware storage beyond our PC’s hard disk drive. Such a situation is encountered when you decide to, say, expand your firm by opening multiple outlets across your district. As the owner, you would also like to be able to access information for all stores, whilst the individual managers aren’t able to see the specifics of their counterparts.
This is where you can start maintaining a data server. These servers are maintained by dedicated personnel and security, and allow you to have a peaceful state of mind. At this stage, let us ponder upon a question: Why would you undertake so much hassle for data?
There are multifarious answers. You might do it to do your tax returns. You might want to ensure that the managers are doing their work honestly. The single biggest reason, however, in the current era is to answer business questions like:
As an owner, boosting profits is the key. The answer to these questions will take you in that direction and the entity that answers is data. Framing and answering such questions comes under the purview of data analytics. You can ask questions about the past (descriptive analytics) as well as the future (predictive analytics).
This is just one example of how data behaves in real life. As we have seen, it can grow really fast and it is indeed powerful. It is not shocking to note that most multinational corporations around the world have dedicated data teams, whose sole responsibility is to analyze the trends and propose strategies to achieve desired targets.
Building a data mindset early on will help to get a grasp of the data quickly. No matter how big or small the dataset is, you will not be fazed. Understanding data is one of the key steps towards gaining insights from its huge chunks, a skill that will be crucial as technology advances, and as data takes centre stage.
Developing a data mindset will help you understand the world around you better. In fact, it will also be the key to venturing into domains like data science and machine learning. Doing so at a high school-level will enable you to stay ahead of the curve and also make better career decisions.
By reading this article, you have already given yourself a very good start!
Build on this gained momentum, sign up for Radicl, our school-centric course to become an early bird in data science today!
Original post at https://pickl.ai/blog/developing-a-data-mindset/