Joel Outschoorn | 17 June 2021
Data analysis is not just a tool or a technology; it is a way of thinking and acting. We have subconsciously been making data-driven decisions to resolve our day-to-day problems for some time.
While terms like databases, analytics, and statistics are mostly associated with the business world, most of us use data to analyse, identify patterns, and make predictions in our daily lives just as often as we do when we are at our desks.
You may not even realise you rely on it for quite so many menial tasks (when was the last time you checked the weather app on your phone?), but you probably use data analytics multiple times a day. Here are some quick examples of everyday applications of data:
Big Data is now ubiquitous. It's getting tougher for us to function in normal society without the presence of Big Data somehow in our lives. Many of the changes are so subtly convenient we barely notice them. How can you maximise these personalised data analytics to make beneficial changes to your everyday life?
The progression of healthcare over the years has been boosted by the volume of data collected and experimented with; ultimately allowing people to live a longer and healthier life. We have been able to produce more personalised self-learning healthcare programs, utilising personal data such as gender, age, weight, medical history and lifestyle.
It has become the norm for us to implement fitness trackers and health apps to compliment an active lifestyle. These devices are actively monitoring our heart rate, sleeping patterns and other key indicators that can be used to benefit a multitude of healthcare purposes. It was estimated that in 2018, around 41 percent of those aged 30 to 39 years used fitness tracking apps to monitor their health. Proactively tracking our movement and eating habits provides us with complete visibility of our physical state allowing us to make effective decisions to govern and regulate our health and fitness goals.
About 1 in 5 Americans now use a smart watch, epitomising the general trend of smartphones becoming our new personal trainers. It is becoming increasingly difficult to accurately manage our physical well-being without some form of personal data tracking.
Data analytical methodologies are also being applied in how we manage our finances.
In 2020, 86.5% of Americans used a mobile device to check their bank balance. Furthermore, in the UK, close to 7 in 10 customers of Halifax (part of Britain's biggest banking group – Lloyds) aged 25-29 had gone paperless or partly paperless, while 62 per cent of those aged 18-24 had done the same. This emergence of online and mobile banking has enabled us to check our live financial status in a matter of minutes. Likewise, the breakthrough of paperless bank statements has made it even easier to comprehend our financial history since we are now given the freedom to export bank statements into .csv files which can be imported into the likes of Excel and Python.
On these analytical platforms, the data from these bank documents can show historical finances which could then be used to explore our spending trends and understand where our money is being spent. This enables us to determine any required adjustments in our financial behaviour to prevent unsustainable spending habits, administer our money more efficiently, and spend money more effectively. Furthermore, it has allowed individuals to develop their own fully customised saving schemes to proactively plan for any upcoming large expenses or investments.
3. Problem solving
Imagine you need to buy a new laptop. Where do you start?
Regardless of your budget, chances are you will find more than one option available on the market and will need to select the one that best fits your need. Aside from the various manufacturers, you will need to choose between a host of features, including RAM, touchscreen, processor, screen size, battery life, graphics card, storage type, operating system, connectivity, etc. The first thing you’ll want to do is to simplify this barrage of information to make it easily digestible.
Let's say you narrow down your list to 4 variable features: operating system, storage type, RAM, and processor. You then rank these variables in terms of their relative importance to you. As a self-employed analyst, you can create a personalised function, with the 4 chosen features as input arguments. This function essentially yields a ranking for certain laptop models, where the larger the outputted value is, the better rating this model has. In a sense, you are creating a database of laptop models with their own uniquely computed rank.
The process of choosing the laptop could be as follows:
This is an example of using the approach of analytics to solve a real-life problem. A complex and abstract problem is broken down into a series of smaller problems that can easily and efficiently be solved using the available data.
These are just three areas of our day-to-day lives where the analysis of ordinary data is being developed to create various benefits to us as individuals. Though data analytics may still be widely regarded as a technical profession, the rise of access to everyday data in such a broad range of areas gives everyone the opportunity to become an analyst in their own right.