Ordinary data, extraordinary results

Routine analytics

While terms like databasesanalytics, 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:

  • Social media sites register anytime there is a visitor or a post to your page.
  • Sensors monitor weather fluxes and instantly report that data and its predictions to your weather app.
  • Sport enthusiasts collect performance statistics for their favourite athletes, utilising this data to form teams for their fantasy leagues.

How to use Big Data to your advantage

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?

1. Fitness

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.

2. Finance

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:

  1. Short-list the attributes important to you in your next laptop
  2. Rank order the importance of attributes
  3. Develop ranking function with the chosen features as inputs
  4. Select models that fit your budget constraint
  5. Score the selected models using the ranking function
  6. Choose the model that best meets your requirements, i.e. the model with the highest rank

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.

Want to become more data-driven? Download our ebook today to find out how.

About Ipsos jarmany

Ipsos Jarmany is a team of expert data analysts and data scientists based in London, UK. Contact us to discuss how we can help you get more from your data.



Defining data analytics and how they impact everyday life

Big data is everywhere

Analytics in everyday life

Smartphone users regularly use fitness apps

Are paper bank statements a thing of the past

Visual impairment

Quantified Health

The breakthrough of analytics is largely dominant in business, generating efficiencies, diminishing mundanity, and driving growth. However, it’s impact stretches beyond the commerce industries, healthcare is starting to benefit greatly from its ability to more quickly detect and therefore treat a broad range of healthcare issues. In this blog we will look specifically at how it can stem the numbers of people suffering from some form of visual impairment.

Health and wellbeing continue to dictate many of the challenges in the world of today and the expenditure of governments to do something about it.

In 2018, total UK healthcare expenditure accounted for 10.0% of its GDP. In 2019/20, the government spent £135 billion on the NHS alone (excluding the coronavirus). More than ever, it is critical that innovative technologies find their way quickly into the NHS.

Big data in healthcare is gaining traction. Research publications alone have risen from 50 in 2018 to 350 in 2020 illustrating the cultural shift of data utilisation in healthcare. ‘Quantified health’ is a relatively new phrase that means the integration of data directly from consumer wearables (pedometers, Fitbits, Muse headbands, etc.), blood pressure cuffs, glucometers, and scales into EMRs (Electronic Medical Records) through smartphones (e.g., Apple Watch, Google Fit, and Samsung Health). They can pick up on warning signs faster by tracking changes in behaviour and other key data points. The net net is a more health aware population and a reduction in healthcare costs.

Blindness epidemic

Beyond fitness, other significant health issues currently being addressed by the world’s data scientists include visual impairment.

According to the World Health Organization, we’re on the verge of a ‘blindness epidemic’. Visual impairments and refractive errors have become a global health issue. An estimated 1.89 billion people are currently living with some form of visual impairment, this is expected to rise to 5 billion by 2050.

The good news is that there are technologies which can help mitigate the impact of visual impairment. Seeing AI, an app developed by Microsoft AI & Research can help. It narrates the world for blind and low-vision users, allowing them to use their smartphones to identify everything from an object, the contents in their wallet or even a document.

However, catching the condition early is key.  Recent medical data indicates that nearly 70% of blindness cases could have been prevented with early detection and screening.

Weather Health Forecast

Diabetic retinopathy is a complication of diabetes, caused by high blood sugar levels damaging the light-sensitive layer of tissue at the back of the eye, known as the retina. When people with diabetes visit their general practitioner, they’re often referred to an ophthalmologist (an eye doctor) who can assess their eyes for signs of diabetic retinopathy. It is one of the leading causes of blindness, resulting in up to 1700 cases each year in adults in the UK.

The disease however is manageable. If detected when a patient is asymptomatic, the more severe outcomes can be avoided. Still, early diagnosis has proven difficult, with Michael Abràmoff, a retinal specialist and computer scientist stating, “we know so well how to treat it, but we simply don’t catch it early enough”. Only half of the population with diabetes get their eyes examined every year, as recommended. But assessing the 4 million people affected by diabetes in the UK and the 400 million + worldwide, is a massive challenge.

American Digital Diagnostics (formerly known as IDx) became the first company to ever receive clearance for an AI analytics platform that makes a diagnosis without physician input. The platform, called IDx-DR, uses Deep Neural Networks (DNN) to detect diabetic retinopathy in primary care. It takes images of the back of the eye, analyses them, and provides a diagnosis — referring the patient to a specialist for treatment if a case which is more than mild is detected. Using the masses of data, the AI system can identify serious cases of the condition, without the need for a clinician.

In effect, this AI technique essentially identifies the problem before it even happens. Its predictive capabilities determine a patient’s future health condition, allowing healthcare professionals to develop treatment plans to prevent visual impairment altogether. IDx-DR’s quick and easy implementation with a three-minute scan, followed by a two-minute computer-aided diagnosis allows a patient to visualise their health forecast in just five minutes.


Visual impairment is a significant threat to our population. Ophthalmologists have widely agreed that it is best to diagnose diabetic retinopathy before symptoms are evident. The development of the IDx-DR as an application of AI has promoted potential integration of large-scale forecasting of serious ailment progression. Healthcare forecasting may just become as common as looking up the weather.