Analytics is something any manager, leader or in fact anyone should know about. Not only because analytics is one of the biggest buzzwords around at the moment but because it will be a game changer in all aspects of life. In today’s data-driven world analytics changes everything, not just in business, but also in fields like sports, healthcare and government. It is hard to think of any aspect of life that won’t be affected by analytics. We have seen books on analytics become global best sellers and the people who are able to apply analytics (sometimes called data scientists) are hailed as having the sexiest job of the 21st Century. So, what really is analytics?
Basically, analytics refers to our ability to collect and use data to generate insights that inform fact-based decision-making. Advances in information technology and a complete datafication of our world now mean we have (or will have very soon) data and insights on everything. This gives us unprecedented opportunities that will transform business, sports, healthcare and government. Let’s first look at the datafication of our world and then at some examples of how analytics are used to turn data into insights.
Datafication – More Data Every Day
Day after day our world is filled with more and more data and the pace of the data growth is accelerating week by week. Data on every aspect of our life is now tracked and stored in databases and analytics allows us to turn this data into insights. Here are just some examples that illustrate the datafication of our world:
- We increasingly record of our conversations: Emails are stored in corporate databases, our social media up-dates are filed and phone conversations are digitized and stored.
- Companies and organisations are creating vast repositories of data keeping a digital record of everything that is going on: Just think of all the data generated daily in our financial systems, stock control systems, ordering systems, sales transaction systems and HR systems. These data depots are growing by the minute.
- Our activities are tracked: Most things we do in a digital world leave a data trail. For example, our bowser logs what we are searching for and what websites we visit, websites log how we click through them, as well as what and when we buy, share or like something. When we read digital books or listen to digital music the devices will collect (and share) data on what we are reading and listening to and how often we do so.
- We increasingly generate data using the ever-growing amounts of sensors we are now surrounded by: Our smart phones track the location of where we are and how fast we are moving, there are sensors in our oceans to track temperatures and currents, there are sensors in our cars that monitor our driving, there are sensors on packaging and pallets that track goods as they are shipped along supply chains, etc.
- Wearable devices collect data: Smart watches, Google Glass and pedometers collect data. For example I wear an Up band that tells me how many steps I have taken, the calories I have burnt each day as well as how well I have slept each night, etc.
- A lot of photos and videos are now digitally captured. Just think of the millions of hours of CCTV footage captured every day. In addition, we take more videos on our smart
phones and digital cameras leading to around 100 hours of videos being up-loaded to YouTube every minute and something like 200,000 photos added to Facebook every 60 seconds. - Internet-enabled devices self-generate and share data. Smart TVs for example are able to track what you are watching, for
how long and even detect how many people sit in front of the TV.
- More data is made publicly available. For instance, weather data is now shared by Met Offices and governments are releasing censor data or land registry data. Also, think of all the data Google collects and makes accessible through tools such as Google Trends or Google Maps.
Analytics Today
We are not only generating vastly more data but our ability to harness and analyse this data has improved massively over recent years. We can now analyse large volumes of fast moving data from different data sources to gain insights that were never possible before. Analyzing large and messy data sets is often referred to as ‘Big Data’ or ‘Big Data Analytics’, which have become buzz words in their own right. Different types of analytics approaches allow us to analyse numbers, text, photos and even voice and video sequences. Let’s look at some practical examples of how analytics are applied in practice today.
Sport: Analytics is widely used to improve the performance of athletes, sports stars as well as you and I. Here are a few real examples:
- You can now get a baseball with over 200 in-built sensors that gives players detailed feedback on their performance. InfoMotion Sports Technologies together with researchers form the University of Michigan are refining a smart ball that tracks and analyses shooting skills and ball-handling mechanics.
- Smartphone Apps such as Run Keeper or Nike + Running use the in-built sensors in your phone so you can track and analyse your own running performance, measuring split times, calories burnt, etc.
- Olympic cyclists use bikes that are fitted with sensors on their pedals that collect data on how much acceleration every push on the pedal generates. This kind of data
provides detailed insights into actual performance and how to improve it. - In tennis we use a system called SlamTracker that records player performance providing real-time statistics and comprehensive match analytics.
- Finally, we have all seen Moneyball – a film based on the real live story of Billie Bean - general manager at the Oklahoma As. He used analytics to identify talent that talent scouts using traditional methods were not able to spot. This allowed him to outsmart much richer teams in Major League Baseball.
- A hospital unit that looks after premature and sick babies is applying real time analytics based on a recording of every breath and every heartbeat of all babies in their unit. It then analyses the data to identify patterns. Based on the analysis the system can now predict infections 24hrs before the baby shows any visible symptoms. This allows early intervention and treatment that is so vital in fragile babies.
We can now use powerful analytics to decode human DNA in a fraction of time it used to take – today it takes just one day to unravel the entire DNA sequence of a human being. With increasing volumes of decoded DNAs come improved insights and powerful predictive capabilities. We can more precisely predict likelihoods of getting certain diseases, which in turn can lead to preventative actions and early interventions. We can also better customise treatments for diseases such as cancer because the DNA code will give physicians information about the most effective ways to treat tumours.
Smart Homes: Our homes are becoming smarter with the ever-growing amount of devices that collect and analyse data. For example, the scales in my bedroom track air quality and temperature levels in addition to my weight, of course, and send this data to my smart phone. My fridge is connected to the internet and will alert me to any faults (e.g. if I forgot to close the door properly). The amount of smart devices will increase significantly over the coming years and analytics will enable us to run smarter, more efficient homes and cities where the central heating system adapts to your patterns of life and where your fridge calls out the service engineer when there is a problem.
Crime Prevention: Fighting crime increasingly relies on analytics to identify and predict criminal activity. Take these examples:
- Our credit card companies monitor our transactions in real time and analytics engines will detect any ‘unusual’ and potentially fraudulent transactions and ‘freeze’ your card before any more damage can be done.
- The police and federal agencies use data analytics to predict terrorist activities.
- Many police forces across the U.S. rely on sensors and analytics to automatically detect and precisely locate things like gunfire. Using tools such as ShotSpotter allows them to respond to any gun incident immediately without the need for anyone to report it. In fact, using this type of analytics police forces realised that 80-90% of gunfire is never reported.
- Many of my clients can now pinpoint their marketing efforts by using analytics on purchase data. Loyalty card or credit card information can be used to identify patterns of behaviour. For example, when a woman becomes pregnant her buying patterns change significantly. Supermarkets can now very safely predict that a woman is pregnant and even in what trimester she is. This can then be used for targeted marketing to cash in on the ‘nesting’ phase when parents spend a lot of money on baby accessories etc.
- Retailers can use analytics to optimize their stock. Traditionally, shops would analyze which items sell the most and stock them. Modern analytics go far beyond that. For example, one of my clients identified that
a particular stock item didn’t sell very often but the people that came in to buy it were big spenders. Therefore was important to always have this item in stock.
- Companies optimise their supply chain performance using analytics. Data from sensors on their trucks or pallets allows them to identify the most optimal delivery route (also taking into account traffic predictions and weather conditions).
- Another client of mine, a leading telecom company, has developed analytics models to predict customer satisfaction and potential customer churn. Based on phone and text patterns the company was able to classify customers into different categories. The analytics showed that people in one specific customer category was much more likely to cancel their contract and move to a competitor than people in any other category. Further analytics now help the telecom company to closely monitor the satisfaction levels of these clients and prioritise preventative actions.
- A large services firm is now able to use analytics to predict that employees are thinking about leaving the company simply based on their usage patterns of sites like LinkedIn, Dice or Monster. Again, this allows them to intervene before the employee actually takes the step and leaves.
