Marketing Specialist, Tech Enthusiast
Big Data's value, popularity, and scale of usage in business today come from a few of the indisputable benefits it has to offer:
The world statistics show that the number of companies using Big Data has grown from 17% to 50% since 2015.
The increase rate is impressive, and the truth is, it’s no longer just a fancy word a cool tech guy would use when discussing what’s behind supercomputer VIKI from I, Robot.
In fact, many large industries have already adopted Big Data technologies and are gaining profit. In this article, I'll look at three industries in which big data is making a dent in 2020.
When discussing Big Data in healthcare, one should distinguish the three aspects of its application. Firstly, it’s the so-called clinical aspect that involves disease diagnosis, treatment, and prevention.
Then comes the management aspect aimed at better management of healthcare institutions by improving their operational and economic efficiency, on both regional and governmental levels.
Finally, the scientific aspect is the one where Big Data can play a truly revolutionary role. Disease studies and drug development are the core areas here.
The fundamental concept lies within the development of electronic health records (EHR). EHR is a system that collects and stores medical and other personal data related to an individual’s health. EHR contains the patient’s disease and ailments’ history, all the hospitalization records and doctor visits, laboratory tests, vaccinations, examinations, allergies, or any other related facts. Apart from serving as a storage of accurate medical data, EHR can control the patient undergo regular screenings and treatments by sending corresponding reminders and notifications.
Many experts claim Blockchain to be a good technology choice for EHR: it fulfills some of its major requirements, such as completeness of data, its protection, and verification.
Combining Blockchain with Big Data and AI makes it possible to use millions of EHR’s for detecting hidden dependencies between a disease along with other side-factors as social, territorial, age-related, demographic, genomic, etc., which in its turn, allows providing a patient with exceptionally individual recommendations. The synergy of these three rising technologies allows creating a diagnostic system of a brand new quality level.
Watson for Oncology by IBM is a prototype example of such a system. Its database includes more than 600 000 diagnosis records as well as 2 000 000 text pages from medical journals and clinic tests. By processing this data, the program can produce the most optimal treatment plan.
A retrospective analysis of treatment plans prescribed says that in regards to lung cancer, Watson selected efficient treatment tactics in 90% of the cases. In comparison, US hospitals averaged the rate of around 50%.
Big Data also represents the core technology for forecasting medical institutions’ spending. It relies on a multifactor analysis of such statistical data as the number of repeat visits, ailments recorded against specific experts and departments, the prevalence of certain pathologies, number of chronic patients, epidemiologic indicators, etc.
British National Health Service serves as a good example here: they use Big Data to analyze the efficiency of surgery sub-departments. Repeated hospitalization, missed doctor appointments, drug supply are the major factors included.
McKinsey’s recent research says that Big Data helps to not only reduce healthcare costs but also contributes to the quality of life enhancement. McKinsey reports that it can be achieved through Big Data capability to unite the data stored in separate sources wholly isolated from one another. For instance:
Optimizing the operational activities is essential for bringing the established treatment practices to a whole new level. Personalized and preventive approaches based on remote monitoring can become the new reality thanks to vast volumes being analyzed.
Every minute millions of people are purchasing by carrying out millions of transactions, so it’s no surprise that retail is one of the top industries leveraging Big Data.
The significant tasks retail is trying to solve from both technological and entrepreneur standpoints are the following:
Any retail business is looking for very explicit recommendations which steps one should take in a particular situation. Nowadays, Big Data analytics can help with that.
One of the aspects of a company’s steady income depends on — is the smart pricing policy. Price correction is one of the principal retail management mechanisms. The studies show that, on average, managers spend half of their work time on goods reevaluation, which still isn’t accurate enough due to human factors. In addition, one cannot guarantee that the process operates such crucial variables as seasonality, stock availability, supply and demand structure, competitors’ activities, etc. That is why large retail chains switch to automatized price reevaluation using Big Data technologies.
Macy’s, which operates 800 stores with 73 trade items, has implemented a similar solution, which has spared the need for pricing correction by 22 times. What’s also important here, the new price wasn’t just somebody’s judgment call but was carefully deduced, relying on the variables mentioned above.
Using the same method, Amazon is adjusting its prices every two minutes.
Apart from the optimizations the technology brings to the existing businesses, it plays a big role in making strategic decisions when developing a new entity. By analyzing competitors’ locations nearby, transport access, potential customers’ income rate, their habits, and preferences, it gives a comprehensive idea of the territory’s commercial potential for future business.
Historically, law-enforcement facilities have been working with huge volumes of data. There are few directions where data-based insights can be of particular value to the law-enforcement establishments.
First and foremost, it’s cybersecurity. As unfortunate as it may seem, but fraudulent activities and malware have become a very often type of crime for today’s police. Big Data analytical function is said to be today’s most efficient instrument for fraud identification and prevention. Big Data algorithms can detect suspicious activities before they actually occur and alert consumers or companies about a potentially fraudulent action. It’s becoming a must for many businesses to take proper care of enterprise data security.
Big Data analytics is also behind modern policing strategies. It allows the police to take a more proactive role in combating criminal activities. By using various sources of data such as the number of arrests, crime rates, fines issued in regards to a particular neighborhood, the police can monitor the areas prone to criminal activities more closely. Predictive policing — is the term used to describe this method in criminology.
Lastly, advanced analytics Big Data offers is an efficient tool for governing establishments on various levels. It gives the authorities unprecedented access to very different kinds of precise information from all over the world to help understand the global picture of a situation and make well-informed decisions. Economy, health, climate, social issues are just a few fields the law enforcement agencies need data science analytics for.
“Information is the oil of the 21st century, and analytics is the combustion engine” — Peter Sondergaard at Gartner.
It’s safe to say that data truly is the new oil, today. But a great opportunity can be a big challenge at the same time.
So if you are having a hard time with structuring and organizing your business data and you feel like there is a better way to deal with it, reach out to me!