For us, Big Data is something that helped Donald Trump win the presidential elections in 2016 and boosts Elon Musk’s Tesla sales. Indeed, Big Data projects are closely connected to the market of business analytics with the volume estimated at about $100 bn in 2016.
Despite being mostly used by the top-line analysts and programmers at IT companies, Big Data analysis has grown commonplace. Follow the article to understand the nature of Big Data and its application spheres.
What is Big Data?
Big Data analysis is a complex of actions aimed at collection, analysis, and systematization of data of >100 Gb total size. Big Data analysis comprises network technologies, servers, software, and technical maintenance.
Big Data is used to:
- store and manage data volumes of hundreds of TB or PT unprocessable by relational databases.;
- organize unstructured data including texts, images, photos, videos, etc.;
- generate analytical reports and implement prognostic models.
Big Data analysis is a bland of programming and analytics, that is why Big Data requires a range of programs. Some of them are given below:
- SAS Eminer — a system of descriptive and predictive modeling;
- Tableau — a data visualization program;
- SPSS — a predictive analysis program;
- Zoho Reports — a program for online reports creation;
- NodeXL — an interactive tool for network visualization and analysis;
- Excel — an old school MS Office product suddenly turns out to be good at Big Data analysis;
- SQL — a programming language for creation, modification, and data management in a relational database;
- Python — a programming language capable of solving analytics related tasks.
The result of data processing looks like a collection of recommendations. It is impossible to predict the outcome of the analysis since sometimes even the slightest changes may influence the final picture. By the way, when properly approached, this technology may not only generate profit but also save lives.
Big Data in business
In the majority of his interviews, Elon Musk stated he leverages analytics to improve cars.
The point is that a Tesla electric car generates a mountain of data. It helps company staff understand the level of drivers’ awareness of the driving experience, charging system, and autopilot mode, etc.
Prognostic analytics sheds light on Tesla’s prospects, which helped raise income. Musk emphasized the role of Big Data in sales increase over the last years.
Big Data saves lives
According to Datasides, the market of wearables in 2015 increased by 171.6%. Therefore, gadget manufacturers obtained more user information. This data can come in handy for medical purposes when designing a full patient portrait, for example. This idea has already been introduced in the U.S.: health trackers have been sending the accumulated information to personal doctors of patients since 2016.
Data about a large number of patients can serve as a base for the recommendation list as part of the most effective treatment, epidemic prognostics, and detection of exposure to diseases that are typical for the citizens of a particular area.
Due to the synergy of Big Data and artificial intelligence, the scientists at the University of Washington can predict the obesity level among the citizens in one or another region. In the course of the investigation, four cities were analyzed: Los Angeles, Memphis, San Antonio, and Seattle. The scientists took 150 000 satellite photos of cities local areas taken by Google Static Maps API. The neural network helped divide the cities into 1695 regions that were later broken into four groups: photos of buildings, roadways, parks, and water areas. Then, the researchers compared the accumulated information with the official data on obesity in each city.
This investigation proved to be pretty effective: area type may help predict the districts with people prone to obesity to the accuracy of 64.8%.
Big Data in urban environment
Data analysis can be used to boost life quality of not only individuals but entire cities. For example, Big Data may optimize traffic. In fact, GPS navigators constantly collect huge amounts of data on roads allowing to see the biggest concentration of cars, traffic jams, and areas with frequent accidents.
Big Data analysis contributed to the installation of water smart sensors in the American city Long Beach. They are used to prevent illegal lawn watering at private residences resulting in fresh water excess consumption. The sensors decreased expenses by 80%.
Big Data application spheres are not limited to the abovementioned examples. Join us at AI Conference scheduled for November 22 in Moscow to find out more.