What is big data

Big data refers to a collection of data that cannot be captured, managed and processed within a certain time range using conventional software tools. It requires new processing models to have stronger capabilities. Massive, high-growth and diversified information assets for decision-making, insight discovery and process optimization capabilities.

What can big data do?

This ever-growing stream of sensor information, photos, text, sound and video data is the foundation of big data.

We can now use this data in ways that were not possible even a few years ago. Now, big data projects can help us:

Treat disease and prevent cancer—data-driven medicine involves analyzing large amounts of medical records and images for patterns that can help detect disease early and develop new drugs;

Feeding the Hungry – Agriculture is being revolutionized by data that can be used to maximize crop yields, minimize the amount of pollutants released into ecosystems, and optimize the use of machinery and equipment;

Explore distant planets - NASA analyzes millions of data points and uses them to model every possibility of landing its rovers on the surface of Mars and plan future missions;

Predict and respond to natural and man-made disasters - Analyze sensor data to predict what earthquakes may occur next, and human behavior patterns help aid organizations provide clues to rescue survivors. Big data technology is also used to monitor and protect refugees away from war zones around the world;

Preventing crime – Police forces are increasingly adopting data-driven strategies based on their own intelligence and public data sets to more effectively Deploy resources and act as a deterrent when needed;

Make our daily lives easier and more convenient - online shopping, mass travel or leisure vacations, choosing the best time to book a flight, or decide Movies to watch next… It’s all made easier because of big data.

How does big data work?

How big data works is that the more you know about anything or any situation, the more reliably you can predict what will happen in the future. By comparing more data points, previously hidden relationships will begin to emerge that will hopefully contain insights into how we can begin to change.

Typically this is done through a process of building a model based on the data we can collect, then running simulations, adjusting the values ​​of the data points each time, and monitoring how it affects our results. This process is automated—today’s advanced analytics technology will run millions of these simulations, adjusting all possible variables until a pattern or insight is found that helps solve the problem.

Data is increasingly coming to us in unstructured form, which means that data cannot easily be put into structured tables with rows and columns. Much of this data is in the form of pictures and videos - from satellite images to photos uploaded to Facebook or Twitter, as well as emails and instant messages and recorded phone calls. To make sense of all this, big data projects often use cutting-edge analytics from artificial intelligence and machine learning. For example, by teaching computers to recognize what this data represents—through image recognition or natural language processing—they can reliably identify patterns faster and more reliably than humans.

Over the past few years, there has been a strong trend toward big data tools and technologies through “as-a-service” platforms. Businesses and organizations rent server space, software systems, and processing power from third-party cloud service providers. All work is performed on the service provider's systems, and customers only pay for whatever is used. This model makes big data-driven discovery and transformation accessible to any organization and eliminates the need to spend significant amounts of money on hardware, software, real estate, and technical staff.

Problems with big data

Today, big data has given us unprecedented insights and opportunities, but it has also raised concerns and problems that must be solved:

Data Privacy – The vast amounts of data we now generate contain a lot of information about our personal lives, much of which we have a right to keep private. Increasingly, we are being asked to balance the amount of personal data we leak with the convenience provided by big data-driven applications and services. Who do we allow access to this data?

Data Security – Even if we agree that someone has our data for a specific purpose, can we trust them to keep it secure? Is the existing legal framework capable of regulating data use on this scale?

Data Discrimination – When everything is known, will it be acceptable to discriminate against people based on data about their lives? We already use credit scores to decide who can borrow money, and insurance is largely data-driven. We can expect more detailed analysis and assessment, and must note that this is not done to make life more difficult for those who already have fewer resources and access to information.

Facing these challenges is also part of “big data”. They are certainly a major part of the debate about the use of big data in academia. However, they must also be addressed by those who want to leverage big data for their business. Failure to do so can result in hefty fines as anything done relates to personal data. We see time and time again that failure to address these issues is often one of the reasons big data enterprise initiatives fail.

When people first start talking about “big data” it is sometimes seen as a fad – the latest trendy tech term that will be talked about for a while and then the next big thing quietly forgotten. This has not been proven to be the case yet - in fact, while newer popular languages ​​have popped up, Big Data is still the driving force in all of them. The amount of data available to us will only increase, and analytical techniques will become more capable.

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