What does the Big Data Revolution Mean for Us?

Companies dealing more and more with Big Data and see the needs for a forward-looking use of Big Data. In this case, certain trends in the data-race emerge.

Data science as a benchmark

The shortages of data scientists worldwide has created a large gap in the industry. Companies are trying hard to fill in this gap to maintain competitive advantage.

“A data scientist represents an evolution from the business or data analyst role. The formal training is similar, with a solid foundation typically in computer science and applications, modeling, statistics, analytics and math. What sets the data scientist apart is strong business acumen, coupled with the ability to communicate findings to both business and IT leaders in a way that can influence how an organization approaches a business challenge. Good data scientists will not just address business problems, they will pick the right problems that have the most value to the organization.” (IBM)

The massive data generated from our daily digital interactions brings new opportunities to our businesses. We must be able to analyze, investigate and talk to all the data that flow at a frantic pace in our businesses. A data scientist acts as an intermediary between different departments within the company and solves complex problems based on a strong scientific expertise.

Data Scientist was originated from the birth of data science, a new discipline that combines elements from different fields including mathematics, statistics, computing, visualization and data modelling. In fact, data science makes the knowledge extraction from both internal and external data to the organization.

Artificial Intelligence (AI) and deep Learning

Besides the scientific data analysis by trained data scientists, another trend is emerging in the field of Artificial Intelligence (AI). What initially sounds like science fiction is now already taking place in the framework of Big Data projects in many global corporations.

Speaking of AI, a machine can perform a series of programming so that the returns of specific information processing tasks may lead to a known result. More surprising, the future AI with deep learning is able to work just like our brains in the form of synapses and neural networks through the development of programs. The information processed by each network can supply information to other networks in the deeper layers of the machine; hence the idea of learning becomes “deep”.

Human Data Interfaces

Human data interfaces allow human-machine interactions. For example, personal assistants like Apple’s Siri meet not only the function of a pure data query, but to be able to communicate with people, to solve individual problems and constantly learn. As part of the Industry 4.0, human data interfaces are intended to help a smart factory to be able to make decisions by targeted business managers. This is an exciting trend which is still in the test phase; but it will certainly provide new insights in the near future.

Data security

As in all areas of IT with data processing plays a central role, data security and privacy cannot be neglected with Big Data projects. The Big Data adventure is associated with a number of concerns that may be legitimate. In addition, technological advances are always accompanied by new security vulnerabilities that require time to be filled, so there is still a gap in security.

It is important to divide staff in advance, which is responsible for each task to avoid misunderstandings and potential security loopholes. In addition to internal security, a legal clarification of the data processing is mandatory. This also means that the purpose of data collection should be clearly defined.

Conclusion

Big Data holds the future of a number of technological revolutions and will expand the IT market. What we read in the science fiction novels 20 years ago such as a well-established process in a smart factory will soon become mainstream in the near future. Fast pace in digital development in the industry is always challenging. Everyone works in the Big Data field needs to clearly understand the trend, opportunities and risks.