1. Knowledge-oriented companies Progressing:
Understanding, managing and manipulating large volumes of data has become something ubiquitous and that most companies have the ability to deal with. 2018 will be the year in which successful companies will move beyond the simple manipulation of data, to take advantage of the ideas that big data can reveal. The focus will be on carrying out effective data mining to meet the requirements of the organization and for the accurate orientation of products and services. A knowledge-oriented approach will facilitate a different level of customer experience, competitiveness, advanced security, and operational efficiency. For example, an electronics company invested most of its marketing budget in conventional channels, including paper, television and the display. However, when analyzing the consumer decision process, it was observed that most of the clients were browsing the websites of the retailers and less than 9% visited the brand’s website. It was enough for the company to go from conventional advertising to show content from the websites of its distributors so that the sales of its e-commerce, increased 21%.
2.Cross-sectional (entire company) Analytics
Analytics should not exist only in departments such as marketing or risk management, it should be present throughout the company. Data analytics will be used to understand business dynamics and reveal ways to increase efficiency. The insights generated from different departments will be integrated in order to obtain an enterprise level strategy and thus eliminate redundant processes to increase efficiency, growth and productivity.
3. Applications in cyber security:
We have reached a level of digital technological dependence of such magnitude that cyber attacks have also become much more unbridled. Apart from attacking global financial institutions, cyber criminals are also targeting personal data and devices and more cases of ransomware have been recorded worldwide. Cybersecurity is one of the biggest concerns in the industry – continued protection is only possible through big data analytics – it will become an important investment area and grow rapidly. Businesses and government agencies will update their security systems to the next generation of software that deals with ultramodern security threats. According to Cybersecurity Ventures’ Cybersecurity Market Report, cybersecurity spending will exceed one trillion dollars from 2017 to 2021.
4. IoT & IoP:
There will be a transition from the “Internet of Things” to the “Internet of People”. The productive analysis on human behavior, interactions and other cognitive areas will grow and begin to filter all vertical sectors of the industry. For example, hospitals will increasingly use machine learning techniques to predict the likelihood of a disease relapse. This will allow them to prepare the readmission of a patient precisely at the time of initial discharge.
5. Reduce the talent gap:
It is obvious that the talent gap in data analytics will skyrocket as demand expands. It is hoped that organizations and academic institutions collaborate to generate skills and talent that meet the demand of data engineers. McKinsey estimates that the shortage of personnel in 2017 is about 200,000 for the United States alone, this figure can easily double worldwide. In March 2016, Continuum Analytics (creator of the Anaconda open source analytics platform) launched the Anaconda Skills Accelerator Program (ASAP), a 12-month data science course. According to the industry’s estimate, data scientists receive as much as twice the compensation received by a programmer.
6. Collaboration between scientific companies:
Companies will have to learn and deploy traditional and scientific techniques of pattern matching and artificial intelligence for analytical use cases. For example, techniques for analyzing gene sequences in DNA are used in text-matching algorithms to process mass emails. It is expected to see close collaboration between data scientists and the scientific community, especially from disciplines such as neuro-science, molecular biology, astrophysics, particle physics and organic chemistry. For example, image processing is widely used in “tagging” on social networks, while speech recognition is used in applications such as Siri.
Companies will move from local platforms to cloud and hybrid environments. According to an IDG survey, 44% of apps used by Fortune 500 organizations are already in the cloud, and by the end of 2017, more than 50% of IT applications will move to the cloud as well. This leads to an increase in the demand for agile analysis tools that are simple, flexible and capable of handling various data sources. Hadoop will continue to be increasingly popular, as it allows us to store an extremely large volume of data at a significantly lower price. The proportion of unstructured data in the data warehouse will continue to increase. Hadoop, now, has passed the evaluation phase of business relevance and scalability and its adoption is expected to accelerate sharply. According to a survey of 2,100 CXOs, entrepreneurs and IT executives from 1,300 companies around the world, 49% of respondents agreed that they obtained tangible value through Hadoop. In addition, 45% of the rest was anticipating a considerable benefit in a short period of time. We are moving towards a new era in the domain of analysis. Thanks to big data, pioneering ideas and innovative technologies are emerging in the vertical sectors of the industry, although this is made up of highly disaggregated data. New markets are opening up for the world of analytics, and if these markets want to be an example of innovation and growth, they will have no choice but to take advantage of big data to make 2018 a successful year.