Nowadays, big data is an essential source for private and public enterprises. Many organizations can easily track and analyze the volumes of business data in data mining projects. At the same time, it helps to make adjustments to their business processes accordingly. Yet, you have seen big data make a massive shift in how business is being done. But it would be interesting to see what technology holds for us in the coming year. Hence, let's have a look at top data analytics prediction to consider in 2020.
Gone are the days the data analyst was kept alone in a tower and stayed away from the organization. Time is changed, and the data scientist can interact directly with several teams and also the company's decision-makers. This means that data analysts can communicate with non-technical professionals on technical subjects. In other terms, communication is the mandatory soft skill for implementing business intelligence.
Along with this, business intelligence requires some domain-specific skills such as SQL, tool experience, and also problem-solving techniques. Though the data analyst consulting contains business intelligence and in the coming years. Hence, this trend becomes more prevalent than ever.
Besides these trends, you can expect those conversational analytics, and personal assistant technology also dominates in the field of big data analytics.
According to one study, it shows that more than 50% of all data science-related tasks can be automated by 2020. In fact, machine learning technology and its advanced features can help to drive this automation. This concept can fetch insights which the skilled data analysts can't do manually. However, the entire process is faster and also accurate than the same done by humans. Further, the combination of machine learning and big data can help the organization's efficiency. Along with artificial intelligence, it can deliver high-quality data analytics.
DataOps concept has gained this year and with the advent of complex data pipelines, which required many integration tools. This includes both methodologies, such as Agile and DevOps, to the complete lifecycle of data analytics. From data collection to delivery, DataOps can handle big data analytics in many ways. In fact, it improves collaboration and improvement in quality while using statistical process control for the data pipeline. Therefore, this trend can get essential in the data analytics field.
Both cloud computing trends and edge computing become complementary models to each other. Therefore, this type of trend can reduce latency and data processing costs. In fact, the suggestion of some experts about edge computing will help to improve data security by combining with analytics. At the same time, it allows data processing at the local level and decreases the need to send data through the other networking projects.
Data security concerns can be increased with time. As many companies can opt for data sharing and data analytics. In fact, it is expected that 40% of the huge enterprises can use backups and snapshots to make sure safety, privacy, and also reliability within the management portfolio. Undoubtedly, many enterprises have no intention of using their backed up data with confidential details. In the coming years, more companies can emphasize on data security and privacy protection. It helps to increase the importance of backup.
The Internet of Things is developing a new opportunity which is helpful for data science and data analytics. In fact, the enhancement of smart cities has mandated the requirement for data collection, data processing, and also dissemination. As well as, smart cities data can assist with medical nursing and proactive health care. In 2020, it has been predicted that nearly 25 percent of smart cities can introduce robotics and smart machines at the medical facility. This technology helps to deliver a better user experience to residents.
As a matter of fact, the future trends in 2020, it says up to 45 percent of analytical queries, can be either automatically generated or generated using NLP technology or voice. It provides that analytics tools should be simple to use and access. In fact, this development can allow anyone to analyze the complex data combinations using a widely adopted and user-friendly analytics platform.
As a matter of fact, an implementation of GDPR is the beginning of an era of prioritized data governance. Even though, it has the best impact on many companies in worldwide and also the companies are still to comply with it. However, the rules and regulations have a lasting impact on data processing, handling, and security. Therefore, the demand for data analysts with skills in data privacy and security will remain an all-time high.
In 2020, in-memory computing can get influential due the cost reduction of memory which results in turning more mainstream. Being a mainstream trend, In-Memory Computing is the best solution for a wide range of benefits in the analysis. In fact, the latest persistent-memory technologies have led to a reduction in cost and complexity of in-memory computing.
On the other hand, persistent-memory technology is a new memory tier well situated between NAND flash memory and dynamic access memory. As a matter of fact, many industries are adopting in-memory computing, which helps to increase application performance while delivering the best opportunity for scalability in the future.
In these days, businesses and organizations have many cybersecurity challenges in their hands. The challenge of cybersecurity can grow in number and also the complexity as the volume of big data projects that it targets.
In the final analysis, 'Smart' will be a new norm in 2020 and the future too. In fact, the combination of advanced technologies with big data trends can provide digital disruption in modern businesses. However, all these data and analytics trends will change the methods of industrial processes over the period.
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