Data Science has been growing with us for a long time, so it is worth writing more about this topic. As a business discipline, data science is the opposite of artificial intelligence. One is an unlimited area in which creativity, innovation and efficiency are the only limitations and the other is linked to countless constraints on engineering, management or regulation.

Artificial intelligence – faster and more responsible

It is forecast that by the end of 2024, almost 3/4 companies are expected to move from pilotage to implement artificial intelligence, which will result in a fivefold increase in data streams and analytical infrastructures. Models before coronavirus based on a large amount of historical data, may no longer be valid.Interference in artificial intelligence will allow the learning of algorithms.

Deep fake in picture and sound

Deep fake uses artificial intelligence to manipulate or create content to show someone else. It is often a photo or video that shows one person who has been modified like another person. Deep fake can also be a sound. This technology can be used in a malicious manner. In addition to jokes and financial fraud, it can also be used to discredit businessmen and politicians. Governments are starting to defend against this through social media legislation and regulations. However, the fight against such fraud is just beginning.

Natural Language Processing – NLP

Although initially NLP was popularized as a subset of artificial intelligence, then a separate process quickly evolved. In 2021, the processing of the natural language will be launched to immediately search for information from repositories of large data sets. Processing the natural language will facilitate access to high-quality information, but it may also lead the system to provide them with the business insights they need to develop further. NLP also gives companies access to mood analysis. This will allow them to know how customers think about their brands at a much deeper level.

The expanded analysis will become more common

One of the main challenges that emerged as large data sets develop is to deal with the itself volume of data currently available. Data sets were so big that handling and interpretation of data is now a big challenge. An expanded analyst solves this problem by using ML and artificial intelligence techniques to automate the preparation, sharing, and analysis of data, transforming larger, apparently useless data into smaller, usable data sets. The expanded analyst will undoubtedly begin to mainstream in 2021.

Data and analyzes move to the cloud

Cloud technology is becoming faster, smarter, and more flexible, so this year, many organizations move their data warehouses to the cloud or switch to a hybrid road, using a cloud and local storage connection. Previously, data warehouses were located on physical storage servers. Now, at least some of them have moved to the cloud using service providers such as Amazon, Microsoft and Google. Data security is one of the last obstacles to cloud computing. Many organizations have not adopted the cloud due to security issues because processes such as extraction and analysis of data in the cloud cannot take place if the data is encrypted. At this point, homomorphic encryption appears and helps solve this basic problem.

Geospatial data

Geo-spatial data will be the key to unlocking enterprise transformation. The focus was on large data sets and growing data volumes, but in 2021 we are not forgetting the diversity of data that is still growing as a key enabler for business transformation. It is often due to a new perspective on the company. The use the data from satellites, drone and data that have geo-presence attributes is becoming a key element for your business. In sales and marketing, a better understanding of demand signals through geo-presence helps optimize limited resources and effectively increase market coverage. The increasing emphasis on balanced development shows that geospatial data unlock a number of sustainable development initiatives, such as acquisition. In the past, geospatial data were reserved for those who were experts. In 2021, the company’s ability to combine geo-spatial data with other data and collaborate across the company and across the value chain worldwide will be a key differentiator.

Health revolution driven by Big Data

A health revolution driven by large data sets appears on the horizon and we can start seeing it in action already in 2021. Although health technology is still developing, 2020 and especially the COVID-19 pandemic highlighted the need for a different approach to tackling health problems. Big data is increasingly being used to find solutions to health problems and we are beginning to see the results of these efforts. Thanks to AlphaFolder program it has been resolved one of the biggest biological challenges. He successfully defined three-dimensional protein shapes based on their amino acid sequence. Deepmind’s AlphaFolder was able to solve this problem several decades before its planned deadline thanks to Big Data. The implication is a breakthrough in medicine which can bring breakthrough solutions to the way medicines are manufactured and is likely to lead to solving the problem of cancer, dementia, infectious diseases and more.

Between the new tools, knowledge and objectives mentioned above, there is much to learn about the trends in data science in 2021. Further education and training will be helpful in order to make progress. Data science will increasingly prioritize the integration of the entire spectrum of data and artificial intelligence, including aspects of its statistical base and knowledge, into everyday enterprise deployments. Using the full range of techniques and information available to data scientists will greatly improve the generation of characteristics and data preparation.

In present times of coronavirus, this is difficult to predict, but we assume that the development of the industry will have a lot of common with COVID-19.