Source:prevedere.com

Expert View on The Future of Data Science

Data science solutions have been widely adopted by leaders and starters covering various business fields. It is a combination of math, art, and logic used for bettering and accelerating the development of industries. Moreover, the data scientist career path has been one of the highest-paid, most attractive, and respected. In this article, we will describe what the future holds for data science based on the Light IT data engineering company’s opinion. The experts of this company shared their views on six key transformations to expect, so let’s get started.

1. The amount of data will only be increasing

Technological progress will only encourage people to consume more information and spread more information about themselves. In 2020, a user generates about 2 megabytes of data per second. For this reason, specialists find new ways to store large amounts of information. The currently used methods of storing information look like this now:

Hybrid storage. Cloud isn’t reliable enough to store confidential information about companies, so the most private information would be kept on portable drives, safes, lockers, etc. New ways to improve password practices and restricted access have yet to come.

Source:consoltech.com

Multi-cloud storage. Some companies must consider keeping data in multiple cloud environments, both public and private.

2. Data science will become automated

Specialists find new methods of automating data science. Even though it is meant to automate and optimize various business areas, integration and maintenance of its developments are not automated just yet. Almost everything in this field is on its way to automation, from storing and refining data to final modeling.

Experts focus on making neural networks self-learn and improve feature engineering to save time on further developments. As an example, Google is actively investing in Cloud AutoML that automates the training and designing of models. Some companies make data management more convenient for data scientists to let them work faster and easier.

3. Machine learning will stay the most wanted for business

Source:pcmag.com

Machine learning will be one of the trendiest implementations in the years to come. It covers a great variety of progress-driven tasks such as video, audio, image recognition, neural networks, text analysis, and natural language processing. Specialists put a lot of effort into reinforcement learning, which allows algorithms to work along the sequence of actions successfully. We can see it from how AI plays computer and board games.

Business process automation and forecasts using machine learning are other trends, from which industry players get strategically-important benefits. These benefits let companies reduce the costs of warehousing, logistics, staff, and eliminate human-generated mistakes. Predictive modeling will improve to provide more accurate insights on customers and competitors. Specialists also focus on conversational analytics to make chatbots and voice assistants more efficient to make customer service better.

4. Demand for skilled data scientists will only be growing

Data science is a popular career path throughout the world because specialists can work on exciting projects, implement the most incredible ideas, solve problems, and get paid well. Still, even the most developed countries worldwide have a significant shortage of employees specialized in data science. In 2019, the US companies were seeking over 2 million specialists in this field of knowledge.

Although the IT and digital sectors have most data engineers hired, they also work in other industries such as retail, healthcare, entertainment, transportation, manufacturing, and many more. Apart from data science or machine learning departments, scientists  in this field are engaged in the product, marketing, content, and game development teams.

5. Data security and privacy are expected to improve

Source:csoonline.com

Since massive amounts of data are generated throughout the world every year, some of it can be vulnerable to various types of security and privacy threats. Currently, there are three more specific reasons why data security has yet to be improved:

Lack of well-educated cybersecurity talents;

More elaborate and devastating cyberattacks;

Ignoring or poor awareness of regulatory standards.

However, the number of security-aware users is small. People don’t want to invent complicated and reliable passwords and read privacy policies. As data science evolves, we can expect security and privacy protocols to change according to the applicable laws at locations where they operate. Still, users must also show higher carefulness and awareness of the law to avoid data theft.

6. Fast and actionable data will outshine big data

Event-driven applications help digital enterprises instantly identify and respond to new opportunities or threats. Many businesses focus on fast data, which is far ahead of stream-based analytics. It allows specialists to analyze both current and historical data simultaneously, thus helping to navigate and respond to a particular situation effectively.

Fast data can improve the quality of business analytics. Combined with an integrated development environment, built-in intelligent data processing, and machine learning, fast data simplifies various processes and also starts a new era of business intelligence.

Source:linkedin.com

Conclusion

The critical areas of data science transformations include its automation, the creation of reliable storage for growing amounts of information, and keeping up machine learning best practices. Experts also realize the need to create more skillful talents, enhance privacy and security, and shift focus from big data to fast data. The sector’s changes are about to come gradually, and the hardest part of it would be dealing with security matters in different countries. As data science improves, cyber threats become harder to prevent, so companies must think about making faster progress with data.


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