Data Science Trends for 2021-2022 — What to Focus On in the Upcoming Year

Data science and business have become a perfect duo from the time of AI inception. With the rise of natural language processing, computer vision and other AI innovations, market players have received powerful instruments to build products that transform industries, change the way we do business and approach our daily routine. Automated business operations, data-driven decision making, accurate predictions and insightful analytics are just a few takeaways brought by data science to business.

To prepare your company for the economic rival and ensure you’ll lead the market, read top trends in data science that will dominate the software development landscape in 2021-2022.

Trend #1 Smarter and Faster AI

Artificial intelligence is widely applied across all business sectors, helping companies in addressing their business challenges. If 10 years ago you might have been wondering how Netflix knows your favorite movies, today you’re pretty aware of its smart recommendation system and AI algorithms standing behind it.

In the upcoming year, artificial intelligence promises to become even smarter and faster than it was before. In 2021-2022, companies are expected to shift from piloting and operational AI, which will bring a significant increase in streaming data and improvements in analytics infrastructure.

Data Science Trends 2021-2022 - Innovations in artificial intelligence (statistics) | LITSLINK Blog

In the context of the current COVID-19 crisis, AI solutions, along with machine learning and natural language processing, can provide vital predictions on the spread of the virus. Technology experts have already found a way to use AI innovations to detect infected individuals, spot patterns and develop possible solutions to complex issues. See our AI case study to find out how LITSLINK helped build a people counter software that assists businesses in implementing preventive measures and keep a limited number of people in the building.

Trend #2 Graph Technology and Analytics

What is graph technology?

Many companies mistakenly believe that the more data they gather, the better result they’ll get in the end. By accelerating computer power, they strive to process more information in a shorter time, believing it will provide them with more insights and help find the competitive advantage that will make them a leader in the world of tomorrow.

However, in most cases, it doesn’t matter how much valuable and structured data you process if you lack an understanding of the context. If the relationship between data patterns is missing, you won’t be able to turn information into meaningful insights.

And this is where graph technologies are introduced to map data sets and understand relationships between them. This approach might help you find common patterns, drive contextualization, and develop more valuable products to meet your business challenges.

How can businesses leverage the power of graph technologies?

In the next 3-4 years, we’re going to witness rapid growth in the application of graph technologies. More than 30% of organizations will apply graph analytics software to drive greater contextualization in decision-making by 2024.

New advancements in graph technologies can comb through large amounts of documents, research papers, surveys, statistics and other pieces of information to build connections between data sets and draw relevant conclusions. When applied in business administration, graph analytics might help companies spot issues at the early stage, develop accurate predictions and make data-driven decisions based on the insights they get.

Data Science Trends 2021-2022 - Data-Driven Decision-Making | LITSLINK Blog

In the context of the current coronavirus crisis, graph technologies can assist healthcare professionals in finding common patterns in the spread of the virus, which will contribute to a better understanding of the disease. Thus, a data-driven approach to the issue might help to develop better capacity plans, find new treatment methods and come up with effective preventive measures.

It is also predicted that graph analytics combined with artificial intelligence software will become a top instrument for identifying and predicting natural disasters. Graph technologies can cover the whole solution development cycle from planning to implementation, helping specialists deliver better results and improve the public health system.

Trend #3 Blockchain in Data Science

Unlike fintech and healthcare where blockchain has already become a household term, data scientists only start to explore the whole potential this technology can deliver to the industry.

Blockchain can address the two major challenges data scientists face today. First, decentralized ledgers provide a brand-new way of managing and operating big data. As a rule, you need to structure the information in a centralized manner where all data sets are brought together for further analysis. This process takes some time as you need to gather the information and structure it properly before getting down to “science”.

Data scientists can use the decentralized structure of blockchain to conduct analysis right from individual devices. As blockchain ascertains the origin of data, you can always track where it comes from and validate it.

Data Science and Blockchain - Facts and Stats | LITSLINK Blog

Second, blockchain infrastructure guarantees transparency for complex networks of participants. When you need to have a helicopter view of all the operations, decentralized infrastructure can help you track all the operations, see relations between data sets and check their origin.

Gartner estimates that the majority of permissioned blockchain applications will be substituted by data management systems (DMBS) by 2021. Since DMBS provides a more comprehensive way of operating big data sets, companies will be able to see more opportunities in utilizing data science and uncover at least a fraction of its potential.

Apart from already existing blockchain applications, this technology can provide an appealing opportunity for small and medium-sized enterprises. It can be used to audit existing data sources, build a reliable data-based infrastructure where all participants will be able to track the origin of information and have access to available data resources. Such an approach builds transparency and enhances security of the stored data.

Thus, we may conclude that blockchain is an inexhaustible resource for data science, which, when handled well, can lead to new innovations in artificial intelligence.

Data Science Lifecycle

Keeping an eye on industry trends is vital, but it is only a fraction of what you should know to build a data science product. Understanding the data science lifecycle is essential if you strive to develop a solution that will be in demand on the global market.

The Data Science Process provides a clear outline for the development of your DS solutions. It includes the core milestones your team should follow to build a quality DS software that will meet your business goals and needs.

The lifecycle described below is applied to deliver smart applications, which utilize artificial intelligence and machine learning to build smart predictive models. It can also be customized for exploratory data science projects or for building improved analytics software. However, in this case, some steps might be missing, so you’ll have to adjust the data science project lifecycle to your particular business case.

Data Science Lifecycle - Infographic | LITSLINK Blog

Build Your Next Data Science Product with LITSLINK!

Knowledge is power. Especially when it comes to business, being aware of the industry trends can provide you with a competitive advantage, which will set you apart from your rivals.

To capture the full potential of data science and successfully apply it to your business, read our insightful whitepaper catered to you by the top industry experts.

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