Top 10 Cutting-Edge Data Science Trends You Will Need in 2020

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Digital transformation is a buzzword somewhat but it's also a vital trend for any modern business. It provides for using digital tools for process optimization, customer experience boost, revenue increase. This evolution is based on data. By collecting, analyzing, and applying data insights, companies can make more informed decisions. In fact, enterprises and even sole entrepreneurs generate new values through information. 

Moving to examples of transformation, the Big Data sector definitely helps to leverage your data in the most efficient way. Nonetheless, there are numerous prominent concepts that also benefit businesses. Edge computing, digital twins, blockchain, and other exciting spheres thrive today. Further, find a brief overview of the most useful, urgent, and just impressive trends. Note that we focus on tech aspects, mainly.

Essential Trends in Data and Analytics

1. Automation and Machine Learning

Data science may be pretty complicated and costly because of its scale. Thus, automation facilitates various internal processes, especially, data cleansing, feature engineering, and machine learning. The AutoML method enables automated developing/maintenance of machine learning models. As a result, the entire industry becomes cheaper and more accessible. New players can enter the game, propose new initiatives, contribute to global progress. 

2. Blockchain

Blockchain is a type of a distributed ledger that consists of interconnected blocks. Such a structure eliminates the retroactive data modification, enables higher transparency and security. Both public and private blockchains can benefit healthcare, government, digital identity, and supply chain sectors. Still, the technology is pretty immature.

3. Conversational AI

You should be familiar with chatbots and virtual assistants - AI interfaces that process user requests and return information or make specific actions. Thanks to advanced data processing, these interfaces can process understand text, voice or gestures. For end-users, they provide fast interactions. For data experts, they help to get values and insights. 

4. Data Fabric

As the size of Big Data increases, analytical tasks require more resources. To guarantee timely and accurate execution, businesses should build a reliable infrastructure. Now, it's known as data fabric. Put simply, it's a custom platform that combines and integrates data modules for various tasks. Data fabric ensures secure access, distribution, and smooth processing.

5. Data Security

Undoubtedly, the protection of sensitive enterprise data is an urgent question. Innovations often come with extra challenges that harm security, lead to reputation and revenue losses. In this case, businesses invest in two aspects: top-notch tech protection and full regulatory compliance. Respectively, new tools, standards, and protocols emerge over time. 

6. Digital Twins

This technology is really exciting as it focuses on full digitalization. A digital twin is a copy of a real-world object or system but built in the digital environment. Such twins are real-time simulations based on constant data flow, AI, and ML. They update according to the changes of physical sources, show relevant patterns, help in testing.

7. Edge Computing

In a nutshell, edge computing is an approach to data processing that is done on edge points of any network, right where data emerges. For instance, connected devices like wearables or smart sensors can analyze information without sending it to the central hub. This model is extremely useful when a system must react quickly like in self-driving cars.

8. Graph Analytics

The constantly increasing size of data sets leads to more complex requests from users. Traditional integrations or SQL queries can't fulfill the new demands. Thus, experts can use analytical systems and databases based on graphs. These networks show how things relate so there are numerous potential use cases, from traffic optimization to genome research. 

9. Persistent Memory

Again, the complexity of Big Data requires new tools. As general database management systems fail to provide the required memory size, an innovative feature emerges. Known as persistent memory, it's usually placed between DRAM and SSD in the data center memory/storage hierarchy. It requires further research but the potential is massive. 

10. Real-Time Intelligence

Also known as continuous intelligence, this concept stands for the widespread use of real-time data for analysis. Thanks to better communication technologies such as 5G, improved cloud solutions, and more data from IoT devices, continuous analysis becomes more affordable and powerful. It helps with decision-making and generates relevant insights quickly. 

Bonus: Critical Non-Tech Trends

While the developments above stand for purely technological ideas, there are a lot of data-related trends beyond this category. We call them non-tech trends. Don't underestimate these extras as they also can be crucial to any business, its success, and reputation. 

Talking about non-technological areas you should study today, let's check these points: 

• Data culture: engagement of C-grade executives, company-wide data strategies, general innovation/information awareness.

• Data equity: approach to data as a resource, focus on transparency, customer and employee satisfaction. 

• Data literacy: cultivation of data skills and knowledge, educational initiatives for staff, better engagement on all levels. 

• Data monetization: turning of data businesses own into business values, attention to revenue, cost savings, and corporate market values. 

• Data storytelling: personalized data narratives for both better information gathering and customer involvement. 

Like almost any modern industry, data science is changing quickly. That's why this list isn't extensive - new trends appear regularly. We encourage you to monitor the recent news to spot emerging tendencies as early as possible. We also can't predict which trends will survive and become the new standards. Only time can show which technology is the winner. 

Living in the Data World

It seems that data is the new king of nearly everything. Without proper information processing, businesses become much weaker and tend to lose their market advantages. Well-known and adopted technologies like cloud computing or mobile intelligence meet emerging trends: edge, automated machine learning, real-time analytics, etc.

It's impossible to overestimate the importance of data in modern economies. And it's a good idea to invest in research in addition to already known trends. By spotting the most innovative and fresh areas of development, you can move to them to outperform your main competitors or attract customers. Isn't it great?