The Most Dominant Trends of 2020 in Data Science

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Data is the core resource or raw material for businesses. When effectively processed, it helps businesses in making crucial decisions. Most activities and processes are data-driven. So, businesses, over decades, have struggled hard to gain access to high-quality data.

In the last few years, technology has supported businesses in a big way. Therefore, data science has moved from human-centered activities to those empowered by artificial intelligence.

AI and research in data science have brought excellent opportunities for businesses. Yet, the challenge is to utilize the trends in data science for your growth optimally. So, without wasting any more time, I would directly introduce you to the data science trends that you can benefit from in 2020.

1.    Predictive Analytics

Predictive analytics in business existed even from the times when there was hardly any technology to support it. As the market is becoming more and more competitive, predictive analytics is finding its applications in so many areas of business decision making.

First, we must understand what predictive analytics is. In predictive analytics, the stored data and information are analyzed to find patterns, predict future trends, and list the possible outcomes.

Earlier, predictive analytics was a cumbersome process, prone to errors. It was mostly manual and was also not very speedy. Moreover, the results suffered from human interpretation, and thus bias. So, many businesses found it too risky to rely on predictive analytics.

But, over the years, the use of computers, cutting-edge technologies, and high-speed internet haven’t just made it faster, but the results are incredibly accurate and reliable too.

The reason why predictive analytics finds its place in the list of trends in data science is the use of technologies such as AI and ML. These technologies have made analytics easier for vast chunks of data. This information is segmented to identify patterns.

The patterns are compared to existing and past situations. The comparison is also used to assess future trends. As machine learning empowers the system to learn with time, the assessment is becoming near perfect.

Predictive analysis uses data mining. It further extends to predictive data modeling, and ultimately, business intelligence. Business intelligence was traditionally backward interpretive. But now, with predictive analytics becoming more effective, business intelligence is also leveraging its potential. Predictive analytics, enhanced by AI and ML, has brought the most significant breakthrough for businesses in the field of fraud detection, market forecasting, goal setting, strategy building, reducing risks, and disaster management. 

2.    Automation in Data Visualization & Analysis

As IoT is growing in popularity, data modeling and analysis are gaining prominence. These systems generate large volumes of data. And, considering the quality and quantity of data generated from these systems, businesses must leverage all possible benefits from this data.

As the number of connected elements in IoT systems is very high, the data will have to be processed at a very fast pace to derive maximum benefits. So, what should businesses do to utilize the large chunks of data effectively? Well, it’s simple; go for automation! 

ML & AI will help the data models to evolve from personalized to predictive and ultimately advance to prescriptive, offering solutions to businesses. Automation will support this process by reducing errors; as the errors are reduced, the reliability of the data models increases. Also, modeling is speedy. It is also productive even when the data volumes are huge.

Automation will bring a turning point in data science in 2020. As the more cumbersome tasks are automated, data scientists will be able to concentrate on highly specialized tasks, which can lead to great innovation in data sciences.

Automation in data analysis is a must for large businesses who want to use big data for growth. Businesses opting to go for automation in data visualization and analysis should also try the tools available for data visualization and automation.

3.    DataOps

DataOps is an emerging collaborative data management trend. It improves the communication, integration, and automation of data flows across an organization. DataOps is about continuous cross-team collaboration! This makes the business data management more effective as there is a free flow of data without barriers among all the stake-holders.

DataOps also aims at increasing collaboration between the data scientists and technologists to achieve benefits from data more synchronously and appropriately. Augmented data preparation, in DataOps, uses machine learning to discover new sources, patterns, and anomalies in data. And, it has little to no human intervention required. So, the delays, dependencies, and errors are reduced to a large extent.

In 2020, both DataOps and data analysis automation will help each other grow. As businesses are adopting DataOps, they will require data automation. And, better the data automation, more effective will be your DataOps. AI-driven data analytics with further strengthen your DataOps initiatives. 

4.    Data as Service

Inspired by software-as-service and infrastructure-as service, now the trend of data-as-service is gathering pace. DaaS is backed by the cloud to deliver flexible data storage, processing, and sharing. 

Top benefits of the data-as-service model include less time to set up, better functionality, flexibility, fewer costs, automated maintenance, improved access to data, and less human intervention. Also, the authenticity of information is maintained as there is a single, secure point of update.

Quicker data sharing, therefore, increases productivity in the organization. 5G is a reinforcement to this. With increasing internet speeds, there is no looking back!

Data security, privacy, and proprietary issues are always some of the prime concerns associated with data as a service model. Though the service providers are working hard to mitigate the fears, a lot remains to be achieved in this matter.

Wrapping Up!

As data science progresses to 2020, you can expect so many transformations in the way businesses manage data. Obviously, the changes will involve some challenges for businesses. The first is the modified human resource needs that lead to different human resource requirements.

Highly skilled data scientists are hard to find and retain as their demand is very high. But, technology advances will make your data management so powerful that you won’t have to concentrate on trivial tasks. And you can hire professional data analytics services too.

The second major challenge that the data science trends of 2020 will bring is the security of data. But, so much research is being done in this area. And the state of security is expected to get better with technology, over time.

No, you can’t waste any more time. Despite the challenges, you must keep yourself prepared to welcome the trends in data science. 2020 is almost there. So, start building your strategies now, and advances in data science are there to support you!

You have a list of trends in data science for 2020, and it is upto you to grab this opportunity!

Author Bio

Jin Markov is a Content Writer with GoodFirms, a research firm in the USA. He has expertise in writing on data science.

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