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Data Science Trends for 2021


By NIIT Editorial

Published on 05/03/2021

6 minutes

“COVID-19 has affected the entire world and introduced the new normal”. You may be sick and tired of reading how all this has happened and the consequences of it. But believe you us, it is no different for a field like data science. As per Gartner’s data analytics trends in 2021, organizations will adopt a new approach to working with artificial intelligence. Hitherto, big data used to be a priority for data scientists. However, in 2021, the same is expected to be replaced by wide data, one that also carries a lot of diversity with it. This along with a handful of other field patterns will shape the data science trends for 2021.

 

An Even Smarter AI 

 

Enterprises will push for a smarter Artificial Intelligence system that can be scaled to operations as per changes in supply and demand. Keeping in mind the impact of COVID-19 on supply chains, AI would have to be molded so it no longer needs historical data to be trained on. Instead, data analysts would have to figure out a way to do so with much less voluminous data sets that offer variety so AI could learn more, sooner. 

 

Flexible Data Analytics

 

Large enterprises rely on multiple business intelligence tools. This makes it harder for them to use the insights in a cohesive way to introduce order amongst chaos. When we talk of a flexible, more composable data analytics approach it means that insights from various analytical tools can be curated and integrated towards linear, more impactful outcomes. 

 

Data Fabric as the Foundation

 

What is data fabric? Gartner defines it as the foundational layer that will help effectuate the compostable data we talked about in the previous step. How important could it be in the long run? As per the research firm, an effective data fabric mitigates data integration time by 30% because the layer incorporates multi-variate integration styles. 

 

Increasing Use of Small & WIde Data 

 

Not every organization is capable of harnessing big data, whether it is for logistical reasons or otherwise. A new approach being promulgated is the use of small data. As the name suggests, it is exponentially smaller in volume to big data at the same time coming in with higher diversification. The combined use of both wide and small data makes it possible to train AI models in a rather shorter period. 

 

The Evolving Role of Data Analytics 

 

Data and analytics is usually a specialty that is handled by separate teams not indulged with other departments. As per Gartner, Data and analytics will be reintroduced as a central business function that would interoperate with other teams. The Chief Data Officer, a role that has come to the fore in recent years, will be able to guide data operations better if they have a stronger, more inclusive handle on things with data analytics becoming integral to business operations. 

 

Power to Consumers 

 

Earlier, data operations were restricted by predefined parameters and dashboards on which data scientists operated with limited capacity. However, Gartner predicts, that such legacy dashboards will be substituted by dynamic and customized user insights specific to each business users’ profile and usable by all employers. 

 

Conclusion

 

These data science trends will host a downright impact on the bottom line workers. Recruiters will seek signages from stakeholders, and key decision-makers to try and modify the KRAs of data science professionals accordingly. Knowledge workers with a strong foundation of data science concepts will flourish, as it would be easier for them to upskill in shorter time spans. 

 

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