Top 5 Data Science and Analytics Trends in 2020?

by July 16, 2020

Data Science

Businesses are waking up to the wonders that they can achieve with Data Science, ML and Artificial Intelligence

Artificial intelligence (AI) and machine learning (ML) are two technologies that have witnessed a massive growth trend over the years. In a quest to look for a quick, cost-efficient and innovative way to gain advantages from data science, enterprises are relying more on the use of rapidly growing big data available at their disposal. Data and analytics combined with artificial intelligence (AI) technologies will be paramount to predict, prepare and respond in a proactive and accelerated manner to ensure business continuity during this global crisis and after-forward.

 

Deep Learning v/s Competitive Advantage

In 10 Enterprise Analytics Trends to Watch in 2020, Frank Bernhard, author of SHAPE – Digital Strategy by Data & Analytics, notes that in 2020, deep learning should no longer be considered a buzzword, but a “tempest disruptor in how companies will perform with intelligence against their competitors.” To enable largely unsupervised learning against unstructured data in a bid to return hidden signals, deep learning will free up the time of in-demand data scientists to connect insights to action.

 

Responsible AI

Expect AI to be more responsible. As forecast, by the end of 2024, 75% of enterprises will shift from piloting to operationalizing AI, driving a 5X increase in streaming data and analytics infrastructures.

Within the current pandemic context, AI techniques such as machine learning (ML), optimization and natural language processing (NLP) are providing vital insights and predictions for the spread of the deadly coronavirus and the effectiveness and countermeasure impact.

 

Data will Deliver Extended Business Value

With IDC predicting the global data to balloon to 175 zettabytes by 2025, deriving business value from data is becoming increasingly difficult given the complexity of the data landscape, the need for data governance and the resulting higher costs of analysis.

Significant investments made in new chip architectures such as neuromorphic hardware that can be deployed on edge devices are accelerating AI and ML computations and workloads and reducing reliance on centralized systems that require high bandwidths. Eventually, this could lead to more scalable AI solutions that have higher business impact.

 

Massive Adoption of IoT

According to a report by IDC, investments in IoT technology are expected to reach $1 trillion by the end of this year. A clear indication of the anticipated growth in smart and connected devices. Many people are already using apps and devices to control their home appliances like furnaces, refrigerators, air conditioners and TVs. These are all examples of mainstream IoT technology. Expect more smart devices such as Google Assistant, Amazon Alexa and Microsoft Cortana will allow us to easily automate everyday tasks in our homes, in 2020 and beyond.

 

The Rise of Data Pipelines

As more data is generated, expect this data to be filtered and ready for analytics purposes. The data pipelines will ensure the citizen data scientists can do more with ML models and imbibe key strategies of action formulation.

AI has been critical in combing through thousands of research papers, news sources, social media posts and clinical trial data during the coronavirus pandemic, to help medical and public health experts predict disease spread, capacity-plan, find new treatments and identify vulnerable populations. Data Pipelines combined with AI and other techniques such as graph analytics will play a key role in identifying, predicting and planning for natural disasters and other crises in the future.