Top 5 Data Trends and Predictions for 2021

The past year has been tumultuous, with many lessons still being revealed today. The COVID-19 crisis has stimulated digital transformation, forcing incumbent organisations to digitize their processes, modernise business models, enable data access, and upskill their workforce for a data-driven age. The COVID-19 pandemic has also proven the need for everyone to be data-fluent, informed citizens, as data can be used to inform and misinform us on the state of the pandemic.

As data science is becoming mature, organisations across the world are trying to increase their digital resilience, and become more data-driven in the process. With data science methodologies and technologies, specialised teams have worked on solving critical problems like self-driving cars, protein folding, and algorithmic trading programs. However, the applications of data science are widespread. It's about creating data fluent organisations and societies, where everyone is equipped with the required skills they need to be informed, citizens, and employees. Over the coming years, we will see better tooling across the spectrum of data fluency. Meanwhile, let's explore data trends and predictions for 2021.

1. Future Predictions with Data Analytics

A massive shift toward the cloud becomes a parallel move towards robust data assets and better data analytics. Future-looking platforms are being built around data analysis, and 2020 proved how important business agility is. One of the big leaps we are witnessing that will become more prevalent in 2021 is real-time analytics. Following past data can be informative, but many applications require immediate data when it comes to reacting to unexpected events. And that can make a massive difference to the bottom line. For instance, identifying and stopping a network security breach based on real-time data availability could completely change risk mitigation.

2. Demand of more Databases

Enterprises have been putting databases on-premises over the past 40 years. But in the next year and beyond, we will continue to see a massive acceleration in deploying or migrating databases to the cloud, growing 75% by 2022. This means rethinking the requirements of what's required for bringing transformation to the business that will likely include developing on cloud-native databases and more closely incorporating analytical and machine learning (ML) capabilities.

Databases are an essential part of every enterprise, but now they are critical to accelerating innovation and growth more than ever. There is a convergence of analytical and operational data to support real-time business requirements. Breaking down these silos between teams and systems will help enterprises make faster decisions, recognise new revenue opportunities, more easily meet changing compliance requirements, and save on overall operating costs.

3. Focus on Knowledge Graphs

As per Gartner, knowledge graphs are among the top five emerging data trends that bridge the gap between human and machine. As big data continues to expand, this data becomes increasingly difficult to analyse and make sense of. This is where knowledge graphs come in.

Knowledge graphs are a collection of interlinked descriptions of objects, concepts and events, that helps create a better context for data through linking and semantic metadata. This allows easy analysis, integration, sharing, and unification of data. Represented in the resource description framework (RDF), knowledge graphs provide a framework that allows easy representation of various data, is interoperable and standardised.

4. Augmented Analytics to bring Data Fluency

One of the significant challenges that have come along with the increase of big data is dealing with the sheer amount of data now available. With sources indicating that we generate an estimated 2.5 quintillion bytes of data daily and that we will be generating a whopping 463 exabytes of data just five years from today, datasets have grown so big that handling the interpreting them is now a major challenge.

Augmented analytics solves this problem by using ML and AI techniques to automate the preparation, sharing, and analysis of data, essentially transforming bigger, seemingly unusable data into smaller, usable datasets.

Augmented analytics will undoubtedly become more data fluent. Research by Gartner shows that augmented analytics will become a dominant driver in business intelligence in 2021.

5. Data Visualisation to be Mainstream

In 2020, data visualisation helped us make sense of an increasingly complex world. Creating, critically understanding and evaluating data visualisation will become a foundational skill for every citizen.

We've barely scratched the surface of what data science or big data is capable of. In 2021, we can expect to see more practical applications of big data to solve some of the human race's biggest problems.