10 Analytics Trends to Look Out for in 2021.
A lot of us might have been thinking that data analytics has already offered what it had in store, but as it turns out, there is a lot more to achieve through the power of data analytics. We are witnessing improvement in the techniques and the technologies involved in harnessing the power of analytics as more and more enterprises realize the need for a robust analytics system. Every move in this industry is a trend setter until that trend is broken by a new one. We will look at ten analytics trends that, it is assumed, will dominate the most of 2021 and beyond. Before you mark your probable trajectory in the analytics world or start looking for data analytics courses in Bangalore (because where else will you look!), it is a good idea to go through these.
Increased focus on actionable, accessible, and cleaner data
The last couple of years have been brutal for the new users of data analytics because there has been a severe lack of clarity in terms of best practices. Companies have lost money in their pursuit of a robust analytical workflow without having the right pieces in the right places. Things are changing fast as the industry matures.
Companies are learning more on quality of data rather than quantity. There will be an emphasis on actionable and accessible data. With cloud computing and database as a service taking charge of the industry, things do look bright.
2. Increased use of continuous intelligence
Continuous intelligence is a referent to real time augmentative analysis. Gartner predicts that 50% of the businesses will be running continuous intelligence systems by 2022. These systems analyze current and historical data and provide insights and decision making support almost in real time and regularly. This could become the statistical backbone of businesses.
3. Application of data analytics to monitor and control climate change
Big data analytics and machine learning has been being used to monitor and control climate change for quite some time. Prediction is that these practices are going to be more widely adopted. Instead of small bodies doing fragmented work, we can witness a global effort to monitor different parameters in order to better understand the state of the world.
4. Robust data management with the help of Metadata
Metadata is data that informs us about other data. In short it is data about data. Companies are focusing on curating and interpreting metadata in order to manage and organize data in the databases. Metadata is also playing a part in easing up the process of data mining. It could reduce the data delivery time by 30%.
5. Increased importance of public cloud
Enterprises willing to integrate AI systems in their systems are depending big time on public clouds. In fact cloud providers are supporting the businesses by providing data to insight services. By 2023 almost 90% businesses will be using cloud computing for data analysis.
6. Industry-wide operationalization of AI
According to Gartner 75% of businesses will have operationalized the AI pilot projects. While the pandemic outbreak has rendered most of the historical data irrelevant and models temporarily obsolete, it has also accelerated the process of AI integration. In fact AI is an essential component in terms of supporting reinforcement learning to build new predictive models for the post-pandemic era.
7. Data as a product
Gartner predicts that 35% of the large corporations will engage in data trade via official online marketplaces by 2022. Right now only 10% of the organizations engage in buying or selling data. The growth of data as a product is one of the key trends to look out for.
8. Expanding the boundaries of data analytics
The most relevant term in this regard is X- analytics. X here stands for any word that can be associated with analytics. There are plenty of untapped areas which if analyzed can open new doors for businesses. For instance audio analytics for weather forecast is hardly explored. These unexplored areas are coming to the forefront. This can open up great new possibilities, especially for developing countries like India.
9. Natural Language Processing as an independent tool
NLP has always been understood as a subset of AI. However, businesses around the world have come to realize the applicability of NLP as a standalone tool. It can be used to extract information from sets of unstructured data, as well as to turn textual data into insights. NLP has opened new vistas in many fields, and it will keep growing in importance through the next year.
10. Utilizing dark data
Dark data is the data that stays below the surface, hidden inside networks and machines. IBM says almost 93% of all data is dark. Considerations about tapping into the huge unfulfilled potential of dark data will be one of the key trends in 2021.