Big data, or complex datasets which are far too dense to be processed by traditional computing architectures, is not a new thought. The degree to which data engineers can govern, data scientists can innovate, and data analysts can analyse this gold mine of unfiltered business insights, on the other hand, is novel—or at least currently being developed.
We will be able to accomplish more with big data in 2023 than ever before because of significant gains in computing power, novel data processing techniques, and widespread cloud adoption. Will our analytical capabilities, nevertheless, be able to keep up with the enterprise's rapid data production and aggregation rate and deliver insightful data on time?
One of the terms that is currently getting the most buzz is data analytics. We live in the Digital Era, which goes without saying. Nonetheless, this era is still in its early stages. This suggests that even while Data Analytics is fundamentally changing how we live and conduct business, the field's full potential has not yet been realised.
There is no disputing the fact that business organisations all over the world have been attempting to extract insights from the data gathered; however, they are still having difficulty with problems like poor data quality and the need to locate qualified personnel who would be able to extract these useful insights. As a result, the future of data analytics is one of limitless promise.
What is Data Analytics?
Making sense of something is the definition of analysis, and making sense of the data at your disposal is what data analytics refers to. It is a field that deals with managing data by gathering and storing data from many sources, as well as with the procedures, equipment, and methods that facilitate data analysis. By analysing data, data analytics seeks to discover correlations, gain new knowledge, and identify trends. These practical insights assist firms not only in making decisions but also in forecasting the future and increasing productivity.
Take a look at the following statistics:
- Market revenue for big data and analytics is $274 billion. Each day, around 2.5 quintillion bytes of data are produced.
- In a short period of time, the Big Data industry has experienced phenomenal growth. It soared by 62%, from $169 billion in 2018 to $274 billion in 2022.
- Only 8.3% of investment decisions in 2019 were influenced by defensive considerations. Defensive measures accounted for 35.7% of big data investments in 2022.
What is the Future of Big Data Analytics?
The following trends are anticipated to be the future of Big Data Analytics:
DataOps and Automation for Better Data Analytics
Automation of the data analytics process should be taken into account when you explore the future of big data analytics. This is due to the enormous volume and unstructured structure of big data, which makes automation necessary. Moreover, automated data analytics is helpful for a number of tasks, including data preparation, discovery, replication, and data warehouse upkeep.
The concept of DataOps can be used to understand the future of data analytics. In a word, it says that it's critical to streamline the procedures for storing, analysing, and interpreting big data. This would necessitate improving communication and coordination between various teams and removing the traditional walls that divide the various departments.
Rise of New Job Opportunities
This is one of the most crucial considerations when thinking about the potential of data analytics. The demand for data analytics professionals will increase as a result of the field's spiralling expansion, which will inevitably lead to the creation of new career possibilities. Furthermore, it is anticipated that the emphasis will move from the need for degrees to a focus on those who have the necessary abilities and practical expertise in the analytics industry. As a result, there are several career descriptions that you might pursue, including those for data analysts, data scientists, engineers, architects, statisticians, data administrators, and more.
Countless opportunities in the area of Machine Learning (ML) and Artificial Intelligence (AI)
In comparison to its estimated value of USD 1.2 trillion in 2020, the global Artificial Intelligence (AI) market is anticipated to increase at a CAGR of 23% in the forecast period of 2023–2028. It is anticipated to reach approximately 6 trillion by 2026. On the other hand, from 2022 and 2029, the market for machine learning is expected to grow at a CAGR of 38.8%.
Using the capabilities of Machine Learning (ML) and Artificial Intelligence (AI) will be the main focus of big data analytics in the future. In actuality, the fundamental tenets of augmented data management are AI and ML. They are thought to hasten the automatic administration of metadata, data integration, data quality, database management, and other processes. They are responsible for increased productivity and decreased error rates. Furthermore, machine learning algorithms may definitely make the task of cleaning up such overwhelming amounts of data simpler, as Big Data can be intimidating due to its sheer size and volume.
The Internet of Things (IoT) shall witness tremendous growth
According to IoT Insights' Global IoT Business Expenditure Dashboard's January 2023 edition, the corporate IoT market expanded 21.5% to $201 billion in 2022. The market expanded slightly more slowly than the 23% predicted for the previous year, and 2023 is likely to see even slower growth.
The Internet of Things (IoT) network will continue to grow to include more and more new gadgets that will share data among themselves and produce enormous amounts of data as a result. One can use predictive and diagnostic analytics by using sensor data such as health, location, machine data, error messages, and others. For instance, one will be able to reasonably estimate the amount of time until a machine is in danger of malfunctioning and plan maintenance and repairs appropriately.
Data Management will be a New Challenge
Globally, organisations have been having trouble verifying and sustaining the quality of their data. One of the drawbacks of the future of data analytics is the aggravation of this issue. It is crucial for companies to guarantee the consistency and correctness of source data. The type of data being subjected to the analysis will determine its quality and dependability. Furthermore, the proliferation of new data sources will only make this issue worse.
Importance of Cloud Enterprises
While imagining the future of data analytics, it is important to keep in mind the growing significance of cloud service providers like Amazon Web Services, Microsoft Azure, and Google. There is no doubting that businesses utilising analytical tools are starting to move to the cloud to increase the effectiveness of their business performance. The features that cloud-native applications provide are very helpful in promoting company innovation and agility. Additionally, it makes it simple to scale all capabilities to organisational demands. Another essential benefit of cloud-based data sources is their assistance in supplementing internal data with information from various social media feeds, outside sources, and SaaS solutions.
Augmented Analysis will be more in demand
The experts have dubbed augmented analytics as the future of data analytics. The adoption of augmented analytics is gradually turning into a need due to the routinely spiralling volume of Big Data. Effective interpretation is severely hampered by the mind-boggling volume of data. Bias now plagues the data value chain, as data scientists create their own models and business users identify their own patterns. As a result, important findings are missed and incorrect conclusions are drawn.
As a result, there is a growing understanding that Augmented Analytics represents the direction of Data and Analytics. It is considered to be a remedy for the data chain bottleneck. Automation of the data preparation process, automation of the ML/AI modelling processes using AutoML approaches, and automation of some of the essential aspects of data science all contribute to making this possible. Additionally, it helps in the development of a cogent narrative of essential information combining conversational analytics and NLP.
It is clear that data analytics has a limitless potential. There are still many unknown areas in the pitch, which is still in its infancy. Furthermore, it is impossible to imagine the effect that machine learning and artificial intelligence could have on data analytics. This suggests that there will be many fascinating discoveries made in the future of data analytics, along with untapped potentials and unknowns. The future of big data analytics is no longer constrained by price constraints, despite the fact that many large organisations are already moving towards, if not fully embracing, all of these trends. This gives them an advantage over their rivals.
Data scientists and engineers are coming up with creative ways to sift through the mountains of data and find insights without spending the money of a Fortune 500 company. There will be a significant increase in the use of big data analytics by small and mid-size businesses. For those who take steps to comprehend and embrace it, the future is promising.