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Future of Data Analytics: Looking At What Is to Come

Data is the Language of the Powerholders.– Jodi Peterson

We are living in the Data Era. However, we are still in the early phase of this period. This implies that even though Data Analytics is radically altering the way we live our lives and do our business; the full potential of the field is yet to be fully tapped. It is no denying the fact that business organizations across the globe have been trying to derive insights from data collected; however, they are still struggling with issues like data quality and requirement of finding the right human resources who would be capable of extracting these actionable insights. Thus, future of Data Analytics is one of infinite potential and unlimited possibilities.

In this blog, we shall look at the future of Data Analytics in terms of the future scope of Data Analytics as a field as well as the way in which Augmented Analytics is the future of Data and Analytics. Given the rising significance of data-driven decision making, we shall consider why Data Analytics is the future of everything.

What is Data Analytics?

To analyze is to make sense of something and Data Analytics would simply mean to make sense of data available to you. It is the field which deals with data management through data collection and data storage from disparate sources, as well as with the processes, tools and techniques which help in analyzing it. The objective of Data Analytics is to extract correlations, derive insights as well as discern patterns by way of interpreting data. These actionable insights not only help in driving the decision making process of the organizations, but also in making predictions and improving efficiency.

types of data analytics

The future of Big Data Analytics

Automation and DataOps for Improved Data Analytics

As you happen to think of the future of Big Data Analytics, automation of the process of Data Analytics is definitely an important consideration. This is because the huge volume and unstructured nature of Big Data, necessitates the need for automation. Moreover, when Data Analytics is automated, it is useful in a variety of activities such as Data Preparation, Data Exploration, Data Replication as well as Data Warehouse maintenance.

Future of Data Analytics can be understood in terms of the idea of DataOps. It states in the nutshell that it is important to streamline the processes of storage, analysis as well as interpretation of Big Data. This would entail the need to advance cooperation and collaboration between different teams and do away with the conventional barriers which separate the different departments.

automation and dataops

Rise of New Job Opportunities

While considering the future scope of Data Analytics, this is one of the most important points. The spiralling growth of the Data Analytics field, will naturally result in the rise of new job opportunities as the demand for Data Analytics experts will rise. Moreover, it is quite likely that there will be a shift of emphasis from requirement of degrees to focus on individuals who will possess the requisite skills and have hands-on experience in the Analytics field. Accordingly, there do exist several job profiles which you can aim for: Data Analysts, Data Scientists, Data Engineer, Data Architect, Statistician, Data Administrator and so on.

The different designations within the field of Data Analytics are often confused with each other as they happen to be primarily concerned with the overarching notion of Data Management. In this respect, if you wish to read on the difference between Data Architect and Data Engineer, or between Data Analyst and Data Scientist, do read our blog on Data Architect vs. Data Engineer: Analyzing the Two Data Management Roles or Data Analyst vs. Data Scientist: Understanding the Two Positions.

Infinite possibilities in the field of Machine Learning (ML) and Artificial Intelligence (AI)

The future of Big Data Analytics will all about be harnessing the potential of Machine Learning (ML) and Artificial Intelligence (AI). In fact, AI and ML are the core principles of Augmented Data Management. They are believed to accelerate the process of automatically managing metadata, data integration, data quality, database management and so on. These account for greater productivity and reduced instances of error. Moreover, as Big Data can be daunting by its sheer size and volume, Machine Learning algorithms can certainly simplify the process of de-cluttering such staggering masses of data.

machine learning & artificial intelligence

The Internet of Things (IoT) shall witness tremendous growth

The network of the Internet of Things devices will further expand in order to cover more and more new devices which will exchange data within the IoT umbrella and in turn generate huge volumes of data. By way of utilizing sensor data like health, location, machine data, error messages and others, one will be able to tap predictive and diagnostic analytics capabilities. For instance, one will be able to get a fair idea of how long it would be before a machine is in danger of breaking down and accordingly plan maintenance repairs.

iot applications

Data Management will be a New Challenge

Organizations on a global scale, have been struggling with the issue of ensuring and maintaining the quality of data. Compounding of this problem is one of the negative aspects of the future of Data Analytics. It is important for organizations to ensure the accuracy and consistency of source data. The quality and reliability of analysis is dependent on the nature of the data being subjected to the same. Moreover, the proliferation of new data sources, will only further exacerbate this problem.

Importance of Cloud Enterprises

When you think of what is the future of Data Analytics, you should side by side visualize it in terms of the rising importance of cloud providers like Amazon Web Services, Microsoft Azure and Google. It is no denying the fact that organizations making using of analytic tools are initiating a shift towards cloud for improving the efficiency of their business performance. The functionalities offered by the cloud-native applications are extremely useful in contributing towards business innovation and agility. Moreover, it helps in easy scaling of all capabilities to organizational needs. Another crucial utility of cloud based data sources, is that it helps in further embellishing internal data with data from different social media feeds, third-party sources and SaaS tools.

cloud computing

Continuous Intelligence and Real-Time Insights

The future scope of Data Analytics is all about Real-Time Data Visualization. The future of Big Data Analytics will all be about accessing, analyzing, exploring and visualizing live operational data. Moreover, different data sources can be linked in order to provide for continuous intelligence on real-time basis. The idea of continuous intelligence and real-time insights is based on the principle of data processing, information analysis against historical patterns and instantaneous action recommendation.

Predictive, Prescriptive and “X” Analytics

Future of Data Analytics can be summed up in the idea of “X” Analytics. 2020 witnessed the growing emphasis on Predictive and Prescriptive Analytics. However, the worsening situation amidst the Covid pandemic which only worsened in 2021, organizations started looking for more robust solutions for averting future crisis. So what is X Analytics? Gartner conceptualizes “X” as a data variable; structured or unstructured. So it can even be video analytics, audio analytics or text analytics. When this is combined with Artificial Intelligence tools, it can play a crucial role in predicting and hence planning for and mitigating future crisis, including diseases and natural disasters.

Augmented Analytics is the future of Data and Analytics

Augmented Analytics has been proclaimed as the future of Data Analytics, by Gartner. The Research Company coined the term in 2017 in order to refer to the process of insights automation using Machine Learning (ML) and Natural Language Processing (NLP).  Given the regularly spiralling volume of Big Data, the need to adopt augmented analytics is gradually becoming a necessity. The staggering mass of data makes effective interpretation a major obstacle. The data value chain is currently beset with biasness where data scientists build their own models and business users decipher their own pattern. This results in skipping key findings and interpreting wrong conclusions.

Consequently, there is an increasing acceptance that Augmented Analytics is the future of Data and Analytics. It is being seen as a solution to the data chain bottleneck. This is made possible through the automation of the data preparation process, automation of the processes of ML/AI modeling through AutoML techniques as well as automation of some of the key areas of Data Science. Moreover, it helps in forming a coherent narrative of relevant insights using conversational analytics and NLP.

  • Augmented Data Preparation helps in accelerating the process of data preparation including the activities of metadata development, data profiling, data cataloguing, data enrichment and so on.
  • Augmented Data Science refers to the automation of some of the processes of Data Science such as Model Selection, Model Explanation, Model Operationalization, Model Tuning and Feature Engineering.
  • Augmented Analytics makes strategic use of the combination of AI/ML techniques and Natural Language Query technologies for automating the processes of finding, visualizing and reporting relevant insights and findings.

Thus, it is evident that Augmented Analytics is one of the key aspects of future of Data Analytics. It will definitely help organizations in handling large and complex datasets, in pushing for the organizational processes to become more data-driven and in democratizing AI across data chain as well as access to insights.

augmented analytics


It is evident that the future scope of Data Analytics is infinite. The field is yet in its nascent stage and a lot remain unexplored. Moreover, the impact that Machine Learning and Artificial Intelligence could possibly have on Data Analytics is unimaginable. This implies that the future of Data Analytics is filled with exciting discoveries, yet to tap potentialities as well as uncertainties.

From the career perspective, Data Analytics is definitely one of the budding domains of the tech world. It has got immense relevance in today’s scenario and the future of Big Data Analytics promises attractive career opportunities. We, at Syntax Technologies, provide you with the amazing opportunity of seizing this chance of being a Data Analyst expert. Be a part of our top-notched Data Analytics training and see the difference. Enrol now.

Become a Data Analytics Expert
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