Top 5 trends in data and analytics | 2021

Data and analytics are the foundation of all the megatrends that are happening around the globe.

The massive volumes of information produced from various data sources and used for decision making in the business world today fuel the booming business of data analytics. Data analytics is the process of disintegrating data to find patterns, structures and trends and inspecting them to analyse information. Using analytics, we can save money, create revenue and help in customer satisfaction and customer bonding.  The unprecedented increase of data volumes associated with analytic techniques empowered from advanced AI has led to the emergence of a big data era.

Cloud-based data platforms, data & analytics have a large role in the Covid-19 effects beginning from stabilizing the business to laying the foundations of new processes. In the medical field, it is also used to determine patterns like disease spread and find treatments and make plans for managing the pandemic.  Decision-making becomes more challenging during the stress periods, especially where there is uncertainty about the present and future. For better business analytics & strategic patterns, corporations and organizations use data and analytics technology according to their objectives. The corporation grips the data for making better decisions in terms of creativity, productivity & customer satisfaction. Big data turned out to be progressively recognized with the approach of the latest technologies like Artificial Intelligence (AI), Machine Learning (ML), and Internet Of Things (IoT).

Surely in the coming months, technology is going to be one of the core components when it comes to getting operations up, running, and lockdown restrictions ease. Some of the trends are:

1. Responsible Artificial Intelligence (AI)

With great risks, comes great responsibilities. There is a rapid increase in artificial intelligence with increasingly powerful technology and this technology has seemingly limitless power. While AI has many advantages to it. It has made our lives increasingly comfortable and has helped us consume and advance our knowledge at an incredibly high pace, there are disadvantages to it. There is a potential for an immensely disruptive impact like loss of privacy, lack of control over these automated systems and robots and potential biases in decision making. 

According to Gartner, by the end of 2024, 75% of the enterprises will make a shift from the plotting of AI to the operationalizing of AI. This, they predict will give five times more increase in streaming data and analytics infrastructure. As mentioned above, artificial intelligence is proving to be extremely effective in providing information on the pandemic- about patterns of its spread, effectiveness of the treatment and other countermeasure impacts. AI techniques like natural language processing (NLP techniques), optimization and machine learning are proving to be critical in realigning the new pattern of data into its new constituent of the supply-demand chain.  

It is safe to say that AI is making human life very comfortable and easy. AI has significant potential in helping solve the challenges of today’s world, fueling scientific discovery – helping us understand this universe a little better, and advancing medicine- protect life as we know it. AI deserves its praise and rightfully so. But, the demerits to this is equally great; this will essentially be stealing information and people’s right to privacy. So, it is the ethical and moral duty of the software company to abide by responsible AI practices to augment knowledge which will in turn benefit people and the society as a whole.

2. Internet of Things (IoT)

How Internet of Things (IoT) works is that it is a system of devices that are internet-connected or are inter-related objects which will be able to collect and share information through wireless networks without the need for human interaction. Some IoT devices include wireless internet, home security systems, heart monitors, etc. A car with sensors that tells you to check your engine, warns you about your tire pressure and level of fuel is a great IoT example. 

“What Internet of Things is really about is information technology that can gather its own information. What it does is, it does not tell a human being something, it just does something,” said British Tech Pioneer, Kevin Ashton.

Eric Schmidt, the former Google and Alphabet executive chairman, made a prediction about the Internet of Things, “The internet will disappear. There will be so many IP addresses, so many devices, sensors, things that you’re wearing, things that you’re interacting with, that you won’t even sense it. It will be a part of your presence all the time.

There are a huge number of innovations that aim to change the present scenarios in 2020. Trends in big data analytics are rising as aspects for breaking down IoT-based information that can help improve dynamics. The task of big data in IoT is to process different types of information consistently and keep it away from utilizing storage technologies. Statistics suggest that IoT technology is expected to reach $1 trillion by December 2020. IDC predicts that by 2025, there will be 55.7 billion connected devices worldwide, out of this, 75% will be connected to an IoT platform. These technological advancements usage not just include the IT industry but also for general masses who would be controlling their home appliances using apps. The recently launched HomePod mini, an intelligent assistant powered by Siri and smart home control will be the icing on the cake when it comes to augmented analytics. 

3. Blockchain technology

In layman terms, blockchain is just a chain of blocks. In this context, the chain is the public database and the block is the digital information. So, blockchain technology is essentially a digital recording of transactions that is duplicated and distributed across the network of computers for many advantages like real-time visibility. This is similar to GoogleDocs, where we create a document and other people can assess it in real-time. The document is not copied or transferred, it is instead distributed. Blockchain, of course, is much more complicated than that but it illustrates three critical ideas of the technology –

  1. Digital assets not copied or transferred, they are instead distributed. 
  2. The asset is decentralised- which allows real-time access.
  3. Trust in the asset is created by a transparent ledger of changes, which preserves the integrity of the document.  

In the data and analytics context, blockchain discourse is around providing assets and transaction details along with transparency for complex networks. This technology is perceived as an alternative to data-based management systems (DBMSs) products set up for giants on mainframes. For single enterprise auditing for data sources, Gartner assumes that by 2021, most permissioned blockchain uses will be replaced by ledger data-based management system (DBMSs) In some cases, they will be supplementing the existing data management infrastructure to bridge the gap between the current capabilities and the gap of newer solutions.

Almost every industry has an endless amount of applications for Blockchain. This ledger technology has several applications which include, tracking fraud in finance, securely sharing medical records of patients between them and the healthcare professionals and also act as a better way of tracking intellectual property in business and music rights for artists. The goal of blockchain is to permit digital information to be recorded and distributed, but not edited. The Bitcoin protocol is made on the blockchain. You’ve got of these people, everywhere the planet, who have bitcoin. There are likely many people around the world who own a minimum of some bitcoin. Let’s say one among those many people wants to spend their bitcoin on groceries- blockchain comes in here.

4. Cloud technologies used as never before

According to the e-Commerce Times, Cloud Agnostic Strategy has been into the limelight in this COVID-19 times. Core business services need to spread across multiple cloud providers to reduce the risk of downtime and optimize their cloud costs. Another trend in cloud technologies is Containerization. Its usage has increased since 2013 when the release of Docker and Kubernetes in 2014 was marked. These technologies are subscribed by companies as consumable cloud services where there is no need for system administrators to take care of virtual machines and related infrastructure. Read moreabout the Containerization of PySpark using Kubernetes here LINK

Cloud automation strategies are helping businesses to tackle dynamics of multiple private, public and hybrid cloud environments with limited engineers. Apart from this, public cloud services will be an essential migration of data and analytics innovation. Fundamentally, companies do not have to buy or maintain their own computing infrastructure if they are using cloud services. Although the benefits of cloud technology are incomparable in innovation acceleration, as the migration process out, there will be newer unintended consequences. These are like cloud cost optimization, integration overhead, and governance that needs to be pre-thought of by the data and analytics leaders.

As companies are buying computing in the form of a service rather than in the form of a physical server, cloud computing is shifting spending from the capital expenditure (CapEx) to an operating expenditure (OpEx). Using the cloud to make room in the budget for a new project may be easier than going to the CFO. Large increases in IT spending may be avoided by this. 

The economics of moving the cloud may not be as clear cut for the rest of the enterprise computing portfolio. Instead of focusing simply on cost, cloud computing vendors are increasingly pushing cloud computing as an agent of digital transformation. Moving to the cloud can help companies rethink business processes and accelerate business change, goes the argument, by helping to disintegrate the data and the organisational silos. Some companies that require encouragement around their digital transformation programmes may find this argument appealing; others may find enthusiasm for the cloud waning as the costs of swapping add up.

5. Decision intelligence 

It is predicted that by 2023, 33%+ large enterprises will have analysts working on decision modelling to improvise the existing systems for predictive analysis. The MOOCs online are designing content to prepare the upcoming generation of decision analysts as it opens up advanced disciplines. These disciplines include decision modelling, decision support, designing decision management frameworks. These decision models are based on the Decision Model and Notation abbreviated as DMN. Giants like IBM offer individual offerings to build a Decision Model using their proprietary tools. Briefly, any decision model requires to follow these steps: 

  1. Validate your environment
  2. Review an existing decision model
  3. Create a decision model
  4. Validate the initial decision model
  5. Add rules to the decision model
  6. Test the decision model 
  7. Simulate decision model 
  8. Deploy the decision model

This includes tests to ensure consistent performance and simulations to check the business impact of rule updates before the final deployment.  

Aspiring data analysts are starting to learn about decision intelligence and decision modelling for applying multiple logical and mathematical techniques for the role. There are companies specializing in providing decision management solutions. Although it is not necessary to always automate 100% of the decision all the time because critical decisions need human inputs and only a certain percentage of decisions can practically be handled by a decision engine. Hence, it is important to understand the mark between critical and non-critical decisions, and based on that, decision models need to be built. Good decision modelling results in improved agility, transparency with business user enablement which is most important for the client. 

6. Natural Language Processing

Natural language processing (NLP) is exactly like the Google of data analytics wherein, it allows users to perform queries in natural human language, with either written or voice input. The relationship between computers and human language is the essence of Natural language processing (NLP). More precisely, natural language processing is the computer understanding, analysis, manipulation, and/or generation of natural language. 

Speech analysis in both the aspects- audible speech, as well as the text of a language is referred to as Natural Language. NLP systems capture meaning from an input of words (sentences, paragraphs, pages, etc.) in the form of a structured output (depending on the application, this however varies). 

A diverse cross-section of professionals, including the front-office workers are benefited by having access to data analytics through this technology. This ability will continue to grow in sophistication. As the technology evolves, the software will be ready for you to ask things like, “What is the average spend per customer within a 10-mile radius this financial year versus last financial year?” rather than things like, “What was the average spend per customer this financial year?”

Natural language processing in AI, although, is more than just speech analysis. There are a number of approaches for the processing of human language. These include:

  1. Symbolic Approach: Human-developed rules and lexicons are the basis of the symbolic approach to natural language processing. In other words, this approach is based on the accepted rules of speech within a given language. Linguistic experts materialize and record this for computer systems to follow.
  2. Statistical Approach: Observable and recurring examples of linguistic phenomena is the basis of the statistical approach to natural language processing. Models based on statistics recognize recurring themes through mathematical analysis of large text corpora. The computer system can develop its own linguistic rules that it will use to analyze future input and/or the generation of language output by identifying the trends in a large sample of texts.
  3. Connectionist Approach: Combination of the symbolic and statistical approaches is the connectionist approach to natural language processing. Generally, this approach starts with accepting rules of the language and tailoring them to the specific application from the input that is derived from statistical inference.

7. Augmented Analytics

Augmented analytics enables technologies like artificial intelligence and machine learning to assist with preparing data, generating insights and explaining insights to augment the way people explore and analyze data in BI and analytics platforms. Automating various aspects of data science, machine learning, AI model development, management and deployment augments the expert and citizen data scientists. Augmented analytics along with data analytics software makes use of ML and NLP to understand and interact with data as humans would do, but only on a much larger scale. 

The analysis process is started by collecting data from public or private sources. This can be the web or it can be a private database. After gathering the data, it needs to be prepared and analysis should be performed in order for insights extraction, which should be then shared with the organization, along with action plans associated with the learning. Everything about the analytics and business intelligence process will be changed by artificial intelligence and augmented analytics by just simplifying or eliminating some steps and radically changing and improving others. 

Every company, organization and the government will need an augmented analytics platform to connect to these databases and live data sources, find relationships within the data, create visualizations and aid in telling stories and then human users can effortlessly share their findings across the organization. They will also need ways to work with Big Data that go beyond the usual analytics systems and completely reimagine how users can relate to data. How users experience analytics and BI will be changed by augmented analysis. By serving up insights that humans could never imagine, they will change the world.

What does the future hold?

The future trends of data and analytics are set to change the way systems operate inIT, manufacturinghealthcare, finance & other sectors. During this pandemic, the need is all high for businesses to drive revenues to compensate for the loss during the lockdown. There are opportunities to learn about and incorporate new technologies like graph analytics too for companies’ applications. Moreover, with technology trends, a huge amount of data indeed brings additional challenges like security threats, data privacy, difficulties in data processing, and storage. However, there is no denying the fact that data and analytical systems refer to big value and opportunities for everyone in this ecosystem.

As we see the top 5 data and analytics trends now, here are three key steps to address data and analytics initiatives:

  1. Defining scope for strategic, operational, or governance?
  2. Planning key stages and activities 
  3. Identifying stakeholders for resource management


  1. Gartner Top 10 Trends in Data and Analytics for 2020, Published by Gartner in Oct 2020,
  2. 5 Big Trends in Data Analytics Published by KD Nuggets in July 2020,
  3. Top 5 Data Science and Analytics Trends In 2020, Published by Analytics Insight in July 2020,
  4. 4 Trends That Will Disrupt Your Data & Analytics Strategy in 2020-2021, Published by Towards Data Science,
  5. The IT Roadmap for Data and Analytics, Published by Gartner,
  6. 5 Cloud Computing Trends in 2020 and Beyond, Published by eCommerce Times in August 2020,
  7. Build Decision Model, Published by IBM,