big data analytics

Looking at the top trends in big data analytics with 2021 vision

Big Data is generating quite a wave in the tech world in recent years. As data is the new oil for almost all successful organizations and these businesses are getting more fluent with new technologies, it’s becoming more obvious to find ways to get the right insight from this data. With the advent of different kinds of IoT devices and consideration of influences from social media, today’s businesses are focusing not only on internal operational data but also these external data to bring meaningful insights to drive key decisions.

Big Data is the technology and approach of working with data that is not only huge but also quick and accessible in a variety of formats. There is a comprehensive Big Data operation and a team of brilliant data scientists for every self-driving car by Elon Musk, or for efficient customer service experience by Amazon.

According to Statista, the global big data and business analytics market was worth 168.8 billion dollars in 2018 and is expected to reach 274.3 billion dollars by 2022, with a five-year compound annual growth rate (CAGR) of 13.2%.

The implementation of Big Data across several businesses and sectors has risen due to technological developments and reduced computing resource costs. Big Data is used in the BFSI industry to understand consumer expenditure and patterns and to simplify what are their needs and wants, in the healthcare industry – to strengthen medical staff and to modernise farming practices in agriculture, among other applications. The automobile industry is getting enriched with data from connected vehicle stories through telematics devices. Similarly, other businesses are leveraging this technology and its new trends to develop their products and business models.

Trends that are set to be the new standards of Big Data Analytics Developments are channelling at least one of these factors below:

  1. Accelerating transition in data and analytics – Optimizing AI innovations, increased composability, and more flexible and efficient integration of more varied data sources
  2. Implementing business value through more effective XOps: Helps in smarter decision making and turns data and analytics into an essential component of a company
  3. Allocating Everything: This mandates the flexible integration of data and insights to encourage a wider demographic and products
  4. Data Lake: Almost all organizations have Data analytics head who work with other teams to bring data at one central place to create a data lake for entire organization.

So, here are some of the key trends that will define how data and analytics will be utilised for work, leisure, and everything in-between in the coming years:

  1. Implementing AI into Practice — According to Gartner, 75% of businesses will move from testing to conceptualising AI by 2024, resulting in a 5X growth in streaming data and analytics infrastructures.

Interpreting, comprehending, and deriving conclusions from all of those databases is considerably more difficult. AI, in this situation, can help to interpret all of the data and make predictions about a customer’s future lifetime value based on all we know – whether or not we understand the connection ourselves.

  1. Data Exploration and Interpretation – The purpose of composable data and analytics is to combine components from various databases, analytics, and AI technologies to create a flexible, user-friendly, and usable experience. It allows executives to link data insights to business actions and develop the organization’s analytic skills but will also enhance access to analytics.

Virtual reality would be used to develop new types of visuals that allow us to extract more information from data, while augmented reality can show us how the findings of data analytics affect the world in real-time.

Data exploration is also made easier with the effort of cloud providers such as AWS and Azure. In these clouds, many data services are available that make the entire data engineering and data science process quite easier.

  1. Hybrid cloud and edge computing – With the surge in cyber-attacks, data privacy, and security concerns in clouds; companies are shifting to hybrid clouds. This cloud model enables one or more public clouds to function in sync with one or more private clouds, resulting in a more complete environment where mobile app security is a top priority.

With over 30 billion devices connected globally, the organisations are looking for methods to better utilise this huge data that they create regularly. Thus, Edge Computing is developed where processors are situated closer to the data source or destination rather than straight to the clouds.

  1. DataOps is a flexible and process-oriented approach for creating and allocating analytics. It brings together DevOps teams with data scientists to deliver the technologies, procedures, and organisational structures to enable data-driven businesses. They also embrace change and are continuously on the lookout for new approaches to better understand changing client demands. DataOps is involved with the end-to-end flow of data through a business including eliminating roadblocks that limit data’s utility or availability, as well as deploying third-party “as-a-service” data solutions.
  2. Quantum Computing for Faster Processes – Quantum computing is a relatively new development trend in which large data sets are administered at higher speeds to analyse data at a fast and efficient pace to identify patterns and irregularities in real-time, resulting in more efficiency for businesses around the world. It can instantly evaluate and comprehend the connections between two or more predictive models or the efficiency of algorithms by doing comparisons.
  3. Engineered decision intelligence is a discipline that covers a wide variety of decision-making techniques including traditional analytics, artificial intelligence, and complex adaptive system applications. It involves individual decisions as well as also series of decisions, business operations, and even dynamic decision-making networks.

When paired with composability and common data fabric, it offers new opportunities to reassess or redefine how businesses enhance decisions and make them more precise, reproducible, and verifiable. Its most significant benefit is that it gives a structure for combining old techniques such as rules-based methods with newer techniques such as AI and machine learning.

The concept of Big Data Analytics is constantly evolving and its scope is estimated to rise with time. All of the aforementioned Big Data trends will surely play a significant role in businesses. To accomplish effective digital transformation, businesses must keep focused and updated with continuously changing trends. They must grasp how to use data analytics to better understand their market and remain ahead of their competition. Organizations that understand and address these business users’ data and analytics demands will be successful in 2021 and beyond.

By Roop Singh, Executive Director, ATCS

3 responses to “Looking at the top trends in big data analytics with 2021 vision”

  1. haidersayyad says:

    Amazing post! your given information about data analytics really informative and useful. Thanks for sharing your blog post.

  2. gaurav says:

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  3. Brij Bhushan says:

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