AI Trends

How much data do you think is being consumed by enterprises to enable decision-making each year? Take a guess. 

Well, according to Statista, the global consumption of data was 64.2 zettabytes in 2020. This number is expected to almost triple and touch 180 zettabytes by 2025. If you’re struggling to understand what that means, one zettabyte is equal to one sextillion bytes – that’s 21 zeros after the 1! 

The amount of data available for analysis has had a transformational impact on business leaders and their ability to make critical decisions with the help of data-driven insights. Additionally, there has been an accelerated adoption of digital tools and technologies during the pandemic.  

Companies across a wide range of industries including manufacturing, healthcare, retail, financial services, insurance, banking, consumer electronics, and consumer goods are using the power of analytics to make both strategic as well as operational decisions.  

Business leaders are using next-generation BI tools to make decisions on resource allocation, demand forecasting, supply chain management and even planning better for remote work.  

According to a Grand View Research report, the global digital transformation market is projected to grow at a compound annual growth rate (CAGR) of 23.6% between 2021 and 2028. It was already a USD 336.14 billion market in 2020. This is hardly surprising considering the digitization of all operations across customer, supply chain, and internal processes. According to a McKinsey Global Survey of executives (which was done a few months into the pandemic), the adoption of digital tools was accelerated by three to four years because of Covid-19.  

As a result of these dramatic changes, the field of data science is also changing rapidly with new buzzwords flying past at the speed of light. Will they stay and deliver as promised or will they be lost in the zillions of terabytes out there? 

Let’s take a look at the 7 big data trends for 2022 that will play a key role in shaping every aspect of data science – right from data storage and data management to analytics and AI/ML. 

  1. Data Fabric 

The high volume of data being ingested by businesses requires a unified architecture to optimise the value of data efforts and provide easy access to users regardless of where data is stored.  

The key is to have a layer that makes it easy to bring in data from various sources.  The concept of Data fabric allows flexible and resilient integration of multiple data sources, enabling a 70% reduction in data management efforts. 

Why is this an exciting trend?  

The modern enterprise now has thousands of end-points, data sources and cloud environments. A data fabric architecture ensures consistency in unifying and integrating data from various endpoints and multi-cloud environments.  

  1. Small Data and TinyML 

In some situations (such as in wearables or IoT devices), lack of bandwidth means that “big data” is actually disadvantageous. Where time, compute power and bandwidth are in short supply, “small data” is coming to the rescue.  

In such cases, the ability to analyse small data and use TinyML to automate certain processes will become critical. We’re already seeing the adoption of TinyML in the case of wearables, IP-enabled home appliances, industrial equipment, agricultural machinery, and even in self-learning cars.  

Why is this an exciting trend?  

We believe that in the case of wearables and IoT devices, on-device analytics using very low computer power will be a game changer. TinyML refers to making this happen.  

  1. Next-Generation Chatbots powered by Digital Employees  

The world of data and AI are very closely linked. Big data and the ability to use this data to help machines “learn” is at the core of any AI model. By building the right data and AI models, enterprises can build next-generation, modern chatbots that are powered by “digital employees”. Here, the digital employee is an AI-powered machine that is able to answer queries, process information and, overall, deliver customers with delightful experiences. We believe the future of data will revolve around how companies and business leaders use AI to make life better for both customers and employees.  

Why is this an exciting trend?  

Delivering delight and world-class customer experiences lies at the core of any business. If data and AI can be used to automate customer interactions that are useful and relevant, it could make a huge difference to businesses both in terms of operational efficiency and growth.   

  1. Generative AI 

Generative AI has unlimited potential as artificial intelligence algorithms generate new content using existing content such as text, images, or audio files. This could be used to create synthetic data needed for training machine learning algorithms. For instance, an enterprise could create digital avatars (or fake people) to train facial recognition algorithms without breaching the privacy of real humans. It can also be used to diagnose rare cancers that are photographed infrequently by training image recognition systems to identify the signs. As we make progress on the Generative AI technology, the potential use cases across healthcare and pharmaceutical industries can be game changing.  

Why is this an exciting trend?  

Generative AI has the ability to drive productivity and efficiency into repetitive processes like never before.  

  1. AutoML 

Automated machine learning or AutoML is driving the democratisation of data science by empowering business users to create their own ML apps. Without code or dependency on IT teams, various subject matter experts can use these tools to develop solutions for challenging problems in their fields. 

Why is this an exciting trend?  

Building robust machine learning models is key to building AI platforms that work. With AutoML, it is now possible to automate the process of building simple ML models, allowing data scientists to focus on more complex tasks.  

  1. Convergence of data from different types of IP devices  

Smart homes, smart cars, smart cities – these are becoming a reality today and are fueled by the convergence of the various digital transformation technologies such as AI, IoT, cloud computing, and 5G.  

While effective individually too, the real power of digital transformation, data and automation will be enabled by the seamless integration and convergence of various types of devices or data sources.  

Why is this an exciting trend?  

Because, data is no longer coming in from one type of source. It can come in from equipment on the shopfloor, IoT devices, wearables, smart cars, automation systems, etc. The key is to unlock the value of data from all these disparate sources.   

  1. Cybersecurity Mesh 

Not only are businesses using AI and other digital transformation technologies, so are blackhat hackers. Going forward, organizations will need modern and continuously improving cybersecurity solutions that use a proactive approach to mitigate cybersecurity risks. The enterprise of the future will use modern threat hunting tools to protect organisational integrity.  

Why is this important in the context of data trends? Because data analysis is at the core of understanding cybersecurity risks and tackling them with agility. A Cybersecurity Mesh will enable businesses to use a flexible, composable architecture to integrate stand-alone, best-of-breed solutions to make their business systems more secure.  

Why is this an exciting trend? 

Today, enterprises are responsible for protecting sensitive data, Personally Identifiable Information (PII), protected health information (PHI), intellectual property, and ensuring overall regulatory compliance. Big data comes in with added risks to enterprise security, and we must do whatever it takes to address these risks proactively. A cybersecurity mesh is designed to do just that.  

Merit Group’s Expertise in Data Services  

Merit’s proprietary data collection platforms and machine learning tools extract information from thousands of diverse sources and refine it into valuable insights. Data is further augmented and enriched by a team of qualified experts through online and voice research.  

Our solutions are industry-agnostic. We work with a wide range of clients with unique requirements across domains ranging from healthcare to finance, marketing and construction. 

We work with business leaders and data scientists to offer expert services in the following areas:  

  • Marketing Data: Maximize audience reach with compliant, ethically sourced data to aid marketing teams 
  • eCommerce: Deliver actionable insights from e-Commerce data at scale  
  • Industry Specific Data: Gain competitive advantage with accurate industry specific BI data 
  • News Content Data: Customized, curated news delivered seamlessly to your dashboard 
  • Regulatory Data: Systematic monitoring of global sources for regulatory developments and intelligence 
  • Document Data: Rapid web document collection and data extraction at high volume 

To know more about Merit and our data capabilities, visit: 

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