Top 10 Data and Analytics Trends Ruling the Roost in 2024

Over 95% of businesses struggle to manage unstructured data in their day-to-day operations. Inability to decipher data prevents them from navigating the market successfully, making business forecasts, and customizing their offerings to match the changing market trends. This proves why data analytics is crucial in enterprise strategy planning. By 2030, the global big data and analytics market value is expected to touch $684.12 billion. As more companies embrace data analytics to enhance customer experience, optimize existing business processes, and lower costs, it’s important to take note of the data and analytics trends that will hold the reins in coming days.
Here’re ten trends to behold:  

1. Scalable and Responsible AI

Research and Markets report that AI makes analytics 48% more effective for industry applications. Traditionally, artificial intelligence (AI) techniques were applied to analyze historical data. However, unpredicted events such as the COVID-19 pandemic increase the demand for real-time data analysis. Adaptive machine learning promotes scalable, responsible, and intelligent AI that offers insightful business analytics even with smaller datasets. Scalable AI will enhance learning algorithms, reduce time-to-value, and make business systems and data more interpretable. AI integration will increase the precision of data analysis in the future.
Read more: 6 Ways Artificial Intelligence is Driving Decision Making 

2. Hybrid, Multi-cloud, and Edge Computing

According to McKinsey, 70% of companies will adopt hybrid or multi-cloud technologies and processes. Hailed as the hallmarks of distributed IT infrastructures, multi-cloud management and edge computing enable companies to extend their computing capacity to the edge of their networks. This allows businesses to reach more data-hungry devices as the data is analyzed locally, close to the data source. Edge and multi-cloud reduce latency and improve decision-making with advanced, on-demand analytics. Today, every business generates volumes of unstructured data. Relying on traditional batch-based reporting to analyze big data cannot help anymore. Soon we will see the rise of distributed cloud models powered by hybrid, multi-cloud, and edge environments.
Read more: Future-proof Your Business with 5G, Edge Computing, and Cloud

3. Data Fabric Architecture

Data fabric architecture supports businesses to seamlessly navigate the complex digital business landscape that generates a lot of unstructured data every minute. It allows organizations to adopt a modular approach, known as composability, through which organizations can integrate new capabilities or features as low-code, reusable, individual components. Unlike the traditional monolithic architecture, composability allows businesses to integrate new features and changes to their enterprise applications without redoing their tech stacks. According to Gartner, data fabric reduces the deployment time by 30% and maintenance time by 70%. The ability to reuse technologies and capabilities from numerous data hubs, data lakes and data warehouses is expected to go a long way in tailoring analytics experiences.

4. Data Democratization and Self-service Analytics

The rise of low-code/ no-code digital platforms is accelerating the shift to self-service analytics. These platforms empower non-technical business users to access data, garner insights and make faster decisions. Today, self-service analytics is improving business response, enterprise agility, speed-to-market, and decision-making. InfinCE, a low-code workplace orchestration platform enables seamless team collaboration by extending your ability to integrate multiple business apps. Its data-powered business dashboard software supports marketers and non-technical users to analyze data, glean insights, track KPIs, and make strategic decisions. As data becomes the key to unlocking business value, we will see the democratization of data extending beyond the realms of technical analysts and data scientists to ensure better inclusivity.

5. XOps 

The merger of development (Dev) and IT operations (Ops) has given rise to the “Ops trend.” The list of acronyms with the suffix Ops is expanding pretty fast. XOps aims to bring all these terms (DevOps, DataOps, MLOps, ModelOps, etc.) under one umbrella to advance automation and AI adoption, and minimize the duplication of technologies and processes. XOps enables data and analytics deployments to function effectively in tandem with other software fields. In coming days, more data analytics experts will start using XOps to operationalize and automate their processes in conjunction with the software development cycle. This eliminates data management and insights generation challenges from the very beginning of software development. XOps will augment the power of enterprise technology stacks to deliver high-quality on-demand analytics.
Read more: DevOps: Building a New Culture of Software Development and Delivery 

6. Graph Analytics

Gartner estimates that by 2025, 80% of data and analytics innovations will be crafted using graph technologies. Graph analytics employs deep learning algorithms to correlate multiple data points (entities such as people, events, things, locations, etc.) scattered across various data assets by exploring their relationships. This offers businesses a holistic understanding of the market, customer segments, consumer preferences and behavior, logistics, and risks. Graph analytics improves contextual understanding which enables businesses to identify problems and address them faster. SAP HANA is a leading graph database that comes with built-in processing engines to perform context-based data search. It allows users to access the correct data quickly. In the future, graph technology will be used widely in search engine optimization, fraud and identity detection, supply chain logistics, social network analysis, and so on.
Read more: SAP HANA Helps Unlock Massive Health Data 

7. Small and Wide Data

Until 2020, historical data replicating past conditions was enough to train AI and ML models. Disruptions caused by the COVID-19 outbreak have made such past data obsolete. It means that data analytics professionals should find new ways to use the available data more effectively. “Small data” and “wide data” techniques reduce the volume of data required for training AI models and help extract more value from diverse and unstructured data sources. By 2025, 70% of organizations will switch from big to small and wide data, improving contextual analytics and making AI systems less data-hungry.

8. Decision Intelligence

Decision Intelligence (DI) is a data analytics discipline that analyzes the sequence of cause and effect to create decision models. These decision models visually represent how actions lead to outcomes by observing, investigating, modeling, contextualizing, and executing data. DI helps make faster and more accurate decisions that result in better outcomes. Gartner forecasts that in the next two years, one-third of large corporations will leverage DI to augment their decision-making skills.

9. Generative AI

Generative AI is an artificial intelligence technique that uses existing text, images, and audio files to generate new content. This technique proves to be highly useful in producing new and authentic data that mimics the original in data-scarce situations. Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) are the two key technologies that support Generative AI. By 2025, generative AI will account for 10% of all data produced, up from less than 1% today, states Gartner. In future, Generative AI is expected to augment targeted marketing, drug development, and software code creation.

10. Natural Language Processing

If you’re using Google Assistant or Amazon Alexa, you’ve already experienced NLP in action. NLP supports data analytics in multiple ways by leveraging techniques such as speech recognition, machine translation, chatbots, text classification, sentiment analysis, and so on. It offers business leaders, marketers, salespeople, and researchers with the precise insights needed to make better decisions. Reports show that the rising demand for advanced text analytics is driving NLP adoption in sectors like healthcare, social media analytics, and consumer and market intelligence. Coming years will witness the rise of no-code and low-code NLP platforms that will make AI and ML more ubiquitous.

A Chief Data Officer (CDO) survey held by Gartner in 2021 found that 72% of data and analytics leaders are involved in their organization’s digital transformation initiatives. More organizations are now realizing the tie between building a data-driven business and steering digital transformation. Start your data-driven journey with customized data analytics solutions built by Fingent. Leverage our top data analytics and visualization techniques to boost your business and customer intelligence, optimize strategies, and enhance productivity. Contact us to know more!