The data has become pervasive in organizations, from small-scale firms to a conglomerate, for bringing competencies around big data analytics. Big data integrated with AI, ML, and other high-end technologies are fuelling the fourth industrial revolution.
The data states that globally the big data analytics market will grow at a compounded rate of 30 percent, reaching a revenue of 68 billion US dollars in 2025 from around 15 billion US dollars in 2019. As per the US Bureau of Labour Statistics, for the year 2022, about 90 percent of corporations have pointed toward valuable information as a critical enterprise asset and analytics as one of their most essential competencies.
The evolving technology and longing for business process and operations optimization bring new changes in the data analytics segment. And businesses need to remain updated with these changes & their influence on related technologies to move their business strategies, data processing and management, and data-related functions accordingly.
List of top trends driving change in data analytics
Various emerging trends in the big data segment include AI, data fabric, edge computing, IoT networks, etc. Some top trends shaping the industry and the future of data are listed down.
1. Advancement in AI but Humans to Remain Critical
AI-based analytics is fuelling the data culture with the help of automation and Machine learning for organizations. The technology is continuously improving by augmenting or automating human capabilities, bringing better business values through enhancing the speed, scale & granularity of data to be monitored. However, experts say that humans will still be crucial in the interest of the social and economic divide remain contained. Such as creating policies and regulations, ensuring AI aims at humanity, improving human collaboration across global stakeholders, etc. Humans have a specific way of thinking, acknowledging, and responding to incidents, things, and people around them. Therefore, for the overall management of operations, humans are essential.
2. Data Fabric rising as new Industry Standard
Forrester analyst states that 60 to 73 percent of enterprise data remains unused for analytics. The growth in data sprawl and volume, diverse ecosystem, and different existing management systems reduce optimal data usage. And to become entirely data-driven and overcome data complexity challenges requires enterprises to utilize integrated data architecture and strategy.
Data fabric is an architectural approach that simplifies data access and facilitates self-service data service in an organization. The architectural technique is agnostics to data environment, process, utility, and geography while integrating end-to-end data management capabilities. It automates data discovery, governance, and consumption, enabling businesses to use data to maximize their value chain by providing the appropriate data at the right time, irrespective of where it resides.
3. Composable Data Analytics
Digitization pushes businesses to evolve their process to provide fast and efficient service. And to achieve it, companies migrated to cloud technology and computational and data agility. However, they failed to reach the required level of efficiency.
As a solution, Composable data and analytics provide more agility than the traditional methods and feature reusable, swappable modules that get deployed anywhere, including containers.
It is a process that allows businesses to combine and use analytics capabilities through various data sources across the enterprise. And for supporting effective and intelligent decision-making.
Composable analytics helps reduce the cost of the data centre, even when organizations migrate to the cloud.
As per Gartner Analysts, by 2023, 60 percent of organizations will build business applications, composing components from three or more data solutions.
4. Data Analytics in Edge Computing
The virtual flow of big data forces businesses to change their computing techniques. The traditional computing paradigm, based on centralized data centres and the internet, is not competent enough to handle this endless data flow. It brings challenges like bandwidth limitations, latency issues, and unpredictable network disruptions.
Edge Computing helps move some portion of the storage and compute resources out of the central data centre, closer to the source of data itself. The data gets processed and analysed where it gets generated, rather than at a centralized data centre. And the computed data at the edge, such as real-business insights, equipment maintenance predictions, or other actions, send back to the main centre for review and human interactions. Thus, edge computing, a distributed IT architecture, processing the data at the network periphery is reshaping data analytics and IT.
5. Data Security
Concern over data security is the top inhibitor to operationalizing big data. Security is the area that needs the most improvement and requires finding the right mix of in-house and third-party IT technologies and processes to defend, detect, and respond to potential data breaches.
More than 68% of organizations agree that they take extra measures to protect unstructured data such as texts, videos, photos, and email. IT and data security teams should collaborate more closely and proactively identify threats and vulnerabilities across their data ecosystems.
According to IDG’s State of the CIO survey, about two-thirds (64%) of respondents say security is integrated closely with their IT strategy. And this figure is expected to jump to 82% in the coming three years.
6. Big Data to Small and Wide Data
Organizations are examining small to wide data, structured to unstructured data with the emergence of AI, data fabric, and composable data analytics. These techniques allow us to look for valuable insight within small and even microdata tables. Such as a traditional data source may provide a column for the colour of an item, but an AI-friendly data source can have many columns and features. And these data structures need special consideration from the database engine.
The idea of small and wide data points to a new way of thinking and analysing data allows for a greater contextual understanding that may give a better or more unique insight/solutions.
7. Data Driven Consumer Experience
Data-driven consumer experience shows how organizations collect consumer data and leverage it to improve their customer’s experience. The consumer interaction with businesses is going digital with digital applications, like AI Chatbots and Amazon’s cashier-less convenience stores. The technology shows that each aspect of customer interaction can be measured, analysed, and transformed into valuable insight to improve a business process and products/services.
It leads to the personalization or customization of products and services, and their demand will increase in the coming years.
Big Data: A Solution for Multiple Business Sectors
Big data and these evolving and promising trends are about to transform various business sectors and their processes. As more organizations embrace a data culture and integrate data sources for better insight and driving business decisions, data analytics is coming to the forefront. The shift from secondary function to the forefront of business operations will help bring scalability, improved agility, revenue, and a solution to various other challenges of the enterprises.