In today’s business environment, data analytics is the future. The most important tool for gaining customer insights. Therefore, companies like Microsoft, Amazon, and Google, among so many others, are heavily investing in collecting and enabling data for enterprises. The Big Data space is expected to reach $273 billion by 2023.
Our use of analytics also grows and changes as AI and machine learning develop.
Data analytics is the future because it is changing and growing as AI and machine learning are evolving. In the past, businesses focused on gathering descriptive information about their customers and products, but now they’re focused on pulling both predictive and prescriptive insights from it.
Data analytics is the future of everything, but why is it becoming the center of all success and transformation?
Future Trends in Big Data Analytics
Future of business intelligence and big data is undergoing a major transformation. Multiple revolutionary technologies are converging simultaneously, resulting in many up-and-coming data analytics trends. Gartner predicts that more than 75% of companies will implement fully-operationalized Big Data strategies by the end of 2024.
It is estimated that streaming data and analytics infrastructures will increase by 500% due to this shift. To quickly identify and act on insights hidden in disparate datasets, businesses are increasingly relying on AI, machine learning, and natural language processing.
How does the future of data analytics fit into the picture? Which predictions and trends are most expected to impact the data analytics industry in 2022 and beyond? Why data analytics is the future of everything? Let’s dive into some of the most promising trends of now and beyond.
Machine learning will continue to change the landscape
As a key component of big data, machine learning is another technology expected to significantly impact the future of data analytics.
The sophistication of machine learning is increasing with every passing year. The potential of this technology goes far beyond self-driving cars, fraud detection devices, and retail trends analysis.
Due to the dominance of open-source platforms, machine learning and AI applications were unavailable to most companies until recently. In spite of the fact that open-source platforms were created to bring technologies closer to people, most businesses do not have the skills to configure the required solutions themselves.
With the development of open-source AI and machine learning platforms, commercial AI vendors have begun to provide affordable solutions that do not require complex configurations. As an added bonus, commercial platforms can manage and reuse ML models, which open-source platforms lack.
Due to unsupervised machine learning approaches, deeper personalization, and cognitive services, experts expect computers’ ability to learn from data to improve considerably. Consequently, machines will be more intelligent and capable of reading emotions, driving cars, exploring space, and treating patients.
Another growing trend in data analytics is the rise of event-driven architecture, an approach that orchestrates how events are produced, detected, and consumed and the responses they evoke.
As well as being often viewed through the lens of IT management, event-driven architecture is also making its way into data analytics. Event-driven data fabric combines different event, domain, integration, and semantic data models, as well as governance, to enable data-driven operations.
Despite having a data analytics strategy, many companies in Asia-Pacific lack the necessary skills and processes to derive business value from data analytics, according to a study by IDC.
In addition to hard-to-use tools, they lack timely access to data and issues with data lineage and integrity.
The importance of fast and actionable data will increase
Data analytics is the future because it is also predicted to be shaped by the rise of ‘fast data’ and ‘actionable data.’
Fast data processing can be performed in real-time, unlike big data, which typically relies on Hadoop and NoSQL databases to analyze information over time.
The real-time processing of big data is enabled by stream processing within one millisecond. Organizations gain more value by making business decisions and taking action immediately as soon as data arrives.
In addition, fast data has created a real-time addiction among users. With businesses becoming more digital, consumers expect to be able to access data anywhere, anytime. Furthermore, they expect it to be personalized. According to the IDC research cited above, by 2025, nearly 30% of global data will be real-time.
Business value comes from actionable data. The complexity, multi-structure, and volume of big data make it useless without analysis. Data can be made accurate, standardized, and actionable by using analytical platforms.
Businesses can use these insights to make more informed business decisions, improve operations, and design new uses for big data.
Cloud data will shape customer experiences
Companies can create long-term competitive advantages by implementing cloud-enabled evolutionary leaps to a real-time customer, employee, partner, and supplier relationship. Here’s how:
Using cloud technology, organizations can accomplish tasks that used to take days in minutes. By using cloud-enabled advanced analytics capabilities, companies can gain insights into human behavior at a lower cost and faster. The future of web analytics relies highly on how fast things can get done.
By facilitating rapid development, testing, and deployment in live production environments, cloud technology helps compress time to innovation and, ultimately, time to value. Cloud-based solutions enable brands to quickly test new products, services, and tools by feeding client data. Leveraging the cloud reduces the time and money wasted on projects that once took a year to complete. Marketing can anticipate and respond to consumer preferences and behaviors as they constantly change due to this agility.
Cloud computing enables fast and efficient processing and analysis of large, disparate data sets, such as those from strategic partners or stored on different platforms. Because of the cloud’s flexibility, data sharing and analysis have never been easier or faster.
Redefine customer relationships:
Cloud technology allows companies to understand customers’ needs, wants, and preferences through vast amounts of data processing. By tailoring experiences, they can make sure that they are relevant.
Management can make better decisions faster with cloud-enabled access to data and analytics. Using cloud-enabled social media analysis, marketing professionals can capitalize on trending topics and demographics. Combined purchases of full-priced and discounted items can enable restaurants to recover margins on discounted items.
Access to big data will be made easier with cloud technology
Having access to apps from anywhere is one of the main advantages of cloud computing.
During the next 20 years, big data analytics will likely become so pervasive that it will no longer be the domain of specialists.
Big data will be assumed to be a competency of every manager and many nonmanagerial employees, just as spreadsheets and PowerPoint have assumed the competencies of most knowledge workers today. Almost every business decision will require the analysis of large datasets, just as a simple cost-benefit analysis is today.
Not everyone will need to become a data scientist. Big data analysis will become more accessible through self-service tools. From any device, managers can access the computing power of the cloud with simplified, spreadsheet-like interfaces.
It’s important to note that these are not the only future trends affecting big data but are likely to be significant.
Companies that are currently deciding which big data tools to use and why these trends will help them make better-informed decisions.
A focus on edge data
According to IDC, by 2023, more than half of enterprise-class IT will be deployed at the edge of networks. By 2025, Gartner forecasts the adoption of edge computing to exceed 75%, with both firms estimating it at less than 10% at present.
What is behind the rapid adoption of edge computing?
Over 23.8 billion connected devices generate over 64 zettabytes of data each year – that’s 64 trillion gigabytes of data. There will be more than 41 billion connected devices worldwide by 2025, and the world’s data will exceed 180 zettabytes.
As data grows in relevance, organizations are shifting from on-premises computing to partially or fully cloud-based architectures to liberate themselves from data storage and processing constraints.
Despite this, conventional cloud computing is not equipped to handle the massive, ever-growing amount of real-world data being generated every day. In critical commercial and industrial processes, bandwidth limitations, data relay lags, and network disruptions can cripple productivity.
In turn, this increases operating costs and poses damaging risks. In edge computing, devices on the edge of a network perform processor-intensive, repetitive, mission-critical data analytics.
In fog nodes, only summary data is relayed and sent to cloud storage for further processing.
As an example, AI-enabled sensors may give a building an ‘intelligent edge.’ These sensors can communicate directly with light fixtures and air vents by controlling ambient conditions and sending security alerts when unauthorized entry is detected.
Real-time insights with continuous intelligence
Continual intelligence is one of the most popular trends in Big Data analytics as IoT adoption continues to boom. In real-time analytics, data is processed, incoming information is analyzed against historical patterns, and immediate actions are recommended.
The ABI Research white paper predicts that continuous intelligence technologies will see greater adoption in the near future as streaming analytics and the need for real-time insights gain more traction and the ability to utilize IoT data for business purposes.
IoT analytics will go beyond the operations, maintenance, and control use cases that you typically find in an industrial setting with the widespread adoption of continuous intelligence technologies. All industries will soon be using such technologies to drive organizational change through strategic planning initiatives.
More vendors are offering out-of-the-box solutions that help brands expand their capabilities, such as machine learning algorithms, digital twining, and data visualizations.
Additionally, 5G is poised to become a mainstream technology (although ABI predicts it will not be ready until 2023), triggering a surge in IoT adoption.
While 5G promises fast processing, it’s worth noting that the network offers much more than speed. As a result of its low latency and continuous coverage, the network will be able to support technologies such as autonomous vehicles, smart infrastructure, and automated public transit systems, as well as bring about improvements across a wide range of sectors.
While data streams will become more common as time wears on, Big Data will continue to grow in size, creating new challenges for businesses.
BI-Survey reports that as the amount of data generated grows exponentially, data quality and management are becoming greater challenges. This challenge will only become more complex as 5G becomes more widely available.
In a distributed network, data fabric provides seamless access to data from various sources and allows users to share it. The technology enables unified data management (integration, governance, quality management), eliminates silos, and accelerates digital transformation.
There is no doubt that out-of-the-box solutions will soon be available, even though Gartner predicted that organizations would need to commission ‘bespoke data fabrics’ for a few years to come.
Data Analytics is The Future
Data analytics is the future of everything – it’s inevitable that analytics will gain momentum in the foreseeable future and will become a core component of countless new technology solutions. In business planning, BI and Analytics have replaced strategy as the key requirements.