As the world slowly transitions to a post-covid landscape, organizations are looking at what the “new normal” is shaping up to be.
AI technologies gained immediate traction earlier this year and continue to fascinate businesses considering how and when to use its innovative solutions and platforms. Behind AI, however, is the workhorse that must be attended to behind the scenes—databases and associated data environments.
Data needs to be secure, highly available, and viable for AI and other advanced initiatives to succeed within enterprises.
According to a recent report by Research and Markets, the global big data and business analytics market in 2022 was valued at $294.16 billion. The market value is anticipated to grow to $662.63 billion by 2028.
The market value is expected to increase at a CAGR of 14.48% during the forecast period of 2023–2028, with the software segment being the dominant component.
According to the report, AI and machine learning (ML) are proving to be essential instruments in keeping control over the increasing amounts of data and enabling real-time execution. As much as AI is transforming the landscape, several risks impact widespread adoption. According to Gartner, the growing use of AI has exposed companies to new concerns such as ethical risks, poisoning of training data, or fraud detection circumvention, all of which must be mitigated. Managing AI risks is not only about being compliant with regulations. Effective AI governance and responsible AI practices are also critical in building trust among stakeholders and catalyzing AI adoption and use.
“There is a significant range of risks—social, humanitarian, sustainability—that companies need to pay attention to as well. Companies that are approaching generative AI most constructively are experimenting with and using it while having a structured process in place to identify and address these broader risks. They are putting in place beta users and specific teams that think about how generative AI applications can go off the rails to better anticipate some of those consequences. They are also working with the best and most creative people in the business to define the best outcomes for both the organization and for society more generally,” said Alexander Sukharevsky, senior partner and global leader of QuantumBlack, AI by McKinsey.
AI isn’t the only trending big data technology. According to Research and Markets, there has also been a surge in adoption of big data analytics software by various organizations to deliver enhanced and faster decision making and to provide a competitive advantage by analyzing and acting upon information in a timely manner.
In addition, there has also been an increase in demand for cloud-based big data analytics software among small and medium enterprises, positively impacting the growth of the market.
Data fabric and data mesh ideas and concepts have also gained traction. Data fabric is a data management design pattern leveraging all types of metadata to observe, analyze, and recommend data management solutions. By assembling and enriching the semantics of the underlying data and applying continuous analytics over metadata, a data fabric generates alerts and recommendations that can be actioned by both humans and systems.
It enables business users to consume data with confidence and facilitates less-skilled citizen developers to become more versatile in the integration and modeling processes.
Data mesh is an intentionally designed distributed data architecture, under centralized governance and standardization for interoperability, enabled by a shared and harmonized self-serve data infrastructure.
To support organizations in navigating through new challenges and a rapidly evolving big data ecosystem, Big Data Quarterly presents 2023’s “Big Data 75,” a list of companies driving innovation and expanding what is possible in terms of collecting, storing, and extracting value from data.
The list is wide-ranging, including some companies that are longtime industry leaders and continue to innovate at a rapid pace, as well as others that are newer arrivals on the data management and analytics scene.
Click here to See All 75 Companies
