Today, the amount of data generated and stored globally is enormous and growing steadily. According to a recent report by Forbes, 120 zettabytes of data were archived last year, considering that 1 zettabyte is equivalent to 1 billion terabytes. This is an impressive figure, which would exceed the number of stars in the observable universe and is set to grow exponentially, reaching 200 zettabytes by 2025 (+67% in two years), according to the latest estimates by Cybersecurity Ventures.
Companies are, therefore, faced with increasing volumes of data, often from different sources, which can generate difficulties in managing, integrating and using the data. In response to these problems, data fabric solutions are born, i.e. an architecture that enables the seamless integration of data from different sources, providing an overall view of the entire enterprise data ecosystem.
A problem for the environment
Data storage can be a problem for the environment mainly due to the high energy consumption of data centres, which house a huge number of servers requiring constant power and cooling. This consumption translates into greenhouse gas emissions, especially if the energy comes from non-renewable sources. Furthermore, the production of hardware needed for storage, such as servers and storage devices, involves the extraction of raw materials and manufacturing processes that can cause pollution and environmental damage. Technological obsolescence leads to rapid replacement of equipment, generating a large amount of e-waste that is difficult to dispose of safely. Other factors to consider are water consumption for cooling data centres and the environmental impact of building new data centres to meet the growing demand for cloud services. Data storage, although fundamental today, has a considerable environmental impact that requires more sustainable solutions to reduce its effect on the environment.
Let’s think about the enormous amount of data that a company must manage and what data fabric solutions can offer. It is easy to explain the huge growth this market has experienced in recent years and will experience in the future. In fact, as highlighted by very recent research by Global Market Insights, the global data fabric market is expected to grow from $2.4 billion in 2023 to $25 billion by 2032, with a compound annual growth rate (CAGR) of more than 30% and an increase of around 942% in 9 years.
The shift to cloud computing
In fact, this approach is mainly required by those sectors defined as data-intensive, i.e., those with an increasing need for real-time data, such as retail, financial services, or logistics. However, factors driving market expansion include the rise of IoT devices for monitoring, big data management and the move to cloud computing, which make data fabric an essential technology. For example, IoT devices, of which there are estimated to be more than 15 billion in circulation this year, generate huge amounts of real-time data that must be collected, integrated and analyzed quickly: data fabric solutions enable their management in an efficient and unified manner, facilitating continuous monitoring and analysis. Equally, the shift to cloud computing has increased the need for solutions to manage data in complex, multi-cloud and hybrid environments, ensuring integrated and simplified business information management.
Essentially, companies are adopting a data fabric approach as it enables them to enable three key assets, namely omnichannel, modernization of legacy systems and efficient AI that truly creates value. Through this approach, the company can quickly adapt to business needs by integrating new data sources and formats, transforming them and creating new applications while enabling a digital proposition to a market through multiple channels and modernizing its legacy systems. In fact, a data fabric solution allows, for example, for the decoupling of a somewhat outdated core system and an innovative mobile app, which will thus no longer only dialogue with the old system, which is no longer visible on the surface, but with a data layer that can be changed and modernized over time.
Furthermore, by providing tools for data cleansing and enrichment, it ensures high-quality data, which is essential for making accurate decisions, reducing the need for data silos, eliminating duplicates and optimizing storage and processing. This clearly also reduces the costs associated with maintaining several systems and offers a flexible and scalable solution that is also functional for AI implementation.
Optimizing the data
The data fabric layer enables the rationalization of data, which are then used to feed the artificial intelligence, thus making it ready for use. If data are scattered everywhere and the AI cannot read and understand their meaning, it won’t be easy to implement them correctly. In order to be productive, artificial intelligence needs to know the business context acquiring information about the company, products, etc.: in this sense, companies need to have what Gartner calls “AI-ready data'”, high-quality, well-structured and accessible data, so that AI algorithms can be trained effectively, extract useful insights and improve the quality of decisions, which will then be more accurate. However, the 2023 Gartner IT Symposium Research Super Focus Group found that only 4% of respondents believe their data is ready for AI, 37% said they are well-positioned to achieve this goal, and 55% reported that getting ready will be difficult.
Data quality represents a crucial challenge in obtaining value from artificial intelligence. ,,Therefore, companies must prepare for this by investing in data fabric solutions that enable centralized and integrated data management, ensuring that data is highly quality, well-organized and easily accessible. Developing a data-oriented corporate culture in which everyone contributes to data management is also essential. Only then will it be possible to exploit the full potential of artificial intelligence, gain meaningful insights and make strategic decisions based on reliable data?
