With more and more use cases for AI and all its branches taking shape, big data is surging in relevance as the backbone of these projects—prompting DBAs, IT, data scientists, and more to take a closer look at the information being fed into their forecasts and models.
According to Research and Markets, the global market for big data was estimated at $185 billion in 2023 and is projected to reach $383.4 billion by 2030, growing at a compound annual growth rate (CAGR) of 11.0% from 2023 to 2030.
Big data and AI have a mutually beneficial relationship. AI requires a massive scale of data to learn and improve decision-making processes, and big data analytics leverages AI for better data analysis.
With this convergence, organizations can more easily leverage advanced analytics capabilities such as augmented or predictive analytics to more efficiently surface actionable insights from vast stores of data.
As the saying goes, “garbage data in, garbage data out,” and organizations need to be able to uncover valuable insights from all the noise. Data cleaning can ensure the accuracy and reliability of data.
This process is the pillar of robust and reliable AI applications. It helps guard against inaccurate and biased data, ensuring AI models and their findings are on point. Data scientists depend on data cleaning techniques to transform raw data into a high-quality, trustworthy asset. AI systems can effectively leverage the data to generate valuable insights and achieve game-changing outcomes.
The importance of data quality and master data management cannot be overlooked. Decisions can only be made based on reliable, consistent data. Models can only make accurate predictions if they are trained and supplied with the correct data. More than that, high data quality standards are essential to increase flexibility for business users.
Another area that continues to cause concern is cybersecurity—the protection of sensitive data within the organization and of data being given to AI.
According to the BARC research study, “Data, BI and Analytics Trend Monitor 2024,” security is not adequately addressed in many companies, and there is a need to take appropriate protective measures. Organizations report that there is often a lack of time and resources to implement the necessary technical, physical, and organizational measures and to coordinate them appropriately.
Data security is not just a task for the IT department. A sound security concept and emergency plan should include options for tracing the attacker, measures for recovering the data, and clear action and communication processes to limit financial and non-financial damage. This also includes the obligation to provide information quickly if personal data is affected, in accordance with the applicable data protection laws, the report stated.
With the push to rapidly adopt AI, the cloud remains an important asset. Worldwide spending on public cloud services is forecast to reach $805 billion in 2024 and double in size by 2028, according to the latest update to the International Data Corporation (IDC) Worldwide Software and Public Cloud Services Spending Guide. Although annual spending growth is expected to slow slightly during the 2024–2028 forecast period, the market is predicted to achieve a CAGR of 19.4%.
The drivers for cloud growth include the demand for public cloud services; big data, AI, and ML integration with the cloud; and increased ROI with lower infrastructure and storage costs, reports BARC.
“Cloud now dominates tech spending across infrastructure, platforms, and applications,” said Eileen Smith, group vice president, data and analytics, at IDC. “Most organizations have adopted the public cloud as a cost-effective platform for hosting enterprise applications and for developing and deploying customer-facing solutions. Looking forward, the cloud model remains incredibly well positioned to serve customer needs for innovation in application development and deployment, including as data, artificial intelligence/machine learning (AI/ML), and edge needs continue to define the forefront of innovation.”
A recent survey conducted by Unisphere Research and Radiant Advisors found that new technologies and concepts that form the basis for data fabric and data mesh are on the rise. Utilizing data fabric is gradually gaining acceptance, with 52% of participants maintaining a neutral stance, waiting to gauge its impact.
The implementation faces hurdles, such as transitioning from legacy systems and addressing data governance, yet the optimism surrounding data fabric’s potential to innovate data management remains strong, the survey results indicated.
Key components such as data virtualization and metadata activation are identified as essential for effective data fabric implementation, underlining a cautiously optimistic view toward this design concept.
To support organizations in navigating through new challenges and a rapidly evolving big data ecosystem, Big Data Quarterly presents 2024’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 that continue to innovate at a rapid pace, as well as others that are newer arrivals on the data management and analytics scene.