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.
Read about the full list, via DBTA.
