Erik Capistrano introduces the course on Fundamentals of Big Data and Business Analytics, which will cover introductory aspects, database and data management fundamentals, and business-related big data issues and challenges.

The speaker defines business analytics as a combination of tools and techniques. Business intelligence involves reporting, data exploration, and ad hoc queries, while analytics refers to using statistics, analysis, and interpretation of data.

Big data has various definitions. SAS describes it as large volumes of structured and unstructured data. IBM views it as data generated constantly from multiple sources with alarming velocity, volume, and variety. Gartner defines it by high-volume, high-velocity, and high-variety information assets requiring cost-effective methods. Research highlights big data existing in terabytes, petabytes, and exabytes, with 2.5 quintillion bytes generated daily, processed for predictive and recommendation functions using proprietary and public data.

Big data is defined by four aspects: Volume (the sheer size of data sets), Variety (the diverse types of data from various sources like social media, government, finance, and banking), Velocity (the speed at which data is generated, like Twitter, Facebook, stock market, and Uber data), and Veracity (the uncertainty and interpretability challenges due to the diverse and sometimes conflicting nature of combined data sources). The speaker notes that a universally agreed-upon definition of big data is still elusive, emphasizing the need for continued study.

An example illustrates how big data works: a bank profiles a customer's travel by combining credit card transaction data (proprietary) with public social media posts (Facebook, Twitter, Instagram check-ins) to verify identity, confirm travel activities, and provide personalized recommendations for future purchases or travel.

Big data investments have shown significant returns: 5-6% productivity increase, 10% business growth, and 4.4 million jobs created. It drives growth in manufacturing (billions), retail, and saves money for credit card firms by preventing fraud. The business data market value was $25 billion, projected to grow to $56.4 billion by 2017, and its economic contribution is estimated to reach $15 trillion by 2030. The big data software market is valued at $16 billion, growing at 8% annually, with investments expected to hit $76 billion by 2020.

The big data landscape involves a vast supply chain and value chain with numerous providers and companies offering services, products, software, and hardware to manage and utilize big data, demonstrating a complex ecosystem.

The session concludes by summarizing the immediate benefits of big data, with future modules delving into the fundamental aspects of data management services and business practices required for proper big data utilization, aiming for a comprehensive understanding.