Big data.
It’s a phrase that’s been thrown around for the last two or three decades—maybe too much in some cases. But it’s a short, catchy phrase. It sums up how we want to describe the amount of data we produce and have to deal with today.
To be clear, when we say “big data,” we mean big data analytics. It’s so much data that we can’t possibly grasp it in any human way, at least not reasonably. It’s coming from everywhere, growing exponentially, and coming at us faster and faster every day. In other words, the person-power it would take to process and analyze big data wouldn’t be feasible or affordable. So, we need help. We need data science. And we need a different type of intelligence: artificial intelligence. But more on that later.
Obviously, the use of big data comes with challenges. But big data initiatives are worth the cost and effort because what we can extract and analyze from it helps us understand the world and how it works at a macro-level. It also helps us dig into details and understand what’s happening at a micro-level. For example, businesses create lots of data in the Finance and Insurance industry. So extracting and analyzing big data can provide insights for investors when making investment decisions.
What is big data in finance?
Big data in finance is the immense amounts of diverse and complex data that banks, financial institutions, and investors use to understand consumer behavior, gain insight into possible investments, and create investment strategies. In other words, this data is primarily used by and for the financial services sector.
How big is big data anyway?
How big big data is depends on the amount of data being sourced, also known as data mining. If we were to consider how much data volume the world produces, it’s “at least 2.5 quintillion bytes of data” daily, according to CloudTweaks. That’s 2,500,000,000,000,000,000 bytes.
We usually measure big data—structured and unstructured data—in petabytes (PB) and terabytes (TB). A petabyte is 1024TB or a million gigabytes (GB). To put this amount of data into perspective, let’s use the newest iPhone as an example. Today’s iPhone can store up to 1TB of data. That means 1PB would equal the amount of data 1024 iPhones can store.
Other big-data challenges
Managing big data’s size is an obvious challenge, but big data comes with even more challenges. For example, any origin that produces or stores data can be a big data source, including social media. Thus, we often gather data from disparate sources.
Big data is also ever-growing. So in dealing with an ever-growing amount of data, we must ensure proper data processing, data management, and data integrity. Our data scientists, for instance, spend a good chunk of their time curating and preparing the data to make sure it’s valuable and clean.
Finally, after we’ve ensured data quality, we need AI to help us make sense of the data we’ve curated. In our case, we use natural language processing (NLP) to read more than 20 billion articles, messages, and forums to make sense of the textual data to enable our clients with multiple use cases, including signals for investment strategies, due diligences on private companies, and ESG controversy monitoring, among others.
How big data is used in the finance industry
Big data is used in many sectors and industries, and in some cases, it’s changing financial business models. However, big data technology has been used in the financial services industry in three key ways: to gain stock market insights, to detect and prevent fraud, and accurately analyze risk.
For instance, through machine learning—using computer algorithms to find patterns in massive amounts of data—data scientists can conduct a deeper data analysis in the financial markets beyond stock market data like stock prices, considering factors such as social and political trends. In some cases, this big data analysis can be provided in real time.
Machine learning also helps with fraud detection. It helps mitigate security risks through monitoring and analyzing customer data like buying patterns around credit cards, for example.
Further, machine learning helps with risk management. Investors can rely on machine learning’s unbiased output from alternative and financial data to predictive analytics, helping identify potential risks or great investment opportunities. Banks use these strategies to analyze business borrowers’ potential defaults, for example.
Other areas big data can provide a competitive advantage in the fintech industry:
- Algorithmic trading
- Chatbots and robotic process automation
- Customer segmentation
- Customer satisfaction
SESAMm leverages AI and big data for better investment decisions
SESAMm is a leading NLP technology company, and we serve global financial organizations, corporations, and investors, such as private equity firms, hedge funds, and other asset management firms. We provide datasets or NLP capabilities to enable our clients to generate their own alternative data for use cases, such as ESG and SDG, sentiment, private equity due diligence, corporation studies, and more. With access to SESAMm’s massive data lake, made up of more than 20 billion articles, forums, and messages, our clients can improve their decision-making process.
Request a TextReveal® demo to see how you can leverage big data for your investment decisions today.