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| How big data is transforming the financial sector |
Big data analytics in finance can help businesses to make the right decisions at the right time and improve the overall value of their business in terms of productivity, efficiency, quality, and other benefits. Here’s how big data analytics in finance are transforming the financial sector...
What is big data?
Big data comes from organizations that collect large quantities of information and store it on servers. In this context, servers can be physical ones or virtual machines (for instance, Amazon's cloud computing services). The typical sources for this data are internet transactions, sensor networks and machine logs.
The key component of a big-data application is a programming platform called an analytic engine, which can crunch these gigantic amounts of data to find patterns and predictions.
The term big data came from computer scientist Doug Laney, who published a report in 2008 describing it as the emerging technology that will reshape businesses and industries, government and society. More recently, IBM CEO Ginni Rometty has been quoted as saying: There are three eras of computing—separate but sequential. First there was mainframes; then came PCs; now we're entering what I call 'the era of intelligent systems.'
A big-data application - Second Paragraph: Because these applications require massive amounts of processing power, they can run on clusters or clouds.
Challenges in harnessing big data
In order to understand and extract useful insights from large datasets, there are many challenges that need to be addressed. One issue relates to the quality of data. Not all datasets are trustworthy or valid and cleaning up these datasets is a process that consumes significant time and resources. Moreover, due to biases in human judgments when conducting an experiment or survey, it is difficult for researchers to get a representative sample of observations that captures true population values accurately. Researchers must often reduce the size of their study sample which makes more generalizations possible but also decreases statistical power and lead to less reliable estimates. This can be countered by combining two smaller studies together as this allows for larger samples with increased power.
Advantages of using big data in finance
There are two main advantages to using big data for finance. The first, the aforementioned reduction of risk in trading, can be done by monitoring any event that could potentially change an investment. Additionally, some of these events may have come too late for traders to react and make a profit off them had they not been monitored with real-time reporting tools. The second advantage is speed. Big data allows users to process information more quickly and conduct transactions before others in the industry can do so.
Big data has a few key advantages for those trading on Wall Street. One of these being speed, as many traditional traders would not be able to keep up with real-time information.
Use Cases for Businesses
1. Predictive modeling. Predictive models provide insights into consumer behavior by analyzing past data and predicting how customers will behave in the future, with those predictions based on relationships among variables like geography, demographics, and purchase history.
2. Data integration: Traditionally, disparate data across a company's subsidiaries was stored on different databases and relied on separate IT departments to maintain it.
Benefits of Using Big Data in finance
By analyzing the data and managing it, companies can get a clearer picture of what they're doing. The value of big data comes from its ability to do this in ways that are better than traditional methods. For example, banks have an immense amount of information about their customers and can see trends or pick up on anomalies in their spending habits. Using these trends and anomalies, they're able to detect fraud, safeguard their customers and avoid losses.
All of these benefits stem from one thing – companies are able to use their raw data and do something useful with it. There's no need for guesswork, and no chance of miscalculating what's going on.
big data and finance
If you are still using Excel to crunch numbers, it's time to rethink your strategy. Modern finance relies on insights generated by some very large datasets that require sophisticated analysis and visualization tools. In this blog post, we outline how big data has transformed the banking industry and why banks need to think about their financing capabilities now more than ever. We also explain how SAP HANA Financials Suite can deliver more efficiency and agility when managing customer portfolios.
Here’s an example of how a modern, cloud-based business intelligence solution can deliver more efficiency and agility when managing customer portfolios.
Prominent Players That Are Solving These Problems
As demand for large datasets has increased, a new breed of company - such as Ayasdi, Gradescope, Palantir and Kensho - have sprung up to meet this need. Leading companies such as Goldman Sachs, Thomson Reuters and Bank of America Merrill Lynch are investing heavily into these new companies that offer tailored packages of analytical tools. Banks are particularly keen on improving their risk management strategy which will be key to continuing an uninterrupted flow of credit when the recovery kicks in. Investment strategies also stand to improve significantly by accessing better insights with regards to how to invest. These opportunities illustrate why so many old-school finance corporations are jumping on board with advanced analytic firms.
Organizations That Have Taken Up This Challenge
With technology developing at such a rapid pace, new breakthroughs are being found all the time. As we move into an era where our dependence on information has reached a staggering level, many large companies have begun to integrate data analytics into their work and it has enabled them to glean insights they never thought possible. For example, leading online retailer Amazon uses algorithms that understand which products customers might be interested in purchasing based on items they've browsed or added to their shopping cart. Furthermore, Ford was able to pinpoint why some of its models performed worse than others and adjust its supply chain accordingly.
Furthermore, large companies that were previously reliant on paper for their processes can now make use of automation software instead.
