Financial Data Analytics: Investing in Actionable Intelligence
Over the past few years, financial data analytics has gone through a rapid period of transformation. The data that was used in the 1990s comprises only a small portion of the data that financial firms use today.
This explosion in new data has created new processes to analyze that data. As a result of this growth and change, new opportunities have opened up in the financial sector. Taking advantage of these opportunities, however, has been a continual challenge.
In the world of data science, this increase in data has created what is known as “big data.” In contrast to data from the 1990s, big data has exponentially more data. Some of this data is structured, and some is unstructured. In order to gain a competitive advantage in the financial sector, firms must develop financial data analytics solutions that process this data virtually instantaneously.
The sheer quantity and various types of data, as well as the need to process it quickly, all pose unique challenges for financial firms.
Housing Big Data for Finance
Over the past decade, firms have invested heavily in IT infrastructure to house the growing amount of financial data. In the coming years, businesses will have to continue to invest in storage solutions for financial data. Few financial firms, though, have the resources to construct and maintain data centers large enough to house all the market, customer, governmental and corporate information for which they are responsible. Most firms either outsource this entirely or bring in an IT consultant to help create a storage solution for all their financial data.
Guarding Big Data for Finance
By nature, big data has different types of information: structured and unstructured, as well as confidential and non-confidential. Businesses in every sector must deal with these differences, but companies in the financial industry face greater consequences if they fail to properly handle their data. Leaked information can lead to lawsuits, fines and (in severe cases where criminal intent is shown) imprisonment. The location where files are stored must be protected from physical harm, as well as digital harm.
Processing Big Data for Finance
All the information in the world is only useful if it can be processed. In the financial industry, information is only useful if it is processed instantaneously. Over the past 10 or 15 years, as the prevalence of electronic trading has grown, the financial markets have been accelerated. Because almost all trading is done electronically now, split-second decisions must be made. Even a momentary delay in a financial data analytics system, one that lasts no more than a fraction of a second, might have a significant negative impact.
In today’s world, financial data analytics is only useful if it has an ultra-low low latency. Essentially, it must be live. Regarding infrastructure, firms must have the infrastructure in place to process big data at an extremely high velocity.
The need for instantaneous information also has implications for software. Any financial data analytics software must be built to the highest standards, so that it can process all the data a firm has instantaneously. The programming should be as efficient as possible, so computers can run the software quickly. The interface should be as simple as possible (while still providing all the necessary information), so people can use the software quickly.
Any big data analytics program that companies in the financial sector rely on must be able to merge structured and unstructured data quickly and in a format that users can easily understand. Information from Oracle, Microsoft Office and XML needs to be complemented by data from social media. Any software used should be able to present different types of data quickly and in flexible formats, so users can manipulate it as needed. Users
should not have to waste type reformatting their data or searching for the right information. The analytics software should take care of that work for them.
A Scalable Solution for Financial Data Analytics
Finally, any financial data analytics solution that a firm decides to implement must be scalable. This may be obvious for growing small- and mid-sized companies that cannot afford the upfront costs of a large-scale solution. Even the most well-established, large businesses in the financial sector need a scalable solution, though.
The solution developed should be scalable. It must be able to grow as big data continues to grow. This field has emerged in the past decade, but it is on track to continue growing in the coming years. Anything implemented today must be upgradeable for next year. Hardware will need to be upgraded in the future, but the ability to grow is especially important for software. A program should be continually developed, and it should be built so that its files will be compatible with future software programs.
Find your financial data analysis solution
IKANOW has developed an analytics platform that organizes, enriches and harvests data for actionable intelligence in the finance industry. With this powerful open source software, your financial organization can create the knowledge and confidence that is valued in today’s finance industry. Contact us to take the next step for your financial data analytics.
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