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by James Kipling, Product Manager at Quantrix

Online banking and electronic trading produces millions of data points every single day.

James Kipling, Product Manger at Quantrix
James Kipling, Product Manger at Quantrix

With so much data being created, banks are under more pressure than ever before to meet regulatory requirements – but when you consider the multi-dimensional challenges of capturing, storing and analysing data over years, departments and regions, branches, geographies and even applications, it can be hard to know where to start.

So, what do you need to think about when working with financial data?

Use the right data

It can be tempting to use all of the data that’s available. There’s a lot of it, and more data surely means more insight, right? Unfortunately, it’s not always that simple. Using every bit of available data can often have the opposite effect, with overcomplicated data sets increasing the likelihood of errors and anomalies in your models. Where data is concerned, less can sometimes be more.

Where possible, you should also ensure your application system has a dynamic link to the data source. Any manual processes will increase your chance of data errors, so manual data input and copy and pasting should be avoided at all costs!

Use a flexible model

We live in a multi-dimensional world, and in the banking industry, most data sets will have multiple dimensions to analyse. Will you be opening a new branch next year? Or launching a new product? Models need to be flexible, so you can ask important, forward-looking questions and expect quick and accurate results.

Ask the right questions

With so much data at your fingertips, it can be easy to get lost in the complexity of your models. Asking the right questions is an important part of data analysis, and it’s best to start by defining your primary answer by using the simplest possible methods in the first instance. Once a simple method has been defined, you can then refine your methodology as required – saving you the pain of repeated trial and error. You also shouldn’t forget to sanity check your assumptions. “Given the current economic climate, does this answer make sense?”

Use the right tools

By using the right professional tools (such as natural language formula writing, built-in audit trails and a visual dependency inspectors), you can reduce common ‘efficiency killers’ such as formula writing, error checking and auditing.

Don’t rule out cloud solutions

Once you have selected the right tool, you also need to decide what platform the applications runs on – should it be the desktop, the cloud, or a hybrid solution that does a bit of both?

Many customers simply don’t need the power of a desktop computer to interact with their financial data. They just need a convenient, easy way to access your system. An intuitive web interface is cost effective, offers high adoption rates, and is easily scalable in your environment, or in the environment of a trusted hosting provider.

Make security your number one priority

To combat the threat of hackers, many organisations have strict policies regarding what systems can and can’t be deployed outside of their firewalls. Many vendors who are unable to offer an in-house solution are often disregarded early on during the decision-making process – but don’t let that put you off. These days, many cloud providers offer a higher level of security than in-house deployments, thanks to their ability to easily access and upgrade industry-leading security measures.

Whatever you choose to do, security should be the major factor in your decision making.

And finally… don’t be afraid of embracing new technologies

There are few industries more data driven than banking. It’s an exciting place to be, and with so much data to process and analyse, it’s a sector that also needs to be leading the charge when it comes to new, ground-breaking technologies for managing data.

Of course, every technology will bring potential risks and rewards, but before making any decision, you also need to consider the cost to your business of standing still. It might seem like the safest option – but at a time when data and its uses are evolving faster than ever before, it’s also never been easier to get left behind.