The past decade has seen an explosion in the amount of data that is currently collected and analysed. This could be attributed to the rise in use of smartphones, tablets, sensors and connected vehicles and appliances, among other digital devices.
Much of the technology that people work with now automatically generates data and new insights. This means that companies are able to collect more data than ever before, in turn transforming their organisational structures to be able to make data-driven decisions.
This is because data helps businesses to better understand their customers. In fact, a survey from Deloitte noted that 49 percent of respondents said analytics assists them in making improved decisions, 16 percent said that it better enables key strategic initiatives and 10 percent said it helps them nurture relationships with both customers and business partners.
The true value of data as a prized commodity was never more evident than during the 2018 Facebook-Cambridge Analytica data breach, when millions of Facebook users' personal data was harvested without consent by Cambridge Analytica to be predominantly used for targeted political advertising.
In the financial services arena in particular, data science has become very important, mostly being used for better risk management and risk analysis. Better analysis again leads to better decisions and a subsequent increase in profit margins for financial institutions.
Companies can analyse the trends in data through a selection of business intelligence tools which can also be used to develop credit risk models, forecast financial market trends and predict the impact of new policies, laws and regulations on businesses and markets.
In order to stay relevant and thrive, particularly during this challenging period, businesses need to be good at anticipating what is next and reacting in real time.
Here we look at the ways in which data is able to help businesses.
Understand customers better
Data and the use of behavioural analytics are key to helping businesses understand their customers and the wider markets. In a nutshell, behavioural analytics is an area of data analytics that focuses on providing insights into the online actions of people and is used to identify opportunities to optimise operations in order to realise specific business outcomes.
Behavioural analytics is based on hard data. It uses the volumes of raw data people use while they are on social media, in gaming applications or on marketing and retail sites. This contextualised data can help companies to understand how people are engaging with and responding to their offering and adjust accordingly if something is not working. It helps businesses to identify priority audience segments and clarify their current behaviour in order to create targeted personas to effectively communicate with.
Behavioural credit risk analysis provides lenders with a more complete profile of the customer and insights which enable them to anticipate their habits. By making use of these analytics techniques, lenders can then monitor or anticipate the risk involved and target the right customers to lend to.
Make insightful predictions
Data is not only useful when it comes to spotting previous and current trends. It can also be used to help predict what might happen going forward. Predictive analytics is a category of data analytics aimed at making predictions about future outcomes based on historical data and analytics techniques such as statistical modeling and machine learning.
The science of predictive analytics can generate future insights with a significant degree of precision. With the help of sophisticated predictive analytics tools and models and the right people to read them, any organisation can now use past and current data to reliably forecast trends and behaviours milliseconds, days or years into the future.
Predictive analytics draws its power from a wide range of methods and technologies, including big data, data mining, statistical modeling, machine learning and assorted mathematical processes. Organisations use predictive analytics to sift through current and historical data to detect trends and forecast events and conditions that should occur at a specific time, based on supplied parameters. This means that it can help businesses find ways to save and earn money.
Predictive analytics can also be used to detect and halt various types of criminal behavior before any serious damage is inflected. By using predictive analytics to study user behaviors and actions, an organisation can detect activities that are out of the ordinary, ranging from credit card fraud to corporate spying to cyberattacks.
Better decision making
It is possible to use data to make decisions that can directly affect revenue. This is known as decision science, which is the collection of quantitative techniques used to inform decision making. It encompasses decision analysis, risk analysis, cost-benefit and cost-effectiveness analysis, constrained optimisation, simulation modeling and behavioral decision theory.
It also incorporates operations research, microeconomics, statistical inference, management control, cognitive and social psychology and computer science. By focusing on these areas, businesses can begin to understand their customers, what they need and how to make sure they get it.
Within credit risk this could include knowing which products to upsell to certain people based on their online behaviour and preferences. In financial services, it can help organisations ascertain who to lend to and the risks associated when it comes to dealing with a particular person.
It is important to note that data analysis and the subsequent insights it yields are only ever as good as the people who work through it, such as analysts, engineers and modellers. This is why it is essential to have the best professionals to successfully manage and navigate complex systems, databases and IT packages to pull out the most prevalent and useful data.
The rise in dominance of data is perhaps best exemplified by the rise in people studying for degrees in this field, in particular Masters in Business Analytics.
In addition, IBM reported in “The Quant Crunch: Demand for data science skills is disrupting the job market” that there will be 2.7 million new data and analytics job openings every year by 2020. This presents a prime opportunity for people to learn these skills and enter into a promising career in this sphere.
Recruiting into data analytics
It still remains the case that data is a challenging area to recruit within. This is because the technology boom of the last 20 years has generated more information than organisations know what to do with, and they need people to analyse the data effectively.
The demand for this skill-set is extremely pertinent across financial services, as companies strive to hire the best talent in this competitive and candidate driven market. The challenge comes not only due to a shortage in this skill-set, but also due to being able to source individuals who can demonstrate the right approach to the role. So, what do we mean by this?
While there is a rise in people going down this career path, a good analyst must have the right attributes. This is to say, they need not just the skills to understand and manipulate the data, but also the ability to make business decisions on the back of the analysis and then communicate it effectively throughout the organisation.
It is therefore crucial for analysts to have the right blend of technical ability and the commercial acumen in order to maximise the potential of their role and the value they can bring to their employer. The role of a data analyst is constantly evolving, as organisations continue to adapt to the rapidly changing markets they operate in. The demand for this skill-set will inevitably remain high, making it a career path of choice for many.
At MERJE, we have a wealth of expertise in this area and a wide network of highly qualified candidates to successfully fulfill your data analysis requirements. To discuss this or any of the points raised in our blog in more detail, please get in touch with Eleanore Sykes, our Credit Risk and Analytics Managing Consultant. Email: email@example.com