By Walter Heck, CTO, Helecloud
Data Science (DS) is no longer a technology of the distant future. It is all around us. DS has truncated timeframes and increased the momentum of rollouts across many diverse industries, transforming cautious approaches to new technology that were once planned to take years into necessary and business-critical leaps that are implemented in a matter of months.
DS is a field that makes use of Machine Learning (ML) and Artificial Intelligence (AI) to generate predictions but also focuses on transforming data for analysis and visualisations. In short, Data Science comprises various statistical techniques, whereas AI & ML make use of computer algorithms. A variety of tools are used in data science. They include statistical tools, probabilistic tools, linear and metric algebra, numerical optimisation and programming. Therefore, many believe that Data Science should be the main focus when analysing AI.
DS is a case in point. In a recent PwC survey, 52% of US companies say they have accelerated their DS adoption plans as a result of last year’s events, with 86% asserting that DS will be a ‘mainstream’ technology at their company in 2021.
Data Science is proving hugely disruptive precisely because it is hugely beneficial at many different levels of a business. From overhauling cybersecurity to process task automation, the advantages of its deployment when it comes to Fintech are compelling.
Optimising security with Data Science
To begin with, DS’ ability to sift through data and identify patterns is a powerful addition to cybersecurity responses. As companies move increasingly towards remote workflows, the attack surface increases, and the dangers to the core business multiply. 79% of global executives now rank cyber-attacks and threats as one of their organisation’s highest priorities when it comes to risk management.
Using Data Science routines, organisations can automate the process of evaluating threats to their business. What’s more, this can occur in real time as opposed to a reliance on malware databases which can be slow to update and disseminate. Instead, DS can be used to identify abnormal activity as it happens, allowing for a proactive approach to be deployed and countermeasures to be applied in rapid time.
The uncomfortable fact is that there is a cybersecurity arms race underway, with hackers also keen to deploy DS routines to identify weaknesses in a company’s security. As a result, DS’s use in cybersecurity is undergoing a steady transition from welcome addition to necessary requirement.
Triage with chatbots
The initial deployments of Data Science tools across the financial sector have tended primarily to have been in the field of automation. This allows companies to free up employees for higher-level tasks where human creativity and problem-solving are important facets to prioritise.
The increasing use of DS-powered chatbots is a perfect example of this. While none are sophisticated enough to pass the famous Turing test and exhibit behaviours that could pass as human in all circumstances, give the circumscribed environment of many customer engagement interactions, they can be convincing to casual users.
More importantly, for the companies deploying them, they unlock the ability to answer routine queries at scale. Queries requiring human intervention can be flagged up and seamlessly offloaded to swivel chair operators, with one of the powers of an effective Data Science system being that it learns as it goes on and, if trained properly, can deal with more incidents and requests as it logs more interactions.
DS -powered chatbots can also provide personalised insights that improve customer engagement. These can be customer-facing, such as recommending a product, or internal to a human operative, such as recommending the next best action to take. In fact, a recent State of the Connected Customer survey found
that 62% of customers are open to the use of DS as long as it provides them with a better experience.
Improving business processes
Data Science also holds forth the promise of dramatically overhauling business processes. These come in two main categories:
- Fulfilment of basic requests using task automation
- Simplification of complex processes
Task automation is an easy win, and something that any company undergoing digital transformation will be familiar with. In any organization there are a given number of tasks that are undertaken by human operatives that are repetitive, from updating spreadsheets at the basic level to synchronizing siloed departments in more complex workflows. Where Data Science is unique, however, is that it is not a ‘set and forget’ technology. If trained properly, it can optimize the workflows that it takes over, providing multiple efficiencies that can cascade through an organization as result.
Its ability to analyse large datasets also helps to simplify processes within a company. Often these have grown up organically, especially where they bridge across multiple organizational silos between disparate business units, and Data Science tools provide the means to grant a holistic view of these systems, creating connections between silos and dramatically increasing operational efficiency.
Embracing the Age of Data Science
Taken individually, the benefits that Data Science confers on businesses are impressive. In combination they are compelling. DS can optimise business operations, increase profitability, and help drive innovation throughout an organisation.
Its ability to scale, especially in harness with the cloud, is unprecedented. Many of the companies that successfully coped with the demand increases, either for their services or for customer queries during 2020’s lockdowns, were leaning into Data Science and its ability to quickly spin up to meet surges. And many of the ones that most successfully responded to the challenges created by recent events had already been using Data Science to drive efficiencies throughout their organisation, making them increasingly agile as they did so.
Their successes have made more and more companies look at Data Science and the benefits it can bring them, with many realising that it is one of the most powerful keys to succeed. And this is only the start too. To date, most uses of Data Science have involved overhauling existing business processes, but there is a new wave of innovation on the horizon that has been built with Data Science in mind from the start. This holds the promise of rearchitecting company workflows and operational models entirely, harnessing Data Science to create new opportunities and ways of working that can deliver additional value to companies in ways that would not have been possible before.
IT leaders must focus on embedding DS at the heart of their business strategy. However, DS is complex and will require the expertise of an established partner who can optimise a business’ Data Science and cloud security, help leaders identify and mitigate threats faster and make this an integral part of the network infrastructure.