Why data is often a double-edged sword in financial services

Financial institutions, whether oriented towards corporates or individuals, arguably have more customer data at their fingertips than any other type of organisation. That much we know thanks to the rise of fintech, which has transformed the industry by making banking in particular far more accessible to the average consumer.

But we know unlocking insights from this goldmine is often a major obstacle. With 73% of financial services firms in the UK planning to invest in “better data analytics to enable more informed decisions” in the next 12 months, it is crucial to have the right building blocks in place to enable this.

Every financial services organisation has had to rethink its market position to deal with the impact of the Coronavirus crisis, and how it has reshaped its business model. Nonetheless, digital transformation strategies that may have been hovering in the background are suddenly top of the business agenda.

Keeping pace with the complexity of financial data

The access to, validity of, interpretation of and insights from data are the keys to digital transformation success, and delivering a better user experience in the so-called “phygital” hybrid reality. However, the skills required to unlock the value of data are hard to come by right now, in one of the most challenging recruitment phases we’ve ever known. In fact, according to DCMS research, almost half of businesses (46%) said they struggled to recruit for roles requiring data skills. Add to that the fact that financial services organisations are grappling with legacy technology and scalability challenges in their transition to the cloud, and you quickly see why they’re often restricted in what they can do with the data they have. 

So how do we keep pace with the complexity and volume of data and empower data teams to make it easier for all areas of the business to tap into data-driven insights? Liberate it from being the reserve of small, centralised data teams, who themselves are already overloaded and hit by the continuing war for talent? 

  • Democratise your data – Organisations should democratise data and enable the wider workforce to glean insights directly. Data silos will no longer cut it in financial services, with businesses increasingly using analytics to inform decision-making on anything from compliance to customer experience. It is imperative that every business unit realises the value of the data that both it and the wider business generates.

What this means in practice is using tools that don’t require bespoke coding, and instead enable businesses to access their data wherever it lives, run analytics locally and reveal genuine insights, in real-time.  By extracting, transforming and loading data in the cloud, agile financial services businesses can convert raw data into actionable, analytics-ready data in minutes for new insights and better business decisions.

  • Empower data teams – All too often talented data teams are bogged down with slow data migration and maintenance. Our recent research revealed that two-thirds (66%) of data professionals believed their organisation was wasting too much time on data preparation.

However, manual integration is increasingly becoming a thing of the past, giving way to more automation and low-code/no-code tools that take away a lot of this pain. They’re making it easier for less technical business users to easily analyse data sets, and freeing up valuable time for skilled data engineers to invest in more technically challenging and value-adding tasks,  and taking full advantage of what the cloud has to offer.

  • Eliminate data blind spots – In our aforementioned survey, nearly 40% of data teams admitted they don’t fully understand how data is being used in their organisations. On top of that, a further 44% are concerned about the challenge presented by the diversity in the types of data they work with. All of which suggests certain data types are being left behind in many organisations, leaving new, growing information gaps in their data strategies.

Cloud data and IoT data were noted as the most commonly unavailable or unsuitable sources for business intelligence and analytics. Cloud-native solutions like data lakes or lakehouses take on great importance here, by providing a centralised repository for businesses to collect and store data of any scale and format. When managed correctly, such data stores promise to help create a consolidated, single source of data truth, making it easier to govern, manage and transform into an analytics-ready state.

Making your data business-ready 

In a landscape permanently adjusted by Covid, financial organisations have a vast amount of data at their disposal, presenting an unprecedented opportunity. Capitalising on this can put them in the strongest position possible, unlocking more informed, real-time decision making across all areas of the business. 

Having a holistic, cross-departmental data strategy is the first step, as too often data is siloed, mismanaged or incomplete. Putting the right tools and considered processes in place is a key part of achieving that, and will go a long way towards democratising data for the benefit of all employees. Now is the moment to  complete a major piece of the digital transformation puzzle and accelerate the modern data journey of financial services organisations all over the world. Don’t let yours be left behind.

Joe Steadman profile image

Joe Steadman is the Regional Director, EMEA, Matillion where he has worked in the enterprise division for seven years. Matillion is The Data Productivity Cloud Matillion helps teams get data business-ready, faster — accelerating time-to-value and increasing the impact data can have.

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