Automating data to fuel digital transformation in finance  

Steve Brodrick, Chief Transformation Officer at Alteryx 

Today, finance leaders face the challenge of not only delivering financial success but also providing strategic insights to fuel digital transformation. With the Financial Services and Markets Act 2023 kicking off a new era for UK financial regulation, leaders in the financial sector need to understand how to balance commercial, compliance and strategic issues to guarantee successful digital transformation.  

Nearly every company, large and small, has become its own super-productive data factory, but as data complexity rises, the only way to truly shorten the path to insights and harness its value is to automate the process of data discovery, preparation and blending of disparate data. 

Luckily, with the rise of automation in data analytics lies a solution for the unique challenges faced by financial industry leaders. Automation in data analytics can empower firms to empower domain experts to discover insights from data rather than turn to IT or hire experts to establish a single source of truth for compliance purposes, enhance efficiency, mitigate risk, and deliver more predictable financial outcomes.  

Creating a centralised financial data source 

Finance leaders have a massive opportunity on their hands, delving into the specific industry applications of automation in data analytics. Combining automated analytics solutions with rich technology integration options, embedded genAI features and accessible data science offers the perfect capability for decision-makers with no data science skills to deliver insights via a natural language prompt. Effectively democratising AI for those without deep expertise in AI and data science and further accelerating modernisation by helping individual financial services firms emerge as AI frontrunners benefitting from a competitive edge.  

Whether on-premises, private, public, or multi-cloud, financial data can be stored, accessed, and analysed in many different places. Organisations must focus on having tools in place to find the data across silos and bring it into their analytic process. Consolidating siloed financial data in a governed environment provides stakeholders access to quality data pipelines for a holistic view of accurate financial metrics. This is a goal well worth pursing, but industry leaders need to be conscious of the various challenges associated with organising financial data. The complexity of financial data, often spread across various divisions, subsidiaries, regulatory requirements, and taxation structures, can’t be overlooked. This is where an accessible, self-service platform that boast ease of use, scalability and flexibility proves its worth with a capability to pull in raw data from a range of disparate sources. 

To take an example – advisory tax firm Baker Tilly has realised the benefits of automated analytics, leveraging it to consolidate massive volumes of unclaimed property data from disparate sources like bank accounts and company payroll. This not only slashes report processing time by 50% but also significantly reduces risk to operations.  

Financial leaders can start automating data consolidation and analytics efforts by first, mapping internal and external financial data sources and then preparing them for final integration. 

Automating data collection and entry  

Financial process automation can eliminate repetitive tasks like data entry, invoice processing, and regulatory testing. Allowing technologies to take on these manual tasks can reduce errors, improve forecast accuracy, and allow finance teams to focus on more strategic initiatives in the business. 

The negative impact of manual spreadsheet work is known all well to the financial services industry. Gartner estimates that human data entry errors in finance processes alone add roughly 25,000 hours of avoidable rework at $878,000 per year.  

A dependence on manual spreadsheets isn’t just a financial risk – it’s a drag on productivity. To take an example from another industry vertical, Siemens Energy was buried in error-prone manual tasks using Excel spreadsheets. Therefore, the company implemented a code free, automated platform, saving thousands of hours and generating 200+ new use cases for automation. 

To kick off automating data collection, organisations should scope out small but high-impact areas for automation in highly manual and repetitive finance processes. This will yield a quick return on investment which helps prove the worth of automation initiatives and expand their subsequent use throughout the business.  

Faster reporting and better accuracy  

Once data has been centralised and initial analytic process automation use cases are initiated, finance leaders will really start to feel the benefits of quicker reporting, increased accuracy of data and lower operational costs. 

Real-time data can also reduce exposure and improve responsiveness to new business opportunities. AI capabilities can be leveraged across the organisation for auto-generated narratives in extensive management reports.  

Telecom giant BT was using 140 legacy Excel models to run regulatory compliance reports, which took up to 4 weeks during compliance cycles. To reduce risk and improve efficiencies, the company re-platformed the models to an automated analytics solution, reducing time to insight by 75% and improving the accuracy of regulatory reporting.  

Organisations must start with defining a well-structured business case for automation. Once completed and signed off they can improve the financial close process and gain immediate wins for additional use cases throughout different processes.  

Ensure compliance  

In finance, there is no room for error. If something goes wrong, there is a knock-on effect on the rest of the company that can be a key trigger in losing clients, prospects, and business reputation. Discrepancies from manual spreadsheet manipulation can expose companies and financial clients to unnecessary exposure and losses in money.  

With the rise in analytics transformation in the finance industry, the manual burden of executing audits is relieved. Regulation can be complied with regardless of changes or extra requirements and the compliance process helps to drive accurate financial insights.  

At Bank of America, manually prepping and cleansing data for millions of transactions was taking up to two months, exposing the bank to costly regulatory fines. The bank now uses a cloud automation solution to reduce processing time down to 1 hour, giving the regulatory team time to take corrective action.  

The importance of upskilling and automation cannot be overlooked as a step to achieving automated analytics excellence. Finance leaders need to ensure that a culture of upskilling employees goes hand in hand with automation, empowering domain experts to deliver high-quality work at rapid speed.  

Empower finance domain experts with self-service analytics  

Automated, self-service analytics has the power to make it easier for finance teams to make data-driven decisions quickly. This culture shift allows business users to focus on value-added activities and accelerates the delivery of high-value insights. 

Organisations must empower their domain experts to deliver high-value work at rapid speed. This can be achieved by providing employees with easy-to-use automation solutions that can work alongside their day-to-day tasks. By choosing a self-service platform and creating a flexible learning environment, domain experts will be able to pick up skills at a pace that suits them best.  

Realising its full potential 

The benefits of automation in data analytics are myriad. Adoption allows financial organisations and teams to deliver faster forecasts and decision-making while, critically, also always improving accuracy. By harnessing automation and AI, the financial sector can become more efficient, reduce costs, and increase profitability. That’s going to be appealing for any industry player.  

While every C-suite executive desires to create value from data, very few have a comprehensive enough definition of value, and most admit they struggle to correlate data and technology investments to specific sources of value. However, it is possible to define value through the two lenses of measurable enterprise value, and upskilling.  

But an often-skipped step in this “dynamic value duo” is a simple one – driving accountability to high-impact decisions that were made possible by freeing up time and process waste in the first place. This last step shouldn’t end at defining value as the measurable impact of time savings of tools and technology. Remember to follow up by recognising great talent. Illuminate the art of possible to others, by holding accountability to the more innovative decisions your empowered workforce can make as they engage in automation.


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