Analytics Automation For Accounting And Finance

Analytics Automation For Accounting And Finance

Data analytics and emerging technologies are revolutionising and reinventing the accounting and finance industry. An international survey of accountants conducted by Sage in 2019 found that 90% of respondents believed there has been a cultural shift in accountancy for the next decade, driving changes in hiring practices, business services and attitudes towards emerging technologies.

While accounting plays a critical role in running a business by ensuring statutory compliance and helping companies evaluate business performance through quality financial reports, advances in data analytics and artificial intelligence (AI)/machine learning (ML) technologies have brought about rapid changes in how businesses operate.

In this age of rapid digital transformation, how can accountants and financial professionals take advantage of AI, ML, and analytics automation to unlock the full potential of data and help drive transformative business outcomes?

Data is the lifeblood of digital transformation, with more than 90% of Asia-Pacific leaders agreeing that data analytics is important for their organisations to stay performant, according to a 2021 Alteryx-commissioned IDC survey. Data is now a critical corporate asset, with its value tied to its ultimate use.

Beyond the fundamental processes of bookkeeping and production of financial statements, accounting is also increasingly playing a strategic role in businesses. Management accountants can help lead businesses in setting financial goals, conducting risk assessments to inform critical business decisions and forecasting financial planning with predictive analytics. To do so, accountants and financial professionals need to be data-literate and equipped with the necessary knowhow to utilise existing data effectively – translating raw data into actionable insights.

For instance, the use of accrual accounts can greatly increase the amount of information on accounting statements and impact the final reports and numbers. As the information often comes from different sources, locating and understanding the right data can be time-consuming.

Modern technologies, like AI, are capable of automating repetitive and tedious manual accounting tasks, such as basic data entry, bookkeeping, payroll and bank reconciliation. These technologies can empower accountants and financial professionals to focus on delivering higher-value work and supporting mission-critical business functions, as analytics automation significantly reduces human error and accelerates analytic insights. In the example of accrual accounts, accruals can be automated to be posted monthly instead of quarterly, improving the quality of financial information. This provides accountants with a clearer view of revenue based on credit and future liabilities.

While most companies can recognise the irresistible benefits of data analytics in their finance and accounting departments, they face many roadblocks during the actual implementation.

If there’s one thing finance and accounting departments have in abundance, it is data. Yet, these vast amounts of data, in their raw and unstructured state, are not usable until they have been processed and analysed. Furthermore, viewing data in spreadsheets can be nerve-racking, making it nearly impossible to uncover critical business insights or enable more efficient processes. An Alteryx report revealed that only 13% of data science models get deployed while tax professionals spent more than 50% of their time gathering tax data and less than 30% on strategic tax analysis.

The current reality of most companies’ data analytics journey is fraught with fragmented processes and analytic chokepoints between teams, technologies, and data sources. Time is often burnt on trying to clean, prepare and analyse data, rather than delivering meaningful financial insights and implementing changes based on what the data reveals. As a result, these vast amounts of data owned by companies are sometimes a source of liability rather than a super-powered asset.

Businesses have been making the push towards becoming more digitally ready and data-driven for years, yielding vastly mixed results due to roadblocks and analytic inefficiencies. In the data analytics revolution for accounting and finance industries, there are some common pitfalls: (i) the lack of simple tools to boost workforce data literacy; (ii) shortage of data-literate and data-confident talent, and (iii) failure to cultivate the right data-driven culture.

An Alteryx-commissioned IDC Infobrief titled “Data and Analytics in a Digital-First World” revealed that 91% of organisations report some areas of skills gaps in data and analytics, illustrating the need for data and analytics simplicity across on- and off-premises data sources, formats, and qualities.

Furthermore, 62% of accountants surveyed believe that today’s accounting training programmes will be insufficient in running a successful practice by 2030, with the increase in demand in skill sets such as technological and data literacy.

Exacerbating the lack of data literacy in a company is the lack of the right talent pool that can support businesses’ data analytics and digital transformation efforts. Eighty-nine per cent of Singaporeans say they face challenges at work as a result of not understanding data. For those entering the workforce, a mere one in 10 graduates stated that they are prepared to deal with data at a workplace.

Beyond the absence of simple tools to boost workforce data literacy and shortage of desired talent, businesses should focus on building a stronger data culture across the enterprise. Isolated data analytics initiatives can result in siloed operations and data lakes, hindering many organisations’ ability to access the right data and interpret it in a fast manner. Building an innovation data culture extends data analytics beyond technology and IT teams to include design, engineering and, in this case, finance teams.

Leading this change and creating a thriving data culture requires strong leadership at the top. According to McKinsey, many CFOs have declared a desire to spend more time on digital initiatives but fell short in execution, finding themselves focusing on more traditional finance activities instead. Many finance executives struggle to find a starting point, preferring to lean on colleagues in IT to deploy digital technologies such as data analytics. However, it is important for CFOs to take the charge in helming data analytics initiatives, developing clear objectives and vision that trickle down and encourage cross-functional collaboration between technology teams and the wider business operations.

Ready to join the data analytics revolution to empower your accounting and finance teams? Despite the aforementioned challenges, every journey begins with a single step. To help integrate data analytics into accounting and finance functions in a seamless and efficient manner, businesses should work on building best practices through the following steps:

1. Begin wherever you are

Taking the first step in operationalising data analytics can be daunting for many businesses. Implementing data analytics will not be a linear process and businesses should embrace the process of learning, iterating, failing, and repeating until they figure it out.

Companies that have seen the most success have moved away from labour-intensive processes as they are prone to fragmentation and operational inefficiencies. With a modern analytics automation platform, the accounting and financial teams are empowered to carry out processes with greater repeatability, consistency, and predictable outcomes.

3. Consider the risks and charge ahead

In considering the approach to take for data analytics, businesses should evaluate all perceived risks and get into the habit of performing regular monitoring and quality checks.

Rather than reinventing the wheel completely, businesses should work on building on existing foundations and processes. Cutting-edge solutions that package open-source AI and ML and self-service analytics applications can solve data issues through automated augmented capabilities. Self-service analytics refers to intuitive, drag-and-drop interfaces and code-free/code-friendly platforms that provide a deeper understanding of financial processes.

Self-service analytics is key to speed, democratising data analytics, and putting the power of data in the hands of hundreds of business domain experts, not just one. When financial professionals no longer have to rely on data scientists for analysis and results, finance teams are empowered to derive data-driven insights that drive better business decisions.

6. Drill down into financial operations

With operational analytics, time-consuming manual hours can be reduced significantly with process automation. Data quality checks can be automated, with a final human approving and posting step, if required. Rather than performing hundreds of hours of manual data extraction and accrual calculations from multiple departments, deploy modern analytics that integrate different processes into a single unified platform.

Modern analytics processes also allow businesses to create brand new capabilities through innovation programmes. This allows for the delivery of products and services to be brought closer to the last-mile stage of the project and supports your needs and those of your customers.

Data analytics offers boundless and unlimited value. Look at companies that have successfully integrated data analytics into accounting and finance. For example, audit, assurance, and tax services companies have created automated tax engines to supplant mundane and laborious processes such as tax validation and calculation, saving thousands of man-hours, and vastly improving the level of comfort and assurance of teams working with data. The key capabilities of automation and augmentation are constantly evolving and driving smarter transformation in finance.

For a time, only trained data scientists and analysts could provide answers, limiting data analytics to businesses’ technology and IT units.

The democratisation of data analytics via self-service technologies has cleared the path for accountants and financial professionals, without deep expertise in technology and analytics, to enter the picture.

Businesses can explore no-code/code-friendly analytics platforms which allow technical and non-technical people to share work and collaborate to achieve data breakthroughs faster than ever before. Today’s modern analytics automation platforms provide a deeper understanding of the auditing process, making it easier to close the books more accurately, automate taxes and drive regulatory compliance. As a result, accountants and auditors are now equipped with the ability to engage with data, identify anomalies, and classify potential irregularities in financial processes through simple-to-use and intuitive drag-and-drop interfaces, regardless of their skill and comfort level.

Modern and smart enterprises looking to unlock value from data at the scale and speed in today’s digital-first world are increasingly seeking to remove complexities associated with creating analytic outcomes. Self-service data analytics unlocks the potential of current finance teams and cutting-edge technologies, including automation, AI/ML and analytics automation, to easily unleash the value of data and deliver strategic insights that weren’t previously possible.

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