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Evolution of Technology and the Impact on Accounting Software of Today

We’ve seen several generations of financial accounting systems emerge over the last 4-5 decades. Initially, there were mainframe applications that only completed the most computationally intensive aspects of a financial process. High cost computing hardware drove a lot of the early specialization in application development (and the limited options for same). As computing power became more affordable, minicomputers drove a new phase of application development. Many accounting software providers emerged as did the accounting suite: a collection of related applications.

Since then, computing power has grown exponentially, and computing costs have plummeted. Today’s smartphones routinely possess more computing power and storage than mainframe computers had in the late 1980’s. Programming languages became more powerful too and this allowed smaller development teams to create new financial accounting applications quicker and cheaper.

As a result, accounting technology has been able to improve at an accelerating rate. Unfortunately, many firms’ usage of an accounting software package seems to have lengthened during the same timeframe. In my early career, we used to track the “de-install” rate of leading back office solutions. In the 1990s, customers often hung onto their software for approximately 10 years. In fact, we’d often saw software being replaced as early as 8 years after the initial installation with 80% of customers using a replacement product by year 12.

Today, it’s not uncommon to find companies still using the same product almost 20+ years later. Why did this happen? Many companies perceived the risk of removing a packaged solution as high, while the upside of doing so was low. This was certainly the case for much of the last decade. However, newer solutions now offer a vastly different value proposition and smart accounting organizations would be wise to take note of this.

What’s Different Today

Prior generations of accounting software focused on transactional processing and financial statement generation. The sales pitch for those solutions was often centered around how fast journal entries, checks or reports could be produced. These conversations could have been confused with those of the makers of manufacturing equipment: it was all about speeds and feeds.

Once the domain of transaction processing was conquered, software vendors had to look elsewhere to add value. Where they once had the competitive advantage in transactional processing, it was now a commodity. Worse, the presence of lower-cost computing resources was acting as a dampening force on new software sales. Software vendors had to find new ways to add value or else their markets and products would eventually become stagnate.

As it turns out, ever cheaper technology enabled capabilities like machine learning to become practical. Likewise, the plummeting price of data storage and the availability of sensors (e.g., IoT devices), big data (e.g., social sentiment information), etc. meant that executives could look at more than accounting data to understand what was occurring in and around their firm.

Finance today must:

  • Work with many kinds of data with differing levels of veracity. Accounting data continues to be highly accurate but what happens when you combine it with the purchase intention” data from customer surveys? Other data sources are giving firms the ability to gain better forecasting or prediction insights even though the data is not as immaculate as accounting data might be. Now firms can apply big data and machine learning to almost every line of the P&L to produce superior forecasts for the firm.
  • Work with data that was previously difficult or not cost effective to review. Many firms possess mountains of “dark” data. This is information that has been collected but rarely reviewed. Airlines collect vast amounts of passenger satisfaction survey data but may not do much with it. Manufacturers might have equipment controllers throwing off lots of status data that only gets a cursory review when a machine fails. Today, new multi-dimensional tools make the review of this data possible and more thorough than what time-constrained humans can do. For example, machine learning technologies can spot anomalies and correlations that many humans might find too subtle to spot. This kind of analysis makes Finance staff more valuable to the firm and a great ally to their operational counterparts.
  • Work beyond paper. Newer solutions use technologies like natural language processing (NLP) to permit voice-to-application controls among other things. Already suppliers can speak into their smartphones and interact with your accounts payable module to check the status of payment. NLP frees up accounting personnel from low or non-value-added tasks. Can your employees check on their T&E reimbursement from their smart phone and without speaking to someone within accounting?
  • Forget transactions and focus on reimagined processes. What does a finance process look like when it can be enhanced with chatbots, robotic process automation, exception handling and workflow tools?
  • Deliver “smart” analytics. If you’re still thinking in static report or graphic formats, you’re behind the times. Not only should data be presented graphically (and enable drilldowns to supporting transactions), it should also tell viewers what this data shows (i.e., what are the headlines or takeaways) and what actions others have taken when presented with similar data or trends. The best solutions today take it even future by executing the recommendation via embedded workflow handling logic.
  • Shift its timeframe from past to future. Previously, Accounting has been focused on reporting what already happened. Only during the annual budget cycle did they look ahead. Today’s technologies can do a lot more. Some technologies can detect “future crimes”. For example, one major accountancy has developed an application that scans employees’ emails and text messages for communications that indicate whether someone might be suggesting a bribe. The software looks at whether the communication is moving through high risk countries and then parses the communication looking for phrases like “facilitation fee”. This technology can detect a potential bribe before it occurs.
  • Analyze data across a myriad of dimensions that goes beyond legal entities or cost centres. These additional dimensions could include: Product (e.g., SKU, product line, etc.), Geography (e.g., region, country, territory, etc.), Channel (e.g., direct, web sales, partner, reseller, white label, etc.) or Statistical and operational data (e.g., sensor data).

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What makes the newer generation of solutions so powerful and rapidly evolving is that most are built on newer technology platforms. These platforms often possess:

  • Tools to permit rapid integration with popular third-party tools.
  • Microservices (e.g., ML/AI, NLP, workflow, etc.) that dramatically enhance the functionality of applications.
  • Powerful development tools that allow non-technical people and systems integrators to easily extend the system’s functionality.
  • Utilities to manage the application software (e.g., backup/recovery) without user intervention.
  • Automatic support for a number of smart devices (e.g., phones, tablets, laptops).
  • Connectors to common sensors.
  • Language translation tools.

And while a spreadsheet can be manipulated to do a lot of things your old accounting package can’t do, it will likely never do or support many of the items above. Newer, modern cloud-based solutions provide peace of mind, a better value proposition and room to change and grow as your business evolves.

Implications for Finance and Accounting

There’s a growing digital divide within the corporate accounting world. There are accounting organizations that are putting their teams through punishing efforts to find new insights and value with out-of-date, underpowered solutions. And there are more enlightened organizations that are letting newer technology do a lot of the repetitive work to free-up time for Finance personnel to focus on high-value activities.

Newer solutions are also forcing Finance professionals to work with more squishy, non-accounting data. This will generate new skills requirements for Finance particularly in areas like statistics, data science, algorithm design, social sciences and more. These skill sets are notably absent in many accounting organizations today and rarely found in the curriculums of major business schools, too.

More precise granular data will trigger changes in how accounting evaluates businesses. For example, allocations can be more sophisticated when being able to extract data from operational systems. Moreover, disciplines like Cost Accounting and Management Reporting must be rethought in an IoT (Internet of Things) and smart analytics powered business environment.

Many of the new enabling technologies (e.g., RPA, ML, AI, NLP, workflow, etc.) are best used as tools to change the nature of work and work processes. This means a mindset change is required by accounting teams to reimagine the future of finance and their profession.

The key change: transactions will be a lesser focus and insights or value to the business will be a greater focus.

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