Financial Planning for Digital Transformation

Both Finance and IT work towards the same goals for the company—create growth and value. But the way both functions want to achieve the goals differ, which inherently leads to a conflict. Finance wants to control its SGA expenses and relate it to revenue trends while IT expenses tend to follow the quickly changing technology marketplace.  But as Gartner managing vice president Barbara Gomolski said very rightly “IT (Cost Optimization) also means that simply cutting the IT budget and waiting until the economic environment is more favorable to make digital investments is a flawed approach to remaining competitive.”[i]

So how should the IT department work with finance to better plan for its expenses.   Let’s examine this in the context of top-down and bottom-up budgeting processes. A lot has been written about both the budgeting process and the pros and cons of it.  Applying the top-down approach singularly to IT expenses would lead to a number based on previous year trends.  Adjustments will be based on revenue trends to maintain a specific target of IT expenses as a percentage of revenue.  While this process would be faster and aligned with company goals at a high level it would miss the details and the needs of the IT department itself. This process would also not take into consideration the innovation and modernization investments that might be needed in specific areas.   The investments would be dependent on the current economic environment—if the revenue trend looks good, IT expenses might be increased and vice versa if revenue trends are not positive.   This in turn might increase the technical and competitive risk of the company as digital investments are becoming essential to maintaining competitiveness in any industry. 

In a bottom-up budgeting scenario, the IT function would do a detailed analysis of needs and put together a budget that aligns very well with the goals of the IT function. All the participants would need to coordinate with each other to understand the cost impacts of various projects/changes. It would also foster the innovation and reduction of technical risks with investments based on the changes in markets.  With this method, the IT function may be aligned internally regarding the investments but it might not be fully aligned with the goals of the company since the company must balance the needs for investments in IT vs. investments needed in other functions.

To make sure budgets and financial plans are aligned across functions and there is commitment from leadership, there must be a step of goal alignment with budgets between the top-down and bottom-up approaches. Goal alignment meetings should be conducted with a comparison of investments and budgets to the goals of the company and the function to make sure that prioritization of expense and investment is done based on the strategic goals of the company while keeping in the mind the needs of the functions too.  This prioritization exercise is imperative to make sure the functional goals are aligned with the strategic goals of the company and that the budgeting exercise mirrors this.  

As stewards of the company’s finances, financial planning & analytics (FP&A) analysts should guide the process of alignment of budgets to goals and prioritization of investments within the company and functions. FP&A should partner with business to help them make appropriate decisions regarding the best use of limited resources based on data and analytics.  

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[i] https://www.gartner.com/smarterwithgartner/how-cfos-can-tackle-the-cost-optimization-equation/

Going Back to the Raw Data

In finance, we tend to report based on cost centers and categories. We combine cost centers or cost categories and report on performance based on this. We forecast and load based on those cost centers and cost categories. We are tied to the way we have organized our data, and we tend to invest in projects in order to organize our data in a different way so we can better report if the current system of organization is not working well. 

But in the end, are we looking at and analyzing the raw data? For us in finance, the raw data consists of our journal entries, sales, invoices, etc. How much time are we spending looking at that raw data and making sense out of it? When we look at reorganizing our data, do we spend time putting together rules of how to create journal entries that make more sense so we can do data mining and machine learning? As FP&A professionals on the cusp of harvesting the potential of "Big Data," we must escape the view of cost centers and cost categories and start doing deep dives into the raw data. 

We have a treasure trove of data in the current financial systems and we need to be able to use it. As

an example, lets dive into IT expense analysis. An IT department today in any company may be organized based on reporting structures. The function may have decided to put all the licenses in one cost center to manage the procurement process much better rather than by putting it in different cost centers. So, if the company is spending money on data center, help desk or SAP licenses—the expense for those licenses might come directly into that one cost center. The function could have employees in one cost center working on two different services such as data center and help desk.  To help understand the actual cost of providing data center services or help desk services, one must pull the expenses from various cost centers and categories. One solution is to reorganize or have more cost centers so we can track these separately, but that can cause other issues such as inefficiencies in license management.  No one way of organizing the cost centers is perfect and needs will be changing constantly, so to obtain any insights, we need to go back to the raw data.

Doing the analysis on the raw data we currently have as well as connecting that data to business drivers or operational metrics today does not require huge investments in ERP systems. It requires more manual work of extracting the raw data and doing the detective work.  The majority of that work is done in Excel today. This kind of manual analysis is not sustainable in the long term as it takes time and resources and is prone to errors, but it might be the first step to take to prove the value of such analyses.  It is very important to prove the value of the analytics needed in small pilot projects or proof of concepts so the company can invest in the right software and tools to enable to the analytics. 

So, go out and explore your raw data and breakout of the cost centers and cost categories.