categoryReporting and Analytics

Migrating From On-Premise to Cloud-Based Solutions

Gartner forecasts cloud computing to be a $278 billion business by 2021, as companies increasingly adopt cloud services to realize their desired digital business outcomes. In a separate survey of 439 global financial executives, the research company found that finance is moving to the cloud much faster than expected.

By 2020, 36 percent of enterprises are expected to be using the cloud to support more than half of their transactional systems of record.

With cloud technology promising to provide the speed and agility that the business requires, including generating significant cost savings and new sources of revenue, it’s not surprising the cloud market is experiencing a boom.

While companies have experienced both organic and inorganic growth, many have struggled to keep pace with rapidly changing technology landscape. Their businesses are saddled with legacy technology infrastructures that are slowing progress towards the achievement of strategic objectives as they are slow to react to change.

Given the need to react more quickly, at the same time become more proactive and drive business performance, senior finance executives are turning to cloud-based financial solutions to provide real-time management information reporting and decision support.

Adopting cloud not a simple and quick decision

Looking at the expected growth metrics for the cloud market and then hearing of all the novel opportunities offered by cloud computing, CFOs and CIOs are enticed to believe that it is only a matter of time before the organization’s computing technology and data is skyward.

However, over the course of my career I have come to understand that the only thing right about a forecast is that it’s wrong. Either the forecast is close to reality or very far from reality.

Cost savings is cited most often by senior finance executives as the reason for adopting cloud technology. Considering that cloud is essentially a form of outsourcing arrangement in which client organizations use the internet to connect to a variety of applications, storage services, hardware resources, platforms and other IT capabilities offered by cloud service providers, upfront capital investment and maintenance costs are minimal.

This is because the cloud service provider owns the necessary hardware and other resources needed to deliver these services and is also responsible for employing the staff required to support them. Unlike on-premise solutions where the business has to incur large capital outlays for hardware and IT support staff, cloud is an operating expense (except implementation costs which can be capitalized), that is payable as and when incurred.

With cloud, a business is able to add new licences to its subscription when it needs them and scale its licensing costs with its growth, rather than buying in bulk upfront. This is in direct contrast to traditional on-premise software where companies in anticipation of business growth have over invested in IT systems hoping to add more users to the user list soon after the growth is achieved.

However, when deciding whether to invest in cloud or not, CFOs should look beyond cost benefits. In addition to cost savings, they should also consider tactical and more strategic and structural issues. Unfortunately, the challenge for many finance professionals is that when evaluating investments the focus is solely on cost. We fail to examine and evaluate the non-financial risks and strategic impact of the investment on the business.

Strategic and structural considerations

As I have written in the past, most organizations get caught up in the hype of new technologies and end up making technology investments that are unaligned to the strategy of the business. There is no strong business case for investing in the new technology. Don’t allow yourself to fall into the same trap.

Investing in cloud is not an IT or finance only decision. Decisions of what to migrate to the cloud, when to migrate it, and how to transition from an on-premise environment to a cloud-based environment all require a collaborated effort if the organization is to achieve its stated objectives.

Further, transitioning to the cloud computing environment  is not a matter of flicking the switch up and down. You need to have deeper understanding of the cloud resources (public cloud, private cloud, community cloud and hybrid cloud) available on the market, their delivery models (SaaS, PaaS and IaaS) and how these all fit together into your business and technology model.

Understanding the cloud model will help you determine whether cloud is appropriate in the context of your business purpose and risks. For example, in the case of public cloud, the applications and cloud-based services are positioned as utilities available to anyone who signs on.

If over the years your company has built and strengthened IT capabilities that differentiate you from competitors, migrating to the cloud can mean walking away from your success recipe and expose yourself to vulnerabilities.

Therefore, if you  are planning to migrate your on-premise computing environment to the cloud, take a long-term view of your IT environment and determine what type of applications are candidates for the cloud and which will not be transitioned until the distant future.

Ideally, you should start with applications that have low risk associated with them or those that have a business need that cannot be met using traditional computing capabilities. Once you have build greater comfort and trust in the cloud, you can then scale to include other applications.

The pain of disintegration

It is no secret that many businesses today are re-evaluating their technology infrastructure and leveraging new technologies to respond faster and be more flexible. But is cloud computing right for your business? Is speed to deploy more important to you than having systems that talk to each other?

In a world where each cloud service provider is claiming that their solution is better than the next one on the same shelf, CFOs and CIOs are grappling with the decision of which cloud service provider to go with. As a result, the company ends up doing business with more than one vendor to compensate for the shortfalls of the other system.

The problem arises when the cloud-based application from one vendor is unable to work with an application from another provider resulting in more than one version of the truth. In other cases, the company’s on-premise IT infrastructure does not allow sharing data with multiple cloud-based applications, which in turn results in finance spending time consolidating and reconciling data from disparate systems in excel.

Given that the cloud model depends on providing essentially a standardized package across the board, it is important to weigh the pros and cons of foregoing customization versus rapid implementation. Because the cloud market is projected to grow in the coming years, many IT solution providers are channeling money towards cloud-based solutions. This has resulted in some vendors withdrawing IT support on legacy ERP systems and phasing them out.

In some cases, the vendors have installed upgrades to the same solutions. The problem with these solutions is that they were originally not built with modern business requirements in mind hence they can only get you a few more years of support.

It is therefore important for CFOs and CIOs to be cognizant whether the solution was originally designed as a cloud-based solution, or it is a modified version of a solution initially designed to function in a traditional on-premise client-ownership model.

With data being found everywhere today and advanced analytics playing a critical role in supporting key decision making, delivering one version of truth has never been so important. In order to make sense of all the data at its disposal a company should focus its efforts on centralizing data management and IT policies. Having a single data repository ensures everyone is drinking from the same well for information and insights.

However, in companies where IT governance is weak the tendency is for teams to autonomously adopt cloud-based solutions that meet their individual needs. This is counter-productive to data management centralization efforts as data normally ends up spread across multiple cloud-based systems that are dis-aggregated.

Just imagine a scenario where FP&A, tax, treasury, procurement, supply chain, and other finance functions each identify and select their own cloud solution. Consolidating and analyzing relevant data from these various systems to get a big picture view of business performance in itself is a huge task to complete as that data is divided across many domains.

While each cloud-based move may look beneficial in isolation, adopted together they may increase operating expenses to a level that undermines the anticipated savings.

Control versus no control

Although cloud-based solutions offer more affordable options and more flexible payment plans than traditional software providers, the issue of data control is still a concern. Cyber criminals are getting smarter by the day,and the fact is, whether organizational data resides on the internet or offline on the company’s network, it’s never completely immune to attack.

When it comes to data security, it is imperative for CFOs and CIOs to know that the moment data is no longer in-house, the business may end up having less control over who has access to key systems and data. The fact that you have outsourced your IT systems and data control to a third party does not make your company immune to a lawsuit in the event of a breach. You therefore need to sign agreements that protect you against various types of misfortunes.

Although the cloud service provider employs a team of IT support staff to monitor and perform regular threat assessments and deploy the latest patch immediately, the organization should not relax and assume the house is in order. You need to get strong assurances from your cloud vendor that your business data is safe.

You also need to know where your data is stored, the applicable data laws, how often it is backed up and whether you are able to perform audits on the data. Other factors to consider include end of agreements for convenience. Many software vendors try to lock in client organizations for a significant period of time. This in turn makes it difficult for client organizations to change vendors without incurring costs or changing systems some way.

Thus, when negotiating an agreement with a cloud-based service provider, try by all means to ensure that you are not locked-in for lengthy periods just in case you need to change technology providers in the near future.

Don’t rush to the cloud without a clearly defined vision and road map. Engage in plenty of deliberation to ensure the new surpasses the old, sample technologies with less risk and related influences on the business and then scale the adoption.

Delivering Reliable Insights From Data Analytics

Data and analytics are becoming increasingly integral to business decisions. Organizations across all sectors are leveraging advanced real-time predictive analytics to balance intuitive decision-making and data-driven insights to monitor brand sentiment on social media, better serve their customers, streamline operations, manage risks and ultimately drive strategic performance.

Traditional business intelligence reporting tools with static charts and graphs are being replaced by interactive data visualization tools that enable business insights to be shared in a format that is easily understood by decision makers, in turn enabling better, faster operational and strategic decision-making.

Given the constantly changing business landscape driven by increased competition, macro and geopolitical risks, intense regulatory demands, complex supply chains, advanced technological changes etc. decision makers are turning to finance teams for actionable insights to help them navigate through this volatility and uncertainty.

As business unit managers increasingly rely on finance decision support for enhanced business performance, it is imperative for CFOs and their teams to ensure the performance insights they are delivering are informed and actionable. However, a 2016 survey report by KPMG revealed that 60% of the survey participants are not very confident in their data and analytics insights.

Data Quality or Quantity?

In today’s world of exponential data growth, the ability for finance to deliver reliable and actionable insights rests not on the quantity of data collected, analyzed and interpreted, but rather on the quality of that data. The starting point for any successful data analytics initiative involves clarifying why the business needs to collect a particular type of data.

One of the challenges facing many businesses today is identifying and selecting which internal and external sources of data to focus on. As a result, these companies end up investing in data warehouses full of massive amounts of useless data. To avoid being data rich and insight poor, CFOs need to understand  the role of quality in developing and managing tools, data and analytics.

Before inputting data into any analytical model, it is important to first assess the appropriateness of your data sources. Do they provide relevant and accurate data for analysis and interpretation? Instead of relying on a single source of data for analysis, you need to have the ability to blend and analyze multiple sources of data. This will help make informed decisions and drive business performance.

Further, businesses are operating in a period of rapid market changes. Market and customer data is getting outdated quickly. As a result, being agile and having the ability to quickly respond to changing market conditions has become a critical requirement for survival. The business cannot afford to sit on raw data for longer periods. Capabilities that enable data to be accessed, stored, retrieved and analysed in a timely basis should be enhanced.

Thus, in order to provide business users with access to real-time and near-time operational and financial data, the organization should focus on reducing data latency. Reducing data latency allows finance teams to run ad-hoc reports to answer specific business questions which in turn enables decision makers to make important decisions more quickly.

In the event that finance provides business insights or recommendations based on inaccurate data, analysis and predictions, this will quickly erode, if not extinguish, business trust and shake the confidence of those decision makers who rely on these predictions to make informed decisions.

As data volumes increase and new uses emerge, the challenge will only increase. It is therefore critical for finance to put in place robust data governance structures that assess and evaluate the accuracy of data analytics inputs and outputs.

Work with business partners to set objectives up front

Churning out performance reports that do not influence decision making is a waste of time yet that is what most finance teams are spending their time doing. It is not always the case that the numbers that make sense to finance will also make sense to business partners.

The biggest problem in the majority of these cases is lack of understanding by finance of the business objectives. Instead of collaborating with the business to develop a better understanding of their operations and how performance should be measured and reported, many finance analytics teams are working in their own silos without truly linking their activities back to business outcomes.

To improve finance’s reporting outcomes, the function should take stock of the reports it produces per month, quarter or annually. Then evaluate the nature and purpose of each report produced and what key decisions it helps to drive. It is not about the quantity of reports that matters, but rather the quality of the reports.

Business partners need to be engaged at the start of the process and throughout the analytics process. They need to be involved as finance explores the data and develop insights to ensure that when the modeling is complete, the results make sense from a business perspective

By working with business partners and setting objectives up front, finance will be able to focus its efforts and resources on value-add reports that tell a better business story. Further, the function will be able to assess and monitor the effectiveness of its data models in supporting business decisions

Simplify interconnected analytics

With so many variables impacting business performance the organization cannot continue to rely on gut instinct to make better decisions. The organization has no choice but to use data to drive insights. As a result, organizations are relying on a number of interconnected analytical models to predict and visualize future performance.

However, given that one variable might have multiple effects, it is important for the business to understand how changes in one variable will affect all the models that use that variable, rather than just one individual model. By maintaining a meta-model, the organization would be able to visualize and control how different analytical models are linked.

It also helps ensure consistency in how data is used across different analytical models. Ultimately, decision makers will be able to prioritize projects with the greatest potential of delivering the highest value to the business.

Build a data analytics culture

Advanced data analytics is a growing field and as such competition for talent among organizations is high. Due to their traditional professional training, many accounting and finance professionals lack the necessary data and analytics skills.

More over, decision makers not knowing enough about analytics are reluctant to adopt their use. Because of cognitive bias, it is human nature to subconsciously feel that their successful decisions in the past justify a continued use of old sources of data and insight. What we tend to forget is that what got us here won’t get us there, hence the need to learn, relearn and unlearn old habits.

To move forward, the organization should focus on overcoming cognitive biases developed over the years, and closing this skills gap and develop training and development programs that can help achieve the desired outcomes. Using advanced analytics to generate trusted insights requires an understanding of the various analytics methodologies, their rigor and applicability.

It’s difficult to have people understand if they don’t have the technical capabilities. However, building a data analytics culture does not imply that the organization should focus on developing data science skills alone. You also need to develop analytics translators.

These are individuals who are able apply their domain expertise in your industry or function, using a deep understanding of the drivers, processes, and metrics of the business to better leverage the data and turn it into reliable insights and concrete measurable actions.

Building a data analytics culture that promotes making decisions based on data-driven insights is a continuous endeavour that spans the data analytics life cycle from data through to insights and ultimately to generating value. Successful use cases can be used to further drive the culture across the organization.

A Practical Approach to Using Artificial Intelligence for CFOs – Part I

Part I Leveraging AI in the CFO Suite

In their role as curator of critical information for their company, Chief Financial Officers must create processes and develop systems that filter out noise and focus only on the most important, actionable information. The plethora of data being created is growing at astronomical rates making this role much more crucial and much more difficult. In this article we’ll explore how CFOs can take a practical approach to integrating artificial intelligence (AI) into their operations.

First let’s define AI in a manner that applies to its use in finance.

AI is information derived from algorithms applied to data set(s) normally accomplished with little or no human intervention.

  • Algorithm: a set of steps that are followed to solve a mathematical problem or to complete a computer process
  • Information: knowledge you get about something: facts or details about a subject

In his book, The Design of Business, Roger Martin describes the stages of learning that go from mystery to heuristic to algorithmic. The financial processes at many companies are heuristic, made up of general guidelines but containing many steps, developed by trial and error, and known only to the process owner. These processes lock corporate technology in the minds of one or a few individuals; creating technology risk and a training burden when staffing transitions occur. Developing an AI system and framework to effectively select processes that should incorporate more AI is rapidly becoming a core skill required for CFO success.

Until recently, many financial applications of AI have helped uncover altogether new techniques or capabilities. For example, program trading in the financial markets came about because AI could “crunch” numbers (the price of a basket of individual stocks) fast enough to allow traders to arbitrage an index against a portfolio of individual stocks.

In addition to the speed factor, AI now is being used to replace repetitive, linear tasks and increase our information output capacity. Both uses have wide implications for the CFO, including;

  • Choosing a system architecture that will capture AI most effectively for your organization
  • Managing your talent in a manner that is socially responsible
  • Developing and acquiring talent that captures the benefits of our AI system investment
  • Mastering the ability to manage the Decision Pyramid

How to Leverage AI in Finance

To date, most of the investments in AI for business have to do with specific industries; stock trading, portfolio management, banking and insurance underwriting. Customer development and customer service have also benefited from large investments in AI. The CFO responsibility areas, although ripe with automation, have not adopted AI to the extent these other industries or functions have. The opportunity is vast, but we need a methodology to identify where to start and continue our AI investment.

The main benefits to AI are derived from three aspects; speed, accuracy and volume. Logically, we should apply AI to areas where the total value (increased revenue and reduced cost) of the following three variables is greatest:

  • Speed: The incremental value of time as applied to a process or delivering information
  • Accuracy: The incremental cost of error or lack of precision in a process or information
  • Volume: The incremental cost of each unit of volume in a process or in reports and analysis.

Identifying the value of these different variables is the key to selecting an appropriate AI strategy and developing a work plan to implement it.

There are two main ways AI can enhance the CFO responsibility areas.

  1. As a Process Improvement Mechanism. In this case AI will be applied to the transactional work to complete it more quickly, more accurately and/or more of it.
  2. As a Decision Support Mechanism. Here AI is applied to the data used to create the information in a report or analysis to improve the decision support. This support is enhanced through quicker, more accurate and/or more information.

Illustrations of these two types of AI applications can be visualized using two examples:

  1. Procure to Pay (P2P): Using AI on the P2P process may yield big improvements in process effectiveness which will lead to lower costs and a reduction in errors.
  2. Budgeting and Forecasting: Using AI in the Forecasting process expands the scope of data that can be incorporated into the model, including the shift from exclusively using internal data to expanding the model to include external data. This use of AI will improve decision making by reducing the noise in our outputs due to using more robust input data.

We have a developed a worksheet to assist in targeting where AI will bring you the most value. The worksheet is patterned after the Four Pillars of CFO Success and includes the major CFO technical competencies (i.e. CFO competencies ripe for AI application). Some critical thinking about each competency will allow you to develop a comparative scoring schedule to assist you in building an AI strategy.

Click to get your AI Identification Worksheet

Next Up Tomorrow: Part II The Benefits of AI and What You Will Need to Make It a Success

Automation: The Key to Reducing Your Financial Close & Reporting Period

Ask any finance person what is it that they like less about their job, chances are that they will tell you it is closing the books at period-end. Why is this the case? For many finance functions, the financial close period (month-end, quarter-end and year-end) is one of their most stressful seasons.

Emotions and tempers fly high. Team members put in long hours just to balance the books.

I have heard cases of teams knocking off after midnight for a couple of days and always ask myself why. Expressions such as “I am working late because it’s month-end, quarter-end or year-end” are starting to sound off like a cliché.

Are Finance teams swamped with work to such an extent that they are unable to complete their tasks within the normal working hours of the day or month? Maybe it’s tradition that must be preserved at all cost?

I am not trying to make myself a saint here. I too am a culprit. I have put in long hours at month-end or any other period-end close more than I can remember, and when I reflect back, most of the tasks that I put in the long hours for were mundane and not worthy of their time allocation.

In my experience, there are two main reasons why Finance teams find themselves working round-the-clock at the end of each financial close period – 1) Complacency and 2) Procrastination.

Complacency is the enemy of progress.

It is a common practice for humans to remain satisfied with the way things are and resist any meaningful change. Fear of the unknown makes us skeptical to embrace change.

What we forget is that not all change is bad. As much there is change associated with negative consequences, there is also a type of change associated with positive results.

Although technology has evolved over the years, it seems as if this new dawn is yet to reach the Finance function. In a number of companies, Finance has been very slow to adopt new enabling technologies in order to drive financial process efficiencies.

Take financial reporting as an example. Company stakeholders are increasingly demanding CFOs to deliver better and easy to understand performance information, more timely and credibly. Despite this need, Finance teams are still heavily reliant on manual extraction processes, and delivering stale information.

Finance people are spending a significant amount of time trying to manually consolidate company financial performance results.

So often Head Office teams patiently wait for subsidiary companies to send through their management packs for Group consolidation purposes and when they do reach the Head Office, they have not balanced and contain errors.

The Head Office team sends the management pack back to the subsidiary for rectification. If the latter is unable to rectify the errors, it automatically becomes the Head Office’s problem. This back-and-forth process results in unnecessary effort and time-wasting rework.

Love it or hate it, Excel is here to stay. Nonetheless, I believe there is a place for Excel and not all financial functions are easily performed with Excel. It is high time CFOs acknowledge the limitations of manual extraction processes and leverage financial consolidation software.

Automating consolidation functions results in reduced cycle times of discrete tasks, more accurate results at a lower cost, and also provides management with improved visibility and control, allowing for improved decision-making.

We Will Correct the Errors Later

Many at times procrastination has resulted in Finance teams working longer hours than they should. Accounting errors identified early in the month are not resolved immediately. Rather, the slogan is “We will correct the errors at month-end”.

When month-end comes, because of other issues at hand, these errors are easily forgotten and left to accumulate. What could have been resolved in one month ends up not getting resolved for another six months or so.

An example of this would be a clearing account that is not reconciled on a monthly basis. The balance is allowed to grow month-on-month until there is no more control and visibility of the account.

When the year-end comes, the external auditors want a precise explanation of the item movements within the account but nobody has a clue of when did the problem start and what exactly went wrong.

The auditors make note of a possible adjustment to the financial statements unless clarity is given.

Because the company is not prepared to write-off the adjustment to the income statement, the CFO reassigns resources to work on the clearing account, resulting in excessive time being spent trying to make sense of the historical items and movements.

It is therefore imperative that as soon as accounting errors are identified, these should be rectified in the same period identified.

Fast-Changing Environment Accelerating the Need for a Faster Close

In today’s fast-changing environment, companies need the ability to make better decisions and at a moment’s notice. Changes are happening must faster than before and because of this quickening pace of business, CFOs will forever be under immense pressure to shorten the time-frame of closing the company’s books and finalizing all financial reporting needs.

Regulators, rating agencies, investors and other stakeholders are increasingly scrutinizing company financial results, at the same time asking to receive this information sooner than later. This leaves little room for financial close and reporting processes that require an overabundance of patience or are lacking in clarity and precision.

Despite dramatic improvements in financial technology over the years, why then are CFOs reluctant to upgrade their company’s current systems? Are they dealing with competing priorities? Is it because they view investment in new financial software as a cost rather than an enabler of business performance?

In order to become effective business partners, the office of Finance needs to abandon its “wait-and-see” approach, become more proactive and invest in tools that help accelerate the financial close process and advance the CFO’s role, making it more and more strategic.

Automation is not meant to oust employees. Instead, automating routine financial processes shortens the period-end close and enables Finance to get involved in high-level tasks such as interpreting and analyzing data, generating insights, managing emerging risks and driving strategy execution.

For example, by investing in e-procurement software instead of relying on a highly manual, paper-driven P2P process, CFOs are able to monitor the company’s cash flow position in real time and take advantage of early-discount opportunities.

As the pace of business change continues to accelerate, Finance has to keep up, take a real-time overview of the entire close process, and address the areas that need improvement.

By incorporating technologies, streamlining processes, and reassigning employees, Finance can build a better-documented, more efficient and accurate financial close.

3 Common Pitfalls of Performance Reporting and How to Avoid Them

Recently I met up with a close friend of mine whom I hadn’t seen in a couple of years for a chat and catching up. He is a qualified accountant and finance professional working in the financial services industry in Zimbabwe.

On top of the social chatter, we started discussing the evolving role of finance, in particular finance business partnering and the impact of Industry 4.0 on the profession.

As our discussion continued, we shared experiences, what has worked and what hasn’t worked so far in our respective organizations, as well as the way forward.

One thing that intrigued me was that my friend didn’t by any chance try hard to hide his frustrations emanating from his every day job. Chief among the frustrations was the fact that finance wasn’t highly regarded within his organization as he would have loved it to be.

I probed further trying to understand why he had reached to that conclusion.

Although my friend mentioned that his organization has made reasonable progress in ensuring that finance transforms from the commonly perceived scorekeeper function to a trusted business partner, many business leaders still perceive finance’s role as that of balancing books and providing rudimentary analysis.

As a result, finance is not invited to the decision making table and asked for its contribution.

Keen to find out why business leaders didn’t see any value in engaging finance in the operational affairs, I asked deeper questions until we both agreed that his team was clogging business partners with too many performance reports, his organization lacked a clearly defined data management model, and finance personnel need to be empowered to collaborate with the business effectively.

Far Too Many Reports and Far Too Little Insights

As it turns out, it’s not only business leaders in my friend’s place of work who are constantly being weighed down by a mass of performance reports. There are plenty.

With the volume of both internal and external data increasing exponentially, the demand on finance teams to provide insightful, relevant and timely management information to support fact-based decision making isn’t going down either.

Because of this unrelenting demand for more information it is easy to succumb to the thinking that more is better, resulting in finance teams working round-the-clock to produce reports that neither meet the requests of stakeholders nor offer the business an informed, value-adding view of its performance.

The problem with having too many reports is that the business is forced to track and monitor far too many metrics which, in most cases, are in conflict with one another and offer far too little insight.

Additionally, the business lacks a clear line of sight to clearly analyse past and anticipated performance in order to make better decisions. In today’s exponentially growing data age, decision makers are looking for essential information to make more confident and effective decisions that focuses their attention on activities that truly matter, and provide a consistent view of performance across the business.

To avoid this common pitfall of repeatedly creating useless performance reports that no one dares to read, finance needs to regularly engage with business leaders and take time to clearly understand what information they actually need to achieve their strategic objectives and consequently drive value.

Delivering this information in an efficient and effective manner is key to finance generating business trust as well as empowering the business to proactively respond to emerging opportunities and threats.

Lack of a Robust Data Governance Framework

After seriously discussing the problem of too many performance reports, my friend further raised an important question. What if a business leader requests a specific report and we do not have all the necessary information to adequately support our findings? Before answering him, I fired a question back at him. How are you currently handling these requests?

To my complete surprise he responded, finance produces reports that we believe are correct and if we do not hear back from the business leader all is considered in order. At this point in time, I was quickly reminded of the expression Garbage In – Garbage Out. No matter how logical our thinking and analysis is, as long as the inputs are invalid the results will be incorrect.

The same applies to business performance reporting. In today’s data-driven and tech-enabled economy, optimized and appropriate use of data is central to helping the business make enhanced decisions, create competitive advantage and successfully execute its strategy.

Thus, data quality is imperative, requiring finance leaders to ensure that there is absolute trust in the information provided to the business.

From discussing with my friend, it became clear that a business requires the right data to support its integrated set of defined key performance indicators (KPIs) and to maintain the integrity of this data, it must be supported by a robust governance structure.

Today’s volatile and dynamic business environment means the organization’s strategic priorities are always changing, as well as its information needs. As a result, the reporting function also needs to keep pace with this constantly changing landscape.

Building a cohesive information and data governance framework ensures common KPIs linked to strategic and operational decision making are used consistently across the business.

KPIs, regardless of their focus are only as consistent as the underlying data, and poor data input will produce inconsistent measures, even if they are labelled as the same KPI. This is why getting the basic data structures and data feeds right is so fundamental in providing decision support that can be trusted.

Additionally, due to the fact that different functions of the business use data for multiple different information needs, a robust data governance framework leads to a single version of the truth via enforcement of consistent information standards, creates awareness of where the performance data is housed and how it can be accessed.

Since the main goal of performance reporting is to provide management with real-time information with proactive comparators, a constant review of the information requirements and data governance framework ensures that performance measures remain relevant.

What I also picked up from my friend is that despite significant growth in the potential use of external data to drive better decision making, many businesses remain predominantly reliant on internal data to drive key business performance decisions.

They are grappling to incorporate this type of data across different business processes, mainly because of the differences in the structure of internal and external data sets.

By incorporating external comparators in their decision making processes, management will be able to identify areas where the business needs to ramp up through investments, and often more prominently, where it is already ahead of the competition, but must continue to focus on to uphold or create a new advantage.

Without this crucial information at their disposal, it is impractical for finance teams to clearly understand the major drivers of their business performance, produce insightful analysis, partner with key decision makers and support strategic decision making.

Business Leaders Discounting Finance’s Capabilities

In order to become effective business partners and provide relevant decision support it is imperative for finance teams to get as much exposure as possible to business-wide decision making. Unfortunately, getting this exposure remains a distant dream for many capable finance teams.

Because business leaders see finance personnel only as gatekeepers and not strategic business advisors, there is little motivation on their part to empower finance to collaborate effectively with the business on a larger scale.

As we started discussing the promises and perils of Industry 4.0 with my friend, he was very much surprised with how far behind his organization is in terms of digital transformation and process automation.

My friend is not alone on this boat; many finance teams are still stuck with legacy financial systems, tools and processes; spending a significant portion of their time on low value-adding transactional activities such as arduous data extraction and manipulation or traditional month-end activities, and little time on positive analysis and decision support. This is detrimental to effective performance reporting.

Performance reporting will only succeed if finance teams are suitably equipped to deliver high-quality insights that support decision making empowered with deployment of reporting technologies.

Even if the business presents an opportunity to collaborate, time alone is not sufficient. There is also a need to create an environment that allows finance people to develop appropriate capabilities, some of which may not come innately to many technical finance people.

One way of fostering this environment involves running finance training programmes that focus heavily on softer skills such as leadership development, communication, change management and stakeholder management, as well as on-the-job training for traits such as commercial acumen, and less on the technical or transactional processes which are easy candidates for automation and do not constitute a large portion of finance’s everyday job.

As with any other form of investment, the organization must be able to reap rewards from the training programmes. It makes no economic sense to spend substantial amount of resources trying to boost finance function productivity and in turn get rewarded with mediocre results.

It is therefore critical that finance personnel sent out for training exhibit the right behaviours and have the confidence to work with the business to interpret reports and constructively challenge strategic decision making to drive more effective decision making.

This in turn will assist finance shed its image as a mere service provider, elude mundane tasks, enhance its reputation and become part of the decision making crème de la crème informing business decisions with deep insights and recommendations.

In addition to training programmes, the organization also needs to invest in appropriate enabling technology that help with data analysis and interpretation in real-time, providing the organization with the necessary speed and quality that it requires to stay ahead, as well as allowing finance to demonstrate their analytical skills and become more influential as business partners.

Addressing the above common pitfalls is key to streamlining an organization’s performance measurement and reporting processes, and ensuring that senior management regularly receive relevant, insightful and near real-time information necessary to improve strategic decision making and ultimately business performance.

What other common pitfalls have you experienced in delivering high-quality performance reporting?

© 2019 ERPM Insights

Theme by Anders NorénUp ↑