TagFinance Transformation

Finance Analytics: It’s Not About the Size of The Data

As the need to make impactful operational and strategic decisions in real time increases, CFOs are playing a greater role in the adoption and integration of data analytics in their organizations to support data-driven decision making.

Executives and business unit leaders are increasingly relying on insights produced by Finance to better understand enterprise performance. That is, what has happened, why it has happened, what is most likely to happen in the future, and the appropriate course of action to take.

In an era where data is proliferating in volume and variety, decision makers have realized it’s no longer enough to base key enterprise performance and risk decisions on experience and intuition alone.

Rather, this must be combined with a facts-based approach. Which means CFOs must set up modernized reporting and analytics capabilities with one of the main goals being the use of data as a tool for business decision making.

Appropriately analyzed and interpreted, data always has a story, and there’s always something to discover from it. However, many finance functions are failing to deliver value from their existing data analytics capabilities.

There is a misconception that to deliver actionable insights, the function needs more data for analysis. As a result, the supply of data keeps rising, while the ability to use it to generate informed insights lags badly.

Yet it’s not about the size of the data. It’s about translating available data and making it understandable and useful.

In other words, it’s about context and understanding that numbers alone do not tell the whole story. Finance leaders should connect the dots in ways that produce valuable insights or discoveries, and determine for example:

  • What is being measured, why, and how is it measured?
  • How extensive the exploration for such discoveries was?
  • How many additional factors were also reviewed for a correlation?

Further, to use data intelligently and influence better decision making, CFOs and their teams should recognize that most enterprise data is accumulated not to serve analytics, but as the by-product of routine tasks and activities.

Consider customer online and offline purchases data. Social media posts. Logs of customer communications for billing and other transactional purposes.

Such data is not produced for the purpose of prediction yet when analyzed, this data can reveal valuable insights that can be translated into action which delivers measurable benefits.

Often the company already has the data that it needs to answer its critical business performance questions, but little of it is being aggregated, cleaned, analyzed, and linked to decision making activities in a coherent way.

Exacerbating the issue is the mere fact that the company has a mishmash of incompatible computer systems and data formats added over the years ultimately making it difficult to perform granular analysis at a product, supplier, geographic, customer, and channel level, and many other variables.

There is nothing grand about data itself. What matters most is how you are handling the flood of data your systems are collecting daily. Yes, data can always be accumulated but as a finance leader:

  • Are you taking time to dig down into the data and observing patterns?
  • Are the observed patterns significant to altering the strategic direction of the organization?
  • Are you measuring what you really want to know, what matters for the success of the business?
  • Or you are just measuring what is easy to measure rather than what is most relevant?

CFOs do not need more data. What they need right now is the ability to aggregate, clean and analyze the existing data sitting in the company’s computer systems and understand what story it is telling them.

Before they can focus on prediction, they first need to observe what is happening and why. Bear in mind correlation does not imply causation.

Yes, you might have discovered a predictive relationship between X and Y but this does not mean one causes the other, not even indirectly.

For instance, employee training hours and sales revenue. Just because there is a high correlation between the two does not mean increase in training hours is causing a corresponding increase in sales revenue. A third variable might be driving the revenue the increase.

Jumping to conclusions too soon about causality for a correlation observed in data can lead to bad decisions and far-reaching consequences, hence finance leaders should validate whether an observed trend is real rather than misleading noise before providing any causal explanation.

Certainly, big data can be a powerful tool, but it has its limits. Not all data is created equal, or evenly valuable. There are situations where big data sets play a pivotal role, and others where small, rich data sets trump big data sets.

Before they decide to collect more data, CFOs should always remember data is comparable to an unexploited resource.

Even though data is now considered an important strategic asset for the organization, raw data is like oil that has been drilled and pulled out of the ground but not yet refined to its finer version of kerosene and gasoline.

The data oil has not yet been converted into insights that can be translated into action to cut costs, boost revenues, streamline operations, and guide the company’s strategic direction.

The Finance Function of the Future

McKinsey has published an interesting piece that merits the attention of finance leaders and professionals.

Their Finance 2030: Four Imperatives for the Next Decade include:

  1. Look beyond transactional activities
  2. Help finance lead in data
  3. Improve decision-making
  4. Reimagine the finance operating model with new capabilities

Highlights consist of these key points:

  • Leading finance departments are guardians of enterprise value creation, demonstrating stewardship of their own spend by lowering absolute costs and shifting work towards more value-added activities.
  • Finance leaders further differentiate themselves by spending a greater portion of their time on value-added activities, such as financial planning and analysis (FP&A), strategic planning, treasury, operational-risk management, and policy setting.
  • Achieving the next frontier in finance efficiency and effectiveness will likely require finance executives to shift their thinking from the priorities of the past.
  • Finance staff’s time is valuable, and best devoted to analyses that drive actual business performance.
  • Equip staff in critical roles with the necessary level of experience, leadership mind-sets, and authority to influence the business.
  • Finance departments need a clearly defined master data-management strategy to guide the collection, storage, and interrogation of the rising volume of data needed to perform the types of analytics the business requires.
  • Owing to its central role, the finance function is uniquely positioned to help define the master data strategy for the enterprise.
  • Beyond providing analytical insights, the finance department is also responsible for framing discussions on company performance and the actions needed to improve it.
  • Reimagine the finance operating model with new capabilities. This requires not only a different way of organizing how work gets done, but also a different type of finance professional.
  • Embed digital skills across the finance organization. These capabilities may include programing bot algorithms, using analytics software, or learning how to translate business data into actionable insights
  • Develop a core of business-savvy finance leaders with the stature to engage company leaders as peers.

These are all good points. My thoughts:

  • Over the last decade, finance has progressed significantly in terms of delivering value-added activities. Much of this progress can be attributed to advancements in automation and analytical technologies which are reducing time spent on performing routine, transactional activities. There is still a lot of progress to be made to improve the time spent on FP&A and business partnering in laggard organizations.
  • Achieving the next frontier in finance efficiency is very important. However, when we talk of efficiency it’s imperative to clarify at what. Many organizations have succumbed to the seduction of efficiency resulting in efficiency becoming an end in itself. Efficiency, when it is understood correctly as the best possible use of scarce resources to achieve a valued end, is undoubtedly important.
  • I agree entirely with the need to make sure finance devotes more time to analyses that impacts actual business performance. More often, finance teams are wasting time and resources producing reports that serve little to no purpose at all. Decision makers want to understand the key drivers of business performance and how these can be influenced to achieve enterprise objectives, but they are bombarded with more analyses on what happened, and less on what could happen, how, when and why. Reporting is about communicating. Rather than communicate what you want to say, tell decision makers what they need to know.
  • Data types, data sources and the speed at which data is generated are all continuously increasing at alarming levels; therefore a more effective and efficient enterprise data management strategy is critical. Build a central data repository (to make sure there is one version of the truth) where data is securely stored and can be updated, accessed, or shared in real-time to perform the types of analytics the business requires. Erroneous data is practically useless and even possibly harmful to the business because if management teams are making critical decisions based on inaccurate data, the outcomes could be costly.
  • The idea of framing discussions on company performance and the actions needed to improve it, is challenging for many finance professionals. Our training has taught us to solve problems using a logical approach or deductive thinking. The problem with this process is that there is just one possible solution or a limited set of correct solutions, and does not take full advantage of the creativity within us. We need to embrace both deductive and inductive reasoning, start asking questions on company performance that have never been asked before, challenge rigid rules and tired frameworks, and consider risks worth taking that we would otherwise not take when thinking deductively.
  • I agree entirely with the need to reimagine the finance operating model with new capabilities. To achieve this, finance leaders need to cultivate a culture of continuous improvement. A culture that is always questioning the status quo, and promotes testing of new processes, tools and operating models, including learning from mistakes. Further, traditional accounting and finance skill sets alone are no longer sufficient today to build an effective value-adding finance organization. Instead, cross pollination of individuals with varying backgrounds is necessary.
  • The ideas of embedding digital skills across the finance organization and building business-savvy finance leaders are both positive ones. Nevertheless, development of other soft skills such as collaboration, communication, problem-solving, critical-thinking, adaptability, emotional intelligence and persuasion should not be neglected.

Finance and Enterprise Performance Improvement

In the past several years, the role of finance in business has significantly transformed from being a back office, desk-bound, number cruncher into a more broader, stakeholder-engaging and advisory role responsible for influencing strategic and operational decision making and improving enterprise performance.

Thanks to new innovative digital technologies and business operating models, CFOs and their teams are no longer spending plenty of time working on low-value add and backward looking tasks such as account reconciliations and reporting.

Instead, the explosion in RPA, advanced data analytics and AI technologies is empowering the finance organization to generate, consolidate and analyze data from various sources, and provide actionable insights that add real strategic value to the business.

As a result, commercially-minded and technology savvy finance leaders are cementing their positions as the trusted advisor to the CEO and Board, helping define and execute on the strategic direction of the business.

Today, we live in a world that is dynamically changing and full of surprises and uncertainties.

For example, one day we are dealing with the fallout from geopolitical tensions, the next day it’s natural and man-made disasters disrupting the entire supply chain.

Then a criminal cyber attack bringing the company’s payments and receipts system to a complete halt. This cycle is never ending.

In this environment typified by impermanence – there is no guarantee that existing finance strategies, operating models, systems, processes and talent will continually take your finance organization to new heights.

Although some progress has been made over the years concerning how finance work is done, we are not yet there yet. There is still ample room for improvement.

Lead the charge for change

Familiarity is the antithesis of progress. In order to soar to new heights, finance leaders and their teams ought to be receptive to change.

In spite of the immense potential of new digital technologies to transform the way finance creates and delivers value across the business, compared to other functions, adoption of game changing technologies within finance has been lackluster in the past years.

Does this imply that CFOs should get their hands on each and every latest shiny tool out there? No, technology is simply an enabler to achieve better business decisions.

What is important is for the CFO to understand the art of the possible.

That is the capabilities provided by the new technology to support and adapt the business’ value propositions, processes, pricing and revenue models, strategy execution, and growth.

The CFO should take charge and act as catalyst for change, ensuring that all represented stakeholders have a complete view understanding of what the business is seeking to achieve, what problems you’re trying to solve and what processes you’re looking to make more efficient, and what the investment’s contribution towards the achievement of enterprise objectives is.

Think differently enough to provide an alternative perspective.

Today, finance is no longer just a numbers game. What was typically a role centered on cost, compliance and reporting has now expanded to include strategy, risk decision-making and performance management.

It’s therefore important that finance leaders and their teams invest some time developing an in depth understanding of the business model, strategy, market opportunities and threats, competition dynamics, product portfolio, supplier relationships and customer profiles as this is key to providing a unique perspective that looks across all departments.

Historical performance reporting has been overtaken by trend recognition, forward-looking business and operating plans, real-time metrics, and driver-based rolling forecasts ultimately accelerating the need for the modern finance leader to be more proactive and growth-oriented, rather than being restrictive.

Being a finance leader does not necessarilly mean you should have all the answers to business performance related matters.

What is required is curiosity. Continuously raising key performance questions that ultimately kickstart productive conversations and collaborative efforts across the business.

CFOs with the ability to utilise modern digital offerings to close information gaps across the business, uncover hidden opportunities, accurately predict the future and improve decision making will by far differentiate themselves from their counterparts.

People, People, People

To be successful CFOs must build teams capable and empowered with the right tools and support to deliver a high standard of work across the various finance pillars.

This is essential for freeing up time for the CFO to support the board in driving the business forward.

Whether it’s for themselves or other members of their team, CFOs should continually look at reskilling and upskilling opportunities. The changing dynamics of CFOs’ role mean they need to keep learning to have the business, analytical and data skills both them and their team require.

In addition to reskilling and upskilling team members, CFOs also need to create an environment that encourages testing of new ideas, processes and tools. When teams and individuals are encouraged to explore, great things happen.

The journey to great heights is sometimes fraught with twists and turns and failures. But it is only through ongoing learning from these experiences that we become better. CFOs should never be afraid to test new business models, processes, technologies and skills for fear of failure.

The time to embrace change and transform is now.

Finance as the Custodian of Enterprise Performance Management

The days of having CFOs responsible for only preparing the statutory financial reports of the business and play the role of the bookkeeper are long gone. Today, finance leaders are expected to play the role of the strategic advisor to senior management and the board,and help drive operational and strategic performance across the enterprise.

That is, become custodians of enterprise performance management (EPM) by taking the lead on performance management and delivering informed business insights. EPM takes a completely different approach towards measuring, monitoring and improving enterprise performance.

Instead of assessing business performance in a siloed approach, EPM ensures the business evaluates and monitors its performance holistically. Although a majority of business decisions have either positive or negative financial implications on the health of the organization, evaluating, monitoring and improving business performance extends beyond a sole focus on financial metrics.

In order to perform better in their new business performance custodian role, it is imperative that finance leaders develop knowledge and a deeper understanding what constitutes and doesn’t constitute EPM. 

A simple google search of the words “Performance Management” brings up results that associate performance management to the process of conducting employee performance appraisals and supervising employees and departments to ensure that goals and objectives are met efficiently.

As a result, many people think that performance management is a human resources process that is only people-focused and has nothing to do with finance – Human Performance Management. To a lesser extent, their thinking is correct in the sense that people are part of the process. However, to a larger extent, they are wrong.

Performance management is not entirely focused on carrying out the outdated employee annual performance appraisals or reviews based on isolated individual key performance indicators (KPIs).

Rather, performance management is the integration of multiple managerial methods and or frameworks such as strategy maps, balanced scorecards, activity based costing/management, driver-based rolling forecasts, process improvement, risk management and advanced analytics to support strategic decisions and drive performance.

This is not achievable individually, hence the key word “enterprise.”

As custodians of business performance, finance should play a leading role in implementing a robust EPM framework across the organization. The framework should enable the organization to communicate and translate its strategy into financial and non-financial metrics and targets, monitor its performance, create accountability, and focus its efforts and resources on the key business drivers.

Additionally, the EPM framework should ensure there is alignment between individual KPIs and reward and recognition systems, and corporate objectives, as opposed to mere job descriptions, in order to encourage behaviours which positively contribute to the overall strategy of the business.

When implementing the EPM framework, it is important to ask the following questions:

  1. What do we want to achieve and excel at? This helps define your goals and key value drivers.
  2. How do we know if we are actually excelling at this? This involves defining financial and non-financial KPIs, which are measures that help you understand whether you are achieving your goals
  3. What is the desired level of performance that we would like to see? Defining KPIs is not enough, you also need to define your targets that represent the level of success or failure at achieving your KPIs.
  4. What initiatives should we pursue or engage in to meet our performance targets? These are actions or projects or strategies or processes needed to achieve a target, or improve performance level.
  5. What resources and or investments are needed to achieve our target? Utilizing driver-based budgets and dynamic rolling forecasts will ensure resources are allocated strategically and efficiently.

Asking and answering the above questions helps design and implement management processes and systems that align business strategy to drive accountabilities, decision support and performance improvement.

For example, when KPIs are aligned with business strategy, decision makers will focus on the critical success factors of the organization. On the contrary, if there is lack of KPI alignment, senior management end up getting overwhelmed with an increasing number of performance reports that lack key insights necessary to move the business forward.

As custodians of enterprise performance, it’s important for finance to have the ability and capabilities to consolidate, analyze and interpret business performance in real-time. Rather than simply report on the past, finance teams must be able to explain the reasons behind the numbers (the whys and what-ifs).

Leveraging consolidation solutions enable teams to quickly model and assess the impact of alternative business scenarios and formulate appropriate solutions.  

Given the complexity of today’s business environment, finance leaders need to rise to the occasion and perform the strategic advisory role now expected of them by senior management and the board.

 

Finance Needs To Do More Than Prepare Reports

There is an ongoing discussion about the evolving role of finance and the function’s contribution towards enterprise performance improvement. Thanks to new operating models and emerging technologies, finance has been presented with an opportunity to step up and shine.

That is, focus more effort on providing effective decision support that drives organizational success and less on rote tasks that can easily be automated, outsourced or performed separately in a shared services center.

Providing effective decision support requires a deep understanding of the business in its completeness, the cause-and-effect relationships between business units, big growth drivers and performance drivers. It’s more than producing a complete set of financials on a monthly basis.

By virtue of their training, many finance professionals possess strong technical accounting backgrounds and limited business experience. For instance, preparing external reporting financials that are IFRS-aligned comes natural to them. At any point in time, they are able to interpret a particular standard, paragraph by paragraph, without even making reference to the standard handbook itself.

There is nothing wrong with becoming an accounting standard expert. The problem arises when the entire finance team is made up of financial reporting experts who spend the majority of their time churning out reports just to meet regulatory and compliance requirements and less on driving business performance.

Month-end, quarter-end and year-end reporting are still an important part of running a successful finance organization. It’s important that the financial statements are free from material misstatements and faithfully represent the financial performance and position of the business.

However, the process should not end there. Finance should also be able to interpret the reported numbers, create meaning and simplicity from them as well as communicate a point of view about how the numbers will inform strategic decisions.

It’s therefore imperative for finance leaders to continuously assess the tasks their teams are focused on. Begin with why. For instance, why does your team produce the reports it produces on a weekly, monthly and quarterly basis? What purpose do they serve in informing business decisions?

After you have answered the why question, you should be able to determine whether the activity, report or process is a value add or not.

Any activity, report or process that is not value enhancing should be discontinued completely or streamlined. This will in turn help you free up more time and channel resources towards issues and or initiatives that really matter to business partners and senior decision makers.

Given that individuals are creatures of habit, it can be difficult to let go of traditional practices or old habits.

Unfortunately, sticking with the familiar in a constantly changing environment will not do you any justice. Just because this is how you have always done things in the past and are used to does not mean you should continue on the same path of the tried-and-tested.

In addition to getting rid of old habits that are no longer able to withstand the test of time, it’s also important to ask if the company’s business model is still fit for purpose to address today’s demands and challenges, and more important, is it fit for purpose for the future? With the world changing so fast around us, a business never reaches a point where it has the ideal model.

The operating model needs to continue to evolve. Finance can help shape this model through spending time with business partners and engaging in a two-way conversation about the business and offering its perspective. Communication between finance and the business should not be limited to month-end reports only.

Leveraging our financial expertise, we can help drive change by helping the company identify sources of growth and operational improvements, allocate resources effectively and efficiently, and accelerate its performance over time.

Finance is often regarded as the purse-holder of the company, holding the power to greenlight some initiatives and redlight others.

However, in order to drive innovation and change, finance must learn to see the world not only through a finance lens but also through a business lens. Many finance professionals are conservative and risk averse in their approach. Taking risk is something perceived extraordinary. We need to transition from this kind of thinking.

There is of course balance between taking risk and mitigating risk, but if finance is inclined to opt for the later, value creation opportunities can be missed. It’s therefore critical that we do not succumb to analysis paralysis because it’s easier to lose the big picture of what is needed to drive the company’s success in a myriad of daily transactions or useless data.

In conclusion, if finance is to influence strategic decisions and add value, finance leaders should start asking if their teams are focusing on what really matters to the business or the function.

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.

Finance Transformation: From Efficiency to Effectiveness

Transforming finance into an effective function capable of helping business teams execute strategy more effectively, make informed risk decisions and drive business performance isn’t just about new technology, shared services centres and centres of excellence.

As much as anything else, it is about changing old habits and behaviours. Redefining the role of finance in the business and transforming not only what the function does on a daily basis but also how it does it.

This also involves finance professionals developing an appetite for improvement, constantly self-assessing their modus operandi and establishing how they can perform their work better.

When embarking on a finance transformation journey, it is very important for the CFO to clearly define the core vision, communicate the purpose and goals of the transformation, and articulate the road map for the journey.

Following this approach helps keep the team and key stakeholders focused on the benefits the transformation will deliver.

Identify the Problem First Before Technology

In today’s fast-evolving environment where CFOs are inundated with new technologies to drive their company’s ambitions, it easier to follow the herd without first building a solid investment case.

The thinking is that if we implement a new ERP platform we have successfully transformed our finance function. This not always the case.

Although technology empowers the finance function to evolve to a modern global finance function your focus should extend beyond standardizing and streamlining workflow. New technology should enable you do what hasn’t been done before.

For example, your transactional processes (AP, AR, Journal Entries, Bank Reconciliations, Reporting and General Accounting) are all heavily manual which is causing you to close your books at least 20 days into the new month leaving little room for FP&A and business partnering.

In this instance, you would want to take advantage of a new cloud-based ERP solution that provides you with a standardized IT infrastructure and help you streamline existing decentralized and highly fragmented reporting processes.

Instead of investing in the new ERP platform just for the sake of investing, you first focus on the end-to-end processes, identify the problem(s) that need solving and look at the expected benefits beyond cost reduction.

As a result, you are now able to close your books earlier and free up finance to focus more closely on business partnering and deliver performance insights and decision support to assist in achieving the company’s growth objectives.

Headcount Reduction versus Productivity Increase

Reducing headcount, achieving transactional efficiency or improving control and governance processes should not act as the sole focus of your transformation journey. Many companies make the mistake of believing that finance transformation is all about reducing FTE costs.

Subsequently, after reducing finance headcount and making the function leaner, the workload remains the same, at worst increases, making it difficult for the remaining teams to manage the heavy workload. Ultimately, productivity deteriorates.

As much as it is important to benchmark the size and cost of your finance function, what is also key is that you clearly define finance staff roles and requirements as per your function’s vision and how it fits into the broader vision of the company.

This will help you eliminate or combine specific roles where there is duplication of effort. It also helps develop the right talent mix and capabilities.

Think Broad and Analytically

Today, companies are privy to large amounts of data (economic, customer, social media, production, market, competitor etc.) and cannot look at business performance from purely a transactional view anymore.

Rather, they must use such data to interpret strategic performance, benchmark against competitors and craft a more holistic experience for stakeholders. Instead of spending ample time discussing small variances, the focus should be on the big picture. Identifying and discussing key business drivers and big trends.

This requires investing in the finance function’s operational and commercial acumen as this is key to supporting the business across the entire value chain, from product design and development to manufacturing and from brand management to distribution, sales and after-sales.

Thus, one of the goals of your finance transformation should be to develop well-rounded professionals who are able to connect the dots, contribute alternative perspective to strategic conversations and help the business break into new markets and diversify revenue streams.

One of the challenges in many organizations is that business unit managers are self-serving and focus only on their own markets, ignoring the impact of their decisions on other business units. Finance business partners are better positioned to unite these teams.

They are analytical, broad thinkers and understand the cause-and-effect relationship of disparate business unit decisions, including the role of finance in cross-functional collaboration to improve the performance of these operating units.

Leveraging their extensive functional knowledge, business acumen, experience and relationships built from partnering with various stakeholders, FBPs are able to engage business unit leaders into more value-adding conversations.

Rather than act as a barrier and tell them what they can and cannot do, FBPs first seek to understand what is it business unit leaders want to achieve, how and why it is important and then provide informed decision support taking into account the impact on the broader strategic performance.

Change Management

Stakeholder engagement, or lack of it, can signal the difference between success and failure of any finance transformation agenda. Challenges will abound. Targets will be missed. Teams will resist change and prefer to continuously focus on what they know best.

You therefore need to keep all invested stakeholders engaged, aligned and informed of progress. Offering coaching and insight, rotating employees through a wide range of operations, exposing them to challenging projects and allowing them to experiment with new ideas and learn from mistakes all help in this front.

As long as the mistakes are within acceptable limits you want your team to feel empowered to make key performance decisions and at the same time become trusted business advisors.

Although habits and behaviours are not something that can be changed overnight, they are still key ingredients for an effective transformation. A cultural shift and buy-in is therefore imperative.

 

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

Part IV Getting After It: Take the Next Step and Make Your Investment in AI

If you haven’t had a chance to read Part I – Leveraging AI in the CFO SuitePart II – The Benefits of AI and What You Will Need to Make It a Success and Part III Where to Invest in AI, How to Measure the Financial Impact and Select Projects yet, please do so before continuing on.

There are four major investments you’ll need to make to use AI successfully in your business.

1. Develop an AI Strategy: This investment is about learning how to apply AI to your activities and selecting your best course of action. Consider using outside experts to help augment your thinking in this area if you are just starting your AI journey.

  • The first step of strategy development includes learning about AI, determining how it will be applied to the CFO responsibility areas, assessing the value of AI application for those areas.
  • The second step is to gauge the data needs (availability, accuracy, volume) and the cost of “creating” data that can generate the output required. “Quality, effectiveness, efficiency and insight are the four key pillars that really make this valuable stuff…” according to Nick Frost, KPMG Audit Technical Lead Partner.¹ Watch for these characteristics in your data. If they aren’t present, be wary of how you use your final product.
  • Using the value noted in a. above and the cost determined in b. an AI Strategy targeting the areas where AI will have the most impact can be constructed.
  • Skill/System assessment and timeline. Determine where growth in skills and systems are needed. The scope of these needs will also help create the resources required and a timeline. From a risk perspective, consider starting small (high expected return, low initial investment) and allow for greater investment as success is realized.
  • Include a change management plan to assist employees and other stakeholders in understanding the strategy and the impact it will have on them.

2. AI Software Selection: The investment in software will include the cost of the software and the expenses of the internal and external team members working on the process.

  • Use your Strategic Plan to target AI vendors that serve the areas highest on your list.
  1. On premise or cloud solution
  2. Data storage costs
  3. Integration with current systems.
  • If AI is new to you stay small and focused on high return, bite-sized efforts you can learn from.
  • Use your network to validate claims made from vendors in terms of system results, implementation timeline and cost.
  • This investment will include the direct payments for the software and internal costs for the selection team to do their work.
  • Our “AI Capital Investment Analysis” tool will help you summarize and communicate your planned investment in AI.

3. Implementation to Operation: It is important to focus the cultural change required during this stage to create an environment that craves the new learning AI brings to the table. The combination of our team’s desire to use AI wisely and a sound AI system add up to success. If either is missing, there is a good chance your implementation will fail.

Here are the implementation steps:

  • Research and mitigate the risks related to the implementation and data management.
  • Train and hire the skills to manage the system and leverage the new capabilities created by the AI.
  • Identify and manage the risks that are likely to occur because of the implementation.
  • Procure and implement the technology that fits your strategy.
  • Monitor and adjust the AI inputs and outputs to create optimum value for the AI stakeholders.

4. Ongoing AI growth: Your AI strategy document is the road map that will be used to plan AI follow up. It is a living document that requires updating.

  • Manage the ongoing operating costs of the AI system
  • Implement AI applications per the Strategic Plan
  • Change the priorities in the Strategic Plan as necessary
  • Consider new applications (see 1 above)
  • Assess current operating AI systems for optimization annually.

Artificial Intelligence holds great promise for financial professionals. It’s a key ingredient to enhancing the business partnering momentum established in the new millennium. Creating our AI Strategy, securing the skills to choose, implementing and operating AI systems, and growing these capabilities are new challenges demanding the attention of the CFO. Developing more efficient and “smart” transaction systems while improving decision support activities are huge value drivers for businesses today. Our ability to harness the power of AI to these means will be a significant measure of our success.

We’d love to hear about your AI experience (email us at info@erpminsights.com)!

¹Eleanor O’Neill, “How is the accountancy and finance world using artificial intelligence?” CA Today, July 31, 2016

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

Part III Where to Invest in AI, How to Measure the Financial Impact and Select Projects

If you haven’t had a chance to read Part I – Leveraging AI in the CFO Suite and Part II – The Benefits of AI and What You Will Need to Make It a Success yet, please do so before continuing on.

Where the CFO can invest in AI to create a positive impact.

Now that we know what AI is and its benefits for finance, how can a CFO develop a plan around how to apply it in their business? To borrow a phrase from Stephen Covey, Begin with the end in mind. Visualize where you want to be and work backwards, considering what is preventing you from realizing your future today. This step will help prevent you from building AI around current systems and processes that are encumbering your digital transformation.

The next step in identifying where to invest in AI is to summarize the outputs your team creates for the company’s stakeholders. Define output as anything your team delivers to a stakeholder that they use. Examples of outputs include; invoices to customers, financial reports to management, pay checks/stubs to employees, borrowing base to the bank, work papers to the auditor, KPIs to the Board of Directors, credit information requests from vendors, accounts receivable aging report to the credit department, new project investment analysis for the CEO, productivity reports for the COO, etc.

​To be highly effective the implementation of AI is a multi-discipline exercise that will require resources from many parts of the business. A good example of this can be illustrated when using AI to assist in auto invoicing and payment applications. The sales department, manufacturing and shipping departments will provide data that allows these two functions to operate autonomously. The data from these departments will be incorporated into algorithms that function to determine how much, when and to whom to send an invoice; and, how to apply payments when the bank reports them as received.

​ Below are some important criteria to think about when selecting where to apply AI:

​ 1. Stakeholder focused; Serve your most important constituents first – Customers, Vendors, Employees (including management) and Directors

​ 2. Determine where AI has the largest potential impact

  • ​ Where improvements speed, accuracy and/or volume have significant impact
  • ​ Revenue generation
  • ​ Cost savings

 3. Understand the complexity of AI application.

  • ​ Data requirements
  • ​ System requirements
  • ​ Process requirements

Measuring the (financial) benefits of an investment in AI for a business

​Just like any other business case development, it is important to measure the benefits of investing in AI technology. These benefits are either tangible or intangible. Tangible benefits are those that can easily be quantified, you can put a value against. On the other hand, intangible benefits are difficult to quantify, but expected to occur as a result of the investment.

​So, is one set of benefits better than the other? Our answer is no. Both tangible and intangible benefits are important. But only tangible benefits can be used to calculate the financial return of AI investment. This can be looked at from the perspective of additional savings or income generated as a result of AI.

​However, the challenge for many CFOs when it comes to implementing new technological solutions for their companies is clearly defining how success will be measured and quantifying the ROI.

​Since the adoption of AI technologies is not yet widespread but still in the pilot phase we suggest CFOs take a simplified approach to calculating the value of AI projects and follow these steps:

1. Identify a specific problem. Although AI is promising to be a huge game changer for your business, AI is not the answer to all your business problems. Don’t fall into the trap of investing in AI for the sake of investing, or worse, succumb to “herd mentality”. To successfully benefit from AI, first identify a specific problem that may be solved though AI. The AI Identification Worksheet discussed earlier can help you here.

​2. Define the outcomes. What will success look like in your company? What is the result you are targeting, and can this be defined in monetary or percentage values?

​3. Measure the results. After clearly defining the outcomes, the next step is estimating the performance of AI against your baseline measurements or outcomes. The spread between your expected performance and the baseline provides with the expected benefits of the proposed AI solution. Put in place a system to measure the actual results

4. Identify and calculate the costs (investment) incurred in delivering the results. Here you need to consider things like initial investment costs, ongoing support costs and the impact on cash flow.

​5. Calculate the return on investment (ROI). This final step involves calculating the ratio of money gained (or lost) relative to the amount of money invested (the total cost). If the projected ROI meets your hurdle rate, you’ll move ahead with the project. Set up to schedule to review the actual performance vs. the expected results to develop the feedback loop to improve your investment model.

Below is an example of calculating the ROI using the steps above:

1. Identifying a specific problem: ABC Company P2P process is highly manual and incurs annual labor costs of $300,000. During a cost and profitability analysis exercise, Brenda, the company’s CFO established that due to high error rates and rework as a result of these manual processes, the company is incurring additional overhead costs of $100,000 per annum. She remembered that from one of the CFO conferences she attended, the speaker spoke about AI and the technologies potential to drive process efficiencies. She proposes to the Board that the company invests in AI, specifically for improving P2P and test the concept.

​2. Defining the outcomes: After a series of meetings with various functional leaders, stakeholders and consideration of various factors, Brenda presents to the board her findings. By piloting AI for the P2P function, the company stands to achieve annual labor cost reduction of 10% and overhead reduction of 15%. The Board approves the project, expecting savings of $45,000 excluding the potential benefits from higher accuracy and improved vendor relations.

After conducting a thorough market analysis of the suitable AI solutions available, with the support of the Board, Brenda engaged the services of FinancePro, a cloud-based software provider specializing in AI software for the CFO office.

​3. Measuring the results: After conducting a thorough market analysis of the suitable AI solutions available, with the support of the Board, Brenda engaged the services of FinancePro, a cloud-based software provider specializing in AI software for the CFO office. It is now 12 months since the pilot project went live and the Board wants to know if the company managed to achieve the 10% labor cost and 15% overhead cost reduction targets. Brenda compares last years’ costs against current years’ costs and her targets of 10% and 15% cost reductions have been met. In year 2, the company estimated benefits of $60,000.

4. Identify and calculate the costs (investment) incurred in delivering the results: Although the cost reduction targets have been met, Brenda believes that these figures evaluated in isolation are not helpful for evaluating the overall investment. She therefore decides to identify and calculate the total cost ABC Company incurred in meeting these targets. She takes into account all initial costs such as license fees of the new AI software, implementation costs and employee training costs for the full amount of $30,000. She also calculates ongoing costs such as maintenance and support, communications and data storage costs which amounted to $20,000.

​5. Calculate the return on investment (ROI): This is calculated as follows

​ • She uses a cash on cash analysis to determine the 2-year ROI:

​In this example, ROI is calculated by taking the total financial benefits ($105,000) subtracting the total financial costs ($70,000), dividing by the total financial costs then multiplying by 100 to arrive at the ROI (50%). This calculation is over a 2-year period but can be applied on an annual basis as well. We have developed a simple model to help you summarize and compare your AI projects. Use it to:

  1. ​ Analyze and select AI projects,
  2. ​ Get your executive team familiar with the financial benefits of AI and,
  3. ​ As a performance measurement and improvement tool once an AI project has started.

Click here to get your AI ROI Calculation model.

Next Up: Part IV Getting After It: Take the Next Step and Make Your Investment in AI

© 2021 ERPM Insights

Theme by Anders NorénUp ↑