Human or Machine Intelligence? Augmentation Key to Better Forecasting

Forecasting is an invaluable process for any business. A forecast can play a significant role in driving company success or failure. For example, high forecast accuracy helps a business anticipate changes in the market, identify growth opportunities, reduce risks, analyze  root causes of performance and proactively respond.

On the other hand, forecasts that are poorly designed, based on weak assumptions often result in unintended consequences.

Preparing highly accurate and reliable forecasts to support decision making is one of the major challenges faced by performance management teams across sectors and industries.

Traditionally, business performance forecasters have relied on past performance to predict future performance. In a perfect, static world the formula works well. However, as we all know the world is not static. The only thing that is constant is change.

Volatility, uncertainty, complexity and ambiguity are at an increasingly alarming level. Further, new technologies are transforming how we do our work now and in the future.

A number of manual processes have successfully been automated. Where businesses have previously relied on financial data alone to make strategic decisions, the dawn of the digital age has brought new meaning to non-financial data.

The new world of algorithm-powered machines

The traditional approach of forecasting is highly manual and time-consuming. People spend a significant amount of time gathering, compiling and manipulating data in spreadsheets.

Most of the time, the data used to predict the future and create forecasts is historical financial data residing in the company’s ERP systems.

Unfortunately, in today’s rapidly changing world the future doesn’t sufficiently resemble the past.

As the new digital era continue to unfold, more and more data (financial, operational and external) will increasingly become available to support business forecasting.

Given that the traditional approach of forecasting leverages data in structured format to prepare forecasts, with more and more unstructured data available, CFOs and their teams have to rethink the old school forecasting process.

In order to increase the agility of the business to proactively respond to competitor activities, customer, market and industry changes that threaten the achievement of set objectives, or trends that present specific opportunities, the organization should consider all types of data at its disposal and discern what is important and what is not for business performance forecasting purposes.

Artificial Intelligence, machine learning, deep learning and natural language processing are disrupting traditional business operating models and companies are increasingly tapping into these new technologies to drive forecasting processes. These highly powered machines use statistical algorithms and modern computing capabilities to collect, store, and analyze large quantities of data and predict what is likely to happen in the future.

The algorithms are fed with warehouses of historical company and market data and taught to mimic human intelligence. Overtime, through learning, forecasting accuracy is improved.

In addition, NLP algorithms are able to go through a myriad of documents including articles, social posts and other correspondences written in plain text and extract insights that can be injected into the forecasting model.

Humans and machines augment each other

It is no secret that machines have a superior advantage over humans when it comes to collecting, storing and analyzing large data sets in real-time. But does this imply that decision makers should rely exclusively on machine intelligence to drive business decision making? The simple answer is no.

When it comes to applying critical thinking and judgement, human beings are much better than machines. Humans are able to evaluate and translate the machine’s conclusions into decisions and actions. Take for instance the forecasting models that are used to predict the future, the best source of information for these models are the domain experts for whom the models are designed.

The domain experts have a better understanding of the models, what assumptions to base the models on including the ability to uncover flaws that others may miss. Software developers, data scientists, AI experts and automation engineers, among others rely on expert judgement of domain experts to hard-code data features in databases that are used to train predictive algorithms.

In one of my articles, Applying Design Thinking to Finance, I highlighted how companies are heavily dependent on analytical thinking in order to drive business performance.

The solution is not to embrace the randomness of intuitive thinking and avoid analytical thinking completely. The solution lies in the organization embracing both approaches, turn away from the false certainty of the past, and instead peer into a mystery to ask what could be

The fact that the past is not a reliable predictor of the future does not necessarily mean that it is not important. History has been known to provide major lessons to us. In the same manner, human judgement can be used to determine which historical data is suitably representative of the future to be included in forecasting decisions.

When data is abundant and the relevant aspects of the business world aren’t fast-changing, it’s appropriate to lean on statistical methods to prepare forecasts. However, even after the forecasting model has been designed and adopted, human judgement is still required to evaluate the suitability of the model’s prediction under different scenarios.

Important to note is that predictive models do no more than combine the pieces of information fed to them. These machines are good at identifying trends and imitating human reasoning. If bad or erroneous data, or good but biased data is presented to the algorithms, issues can arise.

Setting aside human biases

People make decisions based on logic, emotion and instincts. One of the challenges of preparing forecasts in a complex and constantly changing world is setting aside human biases.

Subconsciously, human beings have a tendency to base judgement and forecasts on systematically biased mental heuristics rather than vigilant assessment of facts. System 1 Thinking.

According to Daniel Kahneman in his book Thinking Fast and Slow:

  • System 1 is an automatic, fast and often unconscious way of thinking. It is autonomous and efficient, requiring little energy or attention, but is prone to biases and systematic errors.
  • System 2 is an effortful, slow and controlled way of thinking. It requires energy and can’t work without attention but, once engaged, it has the ability to filter the instincts of System 1.

Personal experiences built overtime make us overgeneralize facts and jump to conclusions. Instead of focusing on both existing and absent evidence, we act as if the evidence before us is the only information relevant to the decision at hand.

As a result, the risks of options to which we are emotionally inclined are downplayed, and our abilities and the accuracy of our judgement are also overestimated.

Further, a focus on the limited available evidence causes us to create coherent stories about business performance including causal relationships that are non-existent. We are quick to ignore or fail to seek evidence that runs contrary to the coherent story we have already created in our mind.

Such actions do not result only in overconfident judgement but also cause us to be overly optimistic and create plans and forecasts that are unrealistically close to best-case scenarios.

By addressing own cognitive biases and enabling collaboration between humans and machines, business forecasters will be empowered to create forecasts that enable faster and more confident decision making.

Machines can only assist and not displace the typically human ability to make critical judgement under uncertainty.

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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.

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Doing The Right Thing For Too Long

Markets and business models are shifting, and so should you keep up with these market changes if your business is to survive and succeed. Compared with the past, the current era of digitization represents an inflection point.

Consider individual trends such as artificial intelligence, virtual reality, Big Data, cybersecurity threats, drones, the Internet of Things, driverless cars, blockchain technologies, and more.

These new technologies have significantly changed the way we connect and interact as individuals, including how businesses deliver products and services to their customers.

Reinventing your business will determine whether you succeed or fail in the digital age. As the saying goes, disrupt or be disrupted. No company, business, or industry is safe from disruption. Today, individual businesses have the potential to compete against multinational companies and win.

These businesses are quick to anticipate market changes and flexible to get ahead of the curve. Sadly, many companies are blinded by their successes and aren’t willing to disrupt themselves. They are not experiencing their desired growth trajectory because they are stuck doing the right thing for too long.

Don’t get comfortable with the status quo and allow your business to get stuck on a strategy and mindset that no longer fit the market.

Here are a few questions to ponder, the answers to which will determine the future of your business:

  • What is at the core of your strategy?
  • Are you in touch with the customers you want to serve? When customers give you negative feedback, how often do you listen and act on it?
  • Are you operating your business on the premise that you know what is best for your customers therefore they are supposed to buy whatever product or service you offer them?
  • Are you keeping up with market shifts or you only know how to grow under one set of conditions or products and services, but not how to survive and strive under another?
  • How robust and flexible is your IT infrastructure to help you innovate, perform your company’s Jobs To Be Done, and scale your business?
  • Are you creating a strong culture that is focused on customers, including a culture that not only embraces change but seeks it out?

Given our world is changing faster, it’s imperative to continuously look for signs that things are changing and think about how those shifts would play out in the short-term, medium-term, and long-term, not forgetting the impact on the execution of your strategy and enterprise performance.

The signs can reveal individually. At times, they are part of a wider trend.

Nonetheless, how you adapt will determine whether you succeed or fail. Keep learning. Learn about innovations in your industry and beyond. Try out new business models and technologies and embrace a philosophy of constant change.

Once you understand how the market is changing and evolving, you can develop the right product or service and strategy that will help you achieve your desired outcomes.

We often talk of the ability to “connect the dots” and “take a helicopter view of the business” as key ingredients for success. But how often are business leaders and their teams doing this?

Across the organization, a culture of “them versus us” prevails. Important decisions are made at a functional level with little or no consideration of their impact at the enterprise level.

Having the ability to grasp the big picture and see how different trends intersect is essential for determining the right path or course of action to pursue.

So, how do you spot market transitions and develop a clear sense of where the market is going?

  • Be curious and hungry for new ideas. Continuously ask tons of key performance questions and pay attention to what’s around you.
  • From time to time, challenge conventional wisdom. It’s easy to stick with what you know about your business model, customers, competitors, markets, or industry but dare to pivot when conditions change.
  • Don’t be nostalgic about the past or worried about protecting what you’ve built in the present. Always be curious about the future and develop a willingness to take calculated risks.
  • Ask existing and would-be customers how they feel about your company’s products, services, and strategy. Instead of turning to sources that reinforce your existing point of view, seek multiple perspectives and cross-reference them as new facts come in.
  • Develop an ability to handle multiple random data points at once. This will help you generate critical market, customer, and business performance insights and make smarter, informed decisions. Be careful to distinguish between the signal and the noise since data can be deceiving, especially when you’re looking for “confirmation” that protects your business model.

Data might not tell you why something is happening, but it does tell you what’s going on.

  • Look for patterns and abnormalities that might suggest something is going on, including any interdependencies.
  • Anticipate all the various scenarios of what could happen.
  • Plan your course of action in response to what’s happening in real time.

As the signals of a market shift increase, the need to act becomes more imperative. Note, monitoring and identifying market shifts, and effectively taking the appropriate course of action is a matter of timing.

If you continue doing the right thing for too long and lack the boldness to disrupt both the market and your own organization, you risk being disrupted and left behind. There is no company that is too big to fail. Neither is there a startup that is too small to succeed.

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How Feasible Are Your Strategic Objectives?

Every organization sets out its goals and objectives, to accomplish its mission and vision. The two often seem like two interchangeable phrases but there is a distinction.

A goal is a desired result you want to achieve and is typically broad and vague. An objective, on the other hand, defines the specific, measurable actions each employee must take to achieve the overall goal.

It is every leader’s job to create a coherent set of feasible objectives or what Richard Rumelt calls proximate objectives. Objectives that define targets the organization is fairly expected to achieve, even overwhelm.

This is essential for ensuring energy and resources are focused on one, or a very few, critical objectives whose accomplishment will lead to a cascade of positive outcomes.

An effective strategy defines a critical challenge or opportunity and clearly articulates how the organization is going to play to win or perform customers’ Jobs to Be Done.

Thus, the objectives an effective strategy sets should stand a good chance of being accomplished, given existing resources and competence.

On the contrary, a bad strategy results in the setting of bad strategic objectives.

Long lists of “things to be done,” are often labeled wrongly as strategies or objectives. Or the desired outcome is simply rehashed with no explanation of how this will be accomplished.

It doesn’t matter how well-thought your strategy is in response to an identified challenge or opportunity. If the resultant strategic objectives are merely a list of things to do, or just as difficult to achieve as the identified key challenge, there has been little value added by the strategy.

In today’s highly competitive, uncertain, dynamic, and complex environment in which a leader’s ability to look further ahead is diminished, it is better to focus on a few pivotal items through taking strong positions, creating options, and building advantage.

First identify the key challenges or opportunities for the business. Look very closely at the changes happening within your business, where you might get an added advantage over competition.

Next, create a list of the issues, including the actions your company should take.

Then, trim the original list to a noticeably short list of pivotal issues and proximate objectives by identifying one or two feasible objective(s), when achieved, would make the biggest difference. Remember, the identified objectives should be more like tasks and less like goals.

Now, focus on the objectives by channeling skills and available resources to accomplish the overall goal.

Once accomplished, new opportunities will open up resulting in the creation of more ambitious objectives. This cycle will help you develop a system that enables the setting of feasible strategic objectives.

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