AuthorPeter Chisambara

Analytics and AI: Humans and Machines are Good at Different Aspects of Prediction

Mostly driven by growth in the IoT, and the widespread use of internet, social media and mobile devices to perform search, send text, email and capture images and videos, the amount of data that we are producing on a daily basis is startling.

Consequently, companies are turning to data analytics and AI technologies to help them make sense of all the data at their disposal, predict the future, and make informed decisions that drive enterprise performance.

Although adoption of analytics and AI systems is increasingly extending in more mission-critical business processes, the implications of these emerging technologies on busines strategy, management, talent and decisions is still poorly understood.

For example, the single most common question in the AI debate is: “Will adoption of AI by businesses lead to massive human job cuts?”

Borrowing lessons from historical technological advances, yes, certain jobs will be lost, and new ones also created. However, machines are not taking over the world, nor are they eliminating the need for humans in the workplace.

Jobs will still be there albeit different from the traditional roles many are accustomed to. The majority of these new roles will require a new range of education, training, and experience.

For instance, nonroutine cognitive tasks demanding high levels of flexibility, creativity, critical thinking, problem-solving, leadership, and emotional intelligence do not yet lend themselves to wholesale automation.

Analytics and AI rely on data to make predictions

As more and better data is continually fed to the machine learning algorithms, the more they learn, and improve at making predictions.

Given these applications search for patterns in data, any inaccuracies or biases in the training data will be reflected in subsequent analyses.

But how much data do you need? The variety, quality and quantity of input, training and feedback data required depends on how accurate the prediction or business outcome must be to be useful.

Training data is used to train the predictive algorithms to predict the target variable, while the feedback data is used to assess and improve the algorithm’s prediction performance.

Undoubtedly, advanced analytics and AI systems are only as good as the data they are trained on. The data used to train these learning algorithms must be free of any noise or hidden biases.

You therefore need to understand how predictive technologies learn from data to perform sophisticated tasks such as customer lifetime value modeling and profitability forecasting.

This helps guide important decisions around the scale, scope and frequency of data acquisition. It’s about striking a balance between the benefits of more data and the cost of acquiring it.

Humans and machines both have shortcomings

In the context of prediction, humans and machines both have recognizable strengths and weaknesses.

Unless we identify and differentiate which tasks humans and machines are best suited for, all analytics and AI investments will come to naught.

For instance, faced with complex information with intricate interactions between different indicators, humans perform worse than machines. Heuristics and biases often get in the way of making accurate predictions.

Instead of accounting for statistical properties and data-driven predictions, more emphasis is often placed on salient information unavailable to prediction systems.

And, most of the times, the information is deceiving, hence the poor performance.

Although machines are better than humans at analyzing huge data sets with complex interactions amidst disparate variables, it’s very crucial to be cognizant of situations where machines are substandard at predicting the future.

The key to unlocking valuable insights from predictive analytics investments involves first and foremost understanding the definite business question that the data needs to answer.

This dictates your analysis plan and the data collection approaches that you will choose. Get the business question wrong, conclusively expect the insights and recommendations from the analysis to also be wrong.

Recall, with plentiful data, machine predictions can work well.

But, in situations where there is limited data to inform future decision making, machine predictions are relatively poor.

To quote Donald Rumsfeld, former US Secretary of Defense:

There are known knowns. These are things we know that we know. There are known unknowns. That is to say, there are things that we know we don’t know. But there are also unknown unknowns. There are things we don’t know we don’t know.

Donald Rumsfeld, former US Secretary of Defense

Thus, for known knowns, abundant data is readily available. Accordingly, humans trust machines to do a better than them. Even so, the level of trust changes the moment we start talking about known unknowns and unknown unknowns.

With these situations, machine predictions are relatively poor because we do not have a lot of data to ingest into the prediction model.

Think of infrequent events (known unknowns) that occur once in a while, or something that has never happened before (unknown unknowns).

At least for infrequent events or happenings, humans are occasionally better at predicting with little data.

Generally so because we are good at comparison and applying prudent judgement, examining new situations and identifying other settings that are comparable to be useful in a new setting.

We are naturally wired to remember key pieces of information from the little data available or the limited associations we have had in the past.

Rather than be precise, our prediction comes with a confidence range highlighting its lack of accuracy.

Faced with unknown unknowns, both humans and machines are relatively bad at predicting their arrival.

The simple truth is that we cannot predict truly new events from past data. Look no further than the current Brexit conundrum.

Nobody precisely knew the unintended consequences of the UK leaving the EU. Leavers and Remainers both speculated as to what the benefits and disadvantages of leaving the EU maybe.

Of course, nobody knows what will happen in the future but that doesn’t mean we can’t be prepared, even for the unknown unknowns.

In their book Prediction Machines: The Simple Economics of Artificial Intelligence, Ajay Agrawal, Joshua Gans, and Avi Goldfarb present an additional category of scenarios under which machines also fail to predict precisely – Unknown Knowns.

Per the trio:

Unknown knowns is when an association that appears to be strong in the past is the result of some unknown or unobserved factor that changes over time and makes predictions we thought we could make unreliable.

PREDICTION MACHINES: THE SIMPLE ECONOMICS OF ARTIFICIAL INTELLIGENCE

With unknown knowns, predictive tools appear to provide a very accurate answer, but that answer can be very incorrect, especially if the algorithms have little grasp of the decision process that created the data.

To support their point of view, the authors make reference to pricing and revenue analysis in the hotel industry, although the same viewpoint is applicable elsewhere.

In many industries, higher prices are analogous to higher sales, and vice versa.

For example, in the airline industry, airfares are low outside the peak season, and high during peak seasons (summer and festive) when travel demand is highest.

Presented with this data, and without an understanding that price movements are often a function of demand and supply factors, a simple prediction model might advocate raising different route airfares to sell more empty seats and increase revenues. Evidence of causal inference problems.

But, a human being with a solid understanding of economics concepts will immediately call attention to the fact that increasing airfares is unlikely to increase flight ticket sales.

To the machine, this is an unknown known. But to a human with knowledge of pricing and profitability analysis, this is a known unknown or maybe even a known known provided the human is able to properly model the pricing decision.

Thus, to address such shortcomings, humans should work with machines to identify the right data and appropriate data analysis models that take into consideration seasonality and other demand and supply factors to better predict revenues at different prices.

As data analytics and AI systems become more advanced and spread across industries, and up and down the value chain, companies that will progress further are those that are continually thinking of creative ways for machines to integrate and amplify human capabilities.

In contrast, those companies that are using technology simply to cut costs and displace humans will eventually stop making progress, and cease to exist.

Finance Analytics: Using The Right Data to Generate Actionable Insights

We are living in the information age. Data is everywhere and affecting every aspect of our lives. As the use of data and analytics become pervasive, in future it will be uncommon to find an industry not capitalizing on the benefits they can provide.

CFOs are increasingly leveraging data and analytics tools to optimize operational and financial processes, and drive better decision-making within their organizations. Finance is no longer simply a steward of historical reporting and compliance.

Today, the expectation on finance is to engage with new technologies, collaborate with various business stakeholders to create value and act as a steward of enterprise performance.

That is, deliver the right decision support to the right people at the right time.

Delivering impactful data-driven insights does not happen on its own. You need to have the right tools, processes, talent and culture working well together.

Here are some of the questions you can start asking to kickstart you on this journey:

  • Does your organization have the necessary tools and capabilities to collect and analyze the data (structured and unstructured) and make sense of it all?
  • How robust are your data governance and management processes?
  • How do you define the structure of your finance analytics team? Are you focused heavily on traditional and technical skills or diversity?
  • How are people armed to make better decisions using the data, processes, and analytical methods available?
  • As a finance leader, are you promoting a culture that bases key decisions on gut feel versus data-driven insights?

Both intuitive and analytical decisions are important and can create significant value for the business.

However, I’d recommend against promoting a culture that embraces the randomness of intuitive thinking and avoids analytical thinking completely.

What are you doing with your data?

In a world where data is ubiquitous and plentiful, it can be overwhelming to separate the noise from the important.

According to IBM, 80 percent of today’s data is originating from previously untapped, unstructured information from the web such as imagery, social media channels, news feeds, emails, journals, blogs, images, sounds and videos.

And this unstructured data holds the important insights required for faster, more informed decisions. Take news feeds as an example. Breaking news on economic policy can help you reevaluate production and supply chain decisions, and adjust forecasts accordingly.

But, for fear of missing out, many businesses end up hoarding data with little to zero analysis performed on this data. You don’t need to collect and store each and every single piece of data type out there.

Instead, only data which matters most and is key to unlocking answers to your critical business performance questions. The rest is all noise.

That’s why it’s critical to regularly ask the question, “What are we doing with all the data we have?” This way you would also be able to identify any information gaps that require filling via new data sources.

For analytics to work, people need the foresight to ask the right questions

People play a very critical role in an organizational analytics efforts. Without insightful guidance on the right questions to answer and the right hypotheses to test, analytics efforts are foiled. They cannot direct themselves.

Asking the right questions is key to drawing the right conclusions. Let’s look at the steps involved in making better decisions using data and analytics for one of your product lines. After a strong start, you’re now starting to witness a decline in online revenue but are not sure why.

Before jumping into complex analytics:

  • First, identify the problem that the data need to answer. You already know What happened. However, this is not enough. To address the issue, it’s important that you have a better understanding of the impacted segment and potential reasons for the revenue decline. This is where you start asking questions such as why is revenue declining? When did the revenue decline start? Where did it happen (country, region, city)? What might have caused this (potential drivers)? What actions can we take to address the decline?
  • Then come up with hypotheses of the issue to be addressed. A hypothesis describes a possible solution, such as a driver or a reason behind your business question. Many people make the mistake of simply jumping into data collection and analysis before forming a hypothesis to answer the critical business question. Going back to our revenue decline example, supposedly you have identified several hypotheses and then prioritized two to explain the revenue decline – A slowing down economy and impeding recession causing customers to tighten their purse strings quite a bit, and a new website design with enhanced security features but also additional online check out steps.
  • Based on these two hypotheses you can then identify the exact data that should be collected and analyzed to prove or disprove each hypothesis.

Irrespective of how advanced your analytical tools are, asking the wrong business performance questions results in flawed data being collected, and ultimately poor insights and recommendations. Hence the importance of diversity within the team.

Remember, no data or analytics model is perfect. Finding good data and making sure it’s cleaned and validated is fundamental but the organization also shouldn’t wait for perfection.

It’s easy to get fixated on perfection and fail to see the forest for the trees.

How to Transform Your Business in Times of Continuous Change

In times of continuous change, there are both winners and losers. Some company’s grow to become high performing, innovative and competitive enterprises while others develop into fighters, fighting for survival on a daily basis.

Today’s business environment is constantly evolving, with many factors both internal and external to the organization affecting the achievement of its stated objectives including the level of its competitiveness compared to competitors.

Some of the contributing factors include prolonged geopolitical and economic uncertainty, unresolved trade issues, rapid advancement in technological innovation, increased competition from new market participants, and fickle customers with constantly evolving needs.

As a result, the business has to be adaptive if it is to grow and succeed in such a disruptive environment.

When everything is going well, it’s easy to focus more attention on the good stories and less on what could go wrong. The blue overshadows the red, and this is a major problem in some companies.

These companies allow their past success stories such as successful product launches, increased market share, core technologies, and other organizational capabilities to blind their ability to view the future with a different pair of eyes.

Culturally, they are locked into the old way of working, bound by legacy systems and processes. Little time is spent on reviewing and evaluating the existing business model to establish whether it is still viable or not in these disruptive times.

On the contrary, transformational companies are not satisfied with the status quo. They are appreciative of the fact that past success is not a guarantee of future success.

Just because you are doing well today doesn’t not mean you’re going to enjoy everlasting success.

Business history pages are littered with doom and gloom stories about companies that have collapsed due to lack of innovation and unwillingness to evolve with the market.

Examples of such companies include the technology company Xerox, the retailer JC Penney, the social networking company MySpace, the department store Sears, the high tech company Polaroid, the bookstore Borders, and Circuit City the consumer electronics company.

What do all these companies have in common? At some point in time, they were all mighty industry titans, too big to fail and led by great, smart people.

However, in the midst of their successes they failed to adapt to changing customer needs, new technologies, competition and business models.

Even though these companies had built their businesses from the ground to the top of their respective industries, their death knell was the self belief that no other company was capable of doing better than what they were already doing and unseat them at the top.

Unfortunately, because of this fallacious way of thinking and ignorance they all paid a hefty price.

To avoid having your company join this list of colossal business failures:

  • Don’t get comfortable doing the right thing for too long. Continuously look for opportunities ahead and remember that today’s success can obscure tomorrow’s possible failures.
  • Regularly ask yourselves if what you’re doing and how you’re doing it is enough. It’s about making productive use of the resources available to you to improve your company’s performance and competitiveness.
  • Don’t dwell too much on the past. It’s important to know what has happened, but more importantly you need to understand why it has happened and how your company would perform in the future.
  • Commit sufficient time to analyzing new technologies, industry trends and competitors. Reviewing financials provides a rearview mirror of business performance, and you need forward looking indicators to understand your customers, competitors and the competitive status of your business (in terms of products, core technologies, market share, talent, culture)
  • Stay open minded. As highlighted above, when a company has been successful for too long, very little time is spent on thinking through alternative downside scenarios. It’s so easy to focus on the good news, spurn bad news and avoid discussing negatives. Questions such as “Why haven’t we done it before, What if this doesn’t work? What would we do then? What might make this not work?” are reluctantly answered. As a result, what begins as minor issues eventually develop into major issues. Don’t be a victim of own success to such an extent that you become ignorant of change.

Transforming a business into a high performing, innovative and competitive enterprise is a journey characterized by ups and downs. Consider every challenge, every problem and every piece of bad news as an opportunity to learn and improve.

The Accuracy in your Forecast Matters More than the Forecast Itself

One of the roles of the FP&A function is predicting future business performance and help business leaders prepare for an unplanned future through forecasting and decision support.

Although anticipating the future is challenging given today’s fast-changing environment, looking ahead is increasingly essential.

The world is far, far more complex than we think. Unknown unknowns and known unknowns have replaced the routine, the obvious, and the predicted.

Resultantly, many of the assumptions on which important future business decisions are based are easily refuted with the passage of time. For example, one of the most common outcomes of the typical business planning process is a hockey stick forecast.

These forecasts usually show significant business growth and profitability prospects. The last few years of actual results are flat, and then magically shoot up for future years just like the blade of a hockey stick.

It’s a rare experience to come across a forecast that shows a downward spiral of business performance.

Businesses leaders often present a positive outlook of enterprise performance even if the odds of achieving their bold aspirations are slim. This is emblematic of human’s limited ability to accurately predict the future.

Tunnel vision

In his book The Black Swan, Nassim Taleb demonstrates how humans suffer from the delusion of knowing. We underestimate what the future has in store.

In the same manner, we tend to develop a tunnel vision while looking into the future, making it business as usual, when in fact there is nothing usual about the future.

Instead of acknowledging our unknowledge of the future, we continue to project into the future as if we are experts at it, using tools and methods that exclude rare events or outliers.

Although these rare events are most of the time external to the organization, they play a significant role in influencing the operational and strategic performance of the business.

The problem with many business performance forecasts is that they tend to focus on a single point destination or outcome, including a few well-defined sources of uncertainty ( known knowns) at the expense of others that do not easily come to mind.

The goal is not to predict or forecast all improbable events but rather to have an open mind and acknowledge that the likelihood of your actual future being different to your predicted future is considerably high.

Think of new products that failed to hit the mark with customers, projects that experienced cost overruns or took longer to complete, companies that failed to survive their forecast horizon etc.

The list of forecast horror stories is endless. I am sure in 2003 the thought of Lehman Brothers going under five years later was a laughable idea and outside the company’s projections.

Mitigating the tunnel vision

When it comes to forecasting, most of us adopt the inside view to assess the future performance of the business or any other project.

In other words, we tend to plan and forecast based on the information in front of us, neglecting some sources of uncertainty outside the plan itself. Daniel Kahneman, the well-respected psychologist has termed this WYSIATI – What You See Is All There Is.

As a result, we produce plans and forecasts that are unrealistically close to best-case scenarios. However, there are many ways for any plan to fail, and although most of them are too improbable to be anticipated, the likelihood that something will go wrong is high.

The cure for tunnel vision is taking an outside view of that which is being forecasted. Optimism bias often gets into the way of accurate forecasting leading to some of the horror stories mentioned above.

Thus, to avoid falling victim to optimism bias it’s important that you go through all the statistics of projects or initiatives similar to that being forecasted. This will help you identify an appropriate reference class and use the statistics to generate a baseline prediction which acts as an anchor for further adjustments.

Measure your forecasting error

Even though the world is complex and constantly changing, many planners are still adopting a simple view of the world as evidenced by their click and drag forecasts projecting into the long term future. Simply extrapolating projections from one year into the next is a mistake.

The accuracy of forecasts is more important than the forecast themselves. Do you attach possible error rates to your forecasts and measure the actual error rate after the forecasted horizon has passed? As the projected period lengthens, the larger the cumulative forecasting errors.

Despite evidence of enormous forecasting errors in the past, there is an ingrained tendency in us to ignore failure statistics and believe we are suddenly better at predicting the future compared to our uncomprehending predecessors.

Should we therefore discard predicting the future altogether? No, we first need to acknowledge that what we think we know about the future is not all there is. Our comprehension of the future is limited. From there on we can plan while bearing in mind such limitations.

In other words, we should stop overestimating our known knowledge about the future. We may be good at predicting the ordinary, but not the irregular, and this is where we ultimately fail.

Talking about Digital Transformation

These days I’m hearing quite a number of business leaders talk about digitization, going digital or digital transformation.

Regardless of which term you’re most comfortable with, it’s a good sign that leaders are seriously considering taking advantage of the power of modern technologies to transform their businesses.

An investment in IT is no longer considered a cost to the business. Rather, in highly performing organizations, embracing emerging digital technologies is considered an enabler of business performance.

In these organizations, business leaders are cognizant of the conditions in which technology supports the overall business strategy as well as those in which it helps shape the business strategy itself.

It’s no secret that we are in the digital era. Digital technologies are everywhere. Just think of the significant increase in the pervasiveness and the power of digital technologies in new domains such as cloud computing, robotics, wearable devices, 3D printing, drones, machine learning, blockchain, virtual reality etc.

These new technologies are influencing not only the way humans live and work, but also how we learn, play, innovate, transact and govern.

Your existing strategies will not carry you into the future

Although I’m not able to predict with certainty what the business landscape will look like in the next 5 or 10 years, I strongly believe that organizations that will survive and succeed during this period will be defined by their ability to master and take advantage of the power of these emerging technologies to deliver value to customers.

Traditional, tried-and-tested ideas that propelled your business to where you’re today are no guarantee they will continually move you forward and ahead of your industry incumbents in this digital economy.

Let’s look at Amazon as an example. The global ecommerce retailer started off as an online bookstore and along the way embraced emerging technologies to become a leader in cloud computing services, media and artificial intelligence.

The company did not become successful because of its size. Rather, taking advantage of digital technologies helped it to become a powerful global brand.

Just because your current business model is working does not necessarily mean you should allow it to run its course. It’s critical to always question, refine and enhance your competencies and strategy otherwise you risk becoming irrelevant and falling victim of some successful, but now outdated, past practices.

It’s not all about a set of technologies

Simply overlaying technology, however powerful, on your existing business infrastructure does not work. A change in structures, systems, processes, skills and network relationships is paramount.

Neither does digital transformation entail simple automation of traditional, routine and repetitive processes. It’s also about embracing new rules of engagement and continually experimenting with new approaches and adapting them to suit your performance objectives.

For instance, embracing advanced data and analytics tools to learn about and better understand your customers and solve their problems, challenge your current business model and ultimately alter the sources of your revenues and profits.

Shift your focus from thinking about how digital technologies support your current business to exploring how they could also shape your future strategy and business models. In other words, think of innovative ways these digital technologies can help you create and capture business value.

Further, effective decisions on digital transformation are not hype-driven.

Different companies are at different stages of the transformation journey, experimenting with different technologies to create new capabilities, establish new relationships and identify differentiated drivers of value.

Thus, instead of just mimicking what other players in your industry are doing, acknowledge that there is no one-size-fits-all solution.

Consider how the different forms and functionality provided by digital technologies could influence your company’s strategic actions and provide better value for customers of your products and services.

Don’t go it alone

Success in today’s digital economy depends on your ability to build a network of relationships and co-create value with other digital players. As an example, think of how traditional banks are partnering with fintech and regtech entrepreneurs to fundamentally enhance, transform and disrupt their current business models.

These tech entrepreneurs are ambitious, with bold views on how they can disrupt and reorder the traditional banking model. By taking advantage of different digital technologies, they have challenged and disrupted traditional methods of delivering financial services.

So depending on your industry setting, you need to recognize the role of such specialized entrepreneurs in solving your fundamental business problems in new and effective ways.

As digital technologies increasingly pervade the very fabric of our society and business, are you keeping pace with the change or you strongly believe in your established playbooks to continually help you survive and succeed?

© 2019 ERPM Insights

Theme by Anders NorénUp ↑