All posts by Alexander Pelaez, Ph.D

Alexander Pelaez, Ph.D., is a President of Five Element Analytics, an analytics consulting firm. He has served as a senior executive to a number of firms in healthcare, retail and media. He is also a professor of Information Systems and Business Analytics at Hofstra University.

Preparing for the Economic Rebound

The CEO of a media company recently asked me about my thoughts regarding the near-term and long-term future of the economy due to the COVID-19 crisis. The conversation turned to what type of recovery would we see and how long will it take. What do executives need to know and how can they prepare for the rebound. Here are a few thoughts from a data science perspective.

We aren’t out of the woods yet.

At 5EA, we are modeling the virus spread and population effects as best as possible with the data we have available. We are providing insights to our customers about the virus growth curve, with a focus on NY and projecting when the peak will come. The virus as Dr. Fauci, Director of the National Institute of Allergy and Infectious Diseases, states, “has its own timetable”, and therefore the public’s participation of government recommendations and guidelines is critical to slowing the spread and ultimately the start of the recovery.

Starting the Engine

When we reach the peak of the virus, which will occur in different parts across the country, the economy will be like car accelerating. We should see regional recoveries which would then lead to a more national and then global recovery. The Great Recession of 2008, saw negative GDP from the beginning of the recession in Sept 2008 until the 3rd Quarter of 2009. A key difference between 2008 Recession and our current economy, is it’s strong structural foundation. No other recession has ever been characterized by a voluntary shutdown of an economy. Therefore, according to our preliminary research, we feel that a recovery should be quick and the acceleration will be rapid, targeting somewhere around 4-6 months after parts of the economy starts between May 1st and May 25th.

Be Smart, Use Your Data

Your company’s recovery will be dependent on actions you take now. Start by examining where you would have been had the shutdown. Leverage your data prior and post shutdown to examine changes in your customers, or their behavior. Using this data you can begin to make projections about how your economic conditions will change when the recovery starts and is complete.

The more data you can collect and analyze the better you will be prepared for the recovery. We at 5EA strongly believe that companies that can leverage data science will be in a far better position to explore new opportunities and manage the intermittent shocks that will occur in the coming months.

What are some specific actions you can take

a) Analyze the change in your customers – you may have seen an influx of customers based on the business you are in. If this is the case consider yourself lucky, however, these customers may not stay with you. They may have used your service due to convenience and they may exit when this is over. Conversely, if you have lost customers, many of those customers will come back, but you must stay in touch with them. Now is a good opportunity for brand and product awareness projects.

b) Model your revenue – Many companies, will see a strong dip in revenue. Begin using the growth curves of the virus as a factor in your equations. The virus itself doesn’t have a direct economic effect, but its an indicator of government responses. By modeling the virus in different areas of the country or world, you can get a better idea of how your revenue curves will react to government policy

c) Target – Allow data science models and classifications to help target areas and identify products that will be in demand. Your previous models, if you have them, will assist greatly in this effort. Connect these previous models with new short term models from the above recommendations and you could generate some very specific insights.

d) Research – With the downturn, now might be the time to revisit some data projects you haven’t had the time to do. Every company has a significant amount of data and as such should leverage the time wisely to prepare for the rebound.

In the coming days, we will be providing more information about virus growth, and economic projections. If you have some specific questions or are in need of assistance and would like to talk to one of our data scientists reach out to us at and we would be happy to assist.

Analytics – A Sun Tzu Perspective

Many of Sun Tzu’s writings have been adapted in a number of industries such as investing, real estate, and sales. However, the application of Sun Tzu to analytics is especially interesting and insightful because it plays such as significant role in successful analytics projects.

Sun Tzu said,

“There are five dangerous faults which may affect a general:

(1) Recklessness, which leads to destruction;

(2) cowardice, which leads to capture;

(3) a hasty temper, which can be provoked by insults;

(4) a delicacy of honor which is sensitive to shame;

(5) over-solicitude for his men, which exposes him to worry and trouble. “

How could one relate anything from analytics to these precepts? Very easily, if you are a student of strategy, and of Sun Tzu.

If an data analyst hurries their work or provides information in manner that is not complete, it leads to the the destruction of the work and possibly the destruction of companies or departments. An analysis that hastily attempts to identify a market segment, marketing campaign or competitive price comparison, can lead to disastrous results. This is not to say that speed isn’t critical, on the contrary as we will see in another article, speed is important, but reckless speed, kills.

Second, an analyst, must have the courage to state what must be said. They cannot hide the information or fear retribution from commanders and leaders. It is their responsibility to give their leadership critical, accurate and timely information whether its good or bad.

Third, analysts must leave the ego at the door. They cannot be tempted to lash out that their solution is the only reasonable one. While confidence and conviction are important, analysts must listen to all inputs in order to ensure that their solutions are not reckless. If there is a rush to judgement or just a defense of ones own position, the battle will be lost.

Fourth, if one is afraid to stand behind their decisions, the risk increases of hesitation and an analysis that is at best incomplete. Analysts must be willing to stand by their work, and be willing to be humble about their results, and not worry about preserving an undeserved honor. Honor and praise will come from decisiveness and completeness. However, a good analyst will shun overt and excessive praise because the work is never done.

Finally, our team is very important and data scientists are very difficult to come by; however, if leaders of analytic teams are worried about the sensitivities of their team or try and make things too comfortable, the analytics team  will never be sharp for difficult projects. Training and goals are necessary for the analytics team to excel in what they do, comfort and complacency are the real enemy.

Sun Tzu’s effect on business spans many disciplines. Analytics teams are no exception. We can learn much from Sun Tzu and in our next articles, we will explore more of how to apply Sun Tzu to your data Science team.

The key to our success….

In our company, we face the normal everyday pressures of businesses. We always work hard for our clients and are continually seeking ways to improve. We don’t let ourselves forget that we are humans as well. We get excited and happy when our ideas become reality and we get frustrated and stressed when we just can seem to find the answers. So how do we overcome periodic downturns of creativity? Continue reading The key to our success….

STOP! – Understand and Apply Growth Curves Correctly

Growth curves are a critical part of many different disciplines including the physical disciplines, however, many business,  with the exclusion of financial companies, tend to neglect growth curves due to the preference of simpler, easy to implement linear lines. They are often misunderstood and when incorrectly applied,  lead to very divergent results. If used properly, however,  they are a great way to understand long term behavior of business activity. This article presents a brief analysis of a few different curves and compares them to a linear approach and further examines their application.

Continue reading STOP! – Understand and Apply Growth Curves Correctly

The Importance of ‘1’: A Different Perspective on Data Science

As data scientists, we are always looking for data, more data, different tools, or new techniques. We develop models enabling us to find higher areas of crime, make our society safer, or find ways to assist companies increase their profits or find efficiencies. Data scientists can help us identify patterns to determine what customers will buy, when they will buy it and where it will be bought. It can even assist the customer in making suggestions for cross-selling and up-selling opportunities and determining what customers will buy before they even buy it. The capabilities and opportunities of data science are endless and its uses are boundless.

Data scientists can easily forget the true nature of the data, since the massive amount of data available and the complexity of the techniques clouds each observation. Depending on the dataset, every single observation represents a human being, or a living being. Statisticians and data scientists have always referred to the the size of the sample as ‘n’, for example n=100, meaning 100 observations. However, when looking at large amounts of data, it obscures the most important ‘n’, n=1. ‘N’ equals one (N=1) could be you, your spouse, your friend, a sister or brother, a child or parent. It can be someone you know, or a friend of a friend. It is not uncommon for many data scientists to be working with a dataset and realize, that one of the observations refers to themselves.

When we analyze data, of course we analyze the numbers as they are, but we should inspect and respect the data, not as numbers but as human beings, as members of our community, or as a precious life. Of course we can de-identify the data as a means of protecting privacy, the fact that they represent a fellow human or even another life, such as a dog, cat, or other animal, cannot be ignored and should not be considered contrary to our mission as data scientists.

Data scientists must strive to conduct their analysis under a strong ethical code

When we apply this consideration to data science, I believe we are embarking on a new, moral, ethical branch of data science, which can be called Neohumanist data science. As data scientists, we are given an awesome responsibility to see the environment from a different lens. We are entrusted with the knowledge of how to find the proverbial needle in a haystack, and seek truth in the cloud of information. The decisions made from data science impact society as a whole and can greatly help our community, our country or our planet. Understanding the importance of the findings uncovered and its’ impact on the lives of others, therefore, becomes an entrusted gift, when we work with an unbiased perspective and a goal of finding the truth, wherever it may lead.

Data scientists, statisticians and business analysts should always strive to learn new techniques and perform the analyses requested. However, they should always maintain a moral compass that grounds them with a perspective of their responsibility to N=1. They must strive to conduct their analysis under an ethical code that prevents them from deliberately avoiding finding a preconceived truth to further a cause, regardless of the cause. They must never allow themselves to fall victim to Mark Twain’s statement that there are “Lies, damn lies, and statistics”. Becoming a neo humanist data scientist means they will always try and hold themselves to a standard unparalleled in our society. The knowledge, the data, and the tools provided are a gift, of sorts, and they are entrusted to data scientists to make sure that their work will cause no harm to any person, or living thing.