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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.

Insights

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 →

Insights

Top 3 Reasons Why Analytics Projects Fail

Analytics projects can be very complex and require an appropriate level of expertise. The various stages of an analytics project incude determination of goals, collection of data, cleansing of data, statistical analysis, and presentation of findings. According to Gartner over half of the analytics projects fail, which is determined as a project that fails to meet its stated objects and/or runs over budget or over the stated time.

We have identified the top three reasons why analytics projects fail and it might not be what you think.

1) Lack of a Senior Sponsor

Projects lacking sponsorship of senior leadership have the highest likelihood of failure since they aren’t given the requisite attention of other projects. Disconnected leadership fails to identify or recognize the valuable insights provided by well conducted analytics projects. Further, this dissonance resonates to analysts and data scientists who may not feel their work is valued or may not feel their efforts will be realized and thereby limit their efforts and scope of results. Senior sponsors should be C-level or high level decision makers in the strategic business units, and it is incumbent upon analytics leaders to obtain the necessary sponsorship and support for their teams.

2) Lack of a clear question to be answered

Its the age old question of what are we doing and why for starters. Analytics projects require the inquisitive nature of business units to fuel the analysis. Understanding the nature of the data, and having a clear objective, solidifies the work effort and creates a less amorphous outcome. The question should be business oriented and ask for answers critical to the business such as “Who are our most valuable customers?”, “Which customers are more likely to purchase the higher level product”, and “What happens if I increase/decrease my price and how will it affect long term customer retention”. When these types of questions are asked, analytics becomes a tool empowering business leaders.

3) Lack of reasonable action

“Action is the foundational key to all success”-  Pablo Picasso

Your business units and senior leadership must be willing to take clear and decisive action based on the results provided by analytics projects. Failure to do so results in missed opportunities and complacency. “Let’s increase our price and analyze the results.”, “Let’s focus our latest marketing effort on the highest value customers”, and “Increase our communication on customers who may be ready to quit based on the results” are clear actions from results. The absence of these actions or indecisiveness becomes a clear indicator of a lack of trust in the data, analysis or analysts, and leads to failure.

Action should follow the questions from senior leadership, who are willing to trust the data and analysis as a compliment to the institutional knowledge of the team entrusted to execute on the directions. Analytics projects provide insights, they are not the end but rather a means to an end requiring buy-in and trust at all levels.

Insights

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.

Insights

Trust your instincts

Recently, an executive at an online media firm had asked me to take a look at some data. His team had found some interesting results using some correlations of data points for his web activity. Unfortunately, he wasn’t convinced of what they were saying, because his intuition was telling him otherwise. However, he couldn’t refute the analysis, it was fairly sound. He decided to get another opinion. Continue reading Trust your instincts →