Monthly Archives: October 2017

On the Fallibility of Modern Prophets.

The Lord will never permit me or any other man who stands as President of this Church to lead you astray. It is not in the programme. It is not in the mind of God. If I were to attempt that, the Lord would remove me out of my place, and so He will any other man who attempts to lead the children of men astray from the oracles of God and from their duty.

Wilford Woodruff 
President, LDS Church

I used to think this meant that the the leaders of the church were infallible: incapable of making a mistake or being wrong. It's easy to be snarky and point out that (of course) they are human, but to actually point something out as a mistake well that . . . that's pretty much apostasy, right? At the very least it's a demonstrable lack of faith.

<sarcasm> I find your lack of faith disturbing because you already know only the wicked take the truth to be hard </sarcasm>.

Can we be lead astray?

No. Unequivocally.

What does it mean to be lead astray? The work and glory of God is the immortality and eternal life of man (and woman). It isn't . . . to be right. It isn't to be above reproach. It isn't freedom from embarrassment.

Did Joseph Smith doubt that he had been lead astray when he wrote, "O God, where art thou? And where is the pavilion that covereth thy hiding place?"

Probably not. But if Joseph Smith could suffer torment, isolation, and martyrdom I think public embarrassment is within the scope.

<opinion> The Lord will not risk the immortality and eternal life of his sheep, but he will not protect us from feeling sheepish in the short term. </opinion>

President Gordon B. Hinckley grapples with the fallibility of LDS leadership.

President Hinkley experienced it in a way few ever have or will. Elder Gordon B. Hinckley was called as a third Counselor to the First Presidency on July 23 1981, when President Spencer W. Kimball as well as his two counselors were unable to attend to all their duties. By 1984, Hinckley was the only publicly active member of the First Presidency. After Kimball's death, Ezra Taft Benson became President of the Church. Benson named Hinckley his first counselor and Thomas Monson his second. In the early 1990s, Benson developed serious health problems, removing him from public view. Hinckley and Monson carried out many of the duties of the First Presidency until Benson died in 1994. After Benson’s death, Howard W. Hunter became President and retained Hinckley and Monson as counselors in the First Presidency. Hunter died nine months later. At this point in 1995, Hinckley assumed the mantle of Prophet. Hinkley knew the limits of those called to the presidency better than most. His thoughts:

Gordon B HinkleyPresident, LDS Church

I have worked with the Presidents of the Church from President Heber J. Grant onward. … I have known [their] counselors, and I have known the Council of the Twelve during [these] years. All of these men have been human. They have had human traits and perhaps some human weaknesses. But over and above all of that, there has been in the life of every one of them an overpowering manifestation of the inspiration of God. Those who have been Presidents have been prophets in a very real way. I have intimately witnessed the spirit of revelation upon them. The Lord refined and polished each one, let him know discouragement and failure, let him experience illness and in some cases deep sorrow. All of this became part of a great refining process, and the effect of that process became beautifully evident in their lives.

From the life of President Monson.

2015 October Conference. President Monson is concluding his thoughts when his strength began to fail putting him at risk of a fall. Why doesn't someone help him? With the lights dimmed, very few can see the whole picture as the prophet concludes his remarks. Michelle Cope was there. Her story is a great example of how the Lord carries us at times. She gives the following account:

Most of you probably did not see what was happening behind President Monson at the end of his talk. I was on the floor, just a few rows from the very front of the Conference Center with a clear view of the scene. You might have noticed that President Monson really struggled to finish the last couple minutes of his talk and especially the last 30 seconds. I was afraid for him. I thought he might faint, pass out, or something worse.

And then, my heart melted when I saw behind President Monson was President Uchtdorf – on the edge of his seat, almost half-way standing up, with his arms stretched out, ready to catch the Prophet at any moment if he fell. You could see the worried expression on President Uchtdorf’s face as well as focused determination. He was on high alert and ready to catch him. As soon as President Monson said “Amen”, President Uchtdorf was immediately at his side and carried him back to his seat, safe and sound.

Michelle Cope 

President Monson teaches us, both in word and example, when we are on the Lord’s errand, He promises, “I will be on your right hand and on your left, and my Spirit shall be in your hearts, and mine angels round about you, to bear you up.”

When your life is difficult, when affliction leaves you wobbling and short of breath, don’t worry if no one is by your side, because He has your back.

Having the answers is not the mark of faith. It is only by asking questions that we gain our own greater understanding.*** I find these stories from the lives of two presidents build my faith in the leadership of the church as well as in myself. It is on you to work out your own salvation. The Lord will not put our salvation at risk but, you might by holding an expectation of infallibility in our leaders.

*** Answers are not necessarily included.

Machine Learning Under the Hood: Separating Signal from the Noise.

All data is a combination of signal and noise. Signal represents valuable consistent relationships that we want to learn. Noise is the random correlations and stuff that will not occur again in the future. The combination of signal and noise takes on familiar patterns or shapes that we can use to build a model.

Models can consider varying degrees of signal and noise. On end of the spectrum is the non-model which disregards signal & noise. Consider this common upsell question.

“Would you like fries with that?” This approach requires no model. It disregards signal and noise, rigidly canvassing everyone regardless of who they are or what they ordered:

“I’ll have a salad, hold the dressing. And a bottled water.”

“Would you like fries with that?”

“Carbs? Uh, no thank you.”

On the other end of the spectrum are flexible solutions that consider signal and noise. The predictions gathered are influenced by every piece of data, including outliers which can (and do) skew outcomes. These solutions can be more damaging than using no model at all. Such models lose predictive ability for new data because they are too tightly bound to their original training data. This is called overfitting.

​This post is a continuation of my last post where I went over how machine learning fits into the scope of data science. This post goes a step further to talk about how we use machine learning to separate signal from the noise. There are many machine learning algorithms. Think of them as tools in a tool box. Data scientists use these pre-built algorithms to tease models from their data sets.

A handful of these tools are based on classical statistical methods which makes them easy to interpret. If the model is being used to aid a human to make decisions it’s a good idea to develop the model with these classical methods:

  • Linear regression
  • Logistic regression
  • K-means classification
  • K-nearest neighbors
  • Hierarchical clustering
  • Naive Bayes

There are more options If there is no human involvement in the decision process; think of Netflix making a recommendation, no one reviews the recommendation before you get it. If there is no human element or if there is a high tolerance for opaque black box methods there is an additional group of modern machine learning algorithms:

  • Random Forest
  • Hidden Markov Models
  • Support Vector Regression
  • Artificial Neural Networks
  • Apriori Algorithms

Discussing each of these is outside the scope of this post. But if you are interested in learning more, make sure to subscribe to my email list for data savvy professionals and get a copy of “Bull Doze Thru Bull.”

Take away. If you are evaluating a machine learning project a great question is to ask about the algorithms that were used. LASSO & Random Forest are as close to out of the box all purpose tools as you will find so they are quite common. The classical methods are a conservative choice. The modern machine learning methods are really black box solutions which means they probably tried all of them and went with the tool that performed the best in testing.

What’s the difference between business intelligence and business analytics?

So . .. are you a BI guy?

I get that often and the answer is yes, yes I am. In actuality the answer is, “well . . . sort of. Maybe. It’s more machine learning. At least some people think so.” The quick answer is that Business Intelligence is an evolving industry. If I get into it there is a variety of follow up questions that usually start: “What’s the difference between …”

What’s the difference between business intelligence and machine learning? Even if you google these terms it’s hard to find a good definition. For sure you will find definitions, but not meaning and context for a never ending list of terms in a jargon rich field.

Quoting a Google employee, “Everything at the company is driven by machine learning.” What does that mean exactly? Is that big data? What about data mining? How does that fit in? Is this all just fancy jargon for old school econometrics and statistics?

In the next 3 min i am going to take on the job of getting you up to speed on what all these terms mean in relation to each other. It isn’t enough to have a list of definitions, you need to understand context. That is what I will give you here. Context.

What Business intelligence is . . . and isn’t.

When you think about Business Intelligence you might confuse it for Business Analytics. Business Intelligence runs the business. Business Analytics changes the business. Intelligence directs process. Analytics directs strategy. Intelligence focuses understanding for today. Analytics focuses planning for tomorrow.

BI is real time access to data. Reporting. BI identifies current problems, solutions, and enables informed decision making. Business Analytics explores data: statistical analysis, quantitative analysis, data mining, predictive modelling among other technologies and techniques to identify trends and make predictions. But the two areas are merging as evidenced by these headlines:

  • 5 Ways Machine Learning Can Make Your BI Better
  • Machine Learning: The Real Business Intelligence
  • Machine Learning: The Future of Business Intelligence.
  • Big Data & BI Trends 2017: Machine Learning, data lakes, and Hadoop Vs Spark

How Does  Machine Learning Relate to Business Analytics? What is it?

I’ve heard machine learning described as the brains behind AI. Machine learning is the subfield of computer science that gives "computers the ability to learn without being explicitly programmed." I think of Machine Learning as a collection of pre-built algorithms for building models to predict future outcomes. Business analytics is about using those models on the execution side, putting insight into context and making things happen. In my last post I talked about the difference between data science and data savvy. Business analytics requires data savvy while machine learning is a component of data science.

Data Science?​

Data Science deals with structured and unstructured data. In principle, everything that relates to data cleaning, preparation and analysis lies within the scope of Data Science. Data science is interdisciplinary requiring training in statistics, computer science, and industry. Solo practitioners with specialization in all three areas are rare so it is common to have data science teams: a data savvy manager, an econometrician, & a developer trained in machine learning.

Traditional Research. If you know anything about analytics (or statistics) you are probably familiar with regression: “ordinary least squares”. If not I highly recommend reading the book, Freakonomics. Regression is a mathematical way of drawing a straight line that most closely fits a scatterplot of data.

Regression is the basis for econometrics which is squarely found in the arena of traditional research. As you can see on the venn diagram traditional research blends classical statistics with industry knowledge. The emphasis of traditional econometrics is to use statistical tools to determine causal relationships in data. An econometrician wants to be able to tell why something is happening in the data. They want to tell a story about why you see correlations. And they do that using different variations of the regression technique.

Software development. We all have apps that make our lives easier & more entertaining. A relative few are lucky enough to earn a living developing and/or supporting software, SaaS (Software as a Service). Traditional software development makes processes more efficient. Most of development exists around this. This field requires both Computer Science (coders) as well as industry knowledge. This space is marked by partnerships between clients and their SaaS providers. 

Developers will spend a lot of time and resources understanding their client's existing process to build solutions around industry best practices.

Machine Learning. Which brings us back to machine learning, which is probably not as familiar as software development or traditional research. When a developer uses machine learning, what does that look like? It starts with a dataset. As is the case with traditional research the first step is to prepare the data for analysis. Data prep, munging or data wrangling, as it is called is the most time intensive step. The second step is to separate the data into two parts. Two thirds to 70% of the data is used for training the model. 

One third to 30% is saved for testing the model. A machine learning modeler has a variety of tools at their disposal to build a model of relationships based on the training data. The modeler will then make predictions about the test data based on this model. The more accurate the predictions, the better the model.


At this point you should have a clear idea of what data science is: a blending of machine learning, traditional research, and software development to create predictive models. To contrast BI focuses on dashboards and reporting for the here and now. BI focuses on process. DS focuses on strategy. Data science requires a variety of advanced skill sets which makes data science teams quite common (and full stack data scientists quite rare). Business analytics on the other hand requires data savvy: a survey level understanding of data science topics with the purpose of putting these models to good use by executing on business goals.​

Among these topics a data savvy professional should be familiar with is an understanding of machine learning and the strengths and limitations of the more common algorithms used in machine learning. If you are interested in learning more, make sure to subscribe to my email list for data savvy professionals and get a copy of “Bull Doze Thru Bull.” In my next post we are going to explore these topics and get under the hood with machine learning.​

Are You Indispensable?

Seth Godin's Linchpin is for you. Your boss. Your team.

Linchpin is about leading, and change, and fear, and succeess.

You couldn't write this book ten years ago, because ten years ago, the economy wanted you to fit in. It took care of you . . . if you fit in. Now, the world wants something different. This book exposes a multi-generational conspiracy to sap your creativity.

What if you learned a different way of seeing?
A different way of giving?
A different way of making a living?
What if you could do it, without leaving your job?
(Or joining a network marketing scheme.)

A way . . . to contribute your true self and your best work.

Are you up for that?