Podcast Must Listen: Masters of Scale :: Charity Water

Reid Hoffman’s Master of Scale podcast episode that dropped today is AMAZING on so many levels! “TO SCALE, YOU MUST MASTER THE SKILL OF STORYTELLING”

Link to episode: https://mastersofscale.com/#/scott-harrison-to-scale-you-must-master-the-art-of-storytelling/

It’s the story of Scott Harrison of Charity: Water.

I found within it:

  • Life lessons
  • Marketing lessons
  • An amazing story, amazingly told

I dare you to watch this video “The Spring” and:

  • Not shed a tear
  • Not believe that clean water is one of the most impactful efforts that we can do for civilization
  • Not pull out your wallet to subscribe to this amazing charity (all charities should operate this way!)

Data Engineering is like the Offensive Line in Football

In Football, QBs & RBs get all the glory, but they are nothing without their Offensive Line. In Advanced Analytics, Data Scientists capture all the headlines, but the Data Engineering team is quietly of one of the most important pieces of an Enterprise.

You want to be Data Driven? …you need Data.

Quality Data Data that can be trusted and governed

And available when needed

A Data Scientist can build the most valuable model, but it is always dependent on the data.

As teams evolve to adopt many multiple AI/ML models that depend on the same underlying data, the data pipeline(s) becomes the critical path of the business.

A strong Data Engineering team can make a huge impact on a Data/Decision Science team. Conversely, they can also create a huge drag.

The longest cycles in a ML project is the data wrangling and the production-ization of a model. Data Engineers have the opportunity to drastically reduce these areas and free the Data Scientists to focus on what they do best.

The best QBs take care of their O-Line and are known for giving extravagant gifts (cars, watches, etc.). Data Scientists aren’t star QBs making tens-of-millions, but a simple “Thank You” and acknowledgement of appreciation this holiday season can go a long way!

#TeamWork

AI Wins Again!

AI beats humans again!

https://mashable.com/2018/02/26/ai-beats-humans-at-contracts/#Sg3c4D_8TkqS

These victories are to be celebrated as great feats of technology and advancement, until one day… :/

In this competition, lawyers we’re given 5 NDAs to review and identify 30 legal issues.

Humans averaged 85% accuracy rate; AI achieved 95 percent accuracy.

AI also achieved 100% accuracy in one contract, whereas which highest-scoring human lawyer score was 97%.

So, the tech works, but what is the business case?

Human lawyers took an avg of 92 minutes; AI completed the task in 26 seconds!!!

That is at least several hundred dollars of savings.

I’m not a lawyer, but if I was, this isn’t something that I’d be concerned with at all. I’d welcome this with open arms and run to this now, as a potential competitive advantage to provide a better service at a lower cost to my clients. In theory, I’ll be able to serve more clients as well and/or be able to devote more attention to higher valued services.

As a consumer, I’d look for lawyers that have adopted this kind of technology because I will feel more confident in the quality of the service. And presumably, the service might be a little cheaper (relative to others’ that don’t leverage technology).

How Romantic…

https://www.usatoday.com/story/life/people/2018/10/29/japans-princess-ayako-must-leave-royal-family-after-marrying-commoner/1804881002/

Firstly, I didn’t even know that Japan had a royal family.

Secondly, love conquers all.

Thirdly, don’t feel bad for the princess. She still receives a lump sum of money after leaving the royal family to “maintain her high standard of living”.

Kei Moriya: Big score for the little guy. Keeping the dream alive for us commoners! (to be clear, not my dream, I’m happily married!)

 

Is Sexual Abuse in the Church part of the Institution?

60 minutes lead story this week was about a whistleblower that came forward with evidence of sexual abuse allegations going on in the churches in Buffalo for decades and the leaders knew about the incidences, yet kept looking the other way.

https://www.cbsnews.com/news/whistleblower-says-buffalo-bishop-knew-of-sexual-abuse-allegations-but-did-nothing-60-minutes/

I’m not religious, but clearly these leaders are serving themselves and not serving their people.

This problem seems very pervasive in Buffalo. Is it pervasive throughout other parts of the country as well? I don’t know, but my suspicion is yes. If it were just a region, then it would be easy for the Vatican to purge one region.

The fact that this has gone on for decades and that there are so many cases, makes me believe that this is institutionalized. Were they abused themselves as children in the church? Do these men then seek priesthood in order to put themselves in a position of power over kids to abuse them?

Absolutely disgusting!

Doubly so when it comes from people who are supposed to be the “holiest of holy”

How Much Time do you REALLY Have?

Would you change anything if you, literally, knew when your time is up? Seems we each have an explicit biological clock embedded in our DNA.

This article talks about how researchers found that your Epigenetic clock can calculate biological age and predict your lifespan.

“Some individuals who fill their lives with fitness and healthy habits die younger than peers who live a much less healthy life. New research into the epigenetics of aging sheds some fresh light on the perplexing phenomenon of premature aging.”

It’s based on this research paper.

I thought this was super interesting.

Loads of implications:

  • Would you retire earlier or later based on your biological age?
  • Surely, this is going to have a tremendous effect on the Insurance industry.
    • I wonder how long until the industry adopts this as common practice to set your premiums.
  • Now that we know the marker (or measure), can we work to improve or manipulate it. (this is all over my head, I don’t even know if that’s a valid question)

Smart People are Flip-Floppers

I’m insecure. I have a small fear of commitment. When I make a decision, I’m always wondering if it was the best decision. What’re the unknown unknowns?

Bezos believes that “the smartest people are constantly revising their understanding, reconsidering a problem they thought they’d already solved”

I’m not saying I’m amongst the smartest or smart at all. But this did give me solace, in that, at least I know I might be thinking along the same lines as smart folk.

I’m always curious: Is there a better way?

In the book “Thinking in Bets”, Poker pro Annie Duke says that when it comes to decision-making, decide as if you are betting all of your money on your choice. Don’t take shortcuts based on your biases; seek contrarian opinions and experienced counsel. Talk with folks who have had similar experiences and expertise who can critique your choices and illuminate your blindspots.

I’ll talk to anybody and everybody about anything.

You can always learn something from someone.

And you know what? You’ll probably disagree and hate me for saying this, but Recruiters and Sales folk are amongst the best to speak with because they speak to the most people. So, they often have a good perspective (as long as you understand their bias, you can really learn a lot).

Rise of the Chief Data Officer (CDO)

Image shows number of CXOs in USA for companies with >1000 employees. In other words, only ~5% have a Chief Data Officer (CDO). Yet, how many are undergoing “Digital Transformations” and/or trying to become “Data-Driven” and/or trying to leverage AI (which depends on (good) data)?

I believe that the CDO role is a huge gap at corporations and it presents a huge business opportunity; not to mention a probable necessity going forward in order to, just simply, compete.

If you segment just “Retailers”: CEO = 497 | CMO = 154 | CDO = 8

This means in Retail, only 1.61% of large retailers have a CDO!

My advice: Hire a CDO.

Here’s the ROI: We all know data is siloed. But instead of breaking silos, I see LOBs duplicating data across the org to suit their needs; thereby creating bigger silos. That’s a lot of duplicated expense and effort, as indiv LOBs protect their budget and interests.

Example, one company recently spoke with, Marketing and Analytics use Adobe Analytics. But Data Science chooses to use raw web logs.

A CDO can put the people, processes, and tech in place to streamline data across the org.

N.B. All #’s are from LinkedIn Sales Navigator, so probably not exact, but good enough for % analysis. Also, I included “Chief Analytics Officer” in the CDO category.

Makes “Hew” say Hmm

Life After Death? (if you can afford it)

http://endoftheamericandream.com/archives/ultra-wealthy-elitists-are-having-their-brains-frozen-so-that-they-can-come-back-to-life-100s-of-years-in-the-future

I suppose when you have millions of dollars at your time of death, there is no harm in spending $100K for a lottery ticket to be brought back to life one day.

This is a genius business model. In some regards, I think Evil Genius, but then again, who’s getting hurt? There is no con. But the current owners of the company, whom profit from this venture today, take $100K, put you in cold storage, and head to the beach? No worries about customer service or a customer complaining about bad service!

Does this fee include the revival surgery process?

Let’s say this technology does come to fruition in 50 years or 100 years. You’ve already given away your estate. You’re no longer ultra-wealthy. Are your (potentially ungrateful) great grandkids, who never knew you, going to take care of you?

 

Really Japan?

https://www.zerohedge.com/news/2018-07-24/explosion-sex-dolls-threatens-japanese-race-extinction

Japanese are so interesting. They insist on preserving their strong culture with very strict immigration policies.They’re already one of the oldest populations in the world. Their birth rates have plummeted. And now the men are preferring sex dolls over the real deal?

 

Good Premise, but Dangerous Means?

https://www.manhattanda.org/tomorrow-d-a-vance-ends-prosecution-of-marijuana-possession-and-smoking-cases/

I am all for legalization of Marijuana. It’s long overdue. I don’t know the exact stats, but I’m sure that there are far too many folks currently incarcerated or blackballed with a criminal record because of minor marijuana charges.

But I don’t believe that a DA’s office should be allowed to do this. This is one man, single-handedly, changing the law, isn’t it? Doesn’t that set a dangerous precedence? Why doesn’t this go through the municipal or state government?

Data Wrangling is Career Strangling

Data wrangling is a necessary process when working with big data; most data, in reality. This opinion piece is not to diminish its importance. Nor, is this to be confused with Data Engineering. But I will argue that data wrangling is career strangling, in that it is holding you back in your career progression. Let me explain…

Firstly, let’s agree that the whole basis of big data is to whittle it down to little data, that we call “Insights”. The point of any data analysis is to identify a trend or anomaly. The point of a machine learning model is to find a set of defined patterns or assign a probability.

Observe any Data Scientist or Analyst presentation and the only pieces that get talked about are the Insights and the model. Zero time is spent explaining how the data was wrangled, despite that being 60-80% of the effort.

I am making the argument that data wrangling is low-level, tedious work that is wasted when an expensive resource such as a Data Scientist or Data Engineer or Analyst decides to take this on.

The best consultants know that:

You don’t get paid for the hour. You get paid for the value you bring to the hour

The more time you spend on lower value work, the more you diminish your value.

And if you’re an Analyst / Data Scientist spending a greater portion of your time wrangling data, that’s much less time that you’re spending to understand the data, that’s much less time you’re spending to analyze the data, that’s much less time to you’re spending on delivering business value from the data.

When it comes to big data, I believe that folks are starting to realize that robust software engineering practices need to be put in place to ensure quality of the data pipeline and #datagovernance. …Cue the Data Engineer.

In today’s episode (Aug 14) of the Digital Analytics Power Hour (a wonderful podcast, btw), there was a great discussion about raw data and data virtualization. I didn’t feel that there was any consensus, so I’ll throw in my 2 cents.

A company must adopt a tool or process to virtualize the raw data for the Data Scientists and Analysts. Drawing from software principles, the solution — built in-house or purchased — must be robust, scalable, extendable, and re-usable.

This will save an immense amount of time (and headache).

For example, when working with raw clickstream data, you have billions of atomic events. In most cases, identity resolution is required over a specified period of time. If every Data Scientist or Analyst is starting with the raw data, I guarantee that each will resolve the identity in a different manner (different “code”). This leads to multiple, inconsistent “truths”. The Analysts / Data Scientists should only work from a consistent, consolidated schema for the vast majority of cases.

So, when I say “Data wrangling is career strangling”, it’s because you’re devoting too much time to work with a lower-assigned value.

[Tangential annecdote: I use Salesforce a lot in my work. If I’m to be diligent, the data entry could be up to 4 hrs a week. I hired a VA  on my own dime  to handle this. This allows me to spend more time on higher value (and quite frankly, more fun) tasks. I value my time]

In the end, businesses are results-oriented. If you can produce more positive business results in a shorter time frame, then your career trajectory will move up-and-to-the-right at an accelerated pace.

And it’s a compounding factor. Those that produce results are provided more opportunities. The sooner you produce results, the sooner those opportunities present themselves.

Focus on value delivered.

The faster you iterate, the faster you grow.