My Diet (aka Mastering Metabolism and Preventing Disease)

I’m writing this post because someone close to me was recently diagnosed with a common and treatable form of cancer.  My hope is that these thoughts are helpful for them, and others, as they reexamine their diet and lifestyle that may have contributed to their condition.

My opinions on diet change – rather, are refined – constantly.  This is what I find works for me now, as I spend more time tuning into my body and correlate my body’s response to different stimulus, whether diet, exercise, mental health and more.

I have to mention, that working with my wife’s healthcare company, Parsley Health, can help provide a framework, data and clinical support for making critical changes in ones diet and lifestyle.  I’ll also include some links below that I found helpful in my early education.

Some Basic Truths (As I See Them Now)

  • Sugars and its more complex form, carbohydrates, are generally unhelpful. Unless you are living a hypo active lifestyle (rare in the modern age).  Carbohydrates can be helpful if aggressively training, doing physical labor or want a few extra hours of pep once in a while but otherwise, they cause more harm than help.
  • Eating a high protein / fat / veg diet is generally a good fit for our predominantly sedentary (office work, driving, instagramming) lifestyle.  Historically speaking, we should probably be eating more aligned with ‘famine’ than ‘feast’.
  • Reducing sugar consumption reduces inflammation.  Inflammation, for me, reveals itself as a testy mood (and biting humor!), bad skin, depression, and joint pain.  Inflammation generally leads to really bad things, including in theory, cancer.

What I Eat

What I Eat, Anytime

Caveat: protein and fat are more caloric than carbohydrates so you can’t go apesh*t with this stuff.  If you eat more than you burn, you will gain weight. The good news is that generally, a high fat diet is more sateing and you’ll have less of an urge to binge.

  • Avocados
  • Eggs
  • Oily Fish (canned sardines, smoked whitefish, cured salmon are my favorites)
  • Low Sugar Nuts (macadamia, walnuts, almonds – cashews and other ‘sweeter’ nuts are out)
  • Tart, organic berries (blues, black, straw)
  • Healthy Fats (olive oil, grass fed ghee, macadamia nut oil, coconut oil)
  • Organic, Grass-Fed, More Expensive, Hard to Find, Elitist Unless You’re A Small Farmer, Meat (85% ground, bison, pastured happy bacon, dark meat poultry, low-mercury fish,
  • Plant Based Protein Supplements (pea protein is a good additive to shakes)
  • Salt
  • Spice (cumin, coriander, curry, etc)
  • Heat (fresh chilies, no-sugar hot sauces. Heat becomes a very good friend when you cut our sweetness)
  • Leafy Greens (kale, chard, etc)
  • High Acid, Low Sugar Citrus (lemons, limes, but not oranges)
  • ‘Filler’ Vegetables (cauliflower, cabbage, summer squash, brussels. These guys are saviors – low calorie and often low nutrient, but terrific ‘bases’ to dishes to absorb fat and flavor and keeping you full / busy eating)
  • Coffee / Tea (blend in some MCT powder or cacao butter in the AM and you’ll find it easy to delay your first meal and stay in a ‘faux fasted’ metabolic state until 11a or later)

What I Eat, Once In A While

  • No-Sugar Alcohol (dry natural zero-residual sugar wines, clear liquors like tequila mezcal vodka and gin)
  • Organic Seasonal Fruits (tomatoes, apples, peaches, etc)
  • Rice and Other Unsweetened Gluten Free Carbs (so, I love Schezuan food, and I will half a half-helping of white rice when eating that food, but generally speaking this is a total cheat and should be done on rare occasions.  French fries also fall into this category. I’ll have them when they’re good, but mostly not. Fun tip, french fries + mayo >>> french fries + ketchup. The addition of fat to the carbs helps mute your glycemic response. That response is a double negative when you add super sweet ketchup to the potatoes.  Again, pick your spots for this stuff – maybe 2x a week)

What I Eat, Basically Never

  • Sweet Alcohol and Other Beverages (all beers, all ciders, aperitifs, sweet vermouth, tonic water, sweet industrial wine, aka most wine)
  • Breads / Sweets / Ice Cream / Dessert (this goes out the window when I travel to France, but I absolutely feel it on the flight home)
  • Gluten Free Breads / Sweets (to be clear, these are metabolically disastrous in the same ways as gluten-full breads would be.  Avoid)
  • Sweet Veg with Hidden Starches (corn, bananas, sweet potatoes, sweet winter squash)

Things That I Also Avoid For Other Reasons

  • Gluten (I’ve testing removing this on and off over the past 4 years and am clear that I generally do better without it. I bring it back in the fold on special occasions, with good results, and I have a hunch that my low carb diet helps me manage the G)
  • Dairy (Leads to breakouts for me, so I avoid it)
  • Mid / High Mercury Fish (tuna, mackerel – heavy metal stay in your body forever and removing them through chelation is a pain)
  • Processed Foods (generally speaking, there are almost always nasties hidden in here)

Example Meals

Breakfast

  • Nothing.
    • I don’t want to side track us here but your body is probably designed to fast for upwards of a month, so skipping brekkie is no biggie.
    • Don’t sweat hunger.  A 3 day fast is a good way to get on terms with this feeling.  It’s really not a bad one to master, and I don’t want / mean to sound like a sadist.  It’s not a big deal.
    • A fatted coffee (above) can help you feel like you’ve eaten while tricking your body to think it’s in a fasted state
    • Counter point #1: Humans are almost certainly better designed to fast at night than the morning.  Moving your metabolic clock off your circadian clock is generally a bad idea. That said, evening fasting is much harder to do socially, so I think a AM fast is better than nothing — anything that limits your digestive period is probably a good move.
    • Counter point #2: Per the circadian point above, fasting / low carb probably makes more sense in the winter than summer (fewer available natural carbs, less sunlight)
  • Bacon & Eggs.  Pastured bacon cooked without draining the pan with absorbing veggies (cauliflower, kale, onions, summer squash), fresh chilies, garlic and topped with two pasture eggs
  • Whitefish Omelette.  Veggies cooked with a bunch of ghee, 3-4 quality pasture raised eggs, whitefish, sage.
  • Protein Shake.  I like Parsley’s Rebuild shake but actually find it a bit too sweet for me, so I’ll make it 50% rebuild and then doctor up the fat (MCT, coconut, cacao butter) and protein (naked pea protein) content

Lunch

  • Organic Buffet.  There is a conscious Korean buffet near my office that offers a myriad of healthy, low carb options.  I’ll often go with greens (both collards w/ garlic and kale with pepitas), baked salmon and some chicken salad
  • Nopal Taco Plate.  An awesome taco shop opened in midtown, even more awesome is that they offer a cactus based plate with salsas, beans, guacamole and meat.  I alter my order by skipping the cheese and tortillas
  • Chinese.  Schezuan hot pot.  Tons of flavor, mystery ingredients and very little sugar / carbs outside of the rice which I eat sparingly and sometimes not at all.
  • Baked Chicken, Rice & Beans.  Ok, so this is cheating, but intentionally so.  Sometimes my body NEEDS carbohydrates and I give it some good one.  This is a rare occasion, but perhaps: I had a bit too much to drink the night before, I have a big evening ahead (physically, socially) where I know I’ll use the extra energy, or I’m simply feeling depleted and I know my fix.

Dinner

  • Cauliflower and Ground Meat Stir Fry.  Cauliflower, summer squash, okra, onions, cumin, cardamom, salt, fresh chilies, a little fresh tomato if summer, ground meat.  Sometimes a bit of well sourced chorizo goes in to the pan first to add additional flavor. Top with parsley, lemon and sea salt.  I could honestly eat this everyday.
  • Baked salmon and a ton of veg.  Enough vegetables and you don’t miss the starches
  • Seafood curry.  Full fat coconut milk plus quality curry powder and whatever vegetables and seafood look good that season.  Cauliflower or cabbage can add filler to replace rice.

Dessert

  • Ha, made you look.  Help yourself to a second glass of residual sugar-free wine instead.

Thoughts?  Disputes? Want to fight about nightshades?  Great recipes to share?  Shoot me a note.

Additional Reading


Goldilocks Criteria: Customer Data Platforms

This is the second in a series of posts designed to help managers think about business requirements for selecting enterprise vendors and software.  Please also check out my first post on Business Intelligence platforms.

Customer Data Platforms (CDPs) inspire a lot of confusion.  Best to begin with what they are and what they are not.

CDPs are:

  • A centralized platform for storing all of the user data about all of your users
  • A platform that can be used by non technical employees to activate / action upon user data
  • An safe-haven for secure user data management, compliant with the latest regulations and best practices
  • A bridge to combine your user data with external data sets
  • A rules engine for user segment management.  Want to build cohorts of users who opened an email and clicked on a Facebook ad – no problem
  • A platform for collaboration, breaking down individual business unit data silos

CDPs are not:

  • CRM solutions designed for sales or support teams to manage intricate customer interactions and workflows
  • DMP solutions focused only on anonymous cookied / IDed users (though they are coming close to covering this feature set)
  • Tag Management solutions designed to wire up various vendor libraries and SDKs.  Many CDPs were Tag Managers, but I think the historic focus on tag management is a disadvantage to be a best of breed CDP.  Just because you were a horse, it doesn’t make you a better car

And why do people integrate Customer Data Platforms?  Centralizing user data, strengthening the intelligence around it, and democratizing access to use it should impact business goals across the board from decreases systems costs to improved conversion rates.

The basic ins and outs of a CDP.

Given all of this, let’s review my Goldilocks (“just right”) criteria for picking a Customer Data Platform:

Connectivity and I/O

Customer Data Platforms are only as good as the pipes that bring data in and out of them.  You want many different roads into the platform from plug and play SDKs / libraries to full read / write APIs.  You also want pre built connectors into the most popular data sources (CRM, event ticketing platforms, etc) and data activation endpoints (ad networks, social media channels, email service providers, etc).

Security and Compliance

As we’ve learned over and over recently, user data security and governance is no easy tasks.  Outsourcing this to a vendor may be a hard decision to make, but it’s often much harder managing and maintaining secure and compliant user data solutions internally.  You want a partner with a tract record of secure data management, comparable customers that you trust and no fear of security audits from your team or others. You also want a partner that is quick to update to changing industry rules and regulations (ex. GDPR).  Internally, you want robust rules, roles and permission settings to partition off sensitive data for specific users and use cases.

Administrative Usability

CDPs are designed to democratize data-driven activities for non-technical users.  As such, you should require a modern, usable UX for non-engineers to get busy with the data.  Some providers require light scripting for segment creation or segment activation. No good. Best to trail the administrative user experience with some of your least technical colleagues before pulling the trigger on a vendor solution.

Identity Management and Identity Resolution

There are a number of features in this functionality bucket, but in short, you want your CDP to consolidate literally all of your available user data into a singular user profile.  This might mean partnering with a device or identity-graph provider to stitch emails to cookies.

This also means flexible data storage limits so that you don’t have to discard potentially valuable user data limits.  At Viacom, a certain % of the US population visits our sites / websites or volunteers their email addresses. That said, our TV signals reach the homes and mobile devices of a much larger user base.  We need systems to allow us to pull all of our data together without worry about a vendor’s storage costs or historic architectural limits.

Real Time Segmentation Updates

You user’s profiles and segments should update in real time as they take actions on and offline.  Many CDPs update segments hourly – which is no bueno. If a user views / interacts with your website or an online ad, their profile should update immediately so they can activate to the next event in your funnel.  Many of the CDPs who came from legacy industries (again, Tag Management) are just not architectured to support real time updates. This is of growing importance.

Integrated and Automated Machine Learning

The next generation CDPs go further than data storage and segment storage.  The best support unstructured data and use machine learning to automatically create useful user segments.  Some even crawl and categorize your content (pages, emails, posts) to find interesting patterns and apply those as dynamic segments to your users.  This is the type of thinking you want to see from your Customer Data Platform partners.

The platform should also support custom data science models – whether run internally within the CDP or through easy and performant read / write APIs.

ML fanboy alert – this is one of my very top considerations when reviewing partners.

Smart Orchestration

Getting your users through a funnel from start to conversion is never easy.  Your CDP should monitor and track your progress and where possible add dynamic intelligence to usher users through funnel events and towards your target goal.  The alternative is intricate manual workflow creation and management, which is hard to set up and even harder to manage against other initiatives.

This dynamic orchestration allows for truly personalized, omni-channel user journeys – experiences and messages that change based on the individual user’s profile properties and the best likelihood of conversion.

Industry Momentum

There is a ton of investment in the CDP space right now.  You’ll want to pick a horse with recent major funding from venture capital or a strategic investors.  Many of these companies will not be in business in two year’s time.

-David


Goldilocks Criteria: Selecting Business Intelligence (BI) Platforms

This is first of a new series of posts dedicated to helping people select data tools and infrastructure. I’ve listed out the ‘perfect’ feature set for a dream product. Of course these features rarely exist in a single solution, but if they did, I’d use it! First up: business intelligence platforms.

There are many fiefdoms in the kingdom of data – from product analytics to predictive models to advanced user-facing data applications – and no single platform will address every need. So for the purposes of this discussion, let’s define Business Intelligence platforms as data platforms that non-technical business users to explore, prepare and present data germane to their work. These tools should support data-driven insights and decision making but should not require a STEM degree or General Assembly workshop to operate.

Turns out this is ancient stuff. Business Intelligence (BI) platform date as far back to the ‘60 – the 1860s – when Richard Devens coined the term in his Cyclopædia of commercial and business anecdotes when Devens used the term to describe how Sir Henry Furnese, a banker, gained an advantage over his competitors by using and acting upon the information surrounding him. Over the past few decades BI tools have matured rapidly, shifting from beastly on-premise data warehouses with text-focused UIs to cloud-based, mobile-first, lithe data platforms designed for non-technical users.

https://www.sales-i.com/a-history-of-business-intelligence

BI is defined, generally, as ‘tools for data analysis and report generation on top of data aggregated from multiple disparate systems‘. Some BI platforms sit on top of separate data warehouses, and some modern platforms serve as the data aggregator / data store as well. BI tools pack a ton of functionality, but are typically narrow scoped. You don’t “do” anything within your Business Intelligence platform, instead you investigate, learn and report on how other systems are “doing”. BI surfaces data to guide decisions made elsewhere.

BI is generally defined as ‘tools for data analysis and report generation on top of data aggregated from multiple disparate systems’. Some BI platforms sit on top of separate data warehouses and some modern platforms serve as the data aggregator / data store as well.

BI tools pack a ton of functionality, but are typically narrow scoped. You don’t necessarily “do” anything within your Business Intelligence platform. Instead you investigate, learn and report on how other systems are “doing”. BI surfaces data to guide decisions made elsewhere.

You will also see BI in the form of Embedded Analytics within various tools – like your CRM system or your Web Analytics platform. Generally, Embedded Analytics help steer micro tasks like, which email subject performed best?, vs. providing a holistic view of data across multiple sources. The best BI tools provide this holistic view – pulling in all of your data to support cross functional views and insights.

So how does this work in practice? A great use case for BI platforms is to create easy to digest OKRs dashboards for your company, teams and individuals. Your BI platform should allow teammates from different business units pull up live views of their progress towards their outcomes / goals… anytime / anywhere… on their phones… without support from business analysis or IT.

OK, enough preamble. Here are the goldilocks (aka “just right”) criteria I look for in BI platforms:

Integrated data warehouse

Traditionally, BI tools sit on top of separate data platforms managed by engineering teams. More recently, a new class of products have emerged that allow you to upload / connect to your data without engineering support. I find this to be a huge advantage as it allows moderately-technical users to get up and running without distracting / relying on external resources. (Self service also leads to challenges with data governance but that’s another story.)

As an example, imagine easily joining together all of the spreadsheets you store in Google Drive / Dropbox with live data connections to Google Analytics / Facebook Analytics / financial data / more and then exploring and visualizing this data as you choose. That’s what these new platforms do all without the help of data engineerings resources.

Data engineering for dummies

Some of the best data scientists I’ve worked with estimate that they spend 80-90% of their time on data hygiene before they can begin analysis and exploration. The same goes for business analysts.

You will also see BI in the form of Embedded Analytics within various tools – like your CRM system or your Web Analytics platform. Generally, Embedded Analytics help steer micro tasks like, which email subject performed best?, vs. providing a holistic view of data across multiple sources. The best BI tools pull in all of your data to support cross functional views and insights.

So how does this work in practice? A great use case for BI platforms is to create easy to digest OKRs dashboards for your company, teams and individuals. Your BI platform should allow teammates to pull up a live view of their progress towards their outcomes / goals on their phones – right before they go to sleep every night!

OK, enough preamble. Here are the goldilocks (aka “just right”) criteria I look for in BI platforms:

Integrated data warehouse

Traditionally, BI tools sat on top of separate data platforms managed by IT teams. Recently, a new class of products have emerged that allow you to upload / connect to your data without engineering support. I find this to be a huge advantage as it allows semi-technical users to get up and running without distracting / relying on external parties. Self service also leads to challenges with data governance but that’s another story.

As an example, imagine easily joining together all of the spreadsheets you store in Google Drive / Dropbox with live data connections to Google Analytics / Facebook Analytics / financial data and then exploring and visualizing this data as you choose. That’s what these new platforms do all without the help of data engineerings resources.

Data engineering for dummies

Some of the best data scientists I’ve worked with estimate that they spend 80-90% of their time on data hygiene before they can begin analysis and exploration. 

https://www.forbes.com/sites/gilpress/2016/03/23/data-preparation-most-time-consuming-least-enjoyable-data-science-task-survey-says/#183675d26f63

What does that mean for BI tools? Any functionality that support easy data manipulation for the sake of improved clarity is awesome. That means – joining data together via drag and drop, changing data types with a click, deduplicating rows without writing SQL is all a huge value add, extending the range of users who can go deep with the data without external assistance.

What does that mean for BI tools? Well, any functionality that support simple data manipulation is awesome. For example – joining data together via drag and drop, changing data types with a click, deduplicating rows without writing SQL, are all huge value adds, extending the range of users who can go deep with the data without external assistance.

Live data! From the cloud! On your phone!

Data that arrives attached to an emails is DOA. This is one of my absolute pet peeves. Further, once people begin offline discussion and editing of the data, the risk of multiple inaccurate versions / views of the same data set commonplace. 

BI tools need to pull from a live backend at all times. When I pull up a link to view a dashboard the data should be (pseudo) real-time, up-to-date, and time stamped clearly with the data last run.

This also means the platform should be mobile-centric. Old timers still want their landscape printouts, but there is nothing more powerful than conversing with colleagues and pulling up live data views on your phone à la minute. 

AI / ML aware

I don’t want to overstate this one as we’re in the very earliest of innings, but your platform should have the foundation of supporting automated machine-learning driven insights. You may not find these immediately valuable (they rarely are out of the box) but in a few years you be getting voice alerts when your data spikes unpredictably in ways you may not have imagined. There is no sense in investing in a platform that is not actively working on automated data insights.

As a start, I’d like to see my platform present basic statistics around the data that I’ve on-boarded. This means simple distribution and correlation reports. As you play with these statistics you’ll be able to more easily wrap your arms around the data at hand, steering deeper analysis and insights. Simple predictive analytics is another good baby step before full blown AI.

This all said, you separately need to invest in training of your teams to take advantage of these statistical insights. Leveling up the data fluency of your team is always more valuable than standing up a wiz-bang technology solution.

Narrative & collaboration focused

A perfect platform would support for metrics-backed storytelling – and not just the sharing of pie charts. That means as a product owner, I can use a BI platform to explore a set of data and then build a coherent, sharable narrative around it. That could manifest itself as a online presentation with live data at different altitudes, supported by text, images, video and other added insights. It also means that I should be able to drawn / pin annotations within the data itself.

Further, the presentation should support active conversation around what’s being presented. Unlimited named user accounts, threaded comments, open annotations, creating next step action item , @ mentions and more are a natural fit here.

Governance gone wild

Sad to say, this is critical. Like supercritical. Like, as soon as you create your second dashboard you need extreme governance otherwise you’ll never find it again or know if the data set that powers it is up to date, approved and official. 

I’ve seen smart approaches here and they center around clear labeling of the data, its origins, similar / duplicative data and more. Having easy way to validate data as “best” or “official” helps too. Ultimately, ML/AI will be a huge help in this arena.

An integrated, dynamic “data catalog” that shows you the breadth of your data, its lineage, stamps of approval, and error reporting is also must-have.

User-level data FTW

BI tool typically play in the aggregated, anonymous altitude. You can see how all your site visitors behave, customer acquisition by location, sales by campaign, etc. Data is viewed on the content, page, campaign, location level – but rarely at user level. In a perfect world, a graph model would be deployed at the atomic event level allowing pivots at the above altitudes but also down to the user level.

A new breed of system called Customer Data Platforms is jumping into the fray here, promising a single view of the user. These CDPs are being leveraged today by Marketing and Sales team but the application of this user-level view to more typical BI use cases is immense. Perhaps CDPs are the topic of the next post in this series…

Live data! From the cloud! On your phone!

Data that arrives embedded within emails or as an excel attachment is Dead on Arrival. That is one of my absolute pet peeves. Further, once people begin to discuss and edit the data set, the risk of multiple versions / views of the same data becomes legitimized. 

BI tools need to pull from a live server at all times. When I pull up a link to view a dashboard the data should be (pseudo) real-time up to date or time stamped clearly with the data last run.

This also means the platform should be mobile-centric. Old timers still want their desktop-focused printouts, but there is nothing more powerful than conversing with colleagues and pulling out live data views on your phone à la minute. 

AI / ML aware

I don’t want to overstate this one as we’re in the very earliest of innings, but your platform should have the foundation of supporting automated machine-learning driven insights. You may not find these valuable right away (they rarely are) but in a few years you should be getting voice alerts when your data spikes unpredictably. There is not sense in investing in a platform that is ignorant to this coming trend.

To start, I’d like to see a platform present basic statistics around the data that I’ve onboarded. This means basic distribution and correlationsinformation. As you play with these basic metrics you’ll be able to more easily wrap your arms around the data at hand, informing deep analysis and insights. Simple predictive analytics is another good baby step before full blown AI.

This all said, you separately need to invest in training of your teams to take advantage of these statistical insights. Leveling up the data fluency of your team is often more worthwhile than the data platforms that they utilize.

Narrative & collaboration focused

A perfect platform would allow for metrics-backed storytelling, and not just the sharing of data dashboards. That means as a product owner, I could use a platform to explore a set of data and then build a coherent, sharable narrative around it. That could manifest itself as a online presentation with live charts (naturally) surrounded by text, images, video and other added insights. It also means that I should be able to drawn / pin annotations to the data itself.

This also means that the presentation platform should support conversation around what’s being presented. Unlimited named user accounts, threaded comments, open annotations, tasks lists, @ mentions and more are a natural fit here.

Governance gone wild

Sad to say, this is critical. Like supercritical. Like, as soon as you create your second dashboard you need this otherwise you’ll never find / know which data is most recent, best, approved and official. I’ve seen smart approaches here and they center around clear labeling of the data, it’s origins, similar / duplicative data and more. Having easy way to validate data / views as “best” or “official” helps too. Ultimately, Machine Learning will be a huge help in this arena.

An integrated, dynamic “data catalog” that shows you the breadth of your data, its lineage, validations and error reporting is also must-have.

User-level data FTW

BI tool typically play in the aggregated, anonymous altitude. You can see how all your site visitors behave, customer acquisition by location, sales by campaign, etc. Data is viewed on the content, page, campaign, location level – rarely at user level. In a perfect world, a graph model would be deployed at the atomic data layer allowing pivots by the above altitudes but also on the user level.

A new breed of system called Customer Data Platforms is jumping into the fray here, promising a single view of the user. These CDPs are being leveraged today by Marketing and Sales team but the application of this granular view to more typical BI use cases is immense. Perhaps CDPs are the topic of the next post in this series…


Get to know a few of Viacom’s data scientists

Here’s a great profile on a few of my Data Science colleagues here at Viacom. So excited to see a few of my hires (Matthew and Preeti) profiled!

Viacom has a strong track record of hiring data scientists with deep academic backgrounds who have also completed business training boot camps.  Matt and Preeti were graduates of the Insight Data Science Fellows Program – an intensive 7 week post-doctoral training fellowship bridging the gap between academia & data science.

With any new hire, there is a learning curve.  The transition can go smoothly if there are mentorship opportunities and other senior data scientists in place before the new cadets arrive.


Selecting what to work on is often more important than the work.

I’ve recently taken on a new role at Viacom as corporate vice president of data strategy.  Here, I sit within a small group of ‘data mercenaries’ looking across the org looking for opportunity to scale how we work with data science and data platforms.  It’s an fun pivot from past roles leading and building teams, and the opportunity seems enormous.  In addition to taking on specific data projects / products, we’re asked to look across ALL of Viacom’s data efforts and assets and help everyone do more.

Our first step is to install a new process for defining the work each data group takes on.  The benefits of this pre-work are multifold and tremendous.  We now have a shared methodology for documenting the goals, expectations and ROI for each project – leveling the playing field for each team to get the understanding, buy-in and support they need.  Here’s a peek into our process:

  1. Problem Statement – a single S.M.A.R.T. sentence that clearly defines the scope of the work at hand, the expected outcome metrics and the timeframe for delivery.  Getting this right and agreed upon can take days.
  2. Context – the landscape and rationale why we’re taking this project on.
  3. Success Criteria – measurable KPIs that will allow us to prove the projects value.  The project shouldn’t move forward without these.
  4. Scope – where do we start, what do we leave out (for now).
  5. Decision Makers – who’s the boss.
  6. Stakeholders – following a RACI approach, who ultimately owns the projects (Accountable), who’s supports this person (Responsible), who’s been Consulted and who’s been Informed.
  7. Constraints – what movable / immovable roadblocks have we identified before taking on the project?

We boil the above into a single page that can shared broadly across the org so that everyone from our most senior managers to the most junior data engineer know what we’re shooting for and what we’ll be measured against.  This also allows us to look across projects and determine where best to spend our resources.

It’s early days, and we’re always learning how to improve the quality of our process estimates, but this big organization just got a little bit more aligned and folks are excited by the newly created clarity.


Using data science to find the right influencers for specific social moments.

It’s been a busy inaugural year for Viacom’s Social  Data Strategy team.  We were founded last Fall with the mission of using advanced analytics and data science to support the growth of revenues from Viacom’s massive social footprint of about a billion fans.  I’m proud to say that we’re well on our way.

One of the first new tools we’ve released is the Social Talent Platform (STP), a data driven, fit-assessment platform that helps our social casting teams identify the best social talent for a particular campaign.  There are a number of great data sets in the market that follow and classify social talent, but none of them can tell you how good a specific influencer might be for a specific project – especially integrated marketing projects that include both internal Viacom content and external advertisers.  Sensing this gap in the market, and knowing that our content teams and advertisers want both art + science to inform their decision making, we created a proprietary platform or unique data sets, algorithms and visualizations.

We followed the standard data product blueprint:

Data Acquisition > Data Management > Data Modeling > Data Storytelling

 

  1. Data Acquisition.  Here we partnered with the best social influencer data companies, social listening data companies and machine learning toolsets in the business.  As many of our deals included custom features, vendor selection and deal structure required pure-play business development and product strategy chops.  For the STP we also leveraged unique data sets that are not typically consider when searching for social talent.   The ingredients make the dish!
  2. Data Management. We connected our data together using licensed data aggregation platforms and custom data environments.  In addition to building a custom database of social talent entities, we also built social profiles for content and advertisers.  Mapping together entities from within disparate data sets was a challenge here, as it is in most data efforts.
  3. Data Modeling.  Two PHD data scientists on my team built proprietary algorithms to compare these entities – influencer, content, advertiser – together to find the perfect fit for each use case.  We looked across the dimensions of audience demographics, topic overlay, post emotionality and more.  We consider non-social data in our models used time series data to predict increased future engagement.
  4. Data Storytelling.  We built a custom front end JavaScript application to allow our talent, content and advertisers to see the results of our custom search tool.   At a creative company like Viacom, outcomes presented via spreadsheets just doesn’t cut it.  This is one of my favorite parts of the project as it pulls on all of my past experience in traditional application development.

The result is a patent-pending, bespoke data platform that helps create more engaging social content.  Kudos to the entire team on this one!


My ketogenic experiment, aka ’empty is the new full.’

I celebrated my first Father’s Day last weekend with a 48 hour fast.  I still know how to party!

My goal was to experiment with the ketogenic diet, which is said to have numerous benefits including weight loss, inflammation reduction, cancer management / prevention and improved mental clarity / performance.  I am lucky to not suffer from any major known ailments, but I wanted to give it a try as I’ve learned deeply over the past few years that I am truly what I eat and the idea of a “self clean” cycle is quite appealing.  I cut out gluten in 2014, dairy in 2016 and, well I guess now “food” in 2017.  I kid, a bit.

Ketosis or “keto” is a metabolic state where you body turns fat > ketones > energy, instead of the more modern use of carbohydrates > energy.  You can enter a ketogenic state by either truly fasting or tricking your body into thinking it’s in a fasted state.  You do the latter by consuming ~75% of your calories from fat, ~20% from protein and ~0-5% from carbohydrates.  With these macro ratios, you can stay in a state of ketosis indefinitely. 

Proponents of ketosis argue that the human body evolved – and thrived – in a state of feast and famine, which is quite opposite to today’s super consistent / available / non-seasonal calorie bonanza. 

Gentlemen, Stop Your Engines

Test strips – doing well

I prepared for my keto experiment by enjoying a low carb, high protein, high natural wine dinner on Saturday.  On Sunday and Monday, I treated myself to a few coffees blended with coconut oil and medium chain triglyceride (MTC) oil.

The fast went surprisingly well.  Only at 4p on day two was I slightly bothersomely hungry, and that passed within an hour.  I limited my physical activity, but felt good and slept well.

I entered at ketogenic state (as measured by urine test strips) mid-day day one, with my ketone levels increasing throughout the fast.

Maintenance

For day two dinner, I broke my fast with a dinner of fatty bacon, mixed in with summer squash and mushrooms.  Not bad at all.  I thew in a ‘ dessert’ of coconut milk, avocado and a half packet of stevia (gross by darn my sweet tooth).

Chicken Wings + Cauliflower. Awesome.

My break-fast mealThe amount of fat required to maintain keto is daunting.  Especially as our modern western brains attempt to unpack all of the “low fat” marketing that clogs the airwaves.  As I entered ketosis, I became keenly aware of all of the high sugar products marketed to us – ice cream, burgers, breads.  I wanted them badly!  I think the key is maintaining a diversity of lipids.  Olive and coconut oils are saving my ass, especially as I find ghee a turn-off (must be from my dairy-free palette).  Nuts – macadamias are the best combo of fat minus carbs – also help out a bunch.  And the coffee delivery method is key – my morning recipe is 1tsp MCT oil, 1 tsp coconut oil, 1 tbs ghee and a pinch of cinnamon.  Delicious.

I began exercising day three and felt great.  A short HIIT run felt better than average.  There is research indicating that oxygen efficiency is boosted in keto.  Divers particularly enjoy keto as it greatly increases their dive and breath holding times.  Strength training felt good, perhaps because it felt good to move after a few days of fasting.

Early Outcomes

It’s been a less than a week, but here are some of the notable takeaways:

  • I feel pretty fantastic.  Euphoric really.  That’s awesome
  • My energy has been very good, and constant throughout the day
  • My skin has improved.  Sugar starvation is great for inflammation, including breakouts
  • I’m rarely hungry

However, it’s nearly impossible to eat out (“I’ll have your fattiest steak please, with a side of olive oil” has been said more than once this week) and my cooking options are quite limited.  As some of who loves food / cooking / taste diversity this is a problem.  It’s also a bit painful to remove even more from my diet, especially after already maintaining a gluten and dairy free lifestyle.

Next Steps

All in all, I’m super pleased with this experience – both the fasting and the state of nutritional (non-fasting) ketosis.  I’d like to continue to experiment and practice both in the future and add them to my toolset when I need a boost or change of pace.  I’m already thinking about the following regimens:

  • Keto Mornings: eat early the night before (6p) and mini-fast until lunch the next day, with only a MCT/coconut oil coffee in the AM.  (2x a week)
  • Keto Quick-Weeks: fast Sunday PM – Monday AM, and then do strict keto through Wednesday / Thursday.  (1x a month – 1x a quarter)

My thinking here is that a break from our carb driven diets has to the a helpful change of pace for our metabolisms.  The euphoria is a nice bonus too!

This meal meets the appropriate ratios.  Watch out for those tomatoes! Carbs are hidden everywhere, sigh.

Resources

  • Dr. Dom on Tim Ferriss.  Even though Dr. Dom’s built like a linebacker, he’s done some of the most thorough metabolic research in the business.  The links in the podcast’s ‘show notes’ are excellent.
  • Carb Counting Spreadsheet.  Good reference.  You will google a lot on ketosis, and often find yourself disappointed.  “brussels sprouts have 1.8g net carbs!  man!”)
  • Ruled.Me.  Great overall resource, including this veggie guide.
  • Keto SubReddit.  Dig carefully.
  • Eating Academy.  The hard science.

The dark ages of tech usability.

The future is bright my friends.

Our kids will laugh at photos of subway cars, packed with hunch-backed phone swipers.  They’ll mock our cable / dongle filled existence.  They’ll pity the eye strain and radiated pockets that we burden ourselves with to ensure we don’t miss a single Instagram live story…

The original “wireless”

At least I’d like to believe the future is bright.

There are serious challenges ahead for technology and mankind in general, but there is a version where technology integrates seamlessly into our daily lives and we all become more human again.  This version of the future sits next to visions of unlimited clean energy, desalinated water for all, repopulation of the earth’s lost species and the Mets gaining respectability again.

Google Glass 1.0 was a hot mess, but the ergonomics of posture-friendly wearables was on the right track.  I greatly look forward to friends and strangers looking each other in the eyes again as we pass on our way to our driverless rides home.