The Evolution of the T-Shaped Skill Set: Building a High-Performance Box-Shaped Product Management Team

The concept of a T-shaped professional is a good one. It suggests the need for both a breadth (top of the letter “T”) and depth (central pillar of the “T”) of skills to excel in your role.

McKinsey & Company adds “For any given role, some skill requirements are universal. Every team member may need to be comfortable working with data, or solving problems in a structured way, for example. Beyond those basics, however, they will also want to develop a deeper understanding of topics that allow them to make a real difference in their job… The result is a T-shaped skills profile, with a broad set of generally applicable skills, supplemented by a spike of specific expertise.”

T-shaped skills are especially important in Product Management, a renaissance role that requires a mix of hard research/design/data/technical skills and softer collaboration/communication/consensus-building skills across diverse internal and external partners. In my product leadership roles, I work to develop both my own T-shaped skills and also to develop an organization of strong, diverse Ts that build something unique when combined together – the box-shaped product team – a synthesis of individual competencies that is more than the sum of its parts.

The Top of the T: The Default Product Manager Skill Set Curriculum

I love working in product management as it allows me to consistently leverage a highly diverse set of skills ranging from business strategy to experience design to quantitative analysis. There are parallels in my personal life. I’m a lifelong musician who started on the piano but then added guitar, bass, drums, production, vocals, and more. 

Even better, the diverse skills of the PM are constantly changing. A decade ago, the archetype product manager was an ex-Google engineer, immersed in coding and architecture. Half a decade later, the pendulum swung to customer-centric experience design. Things have shifted again and now I consider the product role fundamentally a data/business one, albeit one that requires a constant conversation with operational, technical, design, quantitative, and other specialized teammates.

I actively engage my teams in an ongoing dialogue to define and refine the contents/curriculum of this “top T”, and push to foster a culture of continuous learning and development of these different skills through various means including 1:1s, pairing, book clubs, external mentorships, online education, and more.

The Pillar of the T: Each Team Member’s Specialization

Beyond the foundational skills, I encourage every team member to cultivate deep expertise in specific skill segments of the Product Management curriculum that resonate with their interests and abilities.

Teams are better when we fully support – and enjoy – each team member’s natural strengths and talent. This is why they’re here. Peter Drucker perhaps went overboard here when he suggested “Focusing on strengths is development, whereas focusing on weakness is damage control” and “to focus on one’s weakness at work is misuse, if not abuse of the person”, but Drucker certainly knew where the upside was.

A case in point is my experience leading Viacom’s Social Data Product team. Each team member was encouraged to hone their unique strengths. Brian, with a technical predisposition, delved deep into algorithms alongside our data scientists. In contrast, Tanmay excelled in public speaking and presentation and refined those skills to perfection. The collective growth in individual specializations (plus foundational PM skills) up-leveled the overall impact of our team and both individuals to executive management roles.

A Super Team: A Symphony of Diverse Ts

The result of growing both the breadth and diverse depths of product management skills is a product team of specialized experts, with a common framework of foundational skills to get jobs done. The organization has superpowers all across the top of the T and teammates who are pursuing their passions, interests, and strengths. It’s a dynamic where every team member’s depth amplifies our collective capability, fostering an environment of shared learning and mutual growth.

Together we form a box shape that falls outside of the English alphabet, with a rising, rock-solid top-of-the-T with deep strength across multiple functional areas. 


In essence, the orchestration of T-shaped professionals into a cohesive unit is akin to a musical group of varied instruments creating a harmonious song. Each instrument, with its unique tone and timbre yet familiar with the score, contributes to the grandeur of the ensemble. Similarly, in the world of Product Management, the mix of shared skills and diverse specializations culminates in an organization that is skilled, innovative, and ready to tackle the multifaceted challenges ahead.

Healthcare Roadmaps: Crafting Unique Patient-Centric Healthcare Experiences Amidst Other Priorities

In my years leading product and technology teams across various healthcare companies, balancing unique patient experiences with the integrity of foundational medical care has always been a core focus. Our objective is clear: enhance the patient journey without compromising quality.

In healthcare, we cannot risk gimmicks as our customers – patients in pain – expect excellence. This blog post explores how to strike the right balance between supporting core healthcare experiences and building truly differentiated features while utilizing limited technology resources effectively.

Whether dealing with chronic health conditions (Parsley Health), depression/anxiety/PTSD (Mindbloom), poor sleep (Proper), or long-term back pain (Vori Health), there are lofty standards of patient care to meet and we cannot spend all of our time on delightful, yet risky, whiz-bang features. This said, without creating a truly differentiated and delightful patient experience, these nascent companies cannot get a leg up on the entrenched industrial healthcare complex or other venture-backed competitors.

The Nuance Between Product Vision and Product Strategy

When developing any product, it’s essential to have both a clear product vision of long-term goals as well as a product strategy of critical near-term milestones. 

The product vision sets your 5-10-20-year north star goals for the project. The product strategy is the collection of near-term milestones that allow you to continue on your journey toward the vision. The early strategic milestones in nascent companies are often behind-the-scenes moments like fundraising, key hires, or closing early sales deals. 

Said another way, you can’t be a visionary if you fail to survive your early journey. The good news is that you don’t always have to create a well-tuned finished product to achieve these early milestones – for example, your operational margins need not reach their long-term targets and your technology stack may have technical-debt-by-design in the early stages.

Although a polished product isn’t an immediate requirement, a compelling value proposition is vital to attract investment, secure talent, and build customer trust. Striking a balance between foundational robustness and innovative flair is essential. Striking a balance between innovation and sustainability/durability is crucial.

A Balanced Approach

Accepting both the need to differentiate and that the product isn’t “done” in these early years, I tend to advocate for the following resourcing mix when starting out in healthcare:

  • 30-40% for elevated activation/onboarding experience
  • 30-40% for core healthcare experience
  • 10-20% for high-value differentiated feature development

1. Elevating Onboarding/Activation Experience (30-40%) 

An effective onboarding process is foundational to be successful as you can’t have a healthcare business without patients. In most of my roles, we’ve created dedicated teams focused on moving users through the awareness > consideration > conversation > activation funnel.

Excellence here requires continuous testing, quality analytics, a learning culture, and a fastidious mindset of experimentation, iteration, and improvement.

At Vori Health, we took a number of steps to improve our onboarding funnels, including:

  1. Adjusting our technical architecture for agility, breaking these components away from our other core services to allow for faster development and deployment
  2. Investment in specialized analytics instrumentation, including privacy-compliant session recording and anonymized aggregate analytics
  3. A heavy focus on qualitative insight, through in-flow surveys and 1:1 interviews

We combined these approaches with deep cross-functional collaboration with our clinical and operational teammates to completely rethink the order of our onboarding sequence and drive a 700% improvement in our first-visit conversion rates.

2. Core Telehealth Experiences (30-40%) 

There are no shortcuts in healthcare once patients are onboarded and are receiving care. The patient/provider experience needs to work flawlessly and ensure patients have frictionless access to the care they need via common features like scheduling, messaging, video conferencing, care plans/notes, and other medical-specific features (i.e., lab, imaging, testing integrations).

In an early-stage healthcare company, these foundational (aka commodity) telehealth features rarely need to be differentiated.  As such, selecting a partner/vendor for these core telehealth services is a prudent path.  Some things to keep in mind as you review vendors:

  • Compliance features. HITRUST and SOC2, in addition to HIPPA
  • Mature developer documentation, APIs, and SDKs 
  • Realistic roadmaps juxtaposed with feature release notes that demonstrate a proven history of delivery
  • Robust partner integrations to share the load across the telehealth service landscape
  • Outstanding customer references
  • Strong revenues and/or investors to ensure the company is in it for the long haul

Interestingly, as your healthcare business grows, there are very compelling reasons to move away from vendor solutions, including costs as your scale, opportunities to differentiate your patient experience up and down the journey, and creating both small and large feature-specific operational efficiencies (at scale, every 1-2% efficiency boost has major impacts on COGS and margins).

3. Differentiated Feature Development (10-20%) 

Consumer-centered onboarding and standard telehealth features are unfortunately not enough to win for patients, payers, and investors.  As such, I always withhold a stream of technology resources to develop unique features that differentiate the company and build intellectual property/enterprise value. These features must address specific patient needs and leverage cutting-edge technologies. Here are a few recent examples:

Vori Health (Musculoskeletal care)

We built “Motion Guide”, a computer-vision-assisted physical therapy app that uses pose estimation ML models to track patient movement and then provide real-time and personalized corrective feedback.

Proper (Sleep health)

We built a sleep behavior tracker that helps customers understand sleep-impacting and sleep-promotion activities.  There are 101 sleep trackers on the market, so we build something new, based on the leading clinical evidence – cognitive behavioral therapy for insomnia (CBTi) – in a beautiful engaging package.

Selection of these product efforts and features is of course hyperspecific to each business and should be done with the very best product discovery/experimentation/validation rigor that Marty Cagan and others speak to so eloquently.  

Creating differentiation is both an art and a science and if you are relying only on gut instinct you are bound the fail. Sit with your customers, brainstorm widely, test ideas cheaply, prototype quickly, and keep iteration until you get closer to the truth. This is the fun stuff, for sure.

Striking the Right Balance

In conclusion, creating differentiated healthcare experiences while maintaining high standards requires a well-balanced approach to resource allocation. The mix of resources between activation, core experiences, and differentiated features should be an ongoing conversation without a one-sized-fits-all formula. To succeed, a combination of creativity, customer engagement, and rigorous testing is essential. If you’d like to discuss this topic further, feel free to get in touch.

Embracing Failure: Accelerating Product Development with Insight and Iteration

When building consumer and data products, product decisions are most often incorrect (up to 80-90% of the time!) As such, the key to successful product development lies in the ability to quickly gain insights and iterate.

Success isn’t found in initial perfection but in the adeptness to rapidly glean insights and iterate. Being wrong is not a setback but a stepping stone that propels the innovation engine.

It’s crucial to understand how wrong you are, why you’re wrong, and what the next iteration should entail. High-performing technical teams embrace failure, creating systems and processes to learn from mistakes, and shorten iteration cycles for faster progress. They are adept at engineering systems and processes that not only absorb mistakes but transform them into learning opportunities, accelerating progress.

In this blog post, I will share tips and strategies to enhance your iteration speed, foster a learning culture, and bolster technical efficacy

Cultivate a Learning Culture

Celebrate being wrong, but with the condition that the company learns from its mistakes. Encourage an environment where failure is seen as an opportunity for growth and improvement.

Maintain a Hypothesis Backlog

Create a backlog prioritizing and estimating hypotheses, treating them like a regular product backlog. Include detailed descriptions of each bet, strategies for validation, and the outcomes and learnings associated with each experiment.

Optimize Technical Architectures for Speed

During the early stages, prioritize speed over product fidelity, automation, and resilience. Choose technical architectures that support rapid iteration.

Avoid overbuilding backends for initial product iterations, as they are likely to change significantly.

Prioritize Analytics Instrumentation

Each product venture should include a ‘success metrics’ component, including an analytics tracking plan. This ensures the entire team understands what they need to measure and validates that the necessary data hooks are in place before launch.

Don’t Wait for Statistical Significance

It’s rare for an early product to accumulate enough usage for statistically significant insights. Therefore, don’t wait for it. Balance early data with heuristic instincts while keeping a close eye on biases. Don’t hesitate to make informed decisions based on the available information.

Document and Centralize Insights

Build a knowledge base to store outcomes from experiments. Treat these insights as invaluable resources for your organization. Encourage external teams, such as marketing, to contribute their experiment outcomes to this knowledge base. Invest time in structuring this repository effectively.

Share and Celebrate Outcomes

Embrace failures that lead to valuable insights. Share these learnings widely within the organization to foster a culture of continuous improvement.

Plan Next Iterations

Design subsequent sprints quickly based on the new insights gained. Roadmaps often require significant pivots as you accumulate more knowledge, so be open to adapting your plans accordingly.

Revisit Experiments Over Time

Consider repeating experiments when appropriate, as the outcomes can be influenced by factors such as user preparedness, technical feasibility, and changing foundational elements. What didn’t work previously may succeed now due to altered circumstances.

Monitor and Improve Iteration Cycle Time

Regard product iteration cycle time as a core company Key Performance Indicator (KPI) and actively work to enhance it over time.

By embracing failure, fostering a learning culture, and implementing efficient processes, your technical team can significantly improve product iteration speed. Remember that the path to success involves acknowledging mistakes, gaining insights, and continuously adapting based on newfound knowledge. With these strategies in place, your organization can thrive in the ever-evolving landscape of product development.

Whose Product is it Anyway? Nurturing the PM/CEO Relationship

In early-stage leadership product roles, the fit between the product lead and the CEO is paramount. When there is clarity and alignment, the product leader becomes supercharged. However, when this connection is lacking, it can lead to a breakdown.

In this article, I’ll explore tips on how to identify a strong product opportunity during the interview process and how to nurture the PM/CEO relationship for greater success.

Understand the CEO’s Vision for the Product Role

The product lead role is delightfully wide-ranging, requiring a diverse set of skills ranging from business acumen and strategy to technical delivery and operations. This broad scope is why I love working in product management. However, it is crucial to grasp which specific aspects of the product role the CEO expects you to emphasize. Having clarity on whether the role is more focused on vision/strategy or operational/services will set the stage for a strong partnership.

Recognize the CEO as the OG Product Lead

Almost universally, the CEO or Founder serves as the original product lead within their organization. They often have formal product management experience or assumed PM responsibilities during the company’s founding. Over time, the CEO’s role evolves to encompass fundraising, sales, talent management, and more. There comes a point when the product function should be professionalized under dedicated leadership, but this transition can be challenging for the CEO. It is critical to discuss expectations regarding this handoff and the division of labor moving forward. Expect some backseat driving or ‘takebacks’ in responsibility post-handoff. This transition must be discussed openly and continuously.

Manage Your Manager

The product role is highly collaborative, and at leadership levels, the responsibility for people/peer management often outweighs the ‘hard’ skills typically associated with an individual contributor PM (such as design, data, tech, road-mapping, etc.). Devote time to building relationships with your executive leader. I recommend a combination of dedicated 1:1 meetings and clear documentation at different altitudes, from up-to-date high-level product strategy documents to granular roadmaps. Humanize this relationship by creating an environment where you can discuss what’s going well and what needs further development. Prioritize conversations about “how we work together” as much as “what we’re working on.”

Keep Your Eyes Open

As a product leader, you have a unique perspective on the strengths and weaknesses of your executive leader. While you can nurture most relationships to success, some relationships cannot be improved. If you genuinely cannot regain confidence in your executive leader or restore the relationship, it may be a sign that you should start exploring new professional opportunities.

By focusing on these key aspects, you can strengthen the PM/CEO relationship and drive success in your early-stage leadership product role. Remember, the fit between the product lead and the CEO is essential for a thriving partnership. Open communication and mutual understanding are key ingredients for fostering that fit.

Supporting Sweet Dreams: A Product Journey from Concept to Impact with Proper Sleep + Behavior Tracking App

As the Product Management Leader at Proper, a Redesign Health portfolio company focused on sleep health, I had the incredible opportunity to spearhead the creation of a groundbreaking consumer-facing product that changed the way our customers understand and optimize their sleep. Our journey from ideation to delivery was a remarkable adventure that brought together sleep science, novel user-centric design, and our passion for enhancing people’s lives through better sleep.

Inception and Ideation

The journey began with a vision to empower our customers to take control of their sleep quality and duration with the most research-supported intervention for poor sleep, Cognitive Behavioral Therapy for Insomnia (CBTi) – and to promote our consumer products (evidence-based, premium sleep supplements).

The science is clear: behavior change changes sleep quality. We recognized the growing importance of sleep in overall well-being and were eager to provide a tool that allowed users to bridge the gap between their daily behaviors and the quality of their sleep. Our goal was to create an intuitive, data-driven app that helped users uncover new insight into how their daily routines impacted their sleep patterns.

The world didn’t need another sleep tracker; rather we needed to build a behavior tracker that used sleep tracking metrics as core outcomes.

Our product and design team conducted thorough market research, analyzing user needs, pain points, and existing solutions. Armed with insights, we embarked on an ideation process that encouraged diverse perspectives. The world didn’t need another sleep tracker; rather we needed to build a behavior tracker that used sleep tracking metrics as core outcomes. Brainstorming sessions and design thinking workshops with customers and clinicians fueled our creative process, leading us to a clear vision of the Proper Sleep + Behavior Tracker.

User-Centric Design and Iteration

To ensure our app truly resonated with users, we adopted a user-centric design approach. We engaged in user interviews, focus groups, and usability tests to gather feedback at every stage of development. This iterative process allowed us to refine the app’s features, user interface, and overall user experience.

The app’s core functionality was designed around the concept of self-reporting sleep-promoting and sleep-detracting behaviors. Users could effortlessly input behaviors such as bedtime consistency, media exposure before sleep, and dietary choices. These inputs, combined with sleep data from wearable devices or self-reported, formed the foundation for our data-driven insights.

In close collaboration with our product design partners, XXIX, we executed this vision with a novel visual identity that supported touch-friendly mobile inputs and circular, clock-inspired data visualizations.

The Proper Sleep + Behavior Tracker revealed correlations between sleep-promoting and sleep-impacting behaviors.

Testing and Validation

Testing was a pivotal phase of our product development journey. We created prototypes to simulate the app’s interactions and visualizations, seeking user feedback on usability, clarity, and overall appeal. Our team’s commitment to continuous improvement drove us to refine the app’s mechanics and aesthetics based on the invaluable insights from these tests.

During this phase, we fine-tuned the data algorithms that correlated sleep behaviors with sleep quality. Rigorous testing ensured the accuracy of our trend detection and data visualization components. Through this iterative process, we evolved from an initial concept to a robust product ready for deployment.

Delivery and Impact

The culmination of our efforts was the Proper Sleep + Behavior Tracking app — an elegant fusion of sleep science and user-centric design. The app empowered users to recognize the effects of their behaviors on their sleep quality and duration. Daily behavior inputs were artfully visualized alongside sleep data, providing users with real-time feedback on their sleep hygiene practices.

We optimized our data visualizations for both mobile and web devices, ensuring a seamless experience for users regardless of their preferred platform. Over time, the app’s trend analysis revealed patterns and correlations that allowed users to make informed decisions about their sleep-promoting and sleep-detracting behaviors.

The app was also leveraged by our team of Sleep Coaches, who worked 1:1 with Proper’s best customers to understand and improve their sleep outcomes.

In Summary

The Proper Sleep App represented more than just a technological innovation; it symbolized our dedication to improving lives through meaningful data insights. Our journey—from ideation to delivery—showcased the power of collaboration, user-centric design, and iterative development. The impact of the app extended beyond the digital realm, influencing positive behavioral changes and promoting healthier sleep habits.

As a Product Leader, being part of this journey has been a privilege. It reaffirmed my belief in the transformative potential of user-focused product development. The Proper Sleep + Behavior App not only enriched our product portfolio (supplementation is of course a data-backed, sleep-promoting behavior) but also enriched the sleep experiences of countless users. As we continue to evolve and innovate, this project remains a testament to our commitment to enhancing lives through thoughtful, data-driven solutions.

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.


Goldilocks Criteria: Selecting Modern Business Intelligence (BI) Platforms

This is the 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.

For the purposes of this discussion, let’s define Business Intelligence (BI) platforms as data platforms that non-technical business users to explore, prepare, and present data germane to their work. BI tools are crucial for making informed decisions.

Turns out this is ancient stuff. BI platforms we first utilized back in the ’60s – 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.

We’ve also come a long way since the 1960s, evolving from cumbersome on-premise solutions to nimble, cloud-based platforms. Today’s BI tools are designed for accessibility, allowing professionals without a technical background to glean insights easily.

BI tools serve a specific purpose: analyzing and reporting on data consolidated from various sources. 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. They don’t execute tasks but rather provide insights that inform actions taken on other platforms.

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 helps inform micro insights like, which email subject performed best, as opposed to providing a holistic data view of data across multiple use cases. 

So how does this work in practice? A great use case for BI platforms is to create easy-to-digest OKR dashboards for your company, teams, and individuals. Your BI platform should allow teammates from different business units to 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 has emerged that allows 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 / Meta 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 engineering 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.

What does that mean for BI tools? Any functionality that supports 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, and 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.

Live data! From the cloud! On your phone!

Data that arrives attached to an email 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 is 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 ready

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 onboarded. 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 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 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 an 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 draw / 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 an 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 a must-have.

User-level data FTW

BI tools 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, and location level – rarely at a 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…

In the Press: 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.