Industry Trends and Management Principles

Importance of Data

Data (information) helps propel us to the answers and insights (data informs, doesn’t drive decisions). However, we need collaboration –> Someone to lead and make sure we ingest the data, aggregate and store it, and visualize it (for ourselves and shared in analytics for others). Then, someone to exploit the data. Thus, a culture of collaboration is just as important and bringing different skillsets together. It’s not the tools that are the problem, but need the right organizational structure and the right people (expertise), to both support the data and its usage. Similarly, the problem is usually not the tech, the challenge is changing the culture and processes to allow people to work harder.

Data Security and Openness

It is important for data to be both secure (protect people) but also shared (allow more researchers to find insights in the data and consequently the public to benefit). It is difficult to focus on both, but completely possible. You address privacy and security at the beginning, assess value and risk, but don’t look at security and data openness as two goals in tension. When data is more secure with less breaches, the more institutions and the people who lead them are willing to be open with their data. It is also important to be transparent with the public of what we are not releasing, being transparent with what we have at the lowest level. Hybrid data warehousing is a good compromise – Production environment in a physical data center, and testing environment in the cloud.
To encourage data to be shared, it is up to the people consuming the data to both prove the data is secure and that it will provide value because those are the two biggest excuses institutions use for not sharing their data (unsecure and won’t provide value). In addition, regarding research, we must monetize data citations instead of only publications – incentives to not withhold data to maintain data rights.
To enable the data to shared, systems should be constructed with Service Oriented Architecture –> have APIs → build a library around them and build a unique user base.

Data Quality and Other Goals/Tools for Development

  • Visualizations
  • Be iterative – better to redesign (or re-use materials) 30x to learn from failures than spend everything building one complex design
  • More data
  • Include the confidence level of the data, data should be highly structured and organized
  • Understand what type of analysis of your data will benefit the public the most – Descriptive (describing the data) vs Predictive (predicting future data, inductive reasoning) vs Prescriptive (predicting outcomes of decisions). Ideal method of predictive and prescriptive is usually inductive reasoning (starting with the data and creating a theory/hypothesis based on it) vs deductive (starting with a hypothesis and coming to a conclusion).

Examples of Better Data and Usage

  • LEI (Legal Entity Identifier) – “The LEI system was proposed by the Financial Stability Board, a group of finance ministers and central bankers from major economies after the 2008 crisis. It provides a unique ID for legal entities engaged in global financial transactions as a means to promote integration and reduce risks.” Source:
  • XBRL (eXtensible Business Reporting Language) Taxonomy

Safety and IoT

  • Smart Cities – DOT Smart City Challenge
  • Stands in Kansas city → can detect gunshot noise and auto-dispatch cops (DOT)
  • FAA -> Sends data to cockpit, of FAA temp flight air restrictions, weather (DOT)
  • Forecast protests in south america – read 60-70 newspapers everyday (IARPA)


  • Everyone wants to get to the patient outcomes but any solutions (applications or processes) must first help the provider’s workflow in order to get to the patient. Thus, it is better to sell an application that can adapt to any workflow
  • Personalization makes a difference -> need both personalization and scalability.
  • Platform for Care Plans over Point Solutions
  • data standardization – ICD / Snowmed Terminology
  • Analyze parking lot usage in hospitals vs flu incidence rates (IARPA)
  • Car rental company investigated how weather affects how much people rent cars
  • Health – instead of daily/weekly/monthly surveys, different modalities such as wearables or VA telehealth allow for more data than ever before – moment-by-moment data through 24/7 monitoring. While DOD looks for needle in a haystack (one bad actor/terrorist with bad behavior), the Health industry (ie VA) is predictive in that they look for matches/trends in big data, i.e. better diets for certain issues. Data can evolve us from Bad medicine (throwing everything and the kitchen sink at someone’s health issue) to more specific solutions. More data allows us to differentiate between probabilistic (Stochastic) vs causality models.
  • It drastically helps to gameify patients into following their instructions, and helps if the people in their lives are involved also (peer pressure). In addition, regarding patient-facing data, the data must always be contextualized, otherwise the patient won’t use it even if it’s good data that can help them.
  • Facts – Chronic Disease is the biggest cost. 13% of USA’s health spending goes to the last year of a patient’s life.
  • Population Health Tools – John’s Hopkins ACG System, OMOP Framework. Data – Health Level 7. Health data had its birth from copying the Finance industry’s data structures.


  • Farmers can use drones to look at their crops for nitrogen or water deficiency. Drones can cover 1 square mile in 20 minutes, examine down to a square inch (USDA)

Ideas for Research

Happiness by: 1) Square footage of living space, 2) Number of meals eaten per day, 3) Amount of liquid drank per day, 4) How many times you look in the mirror per day

New Technology Coming Out

It is important for us to stay aware of the cutting edge.

Specialized Chipsets – Machine Learning, Math Coprocessors, Authentication at the Chipset level for faster encryption/decryption


General Search Tool:

Economy: Bureau of Labor Statistics – Economy at a Glance
Transportation: National Transportation Statistics

Project Management Process and Dynamics

The Process
David Kantor -> Structure determines performance like a riverbed determines the flow of river
1) Set needs and goals (gather requirements first)
2) Diagnose – do research, set standards, define roles and responsibilities. Talk to lead to figure out how to prioritize and manage people’s expectations of delivery.
3) Develop stakeholder lists, training, metrics, processes, frameworks
4) Implement the idea and execute

Teamwork Principles
The best teams are both hierarchical and collaborative –> Collaborative for brainstorming and bringing together ideas, knowledge sharing and team efforts. Hierarchical for when it’s time to start converging, when everyone has their role to play in providing an answer for a client.

Other Principles
Think about things systematically. Always look at the why’s.
Encourage documentation – it is important not to lose any decisions or insights made in meetings, or lose any industry / process knowledge due to turnover
“Make it ugly” – have meetings saying what’s wrong – no delay in talking about issues and figuring out how to fix them

Stages of Cultural Evolution
(from less to greater complexity)
1) Conformist/Role-Driven – following authority, stability, consistency
2) Achievement/Results-Driven – innovation, results, competition, meritocracy
3) Pluralistic/Relationship-Driven
4) Evolutionary/Purpose-Driven

Three Kinds of Mental Complexity in Adults
(from less to greater complexity)
1) (30% of ppl) Pre-Modern Simple Reasoning – step by step instructions
2) (50% of ppl) Modern Mindset – Value high-iq, scientific method, domain knowledge
2) (20% of ppl) Post-Modern – want to see diff perspectives and understand the whole system.

Problems for Agility Today
History of Organizational Landscape – A fixed process used to be pure innovation at the time, like how agile is cutting-edge right now. Companies, however, made up of more processes, required to be more structured and huge.
Problem – Landscape most organizations face now is inherently complex, which requires an upward shift in mental complexity of organizational leaders (as an OS needs to get more complex to install more complex applications), and greater agility in leadership. And the issue is, most managers/leaders see complex systems as though they are complicated. But instead, an Agile Leader should have the –
Outer Skills – Ability to influence others, evoke shared sense making (how we see the world), skills in facilitating/catalyzing, creatively apply domain knowledge, systems thinking skills
Inner Skills – Complex meaning making, high emotional intelligence (self awareness / management), clear about and grounded in a sense of purpose, permeable to the perspectives of others (values – what we care about), self identity (how we see ourselves)

A consultant helps the customer succeed over their issues, challenges, or problems they may have. The reason consultants charge a lot of money is because they need to be right all the time, tackling the difficult problems quickly and at a high level of quality that the client either does not have the skill or resources to do. Consultants work on the critical business data and provide insights the client has trouble uncovering. In order to do this, the consultant must be more receptive to the client than even their employees, be able to learn quickly the intricate knowledge regarding the client’s service and the problem(s) the client faces.