The quiet transformations affecting health and life sciences

Innovation, disruption, and change lead CIOs’ minds as we explore the future of healthcare and life sciences.

The “Advancing and Enhancing Patient Care” panel was insightful and spanned topics such as innovations with care management, health-based investments, and connecting medical research to life-saving innovations. The panel discussion was lively, and as a result, much was covered. There was also a lot that I didn’t have time to cover within our limited timeframe. I want to introduce new insights; I didn’t have time to share them today fully.

The earlier pioneers in life sciences

New technology is reaching far beyond the bill and paperwork. Artificial intelligence and advanced analytics investments will enable the more intelligent use of multi-sourced data. This newly created data can help fuel clinical trials and accelerate research and development initiatives in the labs. Innovation is driving change in how we interact with patients.

  1. Remote patient monitor
  2. Start drug delivery systems
  3. Biometric trackers
  4. Ingestible sensors
  5. Medication adherence
  6. Diseases management apps

We’re hopeful that the 21st Century Cures Act will accelerate interoperability and patient access. This is a decisive step towards making the theory of continuum of care a reality. The recent MyHealthEData initiative promises to enable better access to patient’s medical information to promote better decision making. The Medicare Blue Button 2.0 initiative has solid traction and over 1,100 organizations already involved spanning 3,000 developers dedicated to making a change in patients’ access. This is compounded with the Patient Access API, which required health insurance exchanges to allow patient access to data through third parties, which went into effect on January 1, 2021.

There still are significant obstacles to providing seamless care, akin to a one-click amazon buy button. We’re not there yet. How will access to video CT scans be provided? To what device will patients download their MRI images? Who’s supporting this bandwidth? In the haste to introduce new technology, software companies forget about their customer: healthcare forgets about the patient experience. We can do better. Connectivity is the answer.

Italy has already figured this out. In Italy’s Lombardy Region (near Milan), the Agency for Innovation and Procurement (ARIA) has created a digital information hub integrating into a single platform more than ten years of health data for 10 million people living in the region. This sets an excellent foundation for virtualized care.

Peter Nichol Recognized as 2021 Top BRM by the BRM Institute

Peter B. Nichol was recognized as a 2021 Top BRM on February 9, 2021 by the BRM Institute.

Each year’s Top BRM list is revealed during #BRMWeek in February. BRM Institute’s global BRM community recognizes the top BRMs that have achieved success through their BRM efforts, strengthened the global BRM community and BRM discipline, enriched lives through excellence in BRM within their organizations, and contributed to the community on a local, national, and global level.

The BRM Institute’s global BRM community recognizes the top BRMs that have achieved success through their BRM efforts, strengthened the global BRM community and BRM discipline, enriched lives through excellence in BRM within their organizations, and/or contributed to the community on a local, national, and global level.

The 2021 Top BRM awards were evaluated based on the following major criteria:

Overall Impact:

  • Explain the impact the BRM has contributed to others around the globe!
  • Share the outstanding accomplishments the BRM has delivered
  • Highlight the amazing organizational accomplishments the BRM has delivered including notable contributions, improvements, discoveries, how they have demonstrated the BRM Code of Conduct, etc.

Leading with Purpose:

  • Bring more personal purpose in the workplace.
  • Identify the convergence of the personal and organization purpose to lead towards happier individuals, stronger relationships and durable communities.
  • Demonstrate how the BRM satisfied their personal or organizational purpose through their work.

Delivering Value:

  • Articulate the value delivered by the BRM engagement
  • Quantify the value realized through BRM organizational empowerment
  • Explain the impact of the value delivered through the BRM’s efforts.

Peter Nichol is a highly respected executive, BRM, and passionate evangelist for the BRM community. As a BRMP®, CBRM®, MBRM®, he is a change champion and has fully embraced the role, capability, discipline, and philosophy of the BRM Institute to achieve powerful results.

Peter is a positive voice among the BRM community. He is a member of the Value-Focused Organization Working Group, collaborating internationally with fellow BRM Institute leaders to amplify value. He was also elevated into the Vice-Chair role of the BRM Institute’s Executive Council (BEC), an advisory team comprised of executive-level individuals from leading organizations from around the globe, with a vision to advance the BRM discipline.

Peter has led businesses through complex changes, including the adoption of data-first approaches for portfolio management, data, analytics, lean six sigma for operational excellence, departmental transformations, process improvements, cloud migrations, maximizing team performance, designing new IT operating models, digitizing platforms, leading large-scale mission-critical technology deployments, product management, agile methodologies, and building high-performance teams. Peter is also a 4x author, MIT Sloan and Yale School of Management speaker, an avid blogger with hundreds of articles on BRM value, innovation, data science, artificial intelligence, and blockchain.

___

“This year’s Top BRM winners are recognized as BRMs within the global community who embody the true spirit of the BRM discipline. Every year, I am blown away by the stories of leading BRM professionals’ significant achievements and how their efforts have strengthened the network of relationships that is the single, global BRM community.

Your tireless passion and devotion to impacting others through excellence in BRM contribute to more sustainable communities on a local, national, and global level. Congratulations on being awarded a Top BRM of 2021 and thank you for doing the hard work to create positive change in the world!” — Aaron Barnes, CEO BRM Institute

___

“As a Master BRM, I am grateful to play a part in shaping the future of the BRM discipline into a single, global BRM community. Whether you’re officially called a business relationship manager, project manager, or agile practitioner, or have another title, the most important part of your role likely involves people. Leaders know that growing, fostering, and maturing cross-functional relationships that embrace an outside-in mindset is are the single most significant contributor to organizational success.

Artificial intelligence, machine learning, and cloud-first technologies are vital enablers, that’s true. However, if everyone is applying AI, everyone is using ML, and most companies are generating automation gains through cloud-first initiatives, then what is the differentiator between these companies?

The difference comes down to people—specifically the relationships those leaders develop. People are the number drivers of quantified business value. I’m excited to launch my 4th book in 2021 titled, Leading with Value. This book directly taps into how leaders quantify and articulate business value. This year will be another challenging year, and I’m confident we can tackle the challenges together as a single, global BRM community!” — Peter B. Nichol

References

BRM Institute. (2021). 2020 Top BRMs. https://brm.institute/2021-top-brms/

Leading With Value

February 9, 2020 — Peter Nichol published his 4th book, Leading with Value: How to Effectively Communicate Business Value.

In Leading with Value, author Peter B. Nichol provides access to secrets of how the best and most innovative companies tackle business value. His unique experience as a software engineer, architect, project leader, and CIO all synergize to provide you with clear, crisp, and actionable insights on how to capture and communicate business value. Based on his experience as a CIO, author of four books, and a data-science expert, Nichol captures the missing key for leaders struggling to quantify what impact they make and why it matters.

BUY ON AMAZON

BUY ON DATASCIENCECIO – AUTHOR SIGNED

Think Lead Disrupt

January 8, 2020 — Peter Nichol published his 3rd book, Think Lead Disrupt: How Innovative Minds Connect Strategy to Execution.

In Think Lead Disrupt, author Peter B. Nichol provides insights into how innovators can continually redesign products, services, and experiences in new and unique forms. Innovative companies don’t just appear. These disruptive companies evolve as a result of individual ideas, beliefs, and values. Individuals working together transform companies with original ideas. Nichol illuminates the mindset of innovative executives and explores how ideas lead to disruption. Based on his experience as a CIO, as the author of three books, and as a digital expert, Nichol captures how you can be part of the idea revolution.

BUY ON AMAZON

BUY ON DATASCIENCECIO – AUTHOR SIGNED

New research to assess project management capabilities using demonstrated competencies

October 23, 2020 — Peter Nichol published new research that explains how to assess project management capabilities using demonstrated competencies.

Leading a new team it’s exciting. Unfortunately, rarely are the teams we inherit high-performing teams.

Taking accountability for a low-performing project management team is stressful. Your peers know the team isn’t performing well. Your business partners give signs that help isn’t welcomed. It’s a tough spot to be in, especially when you just joined a new company.

You know in your gut things are going south. Risks are being escalated. Projects are overspending. Post-production operations don’t exist. It appears that everything is falling apart at the same time. Your experience tells you that just attempting to put out all the fires will never be a sustainable plan.

Where do you start? You start with a demonstrated competency model.

Peter’s research takes into account his 19-years of portfolio, program, and project management experience leading teams at Fortune 100 companies. The research outlines a detailed process to systematically improve the project management capabilities of a team using quantified results. This paper is written for CxOs, VPs, and portfolio executives that are accountable for demonstrating measured project management capability improvements for their organization.

DOWNLOAD THE FULL RESEARCH PAPER – ASSESSING PROJECT MANAGEMENT CAPABILITIES USING DEMONOSTATED COMPETENCIES

Abstract

Abstract — This paper aims to present a practitioner approach to assessing project- and program-management capabilities across individuals, teams, and organizations. Executives must deliver value to business partners to stay relevant, engaged, and employed. Often, the value created doesn’t equate to the net organizational investment. This situation creates challenges for the entire organization. Value isn’t generated at a rate that sustains demand. When delivery is impacted, the expected project and initiative results aren’t achieved, and value isn’t realized. Many leaders blame individuals, focusing on the loudest but not necessarily the most critical areas. By applying a systematic approach to assess the management of an individual project or program, capabilities, the deficiencies within the team become self-evident. As a result, fixing the problem becomes quite straightforward. This paper will explain the method, approach, and technique to analyze and assess a team for demonstrated competency. Executive leaders will have a blueprint of what’s required to effectively assess individuals, teams, and organizational capabilities to align with future business demands.

What will you learn?

  • How to assess a team where everything seems to be going wrong.
  • The steps required to establish a quantified approach to measuring and reporting on project management capabilities.
  • How to roll up team strengths and deficiencies into executive-level visualizations and reports.

New research exploring quantifying project management in a value management office

October 11, 2020 — Peter Nichol published new research highlighting a practitioner approach to achieve value realization in a value management office.

Executives don’t want to have to sell snake oil. Explaining the value of your PMO—when it doesn’t add value—is challenging.

The number one challenge when transforming a failing PMO is changing the value, beliefs, and behaviors of the existing portfolio, program, and project management teams. In a nutshell, the challenge is education. No one wants to be executing projects that are not aligned with strategic corporate goals. Likewise, most employees want to deliver value to the enterprise, they just don’t know how to quantify it.

Peter’s research moves the idea of value management offices from conceptual frameworks to practical step-by-step instructions of what is required to captured, document, and report on value. This paper is written for practitioners who are challenged to deliver value-based outcomes every day.

The paper outlines a specific example of how to quantify value for a project management initiative.

DOWNLOAD THE FULL RESEARCH PAPER – QUANTIFYING OUTCOMES FOR PROJECT MANAGEMENT IN A VALUE MANAGEMENT OFFICE

Abstract

Abstract — This paper aims to present a practitioner approach to applying project-management principles of value realization within a value-management office. Portfolio leaders and program and project managers are confused about how to apply value management in the real world. Project-management offices are being shut down, because they’ve not delivered on the promise to add value to the business. What has emerged to fill this void is the concept of a value-management office. This new organizational construct is narrowly focused on maximizing value for the company’s strategic initiatives. The result is a huge divergence from the traditional project-management office, which has been viewed organizationally as a cost center that provides little if any value to the company. To optimize the value-management office, new techniques need to be applied and integrated into the existing processes and procedures to deliver projects and quantify results. By using a general-structure portfolio, program and project managers can deliver impactful and quantified value to business partners. In this paper, we’ll explore an example of how to achieve value realization. Leaders, by using this model, have a roadmap for achieving quantified outcomes for their value-management office.

What will you learn?

  • How to step-by-step capture, document, and report value for your project or initiative.
  • What are the steps to quantify outcomes?
  • The difference between process maps and value-stream maps.

New research why service catalogs are the blueprint for the future of value management

October 8, 2020 — Peter Nichol published new research highlighting how services catalogs provide the blueprint for the future of value management.

Executives are conflicted about what to do with failing PMOs. They focused on project volume and forget about project value.

As organizations adopt value-based frameworks for delivery new skills are required for success. It’s no longer acceptable to be leading a portfolio that is 90% focused around data and not be able to name a single data cleansing tool or technology. CIOs are looking for executive and business information officers to champion and drive change. It’s hard to do that when you’re not in the know.

By being transparent in the services the new value management office can provide to the organization business partners can begin to understand how to consume there organizationally provided services to maximize value.

Peter’s research envisions the future of a services catalog that is centered around value management offices. Companies are shifting from project delivery to continuous value delivery. To do this successfully the value management office needs to identify services that are core capabilities and defocused on non-core service. Putting in place a service catalog for your vale management office will take the guesswork away from your business partners and make it clear services the office provides.

The paper explains specific examples of how to stand up a service catalog for a value management office and the benefits that will result.

DOWNLOAD THE FULL RESEARCH PAPER – SERVICE CATALOGS: BLUEPRINT FOR THE FUTURE OF VALUE MANAGEMENT

Abstract

Abstract — This paper aims to present a unique approach to implementing a project-management-office service catalog for organizations focused on continuous value delivery. Business information executives and officers must validate investment decisions and demonstrate value achieved. The process of gaining additional organizational and cross-functional executive buy-in is especially difficult when team members, leaders, and executives don’t understand what services the organization’s project-management office provides. The traditional project-management office—centered around processes and templates—is being transformed into a new-age, value-management office that’s hyper-focused on the value realized. Innovators understand and appreciate that if the services the project-management office provides are vague, business partners won’t consume them. The act of creating a service catalog allows for a tailored or agile approach to delivery. This model accepts that not all projects are created equal. More specifically, leveraging a value-management service catalog refocuses the organization. Core capabilities are provided through the catalog, and investments in these areas are doubled down. Alternatively, capabilities that fall outside the core capabilities of the value-management office are evaluated for outsourcing. This shifts the traditional project-management office from a cost center to a value center and makes services offered transparent to downstream consumers.

What will you learn?

  • Why services catalogs are a key element of a functioning value management office?
  • Practical ways to make the services provided to internal customers more transparent and easier to digest.
  • Why CIOs are looking for leaders that drive strategy with innovation?

New research on value management as an organizational capability

October 4, 2020 — Peter Nichol published new research highlighting how value management can be applied organizational as a strategic capability.

Innovative CIOs are realigning project management offices (PMOs) to drive strategic value maximization.

Traditional project management offices (PMOs) focus on process sets, standard templates, and delivering more projects. This focus is important and necessary. Conventional PMOs are not trained, educated, or equipped to focus on strategic projects diving data science, next-gen technology, and business transformations.

The value management office focuses on the most strategic organizational projects and ensures that value is maximized.

Peter’s research explores how innovative companies are reframing the concept of a PMO. These companies are shifting from a “projects delivered” mindset to a “value delivered” mindset. How do you know if your team is a traditional PMO or a value management office? If you as an executive are hearing about process reengineering efforts, communities of practice, or delivery methodologies you’re in a traditional PMO. The focus and priority of traditional PMOs are not on your most critical strategic initiatives—but it should be.

The paper explains multiple value frameworks to discover, capture, and present value to ensure your strategic initiatives achieve optimal results.

DOWNLOAD THE FULL RESEARCH PAPER – VALUE MANAGEMENT as an ORGANIZATIONAL CAPABILITY

Abstract

Abstract — This paper aims to present a practical approach to institutionalizing business value realization as an organizational capability. Business and technology executives are under continuous pressure to justify investments and validate business outcomes. When executives are asked to explain the value generated by their team, department, or company, only then do they realize they lack the in-house expertise to produce the level of quantifiable outcomes expected. Progressive leaders know that the process of discovering, realizing, and optimizing value is an essential organizational capability. This paper presents a practitioner approach offering multiple value management frameworks to assist executives in making informed decisions when building a value-management office. Traditional project-management offices maintain project-management standards, establish best practices, define common languages, develop a resource-management view, and create and maintain project artifacts and tools. Unfortunately, the heavy burden of these core project-management office activities results in no time for strategic planning. The value-management office oversees the execution of all the company’s strategic programs. The company’s hyper-focus on connecting strategy to execution ensures the value maximization of its strategic initiatives.

What will you learn?

  • Why the most innovative leaders are reassessing their PMO functions? What value frameworks are working for agile companies? Which are the most impactful options to hyper-focus on your most strategic programs to maximize value.

New research on applying Six Sigma to quantify outcomes in portfolio delivery

September 14, 2020 — Peter Nichol published new research highlighting how Six Sigma can be applied to quantify portfolio delivery and execution.

As a leader, you’re either joining a company to lead a new team or working internally to improve your existing team. In both scenarios, results matter.

In the quest for improvements, the fundamentals are often overlooked. Once these fundamental elements are in place the objective to quantify improvements becomes more difficult. We need tools that are fit-for-purpose to make portfolio transformations sticky.

Peter’s research connects the basics of delivery for advanced portfolio management practitioner approaches to quantify organizational transformations. The paper explains how to capture and quantify the organizational value to communicate clearly to executive leadership.

DOWNLOAD THE FULL RESEARCH PAPER – APPLYING SIX SIGMA TO QUANTIFY OUTCOMES IN PORTFOLIO DELIVERY

Abstract

This paper aims to present a practical approach to applying principles of Six Sigma to statistically control portfolio delivery within a program-management office. Portfolio executives are continuously charged with transforming a low-performing team into a high-performance team. Often, the methods of transformation are based on experience, and these imprecise methods frequently produce inconsistent results. The transformation from a low-performing team to a high-performing team can be accelerated by using a quantified methodology to define, measure, analyze, improve, and control desired outcomes. This paper presents a practitioner approach to maximizing the organizational outputs from an agile program-management office by leveraging statistics and mathematical principles to tighten process variance. By applying a fit-for-purpose approach to corporate and operational excellence, portfolio executives can dial in and remediate the root causes of portfolio inefficiencies for maximum agility and value realization.

What will you learn?

  • How to conduct a quick-hit portfolio assessment? The foundational for quantification of results.
  • Explanation and examples of four key tools that can be applied to measure portfolio outcomes.
  • Highlights of 21 measures and brief examples of how to apply them to performance.

Building a world-class data-science team

Data science isn’t about special people in special places. It’s about teams.

We’ve all witnessed the wave of innovations that has washed over business models of late. These innovations didn’t surface as the ideas of individuals. The architecture of businesses, business interactions, data collections, and the use of information is so complex that a single individual in a mid- or large-size company wouldn’t have the knowledge to understand all elements required to make the idea a practical reality.

Also, it’s long been proven that heterogeneity enhances group brainstorming. More diverse groups produce better ideas. This concept is especially important when we’re designing data-science teams.

A part of the whole

You’ve probably been told you need to hire one of two individuals. The first is an astute data developer with a grounded understanding of Python, SQL and data storage, PostgreSQL, Unix and Linux command-line knowledge (mainly to run and schedule cron jobs); Python data libraries (Pandas, Scrapy, Keras, Matplotlib, TensorFlow, Bokeh, Scikit-learn, etc.); Flask, Bottle, and Django to host the analysis of the database as a RESTful API, AWS, or Azure-hosting framework; and, of course, AngularJS for presentation results and DS.js to create data visualizations.

If, for some reason, you botch the hiring of the astute data developer, you only have one other alternative—to hire a data academic. This is a theorist who pontificates about changing the world with data but whose experience rarely ventures outside the educational setting and has few practical applications. The data academic understands core statistics, categorical data analysis, applying statistics with R (multiple linear regressions, qualitative predictors, linear discriminant analysis, resampling methods like k-fold cross-validation, hyperplanes, hierarchical clustering), sequential data models (Markov models, hidden Markov models, linear dynamical systems), Bayesian model averaging, and machine-learning probabilistic theory. You hope some of this learning is connected to causality.

Are these two roles important for a data-science team? Of course. If you, by chance, hire both these roles, do you have a data-science team? No, you do not.

Let’s begin with the origins of data science and, from there, we’ll lead into the critical capabilities required to build a world-class data-science team.

From there to here

The foundation of data science originated with five key areas:

  1. Computer science: the study of computation and information
  2. Data technology: data generated by humans and machines
  3. Visualizations: graphical representation of information and data
  4. Statistics: methodologies to gather, review, analyze, and draw conclusions from data
  5. Mathematics: the science of the logic of shape, quantity, and arrangement

Computer science evolved from Turing machines to cybernetics and information theory by the 1900s. Tree-based methods and graph algorithms surfaced in the 1960s. By the 1970s, computer programming and text or string searches popped up. Data mining, data classification, and similar methods pushed us into the early 2000s.

Data technology began before the 1800s with binary logic and Boolean algebra with punch cards. IBM introduced the first computers in the 1940s as DBMS matured. Removable disks with relational DBMS followed into the 1960s. By the mid-1970s desktops, SQL, and objective-oriented programming was the norm. In early 2001, statistical modeling started to emerge, balancing the stochastic data model by using algorithmic models and treating data mechanisms as unknowns.

Visualizations arose prior to the 1800s with cartography and astronomical mapping of charts. Line and bar charts came out in the 1800s, and statistical graphics were depicted by the mid-1800s. The box plot was created in the 1970s, and word or tag clouds started to form in 1992.

Statistics entered the 1800s with theories of correlation, probability, and Bayes Theorem. In the 1900s the concept of regression, times series and least-squares made the rounds. The 1900s introduced the foundation of modern statistics with the hypothesis and design of experiments. By the mid-1960s, we had Bayesian methods, stochastic methods, and more complex time-series methods such as survival analysis and grouping time-series data. Through the 1980s, more developments occurred in Markov simulation and computational statistics, allowing us to better understand the interface between statistics and computer science. By the late 1990s, decision science, pattern recognition, and machine learning were starting to take shape.

Mathematics entered the 1800s with calculus and logarithms. Next, Newton-Raphson introduced optimization methods. By the 1930s, the military had started to adopt theories for manufacturing and communications. The 1960s were booming with networks, automation, scheduling, and assignment problems, which have only matured in recent years.

Understanding the origins of data science helps demystify it and allows you to develop a concrete capability in your company.

Data-science capabilities

Finding success with data science comes down to four factors: people, data, tools, and security.

The most important elements of your data-science team are the people and the capabilities they enable. Next, to get insights—even with the best people—we ultimately need data and access to data. Usually, data is siloed across teams, departments, and systems, making gaining access difficult. Assuming we have the people and access to the data, next, we need tools. Performing analytics necessitates computational and data-storage resources. Fortunately, today we have many open-source options that are more than adequate. Lastly, data security and privacy protection are crucial as data becomes more centralized. With this convenience comes access—which, in the wrong hands, creates risk.

With this understanding of the origins of data science, it’s fascinating to see the mix of conventional capabilities aligned with the less traditional data-science skills that are required for success. Let’s cover examples of data-science capabilities and complementary data-science team skills that are found within world-class data-science teams.

Data-science capabilities

Data-science team skills

  • Stakeholder management: business-relationships management, project management
  • Storytelling ability: executive presence, presentation skills
  • Business communications: clear and timely communication, governance
  • Consulting: need analysis, solutions aligned to goals
  • Problem-solving: lean six-sigma, agile
  • Topical analytics techniques: statistics, root-cause analysis, statistical-process control, value-stream mapping, flows
  • Domain expertise: knowledge of the data, who’s using it and for what purpose
  • Business analysis: experience evaluating and modeling business cases

The ultimate success of a data-science team depends on how well expectations are managed. When expectations are met, the data-science team will be viewed as impactful. Inversely, a weak perception of delivery is a significant reason why data-science teams eventually get disbanded—they focus on what’s cool, not what’s most impactful for the business.

The hidden art of storytelling

It’s idealistic to believe data-science teams can find value in data from day 1, but, eventually, they’ll connect data to new insights. However, often that data is layered across hundreds or thousands of sources, and the team might be months or years away from collecting it all. Most data-science teams begin with a simple set of questions. These questions are challenging but tangible to answer. This approach also limits the data set required to be integrated into an initial proof-of-concept. Sample questions might include some of the following:

  • Which applications in our portfolio have the most significant security risk?
  • Why is the Durham, NC location the most profitable?
  • What type of patient visit will be the costliest next quarter?
  • Is antibody A or antibody B more likely to achieve FDA approval?
  • Which drone should we bring in first for preventive maintenance?

Building a world-class data-science capability isn’t about individuals; it’s about assembling your team. It’s crucial to ensure that essential data-science capabilities and data-science skills are part of your team design. To tap into the power of data science, we require teams to not only extract insights from data but also tell a compelling story. Quite often, we’re left with a lot of data, confusing insights, and no story. Make sure that the team you build can tell a story.