Voices on Project Management

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Voices on Project Management offers insights, tips, advice and personal stories from project managers in different regions and industries. The goal is to get you thinking, and spark a discussion. So, if you read something that you agree with--or even disagree with--leave a comment.

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Cameron McGaughy
Marian Haus
Lynda Bourne
Lung-Hung Chou
Bernadine Douglas
Conrado Morlan
Kevin Korterud
Peter Tarhanidis
Vivek Prakash
Cyndee Miller
David Wakeman
Jen Skrabak
Mario Trentim
Shobhna Raghupathy
Roberto Toledo
Joanna Newman
Christian Bisson
Linda Agyapong
Soma Bhattacharya
Jess Tayel
Rex Holmlin
Ramiro Rodrigues
Taralyn Frasqueri-Molina
Wanda Curlee

Past Contributers:

Jorge Valdés Garciatorres
Hajar Hamid
Dan Goldfischer
Saira Karim
Jim De Piante
sanjay saini
Judy Umlas
Abdiel Ledesma
Michael Hatfield
Deanna Landers
Alfonso Bucero
Kelley Hunsberger
William Krebs
Peter Taylor
Rebecca Braglio
Geoff Mattie
Dmitri Ivanenko PMP ITIL

Recent Posts

Lessons Learned From an Inspiring AI Project

The Project Initiatives That Influenced My Career

Seek Better Questions, Not Answers

A Home for Transformation: Lessons From Fannie Mae’s PMO

Indulge Your Audacious Curiosity—Even if It Means Failing

Unlock the Value of Artificial Intelligence

By Peter Tarhanidis

Artificial intelligence is no longer a tool we’ll use on projects in the future. Right now, many organizations are formalizing the use of advanced data analytics from innovative technologies, algorithms and AI visualization techniques into strategic projects.

The maturity of advanced data analytics is creating an opportunity for organizations to unlock value. The McKinsey Global Institute estimates AI’s global economic impact could climb to US$13 trillion by 2030.

As an example, in the healthcare industry, Allied Market Research reports rising demand for data analytics solutions due to the growth in data from electronic health records, among other factors. The global healthcare analytics market was valued at US$16.9 billion in 2017, and the report forecasts it to reach US$67.8 billion by 2025.

The Evolution of AI Maturity
Gartner describes four growth stages of analytics and value activities. The first is descriptive analytics, which gains insight from historical data on what occurred in the firm or a project. This includes key performance measure reports and dashboards. Second, diagnostics analytics allow you to learn why something happened and the relationship between events. Third, is the use of predictive analytics to develop viewpoints into potential future outcomes. Finally, prescriptive analytics allow you to provide users with advice on what actions to take.

Everyday examples of these solutions range from simple automated dashboards, remote check deposit, Siri-like assistants, ride-sharing apps, Facebook, Instagram, autopilot and autonomous cars.  

Tips on Successful Transformation
Leaders must consider advanced data analytics as a transformational journey—not a complex project. Without thoughtful consideration of the implications of managing AI projects, one may create chaos in adopting these new services.

As a project leader, take these steps to avoid key pitfalls:

  1. Develop your understanding of data science tool kits and technologies and identify any centers of excellence. Start with basics such as descriptive statistics, regression and optimization techniques. You’ll also want to familiarize yourself with technology such as machine learning and natural language processing.
  2. Determine how these AI initiatives integrate into the organization’s mission and vision. This may require a new strategic business plan, optimizing an organization, culture change and change management.
  3. Establish a data governance body and framework to ensure accountability, roles, security, legislative and ethical management of consumer, patient, customer and government data.
  4. Develop strong multiyear business cases that clearly indicate cost versus revenue or savings.
  5. Maintain an agile mindset and leverage design thinking methods to co-create the pilots into products alongside stakeholders.

Please comment below on what approaches you have taken to enable advanced data analytics in your role or in your organization.

Posted by Peter Tarhanidis on: August 12, 2019 01:25 PM | Permalink | Comments (13)

Project Planning Using Canvas

by Ramiro Rodrigues

Project managers: Are you sometimes looking to make plans faster but without being superficial and therefore riskier to the project?

Developed in the 1980s, design thinking is a structured mental model that seeks the identification of innovative solutions to complex problems. Although the concept has existed for decades, it’s only made its presence known in the corporate environment over the last 10 years.

Swiss business theorist and author Alexander Osterwalder similarly sought to accelerate collaborative reasoning when he introduced the Business Model Canvas. Canvas helps organizations map, discuss, rework and innovate their business model in one image.

But a series of proposals for the use of the Business Model Canvas for various purposes outside of business models has also appeared — including innovation, corporate education, product development, marketing and more.

For project professionals looking at alternatives to developing quicker and more collaborative planning, Canvas may sound like a great option. Of all the proposals that come up for the use of Canvas in a project environment, integrating stakeholders may be the best. Canvas brings stakeholders into the process and will help to minimize resistance and increase collaboration, resulting in a better proposal for planning problems and making the project more aligned to the interests of organizations.

But while the arguments put forward for Canvas all seem positive, there is still a dilemma: Can Canvas fully replace the overall project plan and the planning process? Is it possible to do without a schedule of activities, a detailed cash flow, a matrix of analyzed risks — just to limit ourselves to a few examples?

That is probably too extreme.

The general sense is that the integration of Canvas with specific planning — such as the cost plan and the risk plan — is the most productive and generates the best results.

It may be worth asking your project management office for their thoughts.

Have you ever used a Canvas for your project planning efforts? If so, what tips can you share?

Posted by Ramiro Rodrigues on: April 17, 2019 01:05 PM | Permalink | Comments (3)

How to Lean In—and Thrive—in Project Management

By Jen Skrabak, PMP, PfMP

Over nearly two decades in project management, I’ve learned a number of strategies to make my voice heard and advance in my career. Much of that success has come by “leaning in,” as Sheryl Sandberg advocates.

As a woman in project management, I believe the following are key:

  1. Show grit. Demonstrate courage, show your perseverance and never give up in the face of obstacles. Know that it’s a multi-year journey, and you must demonstrate the passion to achieve your long-term goals as a leader in project management.
  2. Be the best. Knowledge, skills, abilities—you need to consistently demonstrate that you’re the best, and not be afraid to speak up and show it. Throughout my career, I have always assessed gaps in my knowledge or experience, and actively worked to close them. For example, although I started in IT, I wanted to transition to the business side to lead business transformation programs. I actively sought out progressive assignments by building a track record of successful projects that became larger in scope and team size with each project, until I achieved my goal of an enterprise-wide program impacting hundreds of thousands of users.
  3. Execute flawlessly. Execution is an art, not a science, and it requires creativity, impeccable organization, exceptional communication and most of all, follow-through. Many of these skills are intuitive in women, and the key is to understand that execution requires the leadership of large teams through four stages:
    1. Awareness: Create the right “buzz” around the project.
    2. Understanding: Teams need to understand their role and how their actions fit into the larger picture.
    3. Acceptance: Teams need to accept the message or change by changing their behavior and taking the appropriate action.
    4. Commitment: To demonstrate true commitment, teams should help champion the message throughout the organization.
  4. Build confidence and trust. Multiple studies support the notion that women are not only better at assessing risk, they are also better at guiding actions and decisions accordingly. Women should use this natural decision-making ability and risk management expertise to build confidence and trust as project leaders.
  5. Communicate clearly and concisely. Keep communications rooted in data and facts, not based on subjective information or personal preferences. Women in leadership roles tend to rate themselves lower than men on key attributes such as problem solving, influencing and delegating, and rate themselves higher than men on supporting, consulting and mentoring. How much time are you spending on communicating the right messages and influencing to gain commitment to your viewpoints versus supporting others?

International Women’s Day is March 8, and this year’s theme is #BalanceforBetter. Please share your thoughts on how we celebrate the achievement of women while we continue to strive for balance for women socially, economically and culturally around the world.

Posted by Jen Skrabak on: March 05, 2019 10:42 PM | Permalink | Comments (11)

Do You Know The 3 Drivers Of Project Success?

by Dave Wakeman

I recently came across some of management guru Peter Drucker’s thoughts on project management. 

As often happens with Drucker’s writing, the lessons he wrote about many years ago are still applicable today. 

In his thinking about project management, Drucker came up with the idea that it really came down to three ideas: objectives, measurements and results. 

Let’s take each of these areas and think about how we should approach them today. 

Objectives: Many projects get stuck before they even begin, due to a poor framing of the project’s objectives. We should be undertaking our projects only when we have moved through the project-planning phase to such an extent that we have a strong grasp of what we are hoping to achieve. 

These objectives shouldn’t be fuzzy or wishy-washy. They should be solid and rooted in the overall strategy of the organization you are performing the project for. 

This means you have to ask the question: “Does this project move us toward our goals?”

If the answer is “yes,” it’s likely a project that should be launched.

If the answer is “no,” it’s likely a project that needs to be fleshed out more, rethought or not undertaken at all.          

Measurements: Drucker is famous for this adage: What gets measured gets managed. 

In thinking about project management, measurements aren’t just about being able to improve project delivery. They’re also essential to ensure the project is headed in the right direction. 

To effectively measure our projects, we need to have laid out key measurements alongside the project’s objectives. 

The measurements should be specific, with expected outputs and completion dates, so you can affirm whether you are on schedule, behind schedule or ahead of schedule. 

At the same time, the measurements should inform you of your progress as it compares to your strategic goals. 

Results: Ultimately, projects are about results. 

To paraphrase another great thinker, Nick Saban: If you focus on doing your job right on each play, you’ll put yourself in a position to be successful at achieving your goals.

Saban coaches U.S. football, but this works just as well for all of us in project management. 

If we are focusing our energy on tying our projects to our organization’s strategy, through this strategy we focus our project efforts on the correct objectives in line with our strategy. Then we use those objectives to measure our progress against the strategy. We should be putting ourselves in a position to get the results that we need from our projects. 

These results should be measured as positive outcomes. In Saban’s case, that’s wins. In your case, it might be a new technology solution, a successful new ad campaign or a profitable fundraising effort. 

To me, reviewing Drucker’s thoughts on project management is a reminder: Even though there is a constant pull of new technologies, never-ending demands on our attention and a world where change feels accelerated, sometimes the best course of action is to step back, slow down and get back to the basics.

 

Posted by David Wakeman on: January 18, 2019 10:02 AM | Permalink | Comments (13)

Machine Learning Isn’t Magic

Categories: Innovation, IT, Lessons Learned, ROI

By Christian Bisson, PMP

Machine learning is one of today’s hottest tech topics.

It’s essentially a type of artificial intelligence (AI) in which you give your software the ability to “learn” based on data. For example, you probably notice how YouTube, Netflix, Amazon and many other companies suggests videos or products you should check out. These suggestions are based on your previous online actions, or those of other people deemed “similar” to you.

For some time now I’ve been working on projects that involve this technology. We often have clients who want machine learning even though they do not know if it’s even relevant to them. Since “everyone is doing it,” they want to do it too.

Calibrating a project sponsor’s expectations is often a good idea. While the automated services generated through machine learning may seem magical, getting to that point involves challenges—and a lot of work.

1. It needs quality data.

The machine will learn using the data it has being given—that data is the crucial starting point. The data that’s available is what drives how the machine will evolve and what added value machine learning can bring to your project/product. For example, if you are trying to teach the machine to recognize vehicles on images it scans, and all you can teach it with are images of small cars, you are not set up for success. You need a better variety of images.

The machine’s ability to learn is directly tied to the quality of the data it encounters.

2. It needs lots of data.

Once you have quality data, you need it in high quantities. If you can only provide the machine with the website behaviors of, say, hundreds of users per month, don’t expect it to have enough information to be able to recommend the best products based on user trends. Its sample will be too little to be able to be accurate.

3. It needs to be tested continually.

Once you have the necessary data, the journey is not over. The machine may learn on its own, but it’s learning based on how it was built and with the data it’s being fed. There is always room for improvement.

4. It’s costly.

As amazing as machine learning is, it is not cheap. So keep an eye on your project’s budget. Machine learning experts can command high salaries, and there is a lot of effort involved with researching the best approach—creating the models, training them, testing them, etc. Make sure the ROI is worth it.

Have you had a chance to work on a project involving machine learning? What challenges have you faced?

Posted by Christian Bisson on: July 14, 2018 08:59 AM | Permalink | Comments (12)
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