Voices on Project Management

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?

By Ramiro Rodrigues

We are experiencing a great contemporary paradox: In spite of state-of-the-art gadgets and collaborative communication tools, which should be streamlining and facilitating work, we feel increasingly burdened with more responsibilities and response requirements.

The clearest side effect is the epidemic feeling that we are always short of what we wish we could have read, produced or done.

Of course, the benefits that technology has brought us in recent decades are indisputable. The production of human knowledge has gained stratospheric scale. The world has become "flat"—economies are now deeply integrated, and long distances have been collapsed by hyperconnectivity. But this also means that a good share of the world's population can now compete for the same professional space as you and your company.

Perhaps this is why recurring publications about better management of time and its countless functions become the focus of attention for the most attentive visitors to bookstores.

When everything is urgent, in fact, nothing is. If everything has the same priority, there is no way for anything to stand out. Perhaps this is the central issue behind the stress so many people feel today. Once the urgency of demands is generalized, it becomes difficult to produce high-quality, timely results.

What’s the solution? Planning, planning and ... planning. Only a good deal of planning — structured and strategic — allows corporate and project leadership to stay focused on real priorities and meet the right attention needs of their teams.

For the individual, planning is also a personal survival tool for organizing and balancing work, personal and social demands.

by Lynda Bourne

Knowledge is organic, adaptive and created—it exists in the minds of people. A person’s store of knowledge is built from their life experiences, their observations, and their formal and informal learning. Consequently, what one person knows will be different to what everyone else knows. Some of each person’s knowledge is explicit, meaning they can explain the rules that apply to it. But much is implicit: intuition, gut feelings and other ill-defined but invaluable insights grounded in the person’s experience.

Information is recorded, held in systems and made accessible to people. Good information management systems contain verified information in a useful format. This information is based on data. Because it is written, it is consistent—but it may not be correct. How the data is interpreted to create the information depends on people’s knowledge and perceptions.

Data Is the Starting Point

Data is a set of observations or measurements. If nothing changes in the world, another person can perform the same measurement or observation at another time and gather the same set of data. Data may not be accurate or reliable but it is based on observed facts about something. The potential for error rests in the way the observations or measurements were made.

The Interpretation of Information

Information is organized data. It provides the answer to a question of some kind or resolves an uncertainty.

However, transforming data into information is not automatic; it requires the input of knowledge. Someone has to look at the data and observe patterns that indicate something of significance or make decisions on what is important in a particular context. Information is refined data in a context that is designed to communicate a message to the receiver of the information.

The problem is different people with different knowledge frameworks will interpret the same set of data in different ways. You only need to listen to politicians arguing about the state of the economy to see how different the interpretation of the same set of data can become. The old adage applies, “There are three kinds of lies: lies, damned lies and statistics.”

When I reduce my knowledge to a codified or written format it becomes available to others as information. But I have no way of knowing how you or anyone else will use or change the information I have created.

Information Management Systems

Changing data into information is the first application of knowledge in an information management system. And the journey from data to useful information may need several passes through the information management system. PMI’s A Guide to the Project Management Body of Knowledge (PMBOK® Guide) identifies:

• Work performance data (gathered by someone during the course of doing project work)
• Work performance information (the data processes by discipline experts into basic information)
• Work performance reports (the basic information, selected, compiled and placed in context to be used by stakeholders).

At each step in this flow, a person applies their tacit and explicit knowledge to the information they have received. They then codify their new knowledge to create another piece of information ready for use by others. The problem with this process in isolation is it is asynchronous and based on individual transactions. This is suboptimal and potentially dangerous. 

However, the model of the information management system above is very common and spans global systems, such as Wikipedia down to simple knowledge repositories in project web portals. What’s missing in this type of system is the knowledge management element, which we will look at next time.

An information system on its own will at best simply make useful information available to people. There is no control over how, or if, the information is accessed or used appropriately. In a full knowledge management system, information is the bridge between data and knowledge:

• The raw data represents values attributed to parameters of something.
• Knowledge signifies understanding of real things or abstract concepts.

More on this next time.

I don’t have a classic project management background, so I spend a lot of time thinking about ways non-traditional project managers can offer up great ideas to people with more traditional backgrounds. 

Sometimes I find that easy. 

Sometimes I find that rather difficult.

I also spend a great deal of time trying to push people past conventional wisdom. 

Again, sometimes that is easy, but most of the time it is incredibly difficult. 

This got me thinking about what I wanted to talk to you about this month: While the truth remains the same, the interpretation of the truth can change. 

What does that mean to project managers? A lot, actually. 

Here are a couple of the things we have always felt were true and how they can be interpreted differently. 

1. Project management is about implementation. As my 8-year-old son might say, “True! True!”

The reality is that project management is about implementation of a project plan with a desired outcome in mind. 

Yet, as we have seen general business matters change, we have also seen that project managers aren’t just involved in implementation — they’re also involved in strategy. 

How is this possible?

Because we don’t just do things, we also have to be in touch with the skills and desires of the organization and our teams. 

This means we do need to implement. But as much as we implement things, we also have to have business acumen that will allow us to offer up ideas, be confident in our ability to think strategically and drive our team toward the results. 

Like improv comedy, a project manager is all about the “yes, and…” 

2. A project manager’s most important skill is communication.

Communication is likely the most important skill for anyone today. But, for project managers, it’s not simply about communication, but communication that enables people to set priorities and take action.. 

Let me explain. 

Poor communication has stopped more projects from being effective than any other thing in project history. 

But good communication alone won’t fix every issue. Sometimes communication isn’t the real issue — instead it’s about also doing the right things. 

That’s why we need great communication in service of doing the right things and getting things done. Communication is key, but communication without commitment to the right things is the real issue. 

The idea that communication and implementation are super important is still true, but why they are true is up for debate. 

What do you think? 

BTW, if you like this blog, why don't you get my Sunday newsletter. There I focus on business acumen, value, and leadership...along with under ideas. If you'd like to get it, drop me a line at [email protected] with "newsletter" in the subject line. 

by Lynda Bourne

Do really good ideas pop into your mind at the most inconvenient moments, like when you’re in the middle of taking a shower? This flash of bathroom brilliance presents two problems:

• There’s no one to share it with (at least in our home).
• There’s no practical way to record it (unless you take a wax crayon into the shower every day).

And typically, that flash of brilliance fades quickly and can be very difficult to reconstruct even a few minutes later. That may explain why Archimedes went running naked down the street shouting “Eureka!” following his flash of bathroom brilliance. This occurred when he discovered the relationship between volume and mass (density/buoyancy) by observing the change in water level as he entered his bath.

How can we unleash this kind of innate creativity on a regular basis and not just in the bathroom?

While everyone is different, there seems to be three key elements to being creative:

1. Make sure you understand what needs to be solved. Most people jump straight into solution mode. Creative people spend the time needed to define and understand the problem without trying to impose a solution.
    
2. Sleep on it. Allow time for your subconscious to work on the problem. It may take one or two weeks. This does not mean forgetting it, however. Set up reminders to keep prodding your creative ideas toward a resolution, whether they are notes you look at a couple of times a day, or a whiteboard with the issue drawn or sketched out. Adding notes and possible concepts keeps your subconscious focused. Remember: You need to stay relaxed and open during this period — stress kills creativity.

3. Make room for quiet time in your day. This allows flashes of brilliance to move from the subconscious into your conscious mind. The key seems to be doing some enjoyable function such as showering or walking, where 90 percent of what you’re doing is automatic and run by your subconscious brain. At the same time, stop worrying about other things, so your conscious mind is just being present.
   
   Then you have the real problem: finding a way to capture the ideas before they fade back into your subconscious. Carrying around a notebook or downloading a recording function on your smartphone can work if you are enjoying a pleasant stroll through a park.

Now, think about your teams and how you work with them to develop creative solutions. Do you call them into a room, dump the problem on them, demand a brainstorming session right there and then wonder why it doesn’t work? Or, do you socialize the problem first, ask people to think about it and discuss it with each other offline, and then call the meeting to see what’s been developed? 

Creativity needs space, time and freedom from pressure. This is the antithesis of most modern work environments where people work in a high-pressure job and are constantly inundated with a stream of “stuff” via technology.

How can you make the time to be relaxed, creative and successful?