Viewing Posts by Lynda Bourne
By Lynda Bourne
I was recently involved in a discussion about why some projects fail and others succeed, even when they’re completed in similar circumstances. The most common determinant of project outcomes—both positive and negative—boiled down to the way the people delivering the project work together. A cooperative and committed team underpins success.
This led me to think about the key requirements for creating a committed and cooperative team. And while the concepts below aren’t new, consistently creating the environment to allow them to flourish can prove challenging.
In my opinion, the three most important factors are:
1) An agreed-upon objective: Defining the project objective in a way people understand is the starting point. For one person, a “great website” may mean a technical marvel with all the bells and whistles. But for someone else, it may mean a simple, easy-to-use presence. It’s up to project leaders to get the team aligned—committing to an objective that’s not going to be delivered creates disenchantment.
2) An efficient team organization. Options can range from self-organizing teams to traditional leader-follower models. What really matters is that the team works in a coordinated and organized way, and this requires good, multidirectional communication to work.
3) Trust between team members. This last element is probably the most important—and least understood. You don’t need to like someone to trust them. In fact, you don’t even need to know someone to trust them. In an emergency, for example, it’s common to see a group of strangers form into a self-organized team and work together—often in quite dangerous situations—so things are stabilized. This is often referred to as “swift trust.” More traditional trust builds on reputation and observed experiences. Either type works, but you need trust. Without that, you’re not going to rely on the other people in the group to do the right thing to help you and the rest of the group achieve your shared objective.
In the modern world, people work on projects in all sorts of ways: virtually, in agile scrums, in traditional hierarchical teams and in myriad groupings. The people may come from one organization or many. Regardless of the group structure, one thing remains true: Project success comes down to effective teamwork.
What are your tips on creating an environment that allows project teams to flourish?
By Lynda Bourne
Every decision involves making a choice between alternatives, with the project leader picking from a number of options. This selection is influenced by information (albeit sometimes insufficient) and preferences rooted in values and ethics. In these circumstances, the modern trend of risk-based decision-making can be seen as a tautology: Every decision involves uncertainty and therefore incorporates an element of risk.
The worst option is to delay a decision until all of the necessary information is available—and, as a consequence, all of the opportunities have evaporated. So how do you make good decisions? The starting point is to accept there will be uncertainty in all true decisions—and the outcome matters. You have to choose between different options while navigating any number of obstacles ahead of you: incomplete information to support the decision, no clear best path and unknown outcomes of some options. The challenge is to make the best decision in the available timeframe balancing risk and reward. No process can guarantee a good outcome every time, but working through a pragmatic process can help improve outcomes.
Your decision-making process needs to:
Getting the weighting right is central to this approach. In some situations, particularly when safety is involved, dull, safe but expensive may be the best choice, particularly if the cost is not particularly significant in the overall project budget. Think about investing in the security layer for any on-line finance or ordering system, for example. In other situations where failure is only going to cause some embarrassment, innovative but risky solutions may be better, particularly if the cost is low. Case in point: No one can predict when a website will become ultra-successful (go viral), but you won’t be successful if you don’t try. Investing US$10,000 in an option that has a 10 percent chance of making US$1 million is a good investment (but prudent managers have plan B ready to roll).
There’s no way to ensure the best decision is made, and often no way of knowing if the decision you’ve taken was the best—you cannot re-run history. But you can measure bad outcomes; the worst decision is no decision. The next-worse option is a late decision. This always costs a lot of money and may result in opportunities being missed. And the next worse option after that are timely decisions based on a wrong premise—usually out of trying to avoid all risk.
If you avoid those pitfalls, you’re likely to be making a well-founded decision. This is the realm of competent managers. But you’ll also need luck on your side to be seen as making a “really good” decision. And for that, you make your own luck, to quote author Ernest Hemmingway. Deciding to make effective decisions is a choice you need to make.
What does your decision-making process look like?
by Lynda Bourne
One thing we learned from 2020 is that it's difficult to make predictions, especially about the future. (Danish physicist Niels Bohr’s words from 1971 still ring true.) Still, the world doesn’t stand still, and project managers need to keep looking ahead. That’s the whole purpose of a project controls function: to produce information that helps us make decisions about the future.
In many respects, project plans (schedules, budgets, etc.) are similar to economic forecasts. For decades, both have been used to make predictions more academically rigorous through mathematical techniques. The problem is these models are suited to the stationary physical world, where everything that happens is governed by the unchanging laws of physics—or to games of chance, in which the probability of something happening can be calculated fairly easily and accurately. They do not neatly apply to the intricacies of a dynamic project or economy.
Two leading British economists, professor John Kay of Oxford University and professor Mervyn King, a former governor of the Bank of England, recently launched a critique of the unrealistic assumptions their peers have added to conventional economics in the book, Radical Uncertainty: Decision-Making for an Unknowable Future.
Their view is that making predictive models more mathematical does not improve the accuracy of the predictions. The models assume the decision-maker and all of the other actors will follow the logic underpinning the model. But we all know the people being modeled do not behave rationally and rarely, if ever, actually work to the plan.
Kay and King call this type of modeling “small world,” as the right and wrong answers can be clearly identified. Projects (and economies) operate in a “large world” occupied by consumers, businesses and government policymakers, and characterized by what they call “radical uncertainty.” People in the large world have to make decisions based on a small part of the information actually needed about both the present and the future. Most of the time we can never really know if we made the best decision, even after the event.
Fortunately, like Alice in Wonderland facing the appearing and disappearing Cheshire cat, people are very good at coping with uncertain situations. And it’s amazing how often we get it right. Kay and King conclude: “Our knowledge of context and our ability to interpret it has been acquired over thousands of years. These capabilities are encoded in our genes, taught to us by our parents and teachers, enshrined in the social norms of our culture.” Human intelligence is effective at understanding complex problems within an imperfectly defined context, and at finding courses of action that are good enough to get us through the remains of the day and the rest of our lives. They are not necessarily the best solutions, but they’re ones that are good enough.
So where does that leave project controls? We have predictable tools such as earned value, critical path and the like built on the basis of predictable calculations. Unfortunately, these calculations are rather bad at accurately predicting actual future outcomes. But is the imprecise information useful?
My thinking is that control tools can provide useful insights, but only if you accept there will always be a difference between the prediction and reality as the future unfolds.
How do you use project controls to chart paths forward into the unknown?
by Lynda Bourne
The world of business is moving toward storing and exchanging documentation in electronic formats—and the transition is swift. While this process has its advantages, my team and I have been working on a major report based on a data set of more than 250,000 records, and the project has highlighted some problems. Namely, as it becomes easier to preserve every iteration of a document, finding useful information becomes harder.
There are two basic types of document storage and retrieval systems with a couple of nuances:
If your organization isn’t using one or more of these systems, it soon will be! You’ll probably find that they solve many problems typically found in paper-based systems, but they also introduce a new suite of issues. Here are some of ways in which these systems fall short—and ways to overcome these challenges:
Establishing one source of the truth. As people become more used to the system, they begin to rely on it. And if something isn’t uploaded, stored or created in the tool, it ceases to exist. You cannot rely on people remembering to do the right thing, and if someone is doing something unethical, they will try to evade the system. The solution lies in system design and automation. Discipline and processes are needed to make sure a document retrieval system contains all of the documents.
Creating one document, one record. Send an email to 10 other people in the organization and you immediately have 11 versions of the one document scattered across various email accounts. (And this is before “reply all” and email trails start to build.) Your document management system needs to be smart enough to recognize identical versions of the same document and archive the 10 copies. However, when someone changes the email (maybe by forwarding it), you have a new document, and the process gets more complex if there are attachments. Here, the solution is a system that can manage families of documents.
Finding what you need—easily. This is the biggest challenge with massive archives of documents (and was central to our work over the last few months). How do you find information? A search based on document contents may seem like the best option, but if you Google “PMI PMP exam change,” you get 891,000 results. And it’s Google’s systems that decide which of the pages it will show you and the sort order. That means if you’re looking for something specific, you may have to dig through a sea of hyperlinks and page titles. This gets even more difficult if you want to check if something did not get documented. A null-result may mean the alleged document does not exist—or it may mean your search terms are slightly ambiguous.
Developing systems that balance providing information that you need against burying you under masses of content requires the wisdom of Solomon. Artificial intelligence can help if the search is routine, but for an important ad hoc search you are probably on your own. One way to help focus searches is by structuring the information, using folders or codes. The problems are minimizing misplaced information and persuading everyone to use the system. Again, system design is central to developing processes that work.
The concept of a paperless project has been around for a while now and electronic document management systems are becoming increasingly common. The challenge that remains is scaling this concept up to the enterprise level and developing tools that can quickly provide you with the information you need from a pool of several million documents.
What do you do to store documents and facilitate the ease of information access?
by Lynda Bourne
Projects mean reports! Many project teams are required to produce weekly and monthly reports for their client as part of a contract, or because of an internal set of reporting requirements. This process comes with challenges:
That raises a big question: Do we need traditional reports? Developments in business intelligence, artificial intelligence and system integrations offer a far more useful solution—putting real-time information in front of the people who really need to know now.
Most of the information on virtually every project (even traditional construction projects) is recorded in various software tools. With a little bit of organization, the data can be brought into a business intelligence (BI) system in real time. The result: a dashboard showing what’s occurring in real time, usually with a drill-down capability to see what has changed and why.
The problem with BI is usually too much information and added noise created by different elements within the tool being updated, edited and corrected at different times. This generates false differences for short periods of time. This is where artificial intelligence (AI) comes in to play two useful roles:
Do reports still have a role? My answer is yes, but it’s a different role. Reports are needed to explain something or to show the results of an investigation or inquiry. For example, a team (or individual) may be tasked to report on the preferred subcontractor to engage for a particular role on a project. The report provides leadership with the information and options needed to make a decision. In fact, this would be a far better use of the time currently spent by PMO and project staff preparing and distributing weekly and monthly reports.
I want to hear your thoughts: Do traditional reports still have a place among project teams?