The Business Driven PMO

GUEST POST by Claire Schwartz

In my last post, I wrote about collecting data to provide information for decision-making. There is no question that data is where we begin, but facts are not enough. What we really need is information—a meaningful interpretation and presentation of the data that gives us insight into a condition or situation.

For example, when I manage a project, I collect data about task performance, such as "Task A" started on January 5 and finished on January 12, it took 27 hours of effort and we spent $3000 on travel. Interesting facts, but they really don't tell me enough to evaluate the task's performance, its impact on the project, or help me make decisions about the remaining work or cost.

It turns out that "Task A" was scheduled to complete on January 14, making it ahead of schedule. Unfortunately, it isn't on the critical path, so there's no impact to the project schedule. We had planned to use 25 hours to complete the work, but even though we were two hours over our plan for the task, we've been running significantly below our labor estimates on the work performed to date, and we're already about halfway to completion. Now for the bad news: the total travel budget for the project is $5000 and we still have two more trips planned. What was supposed to be a one-day trip, costing $1000 for three team members, turned into a three-day trip, costing $3000 because the team got stranded in a Chicago blizzard.

So what makes the second paragraph more useful than the first? In general, the second paragraph tells a complete story that informs, highlights what’s important and clearly identifies the actions or decisions needed. If we further dissect that paragraph, we can see a few specific attributes that make it more meaningful and useful: content, context, contrast and consequence.

Content: In the first paragraph, we know the facts, but the second paragraph takes the time to combine the facts into a story. By adding a little more detail, or content, the reader has a better grasp of “the five W’s”: who, what, where, when and why. Our desire to be brief and direct often results in repeated question-answer cycles, leaving the decision-maker to ferret out the relevant and important information before taking an appropriate action, delaying the process.

Context: While the first paragraph describes the amount of money that was spent on travel, it does nothing to explain the circumstances or context behind the expenditure. By providing the information about the blizzard, the decision-maker better understands why the expenditure was high and in a better position to make an informed decision regarding future travel expenditures.

Contrast: In the first paragraph, we know what date the task finished, but in the second paragraph, we know that it finished late. By including data from the project plan, the second paragraph is able to compare what was supposed to happen with what actually happened. This provides the decision-maker with a much better sense of the problem and apply an appropriate decision.

Consequence: In addition to understanding that a variance exists, we also need to be clear about the impact or consequence of the difference. Sure, the first task took a couple more hours than we had originally planned, but in the overall scheme of things, it really doesn’t require action. The budget variance on the other hand is significant since it pretty much blows our travel budget out of the water.

Turning data into meaningful information to drive a decision isn’t rocket science, but it does require some thought. Next time you put together a report, ask yourself: does this tell the full story? If not, it’s probably just data.

In my next post, I’ll write about presentation in the context of decision-making, including some thoughts on dashboards.

I noticed a recent blog on the PMI site from Saira Karim, called ‘Use Project Management Tools in the Right Context’. Saira started her post with the following:

“Recently I came across an ad for a project management technology application. It was a picture of seven robots in a group, which symbolized humans. The slogan read, “If your team looked like this, any PPM solution would work.”

Interestingly, that’s a Daptiv ad she noticed in the PMI magazine. Saira continues:

“It made me wonder how many organizations actually believe that technology applications do the work and produce results — not humans.

How many organizations and project managers sufficiently analyze their project needs and the compatibility of new technology to their organizations’ existing set-up and processes?

Companies often buy expensive project management applications and then force teams to conform and adapt to the application rather than customize the application to the needs of the people and project.

But buying applications because other organizations use them does not by default mean you, too, will become a leader.

Like with best practices, experience has taught me that technology and tools are valuable — but only if they fill gaps and needs effectively.

Technology is important and can increase efficiency, but in the correct setting and context. Projects are planned and executed by people — therefore technology must complement and be understood by the humans who use it.

Before investing in new project management applications, you must consider things like training, costs and your team members’ willingness to use the tools. Otherwise it could amount to an expensive burden.”

Saira’s post is a great articulation of the importance of the need for tools that adapt to your organization’s business needs. Combining a flexible tool with a PPM roadmap that is improves maturity in successive steps is the key to PPM success.

Have you had experience with tool implementations that have failed (or succeeded)? What are your keys to success?

(Part 1 of a 3-part series)

“Facts do not cease to exist because they are ignored.”
Aldous Huxley

Whether you are a project manager, a PMO director or an executive, most of us want better information. Why? Information is the fuel for decision-making. Whether a decision is about what to have for lunch or about making a billion dollar investment, a decision-maker is reliant on information which is then used to evaluate options and ultimately choose a course of action. But just having information isn’t enough – it needs to be good information. 

What information is collected, how and when we gather it and what we do with it when we have it are all factors that contribute to information quality. Individuals and organizations that are mindful of these tend to be more effective in collecting better information and managing the flow of information across the enterprise than those who take a haphazard approach. So what do the effective organizations know that the others don’t? 

First and foremost, effective information managers recognize that data and information are not the same thing. Data is a set of facts while information is data that has been processed to create meaning. Data is the words, information is the story. Furthermore these managers recognize that good information cannot exist without good data. Good data does not just appear; it is collected through well-defined, well-supported processes. Processes for collecting data clearly define what is being collected and establish clear roles and responsibilities for both providing and collecting it. Attention is paid to timeliness and completeness and includes feedback to data providers so that data quality can be improved over time. 

Effective data collection processes also focus on quality not quantity – they don’t collect data for the sake of data. While its nifty to be able to say  you’re collecting data on 1,000 different ‘key indicators,’ trying to collect data on everything is time consuming and contributes little to the objective of having information for decision making.  Indeed having too much data can feed ‘analysis paralysis’ where the amount of data cannot be synthesized into useable and relevant information. Asking for a few key data elements rather than all the data available not only reduces the time and overhead associated with data collection, it emphasizes the importance of the data that we do collect. 

Consider the example of the manager who, in trying to understand how time is being spent by his staff, defines eight different categories of administrative work. Everything, from reading emails regarding benefits to attending team morale events with co-workers, appear as separate line items in the timesheet.  Over time, and after thorough analysis,  the manager notices: a) The aggregate amount of administrative time reported has increased, and b) The greatest increase is in the item labeled ‘timekeeping.’

But how can we be sure we’re collecting the right data? First, we need to work backwards from the decision.  Not from what the decision will be, but what is the decision about. In other words, not what is the answer, but what are the questions. While we can’t predict all the questions that could arise at any time, there are certain questions that are part of an individual or organization’s decision-making DNA. If your answer can’t be supported with facts, you need to look at your data.

In the timekeeping example above, the real question was not ‘how much time is spent onteam morale events with coworkers’, but rather ‘how much time is available for production or project work (non-administrative)’. With that in mind, a single line item for miscellaneous administrative effort would have provided plenty of data to determine the burden associated with administrative work.  

Collecting data is the beginning of the story for creating better information but it is by no means the whole story. In my next post, we’ll look at how we create information from the data and how that information is communicated all support better decision-making. 

Here's my thoughts on Earned Value, and although it has some issues, you'll see that I actually think it’s a very valuable tool.

I'm an old-time project manager that actually remembers when Earned Value was known as BCWP. That's Budgeted Cost of Work Performed. If you think about it, that's what the EV metric actually measures. And as a former financial officer, I fail to see why it's called Earned Value. First off, if nothing has been delivered yet, I haven't realized any value from my investment in this project. Second, if this measured the project's value it would mean the value is equal to the cost. With no ROI I'm not approving a project like that!

I'm not sure who initially called it Earned Value, but the project management community seems to have bought it hook, line and sinker! I must admit, BCWP is quite a mouthful, but couldn't they have come up with something a little less misleading?

One more note on the misleading name: If I'm in construction or a service industry where I bill based in percent complete, then I literally am "earning value" as we progress. But the customer sure isn't! So I still object to the term.

That said, EV is one of the best tools for tracking a project's performance to plan. Why? Traditional variances like budget or effort variances turn red way too late. A project that ends up 20% over budget won't show a negative variance until it is almost over, and then it's too late too course correct. Further, trending budget and percent complete at the project level, while better, doesn't account for plans that aren't evenly loaded. If we're buying a bunch of hardware near the beginning of the project, that project will look like its trending way over budget very early.

EVM gets past this by looking at the cost - both labor and material - for every task. Yes, there are other methods of EV measurement, but these are the most common ones here. By tracking planned and actual cost and time at the task level, then using percent complete to determine if we are ahead/behind on each task, we get a much more granular view that accounts for variable loads of cost and effort through the life of the project. And then we can roll that up to the project, program, or even portfolio level. Using our CPI and SPI factors, we can get a good reading on how far ahead or behind we are very early in the project, and can then make timely course corrections.

So, while it may be mis-named, Earned Value is a great objective tool for gauging project progress and status. 

News sources everywhere are streaming reports on the state of today’s economy. There is plenty of speculation, conversation and hyperbolae on the need to invigorate the economy with investment. Businesses and government organizations are being pressured to spend more to create jobs and boost the economy. 

But the fundamental question to ask is “At what risk?”

I get this question all the time. Project investments are being made continually. Maybe not at the pace we would like to see, but it’s happening and the state of project execution remains high within organizations.

A colleague of mine and I were recently discussing whether there is a void or opportunity to introduce new thinking in addressing the "at what risk?" question. Let's examine.

First, let's examine the statement itself.  What does “At what risk?” really mean? We can look at it in multiple dimensions. 

Project execution is a common place where we look at risk and risk management, where risk is anything that could potentially adversely affect the schedule, cost, quality or scope of a project. Basically, how does risk impact your project objectives? PMBOK outlines many processes and techniques for managing risk such as the ‘Probability and Impact Matrix for Qualitative Risk Analysis’ and quantitative risk analysis techniques such as ‘Sensitivity Analysis and Decision Tree Analysis’.

Another perspective to view risk and risk management is from the finance angle. We can look at how cost benefit analysis (CBA) can present a financial perspective of risk and we get that from ROI, NPV, etc. But these dimensions are only at the perspective of the project and the projection of potential value. 

There are other dimensions of risk we can discuss, but those have specific vertical applications, like the risk of entering a market or the risk of applying a particular technology.  

These all are very valid methods and processes, but can we answer our earlier question "At what risk?"?  I think so and we can simply address this by asking a simple question: "Are we working on the right things?"

Drawing on portfolio management techniques helps identify "At what risk." But, you may say that risk is already defined as a dimension of portfolio management, and you would be right. Organizations have struggled with implementing portfolio risk models and maintaining them.  

I prefer something simpler. Can I answer our question early in the project lifecycle without spending a lot of energy? Yes. Simply put: alignment , through the identification of what is important at the portfolio level and defining the alignment dimension of your portfolio solves our problem.  Why would we ever want to spend any energy at all on a project proposal if it doesn’t fit within the performance objectives and investment goals of the portfolio?   

From my perspective, answering the “At what risk” question can be a simple top-down exercise that can deliver great value to the organization. But the first question we should always answer is, “Does it fit?”  Then we can focus on evaluating and managing risk in the financial and project execution domains.