Why most project estimates suck…and how Monte Carlo simulations can make them better

Have you ever been part of a project that was late and over budget? I’d be surprised if you haven’t. We humans are famously bad at estimating the future, and project planning is heavily dependent on our ability to estimate the future. Most of us are optimists and some of us are pessimists, but very, very few of us are realists by nature. Monte Carlo simulations can be useful in our estimation process to help us become more realistic about our estimates, and that realism can significantly improve our ability to deliver results more in line with expectations.

We generally recognize our inability to accurately estimate large projects in one chunk, so we break them up into smaller milestones that are easier to estimate. While the work breakdown process is good, the confidence it gives us in our estimates can lead to larger problems. We don’t ask ourselves often enough how accurate we think those estimates are before stringing them together to determine project due dates. If we did, the conversation might go like this:

“How accurate do you think these milestone estimates are?”

“Pretty accurate. We certainly spent a lot of time discussing them and comparing them to past projects.”

“OK. But if you had to put a number on it, would you say they are 100% accurate?”

“Well, let’s not get crazy. I can’t be sure they’re 100% accurate.”

“So put a number on it. How confident are you that they’re accurate?”

“I still feel pretty good about them. I’d say conservatively that I’m at least 90% sure.”

At this point, we’re about to discover some pretty major problems with our assumptions. We typically string together a number of these milestones, which are dependent on each other, and call them the critical path. The end of the critical path is the project due date.

But if we’re only 90% confident our estimates for each milestone are correct, the likelihood of missing our date is pretty high. Let’s say we have five major milestones in our critical path, and we’re 90% sure each is accurate. To determine the probability that all five will come in as expected, we have to multiply .90 x .90 x.90 x .90 x .90. Even with these high confidence rates, we’re now looking at about a 59% chance of hitting our due dates and a 41% chance of missing them. And that’s with only five milestones and really high (and probably unwarranted) confidence in our estimates. The numbers only get worse from here.

So we start missing deadlines and inevitably either pump more money into the effort or start cutting scope. Our original business case and ROI justification for the effort are now inaccurate because it’s going to cost more and produce less benefits. Sound familiar?

Monte Carlo simulations can help us get a better handle on the probabilities of actually delivering on our timeline and budget estimates. Just as I previously demonstrated using Monte Carlo simulations for sales forecasting, a simulation focused on project estimates can essentially become a “what if” model and sensitivity analysis on steroids for project planning. Basically, the model allows us to feed in a limited set of variables about which we have some general probability estimates and then, based on those inputs, generate a statistically valid set of data we can use to run probability calculations for the entire project.

Great. So now we know how likely we are to miss our timeline and budget. So what?

Once we have a more realistic view our our project timeline and budget, we can do far more effective planning. We can develop contingency planning with full knowledge of the likelihood of needing any particular contingency. Having a better sense of potential budget increases or scope decreases in advance of the project start date will help us make better decisions about starting the project to begin with.

We’ll also be able to better plan our needs from other groups in the corporation who might be involved with the final project but not directly involved in the project. For example, we might need to fit a new product launch campaign into an already packed marketing schedule. Will new site functionality require training for customer service? We’ll need to plan time to pull agents off the phones for their training. Setting expectations with these external groups will greatly enhance at least the internally perceived success of our effort. And that certainly counts for something.

Why go through all this complication? Let’s just take all the estimates we get from the team and double them. That should help get ensure we stay within the timeline.

The “double the estimates” approach is one I’ve seen used before. While it does help create timelines that won’t be exceeded, overestimation can also cause problems. Any coordination with external teams will still be a problem if we end up needing them before we originally planned. And over-allocating time, resources and budget can drive up opportunity costs and limit our ability to produce meaningful results over time.

Monte Carlo to the rescue

I created a free, sample Monte Carlo simulation you can download for use in project planning. It illustrates on a small scale some of the possibilities that can occur with even a minor project. We see that even a five milestone effort with 85% confidence in the estimate of each milestone is expected to be more that 20% overdue. But we can also get a sense of the probabilities of various timelines and use it to refine overall estimates.

By understanding the probability of various delivery dates and project budgets, we can better plan scope, business models and contingency plans. We can better coordinate with other teams who will play a part in the ultimate success of the project once it’s complete. In short, we can become realists and, as a result, deliver much better business results.

What do you think? Would this sort of tool help in your planning? What other methods have you used to set better expectations and plan more accurately?

Blinded By Certainty

In reality, very little in our lives is absolutely certain. We can be certain the sun will rise in the east and set in the west. We can be certain death will follow life. And we can be pretty darn certain Steve Jobs will wear a black turtleneck and jeans at his next public appearance.

But we’re certain about a lot more things than we should be.

A recent University of Michigan study by Brendan Nylan and Jason Reifler shows that the more certain we are about particular ideas or situations the more we become blind to facts that discredit our certainty. In fact, in many cases opposing facts are not just ignored but actually strengthen our prior beliefs.  A recent Boston Globe article provides an excellent summary of the research.

From the article:

Most of us like to believe that our opinions have been formed over time by careful, rational consideration of facts and ideas, and that the decisions based on those opinions, therefore, have the ring of soundness and intelligence. In reality, we often base our opinions on our beliefs, which can have an uneasy relationship with facts. And rather than facts driving beliefs, our beliefs can dictate the facts we chose to accept. They can cause us to twist facts so they fit better with our preconceived notions. Worst of all, they can lead us to uncritically accept bad information just because it reinforces our beliefs. This reinforcement makes us more confident we’re right, and even less likely to listen to any new information.

Both the research and the article focus primarily on our political viewpoints, but while reading I couldn’t help but think of people I’ve come across in the business world who were unbelievably certain about their viewpoints based on information or experiences that seemed less than obvious to me. I immediately thought of dozens of people, and I bet you’re thinking of many such people now.

In fact, it was so easy for me to think of other people that fit the bill that I couldn’t help but think the man in the mirror was not immune to this universal human fallacy.

In my experience in the business world, we often assume with undue certainty that past experiences will reflect future possibilities. We say things like, “We tried that before and it didn’t work” or “I know what our customers want.” While our past experiences are extremely valuable and are very important for informing future decisions, we simply don’t have enough of them to blindly ignore changes in circumstances, timing and other variables that could significantly alter results for a new effort.

So how do we overcome our natural instincts in order to make better business decisions?

1. Be aware of the problems with certainty
   You’ve read this far, so maybe you’re awareness is already active. I know that I am reassessing all the things I “know” to try to truly separate what is fact and what is assumption. I very much value all my experience, and I know I make better decisions because of what I’ve seen and heard along the way. But I want to make doubly sure that assumptions I make based on past experiences are tested and validated before I turn them into absolute fact.
2. Actively seek alternate points-of-view
   In my experience, the combination of multiple experiences provides a much more solid foundation for decision making than basing decisions on singular past experiences. Techniques I’ve used, like The Monkey Cage Sessions, are based on the incorporating viewpoints from people in different functional areas and levels of the organization. While it’s acceptable to discount data or opinions that are in opposition to a decision I might make, I want to be sure I’m not simply rationalizing opposing information or viewpoints solely because they are different from my biases.
3. Envision alternate scenarios
   I addressed this some in a previous post, “Obscure and pregnant with conflicting meanings”, where I discussed a technique I called “Scenario Imagination.” I’ve since read an excellent interview with Daniel Kahneman and Gary Klein where they detail a similar and better technique they call “pre-mortem” (which is also a better name than mine). Whenever we make decisions, we have a tendency to assume our decisions are going to produce the best possible results. These pre-mortem techniques have us imagine worst case scenarios to try to dissect potential problems before they occur.
4. Be flexible and plan for contingencies
   Once we admit we’re not 100% certain, we can move forward with plans that are flexible and able to react to changing conditions. To be clear, I’m not saying we should just be wishy-washy and not make clear decisions. What I’m saying is that we should be open to new facts and be sure we have created an environment that allows us to change course when warranted.

If we’re aware of our certainty biases and take active steps to address them, I believe we can significantly improve our decision-making in our businesses.

What do you think? Upon self-examination, have you turned beliefs into facts in your mind? How would you suggest addressing these biases? Or, do you think is all a load of hooey?

The Monkey Cage Sessions

I’ve seen a lot of strategies and “solutions” fail over the years primarily because the solution was crafted before the problem addressed was thoroughly understood.

Many times, the strategy or solution was the result of a brainstorming session filled with type A personalities (me included) ready to make things happen.

You may be familiar with the type of session I’m referencing. Usually, there’s a guru consultant leading the charge. He separates the group into teams and gives them Post-It notes and colored sticker dots. “Write down as many ideas as you can in the next 20 minutes. Don’t think too much. Be creative! No idea is dumb. Stick your ideas on the wall. Now go!” After 20 minutes, a leader from each group presents their best ideas to the rest of the room. Then each person in the room is allowed to vote for maybe six of his or her favorite ideas using the colored sticker dots. A few people are assigned the winning ideas and off we go.

Those types of session frustrate me. I’m concerned there’s too much action, too many unspoken assumptions, and not nearly enough serious thinking.

Over the years, I’ve developed a problem solving technique that I’ve found to work a lot better. I call it the Monkey Cage Sessions. The technique is all about thoroughly identifying the problems from all angles before developing carefully considered, thoughtful and collaborative solutions.

It’s got an intentionally silly name because the process should be fun.

Here’s how it works:

Step 1 – Define the problems.

We start by gathering a group of cross-functional people – ideally from different levels of the organization – together in a room to talk about the problem or problems we’re trying to solve. This could be as simple as enhancing a Careers page on the corporate website or as complicated as building a complete company strategic plan. It’s important to define the general scope of the problem, but it should be defined fairly loosely so as not to stifle the discussion.

The rules of the meeting are fairly simple. We only discuss problems. No solutions. This is a license to bitch. Let it be cathartic.

I usually stand at the whiteboard, marker in hand, and write down everything everyone says. There is no need to be overly structured here, and anything anyone says is legitimate. We throw it all at the wall and we’ll sort it out later.

Sometimes people want to debate whether or not something another person says is really a problem. If someone said it, it’s at least a perceived problem. It’s legitimate. Also, there is often an attempt to offer an explanation for why a problem exists. The explanation is covering for another problem, so that problem should be written down.

People are always tempted to offer solutions, even when they think they’re offering problems. For example, someone might say it’s a problem that we don’t have a content management system. Actually, a content management system might be the solution to a problem. What problem might a content management system solve? Beware of any problem statement that starts with “We need…” and be prepared to break down that need into the problems needing the solution.

Sometimes the problems offered up are very broad and vague. In those cases, it’s important to work with the group to dissect that broad problem into its component parts.

This first session generally uncovers a LOT of problems, but the problem is still usually not completely identified yet. Which leads to…

Step 2 – Categorize the problems

While the chaotic approach of the first session works well to get an initial set of problem descriptions, it’s important to create some order in order to prepare for the problem solving stage. So Step 2 involves writing down all of the problems and sorting them into logical categories. I don’t have any pre-determined set of categories. Instead, I prefer to the let the problems listed dictate the categorization.

Step 3 – Widen the circle

We probably have a pretty good description of the problems now, but we’ve also still likely missed some. For Step 3 we send the typed and categorized list of problems to the original group as well as a widened circle of people. The original group will likely have thought of a couple more issues since the day of the meeting, and the new group of people will almost definitely add new problems to the list. Since this is the final stage of problem description, we want to give this step at least a few days to allow the team to think this through as completely as possible.

Step 4 – Develop the solutions

Finally, we can start solving the problems. Woo hoo!

Now it’s time to gather a subset of the original meeting to start working towards solutions. There should be at least a few days between Step 3 and Step 4. We want to give people some time to think over the full problem set. The group should enter the Step 4 meeting with at least some basic solution ideas. There is no need to come into the room with comprehensive solutions that solve every problem on the list, but the solutions considered should certainly attempt to solve as many problems as possible (without causing too many new problems).

I usually find that by this point many of the solutions are fairly obvious. But there should be good discussion about the relative merits of each suggested solution, and the solutions should be measured up against the problem list to determine how comprehensive they are.

I like to end the meeting by assigning people to lead each of the proposed solutions. Obviously, any suggested solution from this session will need to be fleshed out in a lot more detail, and the leader from this meeting is responsible for determining the viability and solution and then potentially leading the development and ultimate execution to completion.

Subsequent progress is then handled via a separate execution process.

———————————-

I’ve had very good luck over the years using this technique. Some of the primary benefits I’ve found are:

1. Better understanding of the problems
   As the initial meeting wraps up, most people are inevitably feeling enlightened about the problem. They’ve outwardly expressed their own assumptions (which sometimes even they didn’t know they were making) and they’ve understood the perspectives and assumptions of others. They’ve seen the problem in an entirely new light.
2. More comprehensive solutions
   The heightened understanding of the problem and the critically important time between steps to allow the team to be more thoughtful in their ideas. Those ideas are usually pretty all-encompassing solutions to start with, but the discussions in Step 4 lead the team to collectively choose the best of the best of the solutions offered.
3. Better execution
   Solutions are nothing but fancy ideas until their executed. And poor execution can cause even the best ideas to fail. The process of fully defining the problems and sharing that work with wide circles of people is an incredibly important stage that sets the foundation for success in execution. When the execution team provides input in the process and understands the basis for the solution, they are far more supportive in the effort. They are also far more prepared to make the daily, detailed decisions that are often the difference between success and failure.

So, that’s the Monkey Cage Sessions. I hope you find it helpful. If you try implementing the process in your business, I’d love to hear how it goes.

What do you think? Would this process work in your organization? Have you ever used a similar process?

11 Ways Humans Kill Good Analysis

In my last post, I talked about the immense value of FAME in analysis (Focused, Actionable, Manageable and Enlightening). Some of the comments on the post and many of the email conversations I had regarding the post sparked some great discussions about the difficulties in achieving FAME. Initially, the focus of those discussions centered on the roles executives, managers and other decisions makers play in the final quality of the analysis, and I was originally planning to dedicate this post to ideas decision makers can use to improve the quality of the analyses they get.

But the more I thought about it, the more I realized that many of the reasons we aren’t happy with the results of the analyses come down to fundamental disconnects in human relations between all parties involved.

Groups of people with disparate backgrounds, training and experiences gather in a room to “review the numbers.” We each bring our own sets of assumptions, biases and expectations, and we generally fail to establish common sets of understanding before digging in. It’s the type of Communication Illusion I’ve written about previously. And that failure to communicate tends to kill a lot of good analyses.

Establishing common understanding around a few key areas of focus can go a long way towards facilitating better communication around analyses and consequently developing better plans of action to address the findings.

Here’s a list of 11 key ways to stop killing good analyses:

1. Begin in the beginning. Hire analysts not reporters.
   This isn’t a slam on reporters, it’s just recognition that the mindset and skill set needed for gathering and reporting on data is different from the mindset and skill set required for analyzing that data and turning it into valuable business insight. To be sure, there are people who can do both. But it’s a mistake to assume these skill sets can always be found in the same person. Reporters need strong left-brain orientation and analysts need more of a balance between the “just the facts” left brain and the more creative right brain. Reporters ensure the data is complete and of high quality; analysts creatively examine loads of data to extract valuable insight. Finding someone with the right skill sets might cost more in payroll dollars, but my experience says they’re worth every penny in the value they bring to the organization.
2. Don’t turn analysts into reporters.
   This one happens all too often. We hire brilliant analysts and then ask them to spend all of their time pulling and formatting reports so that we can do our own analysis. Everyone’s time is misused at best and wasted at worst. I think this type of thing is a result of the miscommunication as much as a cause of it. When we get an analysis we’re unhappy with, we “solve” the problem by just doing it ourselves rather than use those moments as opportunities to get on the same page with each other. Web Analytics Demystified‘s Eric Peterson is always saying analytics is an art as much as it is a science, and that can mean there are multiple ways to get to findings. Talking about what’s effective and what’s not is critical to our ultimate success. Getting to great analysis is definitely an iterative process.
3. Don’t expect perfection; get comfortable with some ambiguity
   When we decide to be “data-driven,” we seem to assume that the data is going to provide perfect answers to our most difficult problems. But perfect data is about as common as perfect people. And the chances of getting perfect data decrease as the volume of data increases. We remember from our statistics classes that larger sample sizes mean more accurate statistics, but “more accurate” and “perfect” are not the same (and more about statistics later in this list). My friend Tim Wilson recently posted an excellent article on why data doesn’t match and why we shouldn’t be concerned. I highly recommend a quick read. The reality is we don’t need perfect data to produce highly valuable insight, but an expectation of perfection will quickly derail excellent analysis. To be clear, though, this doesn’t mean we shouldn’t try as hard as we can to use great tools, excellent methodologies and proper data cleansing to ensure we are working from high quality data sets. We just shouldn’t blow off an entire analysis because there is some ambiguity in the results. Unrealistic expectations are killers.
4. Be extremely clear about assumptions and objectives. Don’t leave things unspoken.
   Mismatched assumptions are at the heart of most miscommunications regarding just about anything, but they can be a killer in many analyses. Per item #3, we need to start with the assumption that the data won’t be perfect. But then we need to be really clear with all involved what we’re assuming we’re going to learn and what we’re trying to do with those learnings. It’s extremely important that the analysts are well aware of the business goals and objectives, and they need to be very clearly about why they’re being asked for the analysis and what’s going to be done with it. It’s also extremely important that the decision makers are aware of the capabilities of the tools and the quality of the data so they know if their expectations are realistic.
5. Resist numbers for number’s sake
   Man, we love our numbers in retail. If it’s trackable, we want to know about it. And on the web, just about everything is trackable. But I’ll argue that too much data is actually worse than no data at all. We can’t manage what we don’t measure, but we also can’t manage everything that is measurable. We need to determine which metrics are truly making a difference in our businesses (which is no small task) and then focus ourselves and our teams relentlessly on understanding and driving those metrics. Our analyses should always focus around those key measures of our businesses and not simply report hundreds (or thousands) of different numbers in the hopes that somehow they’ll all tie together into some sort of magic bullet.
6. Resist simplicity for simplicity’s sake
   Why do we seem to be on an endless quest to measure our businesses in the simplest possible manner? Don’t get me wrong. I understand the appeal of simplicity, especially when you have to communicate up the corporate ladder. While the allure of a simple metric is strong, I fear overly simplified metrics are not useful. Our businesses are complex. Our websites are complex. Our customers are complex. The combination of the three is incredibly complex. If we create a metric that’s easy to calculate but not reliable, we run the risk of endless amounts of analysis trying to manage to a metric that doesn’t actually have a cause-and-effect relationship with our financial success. Great metrics might require more complicated analyses, but accurate, actionable information is worth a bit of complexity. And quality metrics based on complex analyses can still be expressed simply.
7. Get comfortable with probabilities and ranges
   When we’re dealing with future uncertainties like forecasts or ROI calculations, we are kidding ourselves when we settle on specific numbers. Yet we do it all the time. One of my favorite books last year was called “Why Can’t You Just Give Me the Number?” The author, Patrick Leach, wrote the book specifically for executives who consistently ask that question. I highly recommend a read. Analysts and decision makers alike need to understand the of pros and cons of averages and using them in particular situations, particularly when stacking them on top of each other. Just the first chapter of the book Flaw of Averages does an excellent job explaining the general problems.
8. Be multilingual
   Decision makers should brush up on basic statistics. I don’t think it’s necessary to re-learn all the formulas, but it’s definitely important to remember all the nuances of statistics. As time has passed from our initial statistics classes, we tend to forget about properly selected samples, standard deviations and such, and we just remember that you can believe the numbers. But we can’t just believe any old number. All those intricacies matter. Numbers don’t lie, but people lie, misuse and misread numbers on a regular basis. A basic understanding of statistics can not only help mitigate those concerns, but on a more positive note it can also help decision makers and analysts get to the truth more quickly.

   Analysts should learn the language of the business and work hard to better understand the nuances of the businesses of the decision makers. It’s important to understand the daily pressures decision makers face to ensure the analysis is truly of value. It’s also important to understand the language of each decision maker to shortcut understanding of the analysis by presenting it in terms immediately identifiable to the audience. This sounds obvious, I suppose, but I’ve heard way too many analyses that are presented in “analyst-speak” and go right over the heard of the audience.

9. Faster is not necessarily better
   We have tons of data in real time, so the temptation is to start getting a read almost immediately on any new strategic implementation, promotion, etc. Resist the temptation! I wrote a post a while back comparing this type of real time analysis to some of the silliness that occurs on 24-hour news networks. Getting results back quickly is good, but not at the expense of accuracy. We have to strike the right balance to ensure we don’t spin our wheels in the wrong direction by reacting to very incomplete data.
10. Don’t ignore the gut
    Some people will probably vehemently disagree with me on this one, but when an experienced person says something in his or her gut says something is wrong with the data, we shouldn’t ignore it. As we stated in #3, the data we’re working from is not perfect so “gut checks” are not completely out of order. Our unconscious or hidden brains are more powerful and more correct than we often give them credit for. Many of our past learnings remain lurking in our brains and tend to surface as emotions and gut reactions. They’re not always right, for sure, but that doesn’t mean they should be ignored. If someone’s gut says something is wrong, we should at the very least take another honest look at the results. We might be very happy we did.
11. Presentation matters a lot.
    Last but certainly not least, how the analysis is presented can make or break its success. Everything from how slides are laid out to how we walk through the findings matter. It’s critically important to remember that analysts are WAY closer to the data than everyone else. The audience needs to be carefully walked through the analysis, and analysts should show their work (like math proofs in school). It’s all about persuading the audience and proving a case and every point prior to this one comes into play.

The wealth and complexity of data we have to run our businesses is often a luxury and sometimes a curse. In the end, the data doesn’t make our businesses decisions. People do. And we have to acknowledge and overcome some of our basic human interaction issues in order to fully leverage the value of our masses of data to make the right data-driven decisions for our businesses.

What do you think? Where do you differ? What else can we do?

How to achieve FAME in analysis

In retail, and in web retail in particular, we are drowning in data. We can and do track just about everything, and we’re constantly pouring over the numbers. But I sometimes worry that the abundance of data is so overwhelming that it often leads to a shortage of insight. All that data is worthless (or worse) if we don’t produce thoughtful analysis and then carefully craft communication of our findings in ways that enable decision makers to react to the data rather than try to analyze it themselves.

The most effective analyses I’ve seen have remarkably similar attributes, and they happen to work into a nice, easy-to-remember acronym — F.A.M.E.

Here, in my experience, are the keys to achieving FAME in analysis:

Focused

Any finding should be fact based and clear enough that it can be stated in a succinct format similar to a newspaper headline. It’s OK to augment the main headline with a sub-headline that adds further clarification, but anything more complicated is not nearly focused enough to be an effective finding.

For example, an effective finding might be, “Visitors arriving from Google search terms are converting 23% lower than visitors arriving from email.” An accompanying sub-heading might further clarify the statement with something like, “Unclear value proposition, irrelevant landing pages and high first time visitor counts are contributing factors.”

All subsequent data presented should support these headlines. Any data that is interesting but irrelevant to the finding should be excluded from the analysis. In other words, remove the clutter so the main points are as clear as possible.

Actionable

Effective findings and their accompanying recommendations are specific enough in focus and narrow enough in scope that decision makers can reasonably develop a plan of action to address them. The finding mentioned above regarding Google search visitors fits the bill, and a recommendation that focuses on modifying landing pages to match search terms would be appropriate. Less appropriate would be a vague finding like “customers coming from Google search terms are viewing more pages than customers coming from email campaigns” accompanied by an equally vague recommendation to “consider ways to reduce pages clicked by Google search campaign visitors.” Is viewing more pages good or bad? Why? The recommendation in this case insinuates that it’s bad, but it’s not clear why. What’s the benefit of taking action in quantifiable terms?

Truly actionable analysis doesn’t burden decision makers with connecting the data to executable conclusions. In other words, the thought put into the analysis should make the diagnosis of problems clear so that decision makers can get to work on determining necessary solutions.

Manageable

The number of findings in any set of analyses should be contained enough that the analyst and anyone in the audience can recite the findings and recommendations (but not all the supporting details) in 30 seconds. Sometimes, less is more. This constraint helps ease the subsequent communication that will be necessary to reasonably react to the findings and plan and execute a response. Conversely, information overload obscures key messages and makes it difficult for teams to coalesce around key issues.

Enlightening

Last, but most certainly not least, effective findings are enlightening. Effective analyses should present — and support with clear, credible data — a view of the business that is not widely held. They should, at the very least, elicit a “hmmm…” from the audience and ideally a “whoa!” They should excite decision makers and spur them to action.

————————————–

The FAME attributes are not always easy to achieve. They require a lot of hard thought, but the value of clear, data-supported insight to an organization is immense.

The most effective analysts I’ve seen achieve FAME on a regular basis. They have a thorough understanding of the business’ objectives, and they develop their insights to help decision makers truly understand what’s working and what’s not working. And then they lay out clear opportunities for improvement. That’s data-driven business management at its best.

What do you think? What attributes do you find key in effective analyses?