Context Matters

Numbers have an important story to tell. They rely on you to give them a voice. – Stephen Few

“Are we on track?”

I’m sitting in a small, white-walled conference room.  On my left, a half wall of windows and on the right a row of closets, each packed with office supplies, leftover computer equipment remnants, easels and giant Post-its of past brainstorms wrapped tightly for posterity. The trees outside are blooming and I’m thinking about the upcoming weekend. My boss’s voice cuts through my reverie, “Are we on track?” she asks again, “Are we going to hit the sales target or not?” 

It’s the most common question a Project Manager receives.  In every role, in every industry, in every company I’ve ever worked for, I’ve found myself in this moment.  There is a goal, a sales target, a production target, or a revenue plan. There is always a number we are trying to achieve by a specific future date. We were constantly trying to calibrate, and make a judgement. Are we good? Is there a problem? Is there too little, too much, not enough? What action must be taken? Stay the course or change direction? On that particular spring day, I’m young and inexperienced, and I have absolutely no idea. Zero. I have no words to say to the five faces staring back at me.

In my imagination, I see myself standing up, walking breezily, with ease and authority to the head of the table. A brilliant strategic solution cosmically materializes within me, and I articulate it to the audience with gravitas and aplomb. The crowd breaks out into wild applause, the project is saved, and I’m hoisted onto their shoulders and celebrated as a savant.  Tomorrow I’ll be fast-tracked to the C-Suite, to an office with an actual door, my daily responsibilities elevated to “thinking hard about tough problems.”  The team implements my every suggestion to resounding success. The business grows, we become millionaires.

Of course this doesn’t happen.  Instead, I squirm in my seat under their expectant glares and wait for someone else to speak. The Sales Manager chimes in, “We’re working on that big contract for next quarter and this week’s orders are up. I think we’re fine.” Love that guy, always selling, always optimistic! But then, our Distribution Manager chimes in, “Sure, this week is up, but the first half of the month was so slow we barely shipped any product.” Can both perspectives be true? Maybe, but does either answer our question?

We continue to stare at each other, unsure how to objectively react to these subjective assessments. After a significant awkward silence, an idea forms in my mind. “What does the data say? Can I have a few days to dig in?”  I have no clue what the answer might be, but I’m skeptical opinion based discussion. We’re making decisions based on feelings, not facts. It feels squishy and imprecise. Maybe I can find some facts.

The most vital component of any analysis is the quality of the data going into the model. If your data is incomplete, or inaccurate, so too will be your conclusions. Disciplined data entry, regular validation, these practices are imperative. You may not have every detail you’d like, which means you will also have to get comfortable making assumptions. Assumptions are useful tools when clearly stated and applied to your models with discipline and consistently.

I came to see that the value that I could bring to the conversation was my knowledge of how to extract meaningful data from our systems. As the customer service associate, I was in the system every day, every hour almost.  I knew by heart every category of data captured with each transaction. I also knew how to export it out of our ERP system and into spreadsheets where it could be manipulated and organized. Organized data becomes useful information.

Thank goodness for Microsoft Excel. My first data class at IU, K201, was an introduction to the software and to the conceptualization of data organization in terms of rows, columns, arrays and formulas. A large lecture hall, a heavy manual, and a professor that clearly connected the theoretical examples of how we might one day use this tool out in the real world. This class was gold, worth every single tuition penny I paid. It was one of my favorites; visual, hands-on, practical but most of all: objective.

Armed with these simple tools I set out to answer our question, Are we on track?

My first visual assessment was rudimentary. It began with a simple bar chart, displaying actual sales values for the past six months. This was nice, it was interesting to see the small variances from month to month, but they were all consistently in a fairly narrow range. My logic followed, okay, if we consistently sold this amount it stands to reason that we will consistently continue to achieve this level of sales into the future.

So, I added a different color bar for the future six months. Not very exciting yet, status quo going forward, I have no information indicating that anything will change. But my question isn’t answered yet, “Are we on track to hit our target?”  To see that, we need to switch our view from month over month discrete values to a cumulative sum. We’ll add actual to projection, month over month, calculating the sum total that will be achieved at the end of the year if we continue forward at our historical rate. Now we are getting somewhere!

Oh no. I’m this case, if we do nothing different, if we maintain the pace achieved to date, we will in fact fall short of the goal. We are not on track.

But now a new question emerges. What rate of sales would have to be achieved to meet the goal? Starting from the target, I back out the actuals and calculate the required rate. Now I have two projections in view, first showing our current trajectory and the second a theoretical model of what must be true to hit our targets.

But what about that big contract? If we assume that is awarded next month, does that bridge the gap? Turns out that it depends on what planning assumptions we apply. I clean up the slides and call the team back together.

I’m nervous. With my simple bar and line charts displayed on the wall behind me, I walk the group through the construction of the projection. I state my sources, explain how I verified that the data pulled from the system was accurate, and deliver the message, “We are not on track, but we can get there with the following plan. First, that new contract needs to be accelerated from next quarter to next month. Next, we need to minimize their ramp period to ensure they achieve full expectations this year. We should also ensure there is another contract just behind, as an insurance policy in case this one evaporates.”

Silence as the group pauses. I field a few questions and confirm the assumptions. The conversation then progresses. We are no longer hypothesizing what might happen, the future is modeled in my projection on the screen. Now we are discussing tactics, exactly how we are going to press the negotiations and who we are going to target next.

This view of the future isn’t the rose-colored positive extreme from the sales team, over-reliant on one deal. It also isn’t an overly conservative scenario extrapolated from the highly visible days that the truck shipped almost empty. It isn’t a gut feeling or based on hope and a prayer.  This view is pragmatic, realistic, and supported by unequivocal objective analysis. The results are action oriented, specific, and directly connected our desired result.

After the meeting, I’m pulled aside by my boss. “Thank you,” she says, “Without visibility to the path we were on, we couldn’t agree on what actions should be prioritized.”  It was gratifying and validating. The analysis I performed enabled decisions. If I can continue to provide that service, I will provide value to the organization.

I built a career on this concept: Make data visible, enable objective decisions and drive action. The inputs changed, the targets changed, and the software changed but the approach remained the throughline. My Excel skills have grown stronger over time; I sometimes talk to myself in If/Then statements when working through a thorny problem. I visualize data tables, matrices of data values organized by row category or columns. I value disciplined data entry, with consistent abbreviations and formatting. Sort, group, insert pivot table, insert chart, refresh, and resize. I have a toolbox of formulas stored away in my brain, and that giant K201 manual still sits on my bookshelf.

In the future, when I moved into a formal Project Management role and my Excel projections evolve to timeline estimates in the form of Gantt charts. Project stakeholders want to know if the project is on track. Everyone wants to know if the project is on track. It’s the same question, answered with slightly different data.  Rather than working with sales or cost values, the unit of measure itself becomes time.

The Gantt chart organization is like converting the discrete month over month values to a cumulative sum. The tool considers the duration and sequencing logic for every required task and then projects when the project will complete. Every project starts with a plan and a target completion date. Throughout execution you will collect actual start and completion dates for every single line item. Disciplined data collection through project execution is required. Garbage in, garbage out. Actual dates plus projected dates, compared against baseline plan and target; this is your model. The Gantt is the intuitive, visual representation of this data. The magic is in the relative comparison; are you on track? 

#Objective