Costco Toilet Paper is Soft on the Math

Bathroom. By Dean Hochman.
Bathroom. Photo courtesy of Dean Hochman.

Denominators make everything feel better, including toilet paper.  A really good denominator can help you figure out that you should not buy the bulk package of toilet paper from Costco.  That’s because the real estate you are storing it on is way too expensive.

To understand this, consider your cost of housing.  If you haven’t done so already, you should probably figure out how much you’re paying every month for each square foot of living space in your home.  For example, if your living expenses are $3,000 per month on 1,500 square feet, you’re spending $2 per month for each square foot.

The bulk package of toilet paper occupies four square feet of floor area, which represents $8 per month of storage costs.  The package costs $20 for 30 rolls that will last a family of four about two months.  That’s $10 per month for toilet paper, which seems like a bargain compared to about $15 per month you would pay for the package at a regular grocery store.  But your $5 of savings is sitting on top of $8 worth of real estate.  The unit-cost savings is less than the cost of real estate that it’s occupying.  The Costco toilet paper is just too expensive to forgive real estate cost that it’s imposing on you.

So, how does this relate to workforce analytics?

Appropriate Denominators in Workforce Analytics

Throughout the analysis of the value of your workforce, it is common to talk in numerators.  Number of people.  Salaries.  Benefits costs.  But what is usually more meaningful is to match up the numerators with appropriate denominators.  Number of people this year divided by number of people ten years ago (it’s usually not what you think).  Salaries per month during unfilled vacancies (actually a lot of money).  Executive compensation divided by organizational revenues (a drop in the bucket).  Numerators become more meaningful when you divide them by the right denominator.  And you must experiment and choose wisely.

With truly strategic business analytics, the biggest opportunity for novel insights is the blending of numbers from different strategic pillars.  You could have a finance metric divided by a human resources metric, such as capital invested per employee.  You could take a sales and marketing metric and divide it by people, such as revenues per salesperson.  Ratios from within a VP portfolio are often really easy to pull together because you can usually get them from a single database.  Once you have those easier in-house numbers figured out, it’s vital to get into the difficult metrics.

With the toilet paper example, it is the price of consumer goods are familiar to us as shoppers.  Then unit price is the next level of complexity, looking at price-per-roll.  You need to then seek information that is outside of the shop where the question was first posed, and in this example it’s housing cost.

The Story Changes When Better Denominators Are Chosen

One of my favorite experiences was a health & safety statistic about back injuries from over-exertion.  We knew that a large number of men over age 55 were pulling their backs from over-exertion.  But we discovered that there was a larger denominator of men over age 55, and that their percentage frequency of injury was lower than expected.  By contrast, those entering middle-age at age 45-54 had the highest frequency of these types of injuries.

When I was helping the client figure this out, I had personally pulled my own back at the gym at the age of 46.  I was in defiance about the fact that I was getting older, and trying to prove myself by lifting something that I should not.  I proposed to the client that those age 55+ are too wise for such foolishness, and those under age 45 can handle the challenge because they’re younger, fitter, happier.  I proposed a new interpretation; over-exertions are not about stand-alone physical vulnerability, they are about the disconnect between actual ability and self image, particularly in the social context.  The client liked it.

In order to rock it, each database had to be high quality, allow apples-to-apples comparisons, and have enough fields to break out the data by ten-year age cohorts.  These are critical intermediate steps, and not every organization is there yet.  What is important to notice is that as you improve your numbers, opportunities abound.  You can get stronger each time.  Nothing is so trivial that you can’t make it better with analytics.  And yes, you can afford the good stuff.  If you earn it.

How to Repurpose Leftover Turkey and Leftover Code

Turkey.  Photo courtesy of  Jeremy Keith.

Canadian Thanksgiving has come and gone, and several households are struggling with a conundrum.  What should you do with the leftover turkey?  There are downsides to having this carcass.  It hogs fridge space, you will be eating turkey for days, and some people just hate leftovers.  I know people who are tempted to throw the whole thing in the garbage.  But don’t. Leftover turkey is a great opportunity to whip up some butter turkey or turkey noodle casserole.

When there’s nothing left other than bones, it’s time to make turkey stock. Boiling down a turkey carcass into stock is one of the great wonders of household management.  While the stock simmers, filling your home with great smells, you can accomplish something else.

With workforce analytics this kind of thing happens all the time.  Once you get on top of a major headcount puzzle, you will have spreadsheets and a few pages of code that are available for more than one purpose.  Like turkey leftovers, be bold and repurpose them.

My favorite experience was when I built an entire hierarchy of jobs in order to identify when people had been promoted.  In large organizations it can be ambiguous which job movements are upward or downward.  Often, promotions are not categorized as promotions, especially if they change departments, leave and come back, or get a job temporarily prior to being made permanent.

To get past this obstacle we created a simple reference table that identified where someone was in a hierarchical career ladder, assigning a two-digit code to 1,200 job descriptions.  It was hard and tedious work that was entirely for the benefit of the back-engine of our promotions model.  But we eventually got the promotions model to work at a level of high accuracy, after which the client was able to use the information to influence high-level decisions.  That was the full turkey dinner.

Shortly after we finished this promotions model, we got new demands for work which took advantage of the back-engine.  Our happiest client was the one who just needed the list of rank indicators for the 1,200 job descriptions.  They needed to send emails to a small number of high-ranking people, and with our organizational complexity and some turnover at the top, it was hard to identify who was senior.  What they needed was a rules-based way of identifying who should get their emails.  Looking at our rank tables, they were able to choose seven rank categories and let the code do the work for them.  In the process they uncovered that one executive had been previously overlooked.  Now they were able to get the information out to the right people.

This client got the analytics equivalent of turkey soup.  They just needed the bones from inside — the promotions query — to be boiled down and combined with a few fresh ingredients to create a new, repurposed product that met their needs.

Do you have the opportunity to repurpose your own big wins?  That time you got on top of a major health concern, did you also develop healthy habits that improved other parts of your life?  If you overcame a difficult business relationship, did you also learn what your triggers are, and how to regulate them in future?  At the end of a big project, did you go for drinks afterward and end up with a few new friends?

Sometimes it seems like you’re just working hard to make other people happy.  But if you accomplished nothing in the last year except healthy habits, self-awareness, and more meaningful relationships, would you even recognize that this counts as success?

So put on your wool socks, turn the TV to your guilty pleasures, and curl up with that bowl of turkey soup.  It should feel good.  So take a deep breath and enjoy it.

Fold the Towels First

Towels, by Michael Coghlan
Towels.  Photo courtesy of Michael Coghlan.

This is a quick productivity tip for anyone who feels overwhelmed by the over-abundance of information and obligations.  Fold the towels first.  I first developed this metaphor when I figured out how to “get around to” folding the laundry for my family of four.  There was a big intimidating pile of laundry that I didn’t want to start working on.  So, I just walked up to the pile and pulled out all of the towels, folded them all, and put them away in about five minutes.  I came back to the pile two hours later, and it was about half as big as the last time I looked at it.  There, not so intimidating. Let’s finish the rest of this work.

Similarly, I was able to apply this metaphor to large volumes of errors in spreadsheets full of workforce data.  You see, there is a high likelihood that if you look at all of the problems you need to solve, there is typically one big problem that can be solved really quickly. Think of this as a strike-attack against the intimidation factor.  Just wrap up one big problem then step away from your desk for an hour or for the day.  Come back to your list of woes, and the remaining work should seem far easier.  It works with laundry. It works with big data. And, it could work for you.

Unsubscribe to your biggest spam provider, request a deadline extension on your most unreasonable task, ask for help with that thing that is beyond your ability, or send a courtesy note to that one person you’re worried that you might have offended. It doesn’t always work out this way, but when it does work, it’s incredibly empowering.

Too Much Choice Jams Your Style

Tea and Breakfast
Tea and Breakfast.  Courtesy of Britishfoodie.

Employers are becoming increasingly frustrated that they can’t find perfect job candidates.  And they can’t get perfect information prior to decision-making.  Yet there is an abundance of people and information.  What’s up?

The Paradox of Choice is a book and a TED talk by Barry Schwartz that describes the downside of having too much choice.  Researchers found that consumers presented with more choices in the purchase of jam reduced the likelihood they would buy any jam.  The more mutual funds an employee could choose for their pension plan, the lower the rate of participation in the plan itself.  In these abundant environments after we make a choice we end up less satisfied with our decisions.  It’s too easy to imagine a world where we could have done better.  It makes us miserable.

Schwartz recommends that we consider lowering our standards.  The concept of “sufficing” is key; that we should make choices that are good enough to meet our needs.  If you later discover you could have done better, don’t worry about it.

This attitude is critical to workforce analytics.  Trying to get that one quick hit of novel information should be enough for now.  Just keep the dream alive that you can make progress every day.  Become a little smarter, make a slight improvement, do a fist-pump, and then move on.  Lower your standards, cover more ground, and always move forward.

The Soil Cultivation Metaphor

Hand 1.  Attributed to David Pacey.

One of the greatest pleasures of home ownership is the opportunity to work in the garden.  Gardening is fulfilling for several reasons.  The accomplishment is satisfying and tangible, unlike a lot of office work.  Gardening is great physical exercise, involving a range of low-impact and core-intensive body movements.  Gardeners get time outdoors, bolstering vitamin D intake and exposure to fresh air.  By handling soil, our body develops a resistance to the germs and bacteria, or so rumor has it.  The work is solitary and meditative, improving mindfulness.  The home-grown and fresh-picked produce tastes better and is higher in nutrients.  Gardening is a kind of cure-all for wellbeing.

Soil cultivation is extremely similar to the practice of cleaning a data set.

My first round of soil cultivation was a patch of land that previously had a garden shed sitting on top of it.  There was no organic matter in the soil at all; just a dense patch of dust.  The soil required a major intervention to become useful.  This is also true about a new data set.  It’s great to have new data, but I just know it’s going to require a lot of attention before I can use it properly.  The data will lack clear labels, there will be columns or fields that are useless in some way, and some data points need to be converted.  Sometimes it simply arrives in the wrong format, such as on paper or ascii, or built around a different software environment.  The certainty that I must put work into it in order to get something back, turns this into “real” work.

With data I typically find that some fields are all wrong, and I need to track down or create “lookup” tables that convert the raw data into something that I know is accurate.  I don’t like throwing out data.  I prefer to just keep the dirty data on the left hand side of a spreadsheet, and to the right of a thick, vertical line, create a modified column or field.  The raw data is black, and the modified data is has color, so I know what I’m working with.  I also give the modified data more explicit labels, in succinct but plain English.  Many dubious data fields are suddenly rendered “accurate” by a good label.

As with my fully-remediated soil, my fully-amended data set means that I am ready-to-roll.  I can work with a converted and color-coded batch of cultivated data, culled of garbage and meaningless fields, and turned into something useful.

I know that some people think of a garden as a place where plants grow.  And some people think of data as something that is capable of producing analytic insights.  In both cases, there is something deeply human about taking a mess – soil or data – and turning it into something more.  We advance civilization one step at a time, one cubic foot at a time, one data point at a time.  Sometimes we just need to break a sweat, get some sun, work our bodies, and build immunity.