We tend to think in nice round numbers, such as fives, tens, hundreds. Despite being a nice round number, 2020 will always be the year with the asterisk.
Researchers will seek to account for the social, economic, and political events of the year by assuming 2020 can be “normalized”. This is too simple a concept. If the economy can be represented as a factory that can be stopped and started, then concerns over 2020’s prospects are unfounded. However, this ignores the many activities that require multiple years to complete, such as capital programs, public services, or other planning and permitting activities. The challenge will be to see how activities with longer horizons perform during 2020. It may be many years to get to the new “normal”.
In talking about automation, emerging technologies, telecommunications, Internet of Things, we are witnessing the evolution of “Smart Transportation”. And in many ways, they are correct, but for something to be smart, the implications that the current system is “dumb”. I don’t think “Smart” describes the complexity of transportation.
Transportation began with a man/woman carrying something from Point A to Point B. Seeing everyone in the tribe carrying their own materials, someone says “We could carry more if we lashed the object to a pole”. Together, they can now effectively carry more. The concept of efficiency, per-unit costs, time, etc., became more manageable as people understood transportation allowed for the construction of structures, the movement of food products, minerals, and ideas.
From a technology perspective, we harnessed the elements: wind drove our boats, fire forged the metals that become bolt, nails, airplanes. Over time, we learned to understand risks creating commercial laws/traditions that supported the movement of goods and people. Eventually, humans learned to use boats, animals, sleds, wheels, air, internal combustion engines, etc., each innovation requiring new technological knowledge to be gained and shared. (The history of the wheel!)
In that perspective, in the year 2525, some critics may talk about how simple “our smart technology” will appear. In their mind, today’s “smart innovations” will be the future’s “dumb” system that needs improvement.
Data is an abstraction or a physical activity. When describing data we are measuring one element that may actually have multiple variables that influence its outcome.
On Monday, the LSU Tigers will play the Clemson Tigers for the College Football National Championship. Before, during and after the game, reporters, fans, and announcers will compare many metrics. They will discuss turnovers, first downs, penalties, etc. but the most common statistic (beyond the score) will be offensive yards. Offensive yardage represents many things: the quality of the offensive line (or its lack of execution), each coach’s play-calling, and the quality of the quarterback/receivers play. For example, Joe Burrow’s highlight against Georgia represents a 71 yard pass and that is all. The duration of the play, etc., are compressed into one small data point.
1st & 10 at LSU 20
(3:57 – 3rd) Joe Burrow pass complete to Justin Jefferson for 71 yds to the Geo 9 for a 1ST down
So, when you are watching the game, remember the announcers often describe an action by a single variable, one which is influenced by many things. And for some items, “data” fails to describe the variables that create these memorable moments.
We have all seen or heard this quote from Peter Drucker.
The focus on performance is a byproduct of a data rich world. Deploying “the internet of everything”, provides the ability to improve system performance at a greater degree of granularity if we all can agree upon the desired outcome.
A fan of slapstick/physical comedy, I always enjoyed this skit. Lucille and Vivian are unable to keep up with their chocolate wrapping assignment. They eventually “hide the evidence” that the system is failing, as their confidence turns to panic. (The woman manager actually created a perverse incentive, i.e., no unwrapped chocolates. To avoid being fired, they actually do a worse job than being truthful about their work, or the manager observing to see if they were preforming as expected.)
The manager saw the chocolates were gone. She was delighted, but did not understand the system’s real performance. One could argue that her measurement tools were weak, but her eyesight was sufficient to allow her to believe that no other testing was necessary, the objective was met, no unwrapped chocolates in the other room. Lucille and Vivian do not confront the manager. Their mouths are full of chocolates, thus agreeing to be overworked yet again.
So, when examining ways to manage performance measurements, industrial processing does a good job of discussing flow charts, etc., but it may not necessarily capture the ingenuity of the work bench! And this is where the second Drucker quote serves as a useful counterpoint.
But there may be a better quote… “just remember performance measures are like a box of chocolates.”
Many have postulated what generates transportation corridor development, especially regarding new service options. Often, these discussions involve many users seeking someone to help them solve “their” problem. For example, the shipper will want service alternatives that are reliable and/or at a lower cost than their current operation. Carriers want more cargo on their network. Public sector groups want to see more economic activity, expressed as freight traffic, through their region. (The same could be applied to intermediaries, such as labor, freight forwarders, brokers, etc.). There seems to be no single word that encompasses the “why” regarding how transportation services start and continue over time.
In organizing my thoughts on this topic, I came up with two alternative lists to distill what maybe needed for a transportation service to begin and remain successful. I really don’t know which list is better, so they are presented here for your consideration.
First, the 7 C’s. (I was thinking of something catchy. I think this works..)
Capital-It takes money to get something started. There are barriers to entry, costs of renting/purchasing equipment, etc., as transportation may require large upfront costs before the first shipment occurs.
Carrier-A carrier (or multiple carriers) must be willing to offer that service, possessing the right equipment, skills, etc. to satisfy a shipper’s needs.
Connectivity-The trade lane must service a network, or be tied to networks, so that the cargo does not stop at a midpoint. For example, there are many ports in the U.S., but not all are served by multiple Class I railroads. This could put these ports at a disadvantage for rail dependent cargos. (There are other connectivity issues related to pipelines, roadways, shipper locations, channel characteristics, etc., so don’t think I am only picking on railroads!).
Cargo-There has to be cargo operating in both ways (to spread out the revenue costs for the carrier) or someone is willing to pay for the empty movement, but cargo must be available and willing to pay for that freight service.
Collaboration-For the carrier, shipper and other engaged parties, the service must be seen as an important relationship, not a “one-off” item, to encourage shippers and carriers to be confident the service will continue into the future. This may also require a champion to ensure that everyone is working toward the same goal. (Yes, Champion is a “C” word, but in this context, it is a visionary pushing for collaboration.)
Costs-There is no free lunch. Costs must be set at a level where carriers benefit while shippers receive their desired service levels, and where possible, there are little significant cost on other users/groups.
Climate-Does the business climate support this service? Can the service handle any disruptions or adopt to changing conditions? Given discussions on resiliency, climate may be a good word when discussing risks outside of operational activity.
My alternative term is OARS (like row your boat?)
Operations – The right equipment, permits, labor agreements, etc., to make a transportation service run,
Assets – This category includes the actual transportation equipment and infrastructure (roadways, vessels, trucks, cranes, docks, etc.), and the labor (truck driver, train, customs, services…),
Reliable– Everyone has to commit to making the service “work”, where service risks are minimized, and revenue streams can be managed so that everyone benefits.
Support– Everyone involved understands their role, and works to ensure the cargo, equipment, service, etc., work as expected. In some ways, this final category may be the hardest to maintain over the long term as markets/costs, can change over time.
In reviewing these two lists, there exist many nuanced concepts, but one “C” word seems to be an unspoken, but vital, element: commitment. This requires a commitment to provide the service (carrier), use the service (shipper), and to support the service (public sector/other agents).
Summary:“Everybody Lies”http://sethsd.com/everybodylies was an enjoyable, fascinating book describing how understanding metadata about internet searches can provide information concerning people’s “true feelings, emotions, or opinions”. The book assumes people are more honest when they are anonymously seeking information. Reviewing those searches in aggregate provides information that social scientists may be unable to collect in other formats.
The Main Arguments
Researchers struggle to understand people’s behaviors, needs and their true opinions. In Part I, Data, Big and Small, the author outlines the need to frame social science research based on understanding big and small data. Using his grandmother’s dating advice was a great example of using Big Data (page 25). But there are cautions here, for we can pick and choose what observations we use in making those conclusions.
People will “lie” to researchers for many reasons, such as not expressing their true feelings to avoid judgement by the researcher. In this case, the use of internet searches, often done in private, can provide a way to better estimate broad trends concerning how people understand the world. The main section of the book, Part II, the Powers of Big Data, illustrates the disconnect researchers face when researching topics such as Sex, Hate and Prejudice, Internet, Child Abuse and Abortion, Facebook and Customers. Each topic gets an introduction concerning what people have studied, and how using internet search information can confirm, deny, or provide new insights into the topic.
Throughout the book, there were cautionary tells that having more data may not generate more/useful information or that not every belief can be quantified through the data. His discussion criticizing studies that would find “most Knicks fans live in the New York area” are useless. In Part III, “Big Data: Handle with Care“, the author begins the real discussion: big data can be a boon to good governance and addressing social needs. But the real caveat is that such needs may not be in everyone’s self-interest. There are questions that having more data could introduce more errors, such as Dimensionality, where the odds of finding a correlation between two elements is increased simply because there is just more data to find possible correlation.
Methodology, Evidence, and Context
The report was not an analytically oriented book, but the charts and tables were helpful in illustrating how we “lie to ourselves” when we consider our public disclosures (Facebook posts) compared to our private searches. I went to Google Trends to test a few searches, and it is a useful proxy concerning people’s interest in a topic by time and geography. The book presented, and footnoted, many studies, showing the author’s thoroughness, and would be a useful first document for additional research on some of these topic areas.
The book’s context and layout were very accessible, and the stories engaging. While I would have enjoyed seeing even more tables, charts, etc., such would have reduced the effectiveness of the work (and I could look them up with the references!) There are some graphics in the Ted Talk, which I found very helpful.
I enjoyed the comparison between himself and his brother regarding baseball. I am not a baseball fan, but my father loved football. Cultural references do shape experiences in ways we do not understand when we were children, but these items influence our adulthood’s tastes and desires.
I thought the best part of the whole piece was Chapter 8, Mo Data, Mo Problems? What We Shouldn’t Do, (especially after the A/B testing sections- scary that we are so easy to manipulate!) With more data, comes the assumption that “we” can do more. But does more data mean we have more actionable items, or do we simply have more confusion when making choices. The author mentions the Minority Report, the movie. When discussed in this context, the original story written by Philip K. Dick is even more horrific, as other PreCogs pick up the story at different points. Based on concerns with big data, there exist more ethical challenges that remain to be addressed concerning ownership of our physical and online identifies.
Finally, I liked the honesty of the “conclusion challenge”, especially after mentioning how Freakonomics influenced his professional interest in data research. Seth, if we ever meet, I will buy the first round in celebration of your success in writing such an accessible, fun, and most importantly, insightful book.
The older I get, the more I see this message true. It is easy to assume we are all experts. For a researcher, this is not a good attitude. We all know the one way to do any research activity (process, data, approach, etc.), but in doing so, we often forget the joy that comes from learning something new. It is in that learning, based on recommendations, comments, critiques, etc., that we grow as researchers. But it is in the teaching to others where we learn more.
As a young researcher working at the Port of Long Beach, I answered requests generated from the port staff. (As my time in Long Beach occurred before the Internet became the “knowledge search tool”, I had to understand what people needed and why they needed the information!) After plenty of, “This is not what I need”, “I wanted it like the report you did a year ago”, “how much do you spend on data purchases”, I realized that it was not only understanding their “question”, but knowing what intelligence they needed. So, I asked questions about their request (sometimes the light bulb takes a while to come on…). Surprising, once I took the time to question the requester, the better the research (more timely, focused, etc.) became. (There was a great discussion on the importance of questions by Hal Gregersen on “The One You Feed” Podcast.)
Disclaimer: The following assumes these are internally generated questions. While the same approach could be used for evaluating service consulting requests, there exist other program elements one would add beyond these questions.
The questions fall into four broad categories: Institutional, Skills, Costs, and Review. The Institutional category links the inquiry to the organization’s goals and values. One could argue these are the most important to know, for they outline what is expected, but I would argue they are not the only thing to assess. The Skills category is a self-determination about your ability to provide the answer, while Costs outline what (if any) additional resources may be needed. Finally, the last category is Review, i.e., what can I do better/different in my current work activities based on this request. (Rearranging the 4 categories results in RISC, an appropriate reminder of the possible consequences of bad/misinformed research.)
Institutional: The objective is to provide timely intelligence to support the organization’s mission. In many ways, knowing the right answer but for the wrong question does not help anyone, and researchers must guard against our own biases concerning what we think someone needs. I had to learn to ask the following questions:
Who needs this,
Who asked the question,
When do they expect an answer,
What are their expected outcomes (and by when),
Can you repeat their inquiry back to them in a clear, concise manner,
Will this require an internal review, and if so, who would do that work,
Will this intelligence be used internally or externally,
Who will review this work,
How important is this request when compared to other requests,
Into what format do you want the report (chart, text, etc.)
Is this question related to some legal request, requiring documentation, or following specific guidance goal,
While this require a presentation/training on my part when completed,
What level of confidence are they willing to accept, which can range from a rough guess to a high degree of confidence?
Skills: In many ways, this is the hardest category to consider, for one must be honest. Without this assessment, the researcher may needlessly expose themselves to having their work deemed less than acceptable over time. Some questions may include:
Do I have the time,
Do I have access to the data to complete the task,
Do I have the software/skills to complete the task,
Do I want to do this research,
What happens if I don’t do this,
Is this like previous questions I (or others) have answered in the past,
Can you repeat their inquiry back to them in a clear, concise manner,
Can someone else answer this question better than me,
Do I have the domain knowledge to understand the topic,
Do I need a collaborator,
Do I need some training to answer this question?
Costs: Sometimes there are costs associated with doing business researcher. Not all data is accessible in the format one needs, nor, as people believe is all information “free” on the internet. The researcher must understand the resource costs, but these may matter little to the person who generated the inquiry!
Do I need to purchase data/information services,
Do I need to get a license or right to access the data,
Do I need to purchase software or hardware,
Do I need to hire a consultant because I do not have the skills time or energy to complete this project is anticipated format,
Can I legally share this data, or does it have to be summarized, etc.?
Do I need to pay for training to respond to this request?
Review: After the work is delivered, sometimes it is helpful to review with the inquirer to understand how your research met their needs. And for any professional researcher, this is an ongoing query regarding “do I have the right knowledge to do my appointed tasks”. These questions may include discussions such as:
Will I be asked similar questions in the future,
Do you want to yourself/others to access this information directly without asking me,
Do you need training to access the data themselves,
Do you or I need more domain knowledge,
Did the information satisfy our organization’s needs?
So, what did I do once I better understood internal needs?
After a while, I started to see where most questions centered around “who was doing what where” and “were they successful”. Knowing most questions focused on certain topics, it was easy incorporate those queries into my ongoing data/market research activities. Ultimately, this lead to the development of the Port’s first maritime data mart by integrating PIERS into Oracle with many long-forgotten programs (such as Paradox and Brio). The datamart, using various scripts, generated quarterly market reports for Senior Staff. The information also provided specialized research studies for current or potential clients of the port concerning market patterns.
But people do not “understand the value of information”, something every researcher laments. When I was at the Port of Long Beach, Don Wylie, my boss, instructed me to include on every report “the data was developed by the Trade and Maritime Services using PIERS data”. The following year, there was no debate concerning renewing the PIERS data purchase, nor the value that the Trade Office provided.
In sum, asking the right questions, through a structured approach, can illuminate everyone’s expectations. This should result in more successful projects, while demonstrating the value of a robust internal research mission.
In October 2018, I made a presentation on the challenges of funding highways in Mississippi. As the Domino’s Paving for Pizza campaign started earlier that year, I suggested that Mississippians should only eat Domino’s pizza. This would be a win for everyone, Domino’s sells more pizza, people have better roads without having spent money on highway/vehicle related taxes. (I really liked the pizza/pothole meter, although think of what is happening to your car when you hit a pothole!)
Domino’s fixed two potholes in Jackson, but I am sure there are other potholes in Mississippi.
We talk about others being a legend in their own mind, although we like to think we are “Masters of Our Domain”. When it comes to data and analysis, that domain may not be a physical space, but the information and intelligence one manages/controls. For example, my background has focused on ports, transportation, and freight movements, resulting in my domain knowledge regarding international trade.
But there is more than simply being the Master of One’s Domain to be a solid researcher. One has to know how domain knowledge can shape a research question.
Let’s look at this exchange from “Monty Python and the Holy Grail “, where the troll asks three questions. One of the questions is fairly complicated. The King asks for clarification, based on the domain knowledge gained earlier in the film from two soldiers who possess the specialized knowledge of swallows.
The question concerning the average airspeed velocity of an unladen swallow may only interest researchers examining the physics of avian flight (or Monty Python fans here and here). But having learned something about swallows earlier, the King knew enough about the domain to ask for clarification (in this case, to delay), by asking about another data attribute.
Regarding the query, the question of the average airspeed reflects a question concerning a specific data element, but the second question was based on another attribute, namely the type of swallow. For most researchers, knowing that extra bit of information may make the difference between good research or great research, or in this case, who lives or dies. So, there remains a benefit to being the domain master, as King Arthur reminded Bedevere as they cross the bridge, but only if one learns not only new data but how to apply that information.