A Legacy of Lean Thinking

This article is a reprint from an athlete that I have had the pleasure of training over the last 10 years. She is one of the stars of the Back-to-Back Virginia State Championship team that was the focus of my previous article Trust the Process – Winning is the Result. It is awesome to see how implementing a culture of Lean Thinking into training not only helped our athletes on the field but, it is shaping them for success off of the field.

Reprint from https://www.linkedin.com/pulse/how-data-analytics-helped-my-team-win-two-predictions-kaplan/

How Data Analytics Helped my Team Win Two Championships—And Predictions for Much More

• Published on November 24, 2019

Courtenay KaplanNCAA Division 1 Athlete at Radford University1 article Following

Prior to my first graduate-level course at Radford University, I had no real hands-on experience with data analytics. The idea of using codes and equations to compile data into meaningful sets intimidated me as I was unfamiliar with the process and unsure of the skill level needed to complete these computations. Yes, I took the required information technology class during my undergraduate classes to teach me on how to organize and clean up the data I was given, but I had no idea about the countless opportunities to group, sort and analyze the data in ways that could be useful to taking future action. Luckily, I was able to start my master’s degree in marketing this year and enrolled in business analytics course.

Throughout the semester I started to realize the impact that analytics can have on any operation and how useful it can be if performed correctly.

Not only am I a student at Radford University, but I was a goalkeeper on the Women’s Soccer team for the past four years. During my junior year, my team was fortunate enough to qualify for the championship game in the Big South Conference. While we were preparing for the match, our coach showed us film on the opposing team. Through the program InStat, we were able to analyze statistics about the opposing team such as how many of their shots landed on the goal, how many shots came from varying distances, as well as what percentage of their attacks came from either side of the field, to name a few. The statistics that were laid out were completed through analysis of the team’s entire season and their performance. With these insights, our knowledge of the team grew tremendously, allowing us to predict their attacks or at the very least, be aware of their strengths and weaknesses. This awareness allowed us to form a strategic plan to defeat the opposing team. I believe it even helped me stop a penalty kick in the championship game. We watched every goal their star striker had that season. We knew which foot she favored, as well as which side she consistently shot her penalty kicks to. I knew which direction to dive based on analysis of the statistics we gathered. I can’t imagine heading into a game without these analytics. It would be the equivalent of not studying for an exam or prepping for a presentation. And, yes, we won the championship! In fact, we won back to back championships and I believe statistical analysis played a large part of our success.

The impact that analytics had on my team was a key factor to our success. As a collegiate soccer team can be compared to any business, aiming for wins or in other terms profits, while attempting to avoid losses, or costs. If applied correctly, the impact can shape the course of the organization’s future in a constructive way. As my coach prepared for the following season, he reviewed our own team’s analytics that were summarized in a simple manner so that whether you were a freshman or a senior, the technical jargon of the data was simplified.

Another major impact that analytics can have in the soccer world is throughout the recruiting process. Many young players across the nation are constantly trying to find new ways to stand out and become noticed by top schools. If soccer clubs around the nation were to upload their film to the database such as InStat, each of their players that are looking to play at the next level, could have analyses of their play at the ready for prospective college coaches. Many collegiate coaches are often too busy to watch lengthy highlight tapes of your best players over the course of a year or two. However, if you showed them the statistical analysis of how many shots per game were on goal as well as how many of those shots resulted in a goal, they would start to notice that numbers don’t lie. It would not only make it easier for players to get noticed but assists the coaches in finding consistently well-rounded players. They can compare the statistics of prospective players much easier by having data that is mainstreamed and centralized in one source. The coaches could also see how your play has changed over time to track improvement and consistency as those are driving key factors in finding a solid player.

If soccer club associations added this asset to their teams, I predict that the level of play at the organizations would also improve. Prior to having this program, I would reflect on my play and assume I did well if the final result of the game was in my favor. However, that is not always the case as I review the film from a previous game and see all of the action I had and how I dealt with it. I was able to decipher what my weaknesses were and what my strengths were in any particular game and take that feedback into the following weeks training sessions. Instead of covering all aspects of my play, I was able to focus my training sessions on areas that needed help, making my use of time more productive so that I could be a more effective goalkeeper. This change in training was crucial to a successful season as not only am I reviewing the film from previous games, but so are my competitors. This technology was pushing not only myself, but my teammates to a higher standard.

My mom is an elementary school teacher in my hometown and uses analytics to help drive her instruction. Formative and summative assignments are given throughout each unit of study to help inform her teaching. Formative assessments are benchmarks or checkpoints to make sure understanding is occurring. If formative assessments show students are not learning the material, then my mom knows to adjust her teaching. Additionally, throughout the year students in my mother’s second grade class take a nation-wide exam that evaluates their reading and math skills. The exam is administered three times a year to track the student’s growth and knowledge progression to ensure the teacher is adjusting their lesson plans for every student. These benchmarks give insights into how much information each student is retaining as well as their projected growth. Prior to these analyses, teachers were instructed to teach the curriculum as it is, whether they had excelling students who were bored or struggling students who were frustrated. Data analytics encouraged adjustments in teacher’s methodology as well as revealing insights on how far a student has come over years. I believe that this is a crucial addition to the education industry as this is such a critical age for learning and development in children. I predict that the future holds even more individual programs and lessons that match the student’s preference. The personalized learning programs would be focused on their interests and skill level on a nation-wide scale. The use of technology to help evaluate these students will be key as many young students are starting to become more familiar with technology and the advantages it holds in education. If students had their own personal tablets to complete interactive activities rather than standardized worksheets, it may reveal more useful data.

I predict that business analytics will be incorporated into not only into the bigger, long-term decisions of the organization, but into the small day-to-day operations as well. Organizations will start to incorporate trainings for all levels of the management to be familiar with the use of analytics in their specific job duties and how it can help them improve their procedures, productivity or efficiency. Schools will encourage and create new ways to incorporate business analytics into their courses, earlier on in the student’s education. This will give students confidence in data processing and analytics as well as experience with popular programming systems so they can move confidently in the analytics world. I also expect that the next surge of graduates will have data analytics as one of their expertise that they can bring to their job prospects. Due to the integration of business analytics into more day-to-day operations of organizations, there will be growing demand in graduates who have experience in data analytics. Rather than standing out with experience in data analytics, it will become an expectation upon graduation that you are familiar with analytics, if you want to compete in the job search.

Data analytics holds the power to insights that many organizations, such as schools or sports programs, can utilize to streamline their strategic plans in a productive manner. The key is to hold the data in a centralized and organized system to make the analysis easier as well as have employees that all understand the basics of analytics. The base knowledge will assist the organization in a unified effort of asking perceptive questions. These questions may lead to data queries that hold truths that are yet to be found. I’m excited to see in what other ways data analytics can assist coaches, teachers, professors, students, athletes and many other people who are aiming for success.

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The RPA Myth

As I watch client after client move toward the promise of Robotic Process Automation (RPA) as a solution to their problems, I am amazed at their disregard for the small print.

For over a hundred years organizations have been automating processes. Shortly after the first process was automated, it was noted that automation does not improve a process, it simply gives you more consistent errors in less time.  Although this realization seems obvious when you sit back and think about it, many organizations believe that launching into automation without process optimization will solve their problems.

Beyond common sense, a quick web search will  reveal article after article that support process improvement before automation.  From the Harvard Business Review to one of the leading RPA companies, industry thought leaders are documenting issues related to implementation before improvement.

So, how do we change the current dynamic?

1. Get Serious about Systems Thinking: Both IT experts and process experts claim to be advocates of Systems Thinking, yet both fall into one dimensional thinking as soon as projects start.  RPA is a blending of process and technology.  It is time to truly leverage systems thinking in design, deployment, and operation of technology.
2. Integrate Existing Capabilities: Integrate Agency Performance Improvement Officer (PIO), Continuous Process Improvement (CPI), and Software Development capabilities into a process automation center of excellence that manages integrated process automation and management work cells.  In doing so, ensure an agile approach is developed that borrows relevant methods from Lean to ensure alignment with value and detection of waste and with Six Sigma to ensure processes are properly measured, controlled, and continuously improved.
3. Get Leadership to Accept Transparency: Agency leadership must embrace transparency, understanding that everyone is going to show both good and bad numbers.  Leadership must accept that numbers showing poor performance are not an indictment; they are an opportunity to improve.  Numbers that are always in the green mean they are probably not pushing the envelope hard enough.  At the macro level, an enterprise can be connected at the key stroke level creating an opportunity for performance measurement like never before, but that will not happen if Leadership is afraid of the numbers.
4. Get Real about Risk Management: With great power comes great responsibility.  RPA presents an opportunity for incredible breakthroughs in operational performance and the opportunity to allow humans to focus on innovation and other value adding activities.  The risk is that the robots make errors, very fast, causing serious problems.  Agencies must implement industrial grade risk analysis and management to overcome leadership concerns and to mitigate the risks of serious failure in deployed bots. 

Possibly unlike any technology trend that has come before, RPA presents a critical need for process improvement prior to automation.  As eluded to above, there are numerous risks inherit to having intelligent software conduct transactions in place of humans.  Consider that most tasks completed by humans generate some degree of defects, but they generate them at a pace humans can detect and remediate.  With RPA, these mistakes will be generated and a pace imperceptible by humans.  Imagine if these mistakes are things like amounts on invoices or late fee calculations.  Massive numbers of these mistakes will accumulate without detection  costing organizations millions in losses and litigation.  Alternatively, RPA projects could and should use proven process engineering methods such as Lean and/or Six Sigma to first improve processes into an effective model the generates desired outcomes with quantifiable external and internal performance metrics.  An improved process defined in this way will ensure alignment with organizational value streams and allow instrumentation and reporting of procedural performance.  Ideally proven process monitoring techniques and tools will be used to automatically analyze the massive amount of data generated and hence identify either negative trends or defects before they are out of control.

For those familiar with the use of tools such as Lean and Six Sigma in conjunction with process automation, you know that this approach is not only effective, it is actually easy and usually leads to a reduce project time line from idea to full deployment.  Sure, one can get to an initial deployment by avoiding process improvement and diving straight into automating processes as they are defined by stakeholders, but the cost is multiple iterations of poor performing releases extending the achievement of a stable and fully deployed solution.

So, if you are looking at Robotic Process Automation as a solution, or your RPA efforts are not achieving the results that you expected or were promised, ask yourself if you have an integrated solution and if the problem is the processes or your program. Remember, there is never a silver bullet and that stove piped approaches rarely work.