Implementing a nudge unit flywheel - Every data science shop needs Radar O'Reilly
Updated: Jan 27
How to think of the economics organization in terms of the flywheel effect.
The following is part of our article series: Companies Need A Nudge: Create a nudge unit flywheel to drive happy customers and business success
The TV sitcom M*A*S*H ran in the 1970s and was a funny show about an Army hospital unit in the Korean War. (In case you are wondering, I only watched the re-runs....) This show still holds records for being one of the most watched TV shows in history. What could data science possibly have in common with this TV show? Turns out, quite a bit.
But first, we explore how "incrementalism" significantly impacts a company's ability to scale. Then, in the section immediately following, we show how to solve incrementalism-related challenges with a nudge unit flywheel. We will show how Radar O'Reilly is foundational to your economics organization.
Incrementalism and the impact on data science
In this section, we provide an adaptation-limiting example of "incrementalism." Our example is from the banking industry, though, virtually all industries are subject to incrementalism. This occurs for 2 reasons:
A company did not start in a data-native environment, so transitioning legacy systems to a high-quality information-based strategy environment is challenging.
Data technologies change so quickly, that some companies may have started as data-native, but have fallen into incrementalism because of a failure to keep up with the latest data technologies.
Please consider this as a comparable industry example with which you are most familiar.
A banking example: Banks become big banks mostly through consolidations. The consolidation catalyst may occur from many sources:
Often a big economic downturn is a cause,
Sometimes law changes are a cause, (Think of the reduced interstate banking restrictions in the 1990s) or,
It may be the regulatory change is a cause, resulting in different scale economies. (Think of the CFPB requirements that kick in at the $10B bank asset size. Once a bank goes over $10B in assets, they need to add significantly more risk management infrastructure.)
The following graphic shows consolidations for some of the biggest U.S. banks from 1990 to 2010. Certainly, a similar consolidation trend exists for most U.S. banks. "Eat or be eaten!" seems to be the mantra.
So, what does this mean to data science in banking? In a word, banking suffers from "incrementalism." This occurs for a multitude of reasons, including:
Our human nature to think shorter term (i.e., Recency Bias),
SEC registrant quarterly reporting requirements encouraging short-term reporting and related short-term thinking, and
the consolidation norm specific to many industries.
In the data science world, data is the raw material enabling analytical success. Access to data is critical. Unfortunately, in the incremental organization context, data can be a challenge to locate, access, and utilize. This is one of my favorite relevant aphorisms about data:
Where is the wisdom?
Lost in the knowledge.
Where is the knowledge?
Lost in the information. - T. S. Eliot
Where is the information?
Lost in the data.
Where is the data?
Lost in the damn database! - Joe Celko
The '80 - 20 rule' for data science: Generally, in larger organizations, data can be very silo'ed in different operating groups, different operating systems (aka, systems of record), with various levels of care. Also, because of organizational incrementalism, acquired or some legacy company systems are not always fully integrated into central company systems. Today, with an increasing focus on information security, data accessibility is generally more restricted and may require special permissions. All this creates friction for the data scientist. Often, doing really interesting data analysis and driving actionable business insight is only about 20% of the data scientist's job, the remaining time is spent wrangling data and other administrative tasks. So, this is the data scientist's reality. Is it getting better?
Some days, yes --> better data warehousing, API's, or tool access occurs,
Some days, no --> the next wave of consolidations or more info security rules occur.
If you are in a data science group, especially groups focused on operational analysis and compliance testing, this reality is likely particularly acute. This occurs because you are closely tied to the operating system's data availability.
The compliance testing example: For example, Compliance Testing, especially specific to customer obligations, requires access to a core system of record data and documents. The gold standard is to directly test the customer's communication media (letters, texts, statements, online, auto agent, etc.) against the regulatory, investor, or related obligations. Because of organizational complexity, separate systems, third-party involvement, infosec requirements, etc; automation-enabled testing of customer media may be very challenging.
Please see our article AI and automated testing are changing our workplace for more context.
Building your nudge unit flywheel like a M*A*S*H unit
A practical solution to enhance data availability may be found in the following analogy. A M*A*S*H unit runs with a couple of primary operating groups. Those include the expert doctors, nurses, and orderlies that attend to the patients - think of Hawkeye or Margaret Houlihan. Also, the M*A*S*H unit includes leadership, like Colonel Blake or Colonel Potter. Naturally, economics-related data science shops also have both experts and leaders (the data scientists and the data science leadership)
So far, so good. But frequently overlooked in data science shops is the single most important factor to make a M*A*S*H unit run. That is, Radar O'Reilly. Radar is not just a company clerk, he is the grease that makes the M*A*S*H unit run. Radar is the one that knows how to get things done, knows all the Army supply sergeants, and that knows the company clerks at the other Army M*A*S*H units. As such, Radar knows where to get the raw material to ensure the M*A*S*H unit effectively operates. Radar knows his way around the Army and how to work back channels. In the context of data science, Radar knows where the data is, whom to contact to get the data, how to get the metadata/data dictionary/data ontologies, the nuances of the infosec rules, and how to stay ahead of the next big change affecting data availability. To me, asking an economist or data scientist to run down data is like asking surgeons to buy their own sutures.
The nudge unit flywheel
Integrating economics into your organization
For whatever reason, data science organizations do not always hire the Radar O'Reilly types. An essential attribute is that the Radar O'Reilly types are homegrown. These are often long-time employees that know the data, know the business, and know "where the bodies are buried" when it comes to finding data. They often have deep relationships across the organization. If I was starting a new economics-related data science organization in a big enterprise, my first hire would be Radar O'Reilly. Sure, I would eventually hire a crack team of data scientists, economists, junior analysts, business analysts, relationship managers, application engineers, and license Definitive Pro, Python, R, SAS, RPA / OCR engines, or related tools and storage. But Radar and building out my data organization would come first. Since it is hard to analyze something if your data raw material is elusive and regularly at risk.
The idea is to think of the economics organization in terms of the flywheel effect. [ix] A flywheel effect is NOT about a single momentous event, a single innovation, or a lucky break. The flywheel effect is about the business learning process. The ability to make smaller but cumulatively substantial good decisions via RCT-informed processes and leading to long-term profitable growth and scale. The RCT-based discipline and results are only the beginning. The economics organization is well-positioned to provide enterprise choice architecture and help the executive team make the best decisions. Your economics organization has the potential to be an organizational flywheel. A highly functioning nudge unit will be instrumental and necessary for company success. We provide the four primary nudge unit organizations in the earlier graphic. They are all important. Data and the Radar O'Reilly type are necessary flywheel catalysts.
Definitive Pro: For corporate and larger organizations - This is an enterprise-level, cloud-based group decision-making platform. Confidence is certainly important in corporate or other professional environments. Most major decisions are done in teams. Group dynamics play a critical role in driving confidence-enabled outcomes for those making the decisions and those responsible for implementing the decisions.
Definitive Pro provides a well-structured and configurable choice architecture. This includes integrating and weighing key criteria, overlaying judgment, integrating objective business case and risk information, then providing a means to prioritize and optimize decision recommendations. There are virtually an endless number of uses, just like there are almost an endless number of important decisions. The most popular use cases include M&A, Supplier Risk Management, Technology and strategic portfolio management, and Capital planning.
Next are a few whitepapers and examples of how to make the best organizational decisions:
Third-Party Risk Management: Getting the Most of Limited Risk Management Budgets
Mergers & Acquisitions: Moving from Haphazard to Fact-Based Decision-Making
U.S. Treasury Case Study: Defining the Decision-Making Process (Technology portfolio management)
Citations are found in the article: Companies Need A Nudge: Create a nudge unit flywheel to drive happy customers and business success