Babis Marmanis, CCC’s Executive Vice President & Chief Technology Officer, recently wrote a post on LinkedIn titled The Product Management Practice, Part II, the second in a series defining a highly performing Product Management (PM) practice. Since joining CCC, Babis has strengthened its product management practice, resulting in award-winning solutions that simplify content workflow, data management and licensing. Here are a few key points from his original post. 

This is the second post in a series where I present my view on what a highly performing Product Manager (PM) looks like. My description focuses on the core competencies critical to being a successful PM and, as listed in the first post of the series, form five distinct categories: 

  1. People, 
  2. Analytical,   
  3. Operations, 
  4. Knowledge, and 
  5. Activities. 

Within these categories, I describe the competency as well as the skills required to attain that competency. These definitions can help product leaders set the right expectations with the PMs they work with. I also provide examples of expectations for the PM; however, you should define expectations that account for your special circumstances. The success criteria for a competency ought to be metrics built on the expectations associated with a competency.  

This framework can be used to:  

  1. Evaluate prospective candidates for PM positions, 
  2. Onboard new PMs by setting clear expectations and providing a framework for success, and 
  3. Evaluate your team’s performance and identify areas of strength to be celebrated as well as opportunities for improvement. 

The first post was about the People competencies category. In this post, focus on the Analytical competencies category.  

Analytical Competencies for PM Success
Analytical skills are essential for many roles. They are the qualities that determine how clearly, in what depth, with what efficacy, and ultimately how successfully we can describe and solve a problem. There are six key analytical competencies to consider for a PM 

  • Decomposition,  
  • Pattern Recognition,  
  • Representations,  
  • Abstractions,  
  • Algorithms,  
  • Composition 

Let’s take a closer look at each one of those related but distinct competencies. 

Analytical Competency: Decomposition 

Description: Decomposition refers to the ability to break a complex problem, or system, into parts that are easier to conceive, understand, and therefore solve for. 

Skills: Data representation and analysis, concept modelling, strong linguistic skills   

Expectations: Decomposition is closely tied to our ability to produce an effective problem formulation, which defines the essential elements of the problem that we are trying to solve. It is expected that a product manager can decompose quickly and effectively a problem into parts that can help him/her solve the problem. Successful decomposition leads to fewer changes in the model representation while enabling seamless extensions for growing the capabilities of a system. Hence, the number of changes that are required in the conceptual representation and the effort required to expand the features of a product are both key metrics of success.  

Analytical Competency: Pattern Recognition 

Description: In psychology and cognitive neuroscience, pattern recognition describes a cognitive process that matches information from a stimulus with information retrieved from memory. In our case, we are looking for the ability to recognize patterns in all sorts of situations that are pertinent to our product. 

Skills: The ability to identify concepts or workflows that are similar, as well as dissimilar. The ability to have a structured view of the world formed from data 

Expectations: A product manager is expected to be able to identify patterns related to business concepts and business processes, as well as in collected data that reflect things such as customer behavior, market trends, and so on. 

Analytical Competency: Representations 

Description: A representation is a conceptual reference framework that can be used to express an abstraction. One could use a graph representation to express the relationships between students and institutions. That representation is different from, say, a list of students with the information regarding their institution given as an attribute in the student record. The modeling of a problem is expressed through the representations selected 

Skills: Knowledge of representations for a range of abstractions. Ability to select an appropriate representation for a given problem. Ability to transform information from one representation to another. Ability to recognize isomorphic representations. 

Expectations: Understand the representations that are used today in current products. Analyze the representations for consistency, for completeness, for precision, and so on. 

Analytical Competency: Abstractions 

Description: An abstraction is a concept that acts as a common noun for all subordinate concepts, and connects any related concepts as a group, field, or category. Like representations, abstractions are important to problem modeling. In fact, they are the most important part of our analysis. If the abstractions are inadequate or erroneous then the likelihood of many other things going wrong is very high.  

Skills: Ability to analyze information and form appropriate abstractions. Knowledge of common abstractions. Knowledge of relationships between abstractions. Identification of similarities and differences between distinct abstractions. 

Expectations:  Understand the abstractions that are used today in your respective products. Analyze the representations for consistency, for completeness, for precision, and so on. 

Analytical Competency: Algorithms 

Description: An algorithm is a sequence of steps to transform the input of a problem to its solution. The computational complexity of an algorithm is the amount of effort involved in transforming the inputs to a solution. Software applications are, in effect, compositions of various algorithms; one for each specific task that must be performed by the system. An understanding of known algorithms allows us to evaluate the level of difficulty for solving a variety of problems. 

Skills: Knowledge of algorithms for solving common problems. Understanding the following properties of an algorithm: correctness, efficiency, and efficacy.   

Expectations: Review and understand the algorithms that are used today in your respective products.  

Analytical Competency: Composition 

Description: Once a problem space has been defined and its various parts have been identified, composition enables a solution to emerge from the synthesis of the respective solutions of its parts.   

Skills: Ability to form compositional solutions. Ability to view a space from different angles and at different scales. Ability to understand how different solutions interface and evaluate advantages versus disadvantages. 

Expectations: The composition of different elements will be reflected in the design of a solution to specific problem. Unlike the design, the composition of the solution elements does not require deep technical knowledge. However, it does require a thorough understanding of the appropriate abstractions and algorithms that would be used to compose a solution.    

Like People Competencies, Analytical Competencies are critical to the success of a product manager, and most importantly, to the products or services for which s/he is responsible. The ability to abstractly analyze, understand, represent and present information about the solution is absolutely required in this role. In the next post, I’ll address key considerations related to Operations and product management. 

I’m always interested in hearing other thoughts and ideas on this topic and welcome your input. 

Stay safe! 


Author: Haralambos Marmanis

Dr. Haralambos Marmanis is CCC’s Executive Vice President & CTO, where he is responsible for driving the product and technology vision as well as the implementation of all software systems at CCC. Babis has over 30 years of experience in computing and leading software teams. Before CCC, he was the CTO at Emptoris (IBM), a leader in supply and contract management software solutions. He is a pioneer in the adoption of machine learning techniques in enterprise software. Babis is the author of the book "Algorithms of the Intelligent Web," which introduced machine learning to a wide audience of practitioners working on everyday software applications. He is also an expert in supply management, co-author of the first book on Spend Analysis, and author of several publications in peer-reviewed international scientific journals, conferences, and technical periodicals. Babis holds a Ph.D. in Applied Mathematics from Brown University, and an MSc from the University of Illinois at Urbana-Champaign. He was the recipient of the Sigma Xi innovation award and an NSF graduate fellow at Brown.
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