I attended the Kellogg Alumni event last week at the Facebook campus here in Bay Area. While I got the opportunity to meet some old friends and make some new acquaintances, I was thrilled to watch an expert panel discuss an exciting subject entitled, "Big Data does not Make Decisions - Leaders Do"; thrilled simply because the entire discussion was about what I live everyday in my work, and what I strive to achieve with my team and my organization.

The panel composed of highly accomplished Kellogg alumni (Facebook's CMO, Marketo's CMO, and a Senior Managing Partner at a leading venture firm) plus two of the world renowned Kellogg faculty. They all agree, and in fact strongly promote, that the managers as well as executives (CIOs, CTOs, and even CEOs) have to have some "working knowledge of data science" to be able to ask the right questions and steer the data science and data analytics teams in the right direction so as to solve most relevant business problems; that the right culture and the mindset have to be developed that Big Data (however you define it) by itself or the software and/or analytics tools by themselves are not the end solutions, but that it requires the leaders to recognize the importance of domain knowledge combined with the ability to to connect the market problems for a given vertical with the right set of data.

Another key agreement, closest to what I live and breathe everyday, is that "Product Management" is a critical role in bridging the data science world with the market/business requirements; that a good product manager is most essential to connect the business problems with the data science findings. It is observed that, even while there is a shortage of good data scientists, there is even more acute shortage of good product managers who understand the business domain as well as the data science well enough to make a successful connection.

As I often say: "Data Science is about Discovery, Product Management is about Innovation", the primary focus of a product manager should be the market-centric innovation. Data Scientists (and Engineers) are often driven by precision (and perfection), while good product managers will know the level of sufficient accuracy to take the product to the market at the right time. The three things I recommend that you do to be a successful product manager in the data analytics space are:

  1. Take time to learn the science: if you are looking for some quick learning, many resources are out there (webinars, MOOC classes, etc.) to teach you the subject; even paid training classes (2-day or 3-day) such as those from Cloudera are worth the time and money. If you can and are looking for longer term learning, many schools have undergraduate and graduate level programs in data science and analytics. A product manager with a strong grasp on the algorithms, models, techniques, and tools in data science will not only enjoy the support of the data science teams he/she works with, but also will be able to steer the product roadmap with application of the right models/techniques combined with right data set to the right business problems and market requirements.
  2. Learn by spreading the knowledge: it is given that the product manager have to play a highly proactive role in building the product with the help of data scientists, data engineers, data architects, and other stakeholders on all sides and also in taking it to the market. But, go beyond that expectation and play a highly interactive role in educating the stakeholders and the managers about the data science behind the analytics. Invite managers, executives, engineers, and others for a 30-minute seminar once in a while on a new data science topic; that in itself will be an enormous learning experience for you; after you present the data science model and technique, facilitate the audience to have a discussion on the model's possible application to various business use cases. As part of the lunch seminar series I have been doing at my company, I recently presented on the topic of "Hidden Markov Models" about which I did not have much prior knowledge; as I took upon the task, I did some reading, found the necessary R packages and relevant data sets to build the model and do the predictions for a real world use case; though the preparation took many hours over two weeks, when I presented I did not have all the answers for the questions from the intelligent people in the room (our CTO, solution architects, engineers, and other product managers) about nuances of the algorithm and the model; but that's fine, because the key outcome is that I learned something new and the dialogue made me richer as to how this model can be leveraged for a particular use case!
  3. Engage the cross-functional teams to stay ahead in innovation: the product marketing (for B2B) or consumer marketing (for B2C) and sales people may not attend your seminars for either lack of interest and/or background or they might often be on the field; but they are also key stakeholders in your ecosystem. Marketing and sales people are eyes and ears of the company as they provide the ground-level intelligence which are critical for formulating the company's strategies and tactics; engage them to tap into their field knowledge about what customers are really looking for; often customers and clients understand only tip of the data science and what they really need may be different from what they ask for; so, have the empathy and patience to help the stakeholders understand the difference between descriptive analytics (BI tools and other) and more advanced analytics (prescriptive analytics and predictive analytics with data science); make efforts to collaborate with the marketing teams to generate content, not too technical nor too superficial, to appeal and to be understood by a wider audience. All these efforts will help you garner deeper market knowledge to stay ahead, help you avoid drowning in the day-to-day necessary agile/scrum activities for near-term product development, and actually help build your mid-term and long-term product roadmap.

What are your thoughts and recommendations as a successful product manager in this space? Share with us....