Why every Product Manager should know Machine Learning

Machine Learning (ML) is becoming ubiquitous and every single product out there is attempting to use some flavor of Machine Learning to better address customer problems and delight them. ML is no more a fad and it is not restricted to those familiar use-cases of image recognition, page ranking, spam detection, autonomous cars etc. Appropriate use of ML algorithms is essential to differentiate every product and deliver better value proposition to its customers.

Machine learning is evolving faster than any other technology and it has the power to break new grounds creating more opportunities through providing solutions to problems that were evading us for a longer time. Even products that are not entirely ML dependent, where it already addresses a certain problem, harnessing ML can help the product better address the same problem. ML is soon becoming the de facto technology for every product and its Product Manager, ML offers only two choices (1) Embrace ML effectively or (2) Fad into oblivion. Ideally speaking, there is only one option to pick.

In this entire conundrum of how to embrace ML, can ML offer an effective solution and what are the other alternatives, how ML is better than those alternatives, how to validate the efficacy of ML, and what is the real value delivered to customers? What do you think will be the role of PMs? Please be aware that the context of this blog is to products that are not entirely ML dependent unlike autonomous cars, speech recognition etc.

Great PMs should be tremendously good at defining problem — What is the problem that the product has to address. Great PMs spend time identifying a problem that is worth addressing. Focus on technology and solution is always secondary. Technology always evolves and so does solution. Identifying the right problem provides the right start to identifying the right technology to delivering the right solution. PMs should focus on identifying the problem and it is entirely the responsibility of engineering to identify right flavors of ML based on the problem statement provided by PM.

Then… Why am I bragging about PMs being aware of Machine Learning? I foresee three reasons for it.

Even though, PMs should focus more on the problem and less on the solution and PMs need not play a role in drafting HOW to address the problem using ML, PMs should definitely have the technology acumen to understand the HOW. Understanding of HOW will position PMs to unbiasedly evaluate how exactly ML is better at addressing the customer problem, does it really add significant value. Incorporating ML does not automatically bring results. There is no magic behind ML. ML has to be harnessed in a right way with a right set of data and models to contribute the right value. Rightly so, It is the not the destination that is always important, the journey is also equally important to ensure that PMs took a notice of the efforts by the engineering team and not just the outcome. I always believe in rewarding efforts and not just outcomes. During the journey, PM should speak the language of engineering to comprehend their efforts and to assist them as well. Let me imagine a fictional conversation between my engineer and me after I asked for his assistance to help me predict product revenue based on earlier collected data.

Engineer: Hey Murali, I have used multiple models for classification problem. However, the bias is too high with all those models and we have a problem of underfitting. Guess we need more features.

Me: What are features?

Me: Oh, I have customers age and country.

Engineer: Is there a strong correlation between country and the revenue generated

Engineer: No, the data is not useful we need something else. Unless you provide additional data, I cannot build a good prediction model

Me: Completely lost in the jargons, not sure what data to collect. Notwithstanding, I do not know a dime about ML, I will start doubting the capabilities of my engineer.

Posted on 7wData.be.