What should I focus on as a Product Management Intern?

Eight years back, I answered this question on Quora and some have found the answer useful even today. Let me just reproduce the answer here:

A summer internship can last for a short duration like 10 weeks. The problem is 10 weeks is a very short time, but not too short to make an impact. Hence focus on the tactical areas of product management and very less on the strategic areas.

  1. You must try to do some data crunching – qualitative and quantitative and provide your recommendation on which features monetize better over the others. This helps the PM to be armed with factual reasons as to why a feature should or should not be built (invested)
  2. Benchmark the product against the competition. This will help you tremendously to get a strong foothold of the industry.
  3. Ask for the opportunity to prioritize bugs. This in some places is a dirty job because of the magnitude. This will tell you health of the software and also when you objectively prioritize you earn the respect of Engineering and QA – the value of which is worth its weight in gold.
  4. Participate as the honorary QA tester. You will know the product inside-out. You will also know the process, the politics and bureaucracy involved.

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Data Scientists & Surgeons

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A few years back my dad was diagnosed with inguinal hernia for which a surgery was recommended by the doctors. My dad was admitted to the hospital two days before the scheduled day of the surgery for pre-surgery tests as he was nearing 70 years. A parade of different specialists from the heart surgeon to anaesthetist visited him to make sure that he was fit for the surgery. The night before the day of the surgery, he was administered with enema to clear the stomach and was counselled to be mentally prepared as patients usually get frightened to go under the knife. On the day of the surgery, he was taken to the operation theater and was all set to be put under anaesthesia when the surgeon shouted, “What is his EF?” EF, short for Ejection Fraction, is a measurement, expressed as a percentage, of how much blood the left ventricle pumps out with each contraction. A normal heart has a EF of 55%, where as my dad’s was just 40%. The surgeon reminded that someone with a weak heart will have enough complications to come out of anaesthesia so much so he asked the team to abort the surgery! To arrive at this decision, you did not need a surgeon in the operation theater. The doctors could have determined this well in advance and not waste their own time as a surgery involves an array of para medics like nurses to work in a coordinated manner.

Cut to 2018. We were working on a relatively medium scale data science project which involved data sourcing from multiple vendors. The team had data scientists, product managers and data engineers.

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Modelling by Data Scientists is the third stage in the process and by then there has to be clarity on the manner in which the data is collected/sourced, the validation of data is complete and the data is made available for consumption in a fashion that data modelling can happen seamlessly. In this case, 8 weeks into the project, the Data Scientists unearthed a flaw in the way the data was collected for a particular data source. Some fundamental hypothesis we had could not be validated in the manner we expected because of the flaw in data. Although the project was salvaged in some manner, for me it was deja vu. The Data Scientist was akin to the Surgeon. The surgeon had to abort the surgery in the operating theatre where as the data scientist had to almost abort the project at the stage of modelling.

It is said that Data Scientists spend a lot of time in cleaning and reorganising data, but that falls in the realm of data engineers. Data Scientists also worry about the nature of the data collection but that again falls under the realm of Data Product Managers. If the Data Scientists have to increasingly do the cleanup of data, question the nature of the data collection and find basic flaws in the data, the product managers and data engineers are not doing their job. Don’t expect the surgeon to find out that the blood test appears wrong!

Image Credit: Almondbite3

Eisenhower Productivity Box for Product Managers

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President Dwight Eisenhower was the 34th President of the United States. It is said that Eisenhower had an incredible ability to sustain his productivity almost throughout his illustrious career and life. He is famous for his productivity strategy, now known as Eisenhower box. This is a simple 2 x 2 matrix combining importance and urgency. I have tried creating one for Product Managers based on my experience.

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Let us start with the second quadrant; the tasks which are important but not urgent. They are usually strategic and long-term in nature. You must schedule and plan them. One way to make sure that you do concentrate on second quadrant tasks is to schedule the tasks much in advance on a calendar accessible by all the stakeholders so that you have a deadline for yourself and you will pay attention to these tasks.

The first quadrant items are the ones which eat-up your time. You cannot avoid them, but if you have spent enough time on the tasks in the second quadrant, then the ones in the first quadrant will at least not be a surprise. For example, if you prepare collaterals for stake-holders much in advance, then you can avoid surprises during the time of product release.

The one in the third quadrant is something that is mundane, can be automated or delegated. These have to be done, so that it helps you to accelerate the tasks in first two quadrants, but avoid doing the tasks here yourself. For example, if you need to conduct user testing, gathering participants must be delegated after you review the guideline for gathering participants.

Finally, the fourth quadrant. Things you must stop doing. Depending on your seniority, the content of this quadrant changes.