Author Archives: Vivek Vijayan

About Vivek Vijayan

A Technology Product Manager - I derive satisfaction when my product intelligently makes life easier for users.

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.

Feasibility Sprint for Agile Scrum

Sprint, the iteration, is the basic unit of development in Agile cSrum. Scrum puts emphasis on working product at the end of the iteration that is really done (see here for more on done)  something which is fully integrated and ready to be shipped. However, in my initial days of Scum adoption, whenever we started working on a new product-line, we never achieved what we planned for a sprint in some form – let alone something ready to be shipped. Sprint retrospectives revealed that at the start of a new product line or a major feature, the developers made a lot of assumptions on technical feasibility (like third-party APIs, hardware availability) which also affects how the software architecture is shaped. Mid-way of the sprint, we used to discover that some user-stories would be impossible and a few others would take a lot more time that we expected them to. The user-stories were valuable and useful – but did make a lot of assumptions on feasibility. The assumptions remained unverified during sprint planning resulting in a lot of disappointment for all the stakeholders. That led to the birth of feasibility sprint.

What is a Feasibility Sprint? 

A sprint which is designed to uncover the assumptions and lay the foundations for the sprints ahead with a clear understanding of what is possible and time it takes for something that is possible. The common understanding for this sprint was:

  • Time boxed like any other sprint; usually a bit longer than the usual 2-week sprints. For complex product lines, even a 6-week long feasibility sprint is most often worth the time.
  • “User” Stories revolve around testing complex assumptions in order to understand what is feasible technically in the prescribed eco-system and effort involved in making it feasible. Insist on demonstrable proof-of-concept even if it runs from the developer’s IDE.
  • Acceptance criteria: The sprint is deemed to be “done” if each user story can be classified into one of these: (a) Possible to build (b) Not possible to build (c) Possible to build with workarounds (d) Partially possible.
  • Sprint Review: The team “demos” the proof of concept and understands what is feasible and what is not.
  • After-sprint: The Product Owner will have to assess the impact of the sprint on the user stories and backlog by: (a) Re-writing or splitting user stories (b) Re-prioritising the product backlog (c) Communicate to the stakeholders on the shape the product will take if there is considerable deviation in the product feature or if the timeline is way longer than expected.

 

Challenges 

Feasibility sprint is usually an afterthought as stakeholders are optimistic about their assumptions and timelines. Only after burning the fingers in a short sprint does one realize that an item of a sprint needed some feasibility study. Senior team members must have the experience and maturity to identify an item that cannot go into the regular sprint.

If you are not doing frequent Feasibility Sprints in your organization, it might be an indication that you are aiming only for things which you have complete clarity about and taking almost no risks.

Postscript

Feasibility sprint has a close resemblance to Sprint Zero – which is more to bootstrap a Sprint process for a new product.

What did people search for “Product Management” in 2016?

We are past 2016 by a few hours now. I did this exercise of finding out what people searched about product management (primarily on Google) in 2016 like I did the last year. The queries searched were split into four categories:

  • Employment e.g. “product manager salary”
  • Informational e.g. “what is product management”
  • Skills e.g. “product management certification”
  • Tools e.g. “product roadmap tools”

category-split

Share in 2016 Difference from 2015
Employment 24% -9%
Informational 21% -6%
Skills 37% 16%
Tools 18% -1%

category-ad-split

Queries that saw a huge increase since the last period

“product management courses” 50%
“product management skills” 40%
“product management conference” 40%

Top queries under each category

Employment
“Product manager salary”
“Product manager resume”
Informational
“What is product management”
“how to become a product manager”
Skills
“Product management training”
“Product management certification”
Tools
“Product management software”
“Product roadmap tools”

Soliloquy

  1. Product Managers seem to have an increased interest in up-skilling themselves in the trade in 2016. What remains unanswered is if it is aspiring product managers who contributed to the increase or existing product managers who felt that they needed some sort of a discipline as a practitioner.
  2. In 2015, the employment category advertisement cost was cheap in comparison to other categories, that has completely changed in 2016. Recruiters seem to be shifting their budget significantly to search-engine marketing as that provides candidates with high intent.

Read what this report looked like last year for 2015.

Countering product sentiments within your organisation

Abraham Lincoln once famously said “Public sentiment is everything”. It applies to products even if it is a notch or two less than that for politics. Product Managers are expected to manage ‘user sentiment’ or ‘customer sentiment’ for the product that they manage.  However, the mettle of the product manager gets tested when the ‘sentiment’ is within the organisation – more intensely if people higher than the product manager’s pay scale support the sentiment. Continue reading

3 Questions to Product Manager Suzie Prince

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With ProductMantra today is a Product Manager whose passion is minimalism. With her minimalist philosophy she creates products that are valuable, usable, feasible and desirable. Presenting to you Suzie Prince, who at present is the Head of Product at ThoughtWorks Studios. Typical to our interview series, we asked Suzie three questions.

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How can Product Managers identify tech debts

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Did you know how Amazon Web Services was born?

In early 2000s Amazon was growing quickly and hiring new software engineers, yet they were still finding, in spite of the additional people, they weren’t building applications any faster…The internal teams at Amazon required a set of common infrastructure services everyone could access without reinventing the wheel every time, and that’s precisely what Amazon set out to build — and that’s when they began to realise they might have something bigger.

This is an excerpt from TechCrunch. AWS was an extraordinary solution to a technical debt.

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