Thursday, January 12, 2017

Product Managers Could Explain Data Science Results In Plain English

One of the key responsibilities of a product manager working with a data science team could be to articulate the results of a data science project in plain English to other team members and stakeholders. I take the following approach to do this.

First, I request the data science team to aim for a small success within three months of work. In collaboration with the data scientists and a subject matter expert, I create a concept story (1) that outlines the specific results we aim to achieve. We aim for modest results in a short period rather than aim for very ambitious results in a year (4).

Second, I sit down and have a conversation with one of the data scientists (2) to understand the results of the data science project. I do this during the research phase of the project as soon as the team reaches the projects desired research goals (3). The data scientist will usually share a data file with the results of the data science project. I normally request the data scientist to point out the top three highlights of the research. We then verbalize the results and convert the results into a plain english sentence in a short work session. For example, in a data science project to match data sets, the plain English sentence might say "We were able to improve the match rate between dataset A and dataset B from about 2000 to about 10,000." I then build on the sentence by stating what it means for an end user. For example, the plain English statement might be "When a person looks at a doctor, she is five times more likely to see a hospital affiliation compared to before."

Third, I provide a screen shot of the application area where the data manifests itself to make the data easier to understand for all team members and stakeholders.

A product manager who takes on these responsibilities in the data product team can play a meaningful role (4). It is also a good way to gain credibility not only with the data scientists but also with stakeholders who may not always have a data science background. It might take 6 months and a couple of successful releases for the data scientists and stakeholders to appreciate the role of a product manager. Don't let that stop you. Keep at it and you will succeed.

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1. I might share a sample concept story in a future post, if possible.
2. Experienced data scientists are good at articulating the results achieved.
3. Data science research outcome is later turned into scalable code by a data engineering team.
4. Overstating the scope and impact of a data science project is a common mistake.

Keep Application Teams Posted About Data Science Projects

In some cases the results of a data science project might lead to the creation of vast quantities of useful data. If the resulting data is used by applications, it is necessary to keep application engineering teams informed in advance so that they can be prepared for the increase in available data. They may have to invest in improving their infrastructure and performance to accommodate the new data.

Think of data as water and applications as the hydro electric dam that uses the water to generate electricity. A sudden unexpected deluge of water might overwhelm the turbines. So keep those responsible informed about the possible deluge.

This could be a key responsibility of a product manager working with a data science team.

Wednesday, January 11, 2017

Gene - An Intimate History - Book

I took a few days off from work recently. During that time I read the book, The Gene - An Intimate History by Siddhartha Mukherjee.   I read that the gene is going to change the world, the same way the atom and the bit changed the world.

Advances in technology have enabled human beings to not just read but also write into a gene. The idea of debugging a human being by editing the gene is cause for celebration and cause for concern at the same time. You can read the New York Times review of the book here.

Data Science Teams Are Twice As Big & Work Twice As Long As App Teams

At Castlight Health, I have worked with  two data science teams that developed multiple data products. Based on my experience, I noticed that in a data product development team, the data science and engineering team is usually twice as big as the application development team. In other words, if you are developing a data product, your invest twice as much in the data science and engineering team as you would in an application engineering team in any given period.

Another important fact is that the data science and engineering team needs to work about twice as long as the application engineering team. Think about it this way. If you are developing a Maps product, the maps application engineering team might be about 4 engineers who work for an year to build the product. However the maps data teams will be about 8 people and will work for two years to create and operationalize the first version of the product.

If you are a product manager involved in planning a data product, this is a good insight to share with your stakeholders and investment decision makers. This of course is a rough idea based on data products in the healthcare industry.

Thursday, December 29, 2016

Data Science In Real Life - Course

One of my development goals for Q4 2016 was to understand how data science happens in real life. I took a few days during the holidays to complete a course on this topic taught by researchers at the Johns Hopkins University. Unlike the other courses in the Executive Data Science specialization, this one was not always on topic. It did have very useful insights into what are the things that could go wrong and what are the pitfalls one can avoid. This year I applied the lessons learned from these courses to the data science projects I managed at Castlight. This knowledge contributed in a meaningful way to the success of such projects. The Executive Data Science specialization is a good one for product managers who want to work with data scientists.

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