Managing Data Science as Products

Managing Data Science as Products

Managing Data Science as Products. How data science teams can apply product management practices to solve their biggest challenges

According to an article published by Wharton, the amount of data science jobs have experienced “massive growth — 15 times, 20 times” over the last few years. However, it does not mean data scientists have it easy. With the October 2019 Report from MIT Sloan and Boston Consulting Group citing “seven out of ten companies report minimal or no impact from AI so far”, data science teams are under tremendous pressure to overcome these hurdles and demonstrate impact.

In the 2017 Kaggle State of Data Science and Machine Learning Survey, 16,000 respondents identified the 7 biggest barriers for data scientists at work as:

  1. Dirty data (49.4%)
  2. Lack of data science talent in the organization (41.6%)
  3. Company politics / Lack of management/financial support for a data science team (37.2%)
  4. The lack of a clear question to be answering or a clear direction to go in with the available data (30.4%)
  5. Unavailability of/difficult access to data (30.2%)
  6. Data Science results not used by business decision makers (24.3%)
  7. Explaining data science to others (22.0%)

Aside from dirty data, all of the hurdles faced by data science teams relate to organizational and stakeholder management issues.

No amount of Python or R code will solve such challenges. Instead, data science teams need to immediately employ product management practices to engage stakeholders effectively and demonstrate their value to the organization. Below are some simple steps Data Science teams can put into practice

data-science leadership management product-management agile

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