I thought it would be interesting to connect with the article’s author to get further insight on how companies could address their analytics needs. In the first part of the interview, John Kelly answered questions relating to data science team members, how they relate to each other in an organization, and what major frustrations they face when using data science tools.
John P. Kelly is the Managing Director of Berkeley Research Group, a predictive analytics practice that leverages econometrics and data science to help drive actionable and data-driven growth strategies & products. BRG empowers its clientele by applying data science to key strategy decisions being made in marketing, sales and operations. Some examples include dynamic pricing optimization, loyalty program design, site location analysis, predicting consumer behavior, and reducing churn.
JK: The title “Data Scientist” is tricky as the term has been diluted. Companies are sometimes paying data scientists who are only able to validate data relationships, but not necessarily to find true data relationships. I would say they are three different types of team members:
JK: Overall, I would recommend the centralized approach. The reason is that you have to build a data forward culture and you will build it more rapidly with a close collaboration of folks working with the data. Make sure to put data scientists on the same P&L responsibility so that they have the same goals.
If you start dispersing them to various business units, each unit will have their own dedicated resources and you can start building silos. A Business Unit Manager will assume that his/her Data Scientist is only accountable for the business unit analytics projects and, consequently, won’t share his/her resources. A centralized approach allows you to aggregate a large amount of talent in a pool and allocate them to business units depending on a project’s priority. With this approach, you have visibility of your different analytics projects.
JK: This job title may come and go as companies become more data-centric. It signifies that the company is currently building a data-driven approach and that data has a strong seat at the executive table. Companies think that if they have a CDO, everything related to data should be his/her responsibility and other departments don’t need to get involved. As companies get more mature with data-centered information, it may become a problem. The reality is that data needs to be invested in all departments — the Chief Marketing Officer, Chief Operating Officer, etc. should all be comfortable working with data.
JK: At Berkeley Research Group, even if we do not sell tools, we can make recommendations. As Data Scientists are highly connected on social media and are truly invested in their work, they are going to hear about great tools from their network. So, one way to hear about data science tools is to create a Twitter account, follow the hundred most impactful Data Scientists, and regularly read your Twitter feed; you will see a number of data science tools promoted. There are also organic courses and technical training here in the United States, like General Assembly, where you can hear people sharing information about how they experimented with a product.
JK: It seems there is no substitute to cleaning the data, integrating data streams, and validating them. There are tools that claim they can do it but, in fact, they are only helping on an order of 25% of the time spent. They are not a replacement and Data Scientists are frustrated by that; they don’t find that particular work especially rewarding. Companies need to continuously re-emphasize how important the data preparation stage is.
Another frustration is that a lot of tools pretend they can do everything: they can cook and clean and even raise the kids. Obviously they can’t do that and, consequently, they poison the well for other tools, and data-driven solutions to problems in general. New tools are not the standard and other team members, typically, do not have the tool. When there is no critical mass for a product, it is not always interesting to use it.
JK: I don’t know any companies who claim to manage talent differently for Data Scientists, but I do know what attracts Data Scientists:
This topic is really important because there is such a shortage of Data Scientists in today’s world. They can easily come and choose their location and their platform. In the U.S., there are 3 major concentrations:
Did you enjoy this article? I will be back soon with some additional insight from John Kelly on the most common challenges in terms of organization and why big data investment has not yet impacted companies at scale.
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