Data Science Skills and Business Problems
Discover what skills a data scientist benefits from learning and how the concept of a data scientist, and what businesses expect of them, has developed over time.
By Alex Jones, June 2014.
For years, business leaders and IT organizations have had a challenging relationship. On one hand, IT organizations struggle to keep up with rapidly changing technology, poor communication, talent gaps, and unforeseen challenges. While on the other hand, business leaders face project completion dates that seem to slide across the calendar, cost overruns, and an ever increasing appetite for technology to support and drive business.
Now, with the rise of "Data Scientists" we see the same dynamics play out--- but this time, the new "must have" tech project is people, not software or systems. The parallels are humorous and hopefully shed some light on the confusion that often arises between IT and Business.
Let's explore the development of business analytics and data science.
In 2011, McKinsey published Big Data: the next frontier for innovation, competition, and productivity and in that one report hundreds of companies put on their running shoes and joined the race for analytical talent. Although this was certainly not the first report on the subject, it served to illustrate the business value and potential opportunities that are available (and not just to technology companies).
After describing the opportunity, McKinsey highlighted their forecast that there would be a severe shortage of "Deep Analytical Talent" by 2018 and they outlined the type of professional that would be needed to implement big data initiatives.
Just a few months after McKinsey's report, DJ Patil and Jeff Hammerbacher wrote Building Data Science Teams, in which, they discussed their experiences at LinkedIn and Facebook.
More importantly, DJ Patil gave McKinsey's "Deep Analytical Talent" a name--- Data Scientists. Below is an excerpt from the article describing how the title came to be:
With title in hand, DJ Patil added some key characteristics to look for in a Data Scientist:
For the executive looking for a "Deep Analytical Talent" this article was a welcome expansion to the job description. However, much like IT projects, it also increased the expectations for what a Data Scientist was suppose to be.
From there, the floodgates opened. Over the next few months and years, the job description expanded into what is often referred to as a "Unicorn" of skillsets.
Everyone had a few things to add. Below are just a few examples. (Sources: Drew Conway's Data Scientist Venn Diagram, Gartner, Drew Tierney's Multi-disciplinary Diagram)
With each iteration, Data Scientist began to look more and more like Unicorns and less like "Deep Analytical Talent".
Ironically, the expanded expectations of Data Scientists are a product of their own success. The ability to advise executives, understand deeply technical problems, communicate, (*INSERT ENDLESS LIST*), illustrates that business leaders see Data Scientists as a bridge that can finally align IT and Business in a much more permanent and productive manner.
Unfortunately, many technical focused professionals, see the obligation to develop business skills as a trivial and unneeded task. However, it doesn't have to be!
In an effort to bridge the gap between Business and IT, I expanded from simply looking at the Top 10 Data Analysis Tools for Business to developing a concise guide to get IT professionals and business leaders to structure the problem solving process (which can be challenging after spending a few hours data cleansing or coding) and ensure that both groups understand the strategic need for a project.
In some cases, simply structuring out the problem and working through the objectives can be enough to come to a quick and simple solution.
Essentially, this serves as a basic framework for working through business problems. Although it won't make you a strategy expert, it will help to advance the conversation, align your goals with the business, understand the expected return (which can help to prioritize between projects), or even speed up talking points for a meeting so that you can get back to the comfort of coding.
As you would expect, it flows from top to bottom. "Basics" serve as a reminder of the steps to consider with any problem. For instance, the first item "breakdown and segment" is a reminder that spans customer/ revenues/ products/ etc (ie: segment out your customer base, breakdown products by volume and profit margin, segment revenues by streams, etc).
From there, Finance, Marketing, and Operations are very simplified and loosely defined groupings that help to guide discussion. All of which leads down to the ultimate goal of any project--- the profits!
Here is a link to a downloadable PDF of the cheat sheet.
Original LinkedIn post here.
Alex Jones is a Graduate Student at U. Texas McCombs School of Business.
Related:
For years, business leaders and IT organizations have had a challenging relationship. On one hand, IT organizations struggle to keep up with rapidly changing technology, poor communication, talent gaps, and unforeseen challenges. While on the other hand, business leaders face project completion dates that seem to slide across the calendar, cost overruns, and an ever increasing appetite for technology to support and drive business.
Now, with the rise of "Data Scientists" we see the same dynamics play out--- but this time, the new "must have" tech project is people, not software or systems. The parallels are humorous and hopefully shed some light on the confusion that often arises between IT and Business.
Let's explore the development of business analytics and data science.
In 2011, McKinsey published Big Data: the next frontier for innovation, competition, and productivity and in that one report hundreds of companies put on their running shoes and joined the race for analytical talent. Although this was certainly not the first report on the subject, it served to illustrate the business value and potential opportunities that are available (and not just to technology companies).
After describing the opportunity, McKinsey highlighted their forecast that there would be a severe shortage of "Deep Analytical Talent" by 2018 and they outlined the type of professional that would be needed to implement big data initiatives.
Just a few months after McKinsey's report, DJ Patil and Jeff Hammerbacher wrote Building Data Science Teams, in which, they discussed their experiences at LinkedIn and Facebook.More importantly, DJ Patil gave McKinsey's "Deep Analytical Talent" a name--- Data Scientists. Below is an excerpt from the article describing how the title came to be:
“Business analyst” seemed too limiting. “Data analyst” was a contender, but we felt that title might limit what people could do. After all, many of the people on our teams had deep engineering expertise. “Research scientist” was a reasonable job title used by companies like Sun, HP, Xerox, Yahoo, and IBM. However, we felt that most research scientists worked on projects that were futuristic and abstract, and the work was done in labs that were isolated from the product development teams. It might take years for lab research to affect key products, if it ever did. Instead, the focus of our teams was to work on data applications that would have an immediate and massive impact on the business. The term that seemed to fit best was data scientist: those who use both data and science to create something new.
With title in hand, DJ Patil added some key characteristics to look for in a Data Scientist:
Technical expertise: the best data scientists typically have deep expertise in some scientific discipline. Curiosity: a desire to go beneath the surface and discover and distill a problem down into a very clear set of hypotheses that can be tested.
Storytelling: the ability to use data to tell a story and to be able to communicate it effectively.
Cleverness: the ability to look at a problem in different, creative ways.
For the executive looking for a "Deep Analytical Talent" this article was a welcome expansion to the job description. However, much like IT projects, it also increased the expectations for what a Data Scientist was suppose to be.
From there, the floodgates opened. Over the next few months and years, the job description expanded into what is often referred to as a "Unicorn" of skillsets.
Everyone had a few things to add. Below are just a few examples. (Sources: Drew Conway's Data Scientist Venn Diagram, Gartner, Drew Tierney's Multi-disciplinary Diagram)
With each iteration, Data Scientist began to look more and more like Unicorns and less like "Deep Analytical Talent".
Ironically, the expanded expectations of Data Scientists are a product of their own success. The ability to advise executives, understand deeply technical problems, communicate, (*INSERT ENDLESS LIST*), illustrates that business leaders see Data Scientists as a bridge that can finally align IT and Business in a much more permanent and productive manner.
Unfortunately, many technical focused professionals, see the obligation to develop business skills as a trivial and unneeded task. However, it doesn't have to be!
In an effort to bridge the gap between Business and IT, I expanded from simply looking at the Top 10 Data Analysis Tools for Business to developing a concise guide to get IT professionals and business leaders to structure the problem solving process (which can be challenging after spending a few hours data cleansing or coding) and ensure that both groups understand the strategic need for a project.
In some cases, simply structuring out the problem and working through the objectives can be enough to come to a quick and simple solution.
Essentially, this serves as a basic framework for working through business problems. Although it won't make you a strategy expert, it will help to advance the conversation, align your goals with the business, understand the expected return (which can help to prioritize between projects), or even speed up talking points for a meeting so that you can get back to the comfort of coding.
As you would expect, it flows from top to bottom. "Basics" serve as a reminder of the steps to consider with any problem. For instance, the first item "breakdown and segment" is a reminder that spans customer/ revenues/ products/ etc (ie: segment out your customer base, breakdown products by volume and profit margin, segment revenues by streams, etc).
From there, Finance, Marketing, and Operations are very simplified and loosely defined groupings that help to guide discussion. All of which leads down to the ultimate goal of any project--- the profits!
Here is a link to a downloadable PDF of the cheat sheet.
Original LinkedIn post here.
Alex Jones is a Graduate Student at U. Texas McCombs School of Business.
Related:
- Unicorn Data Scientists vs Data Science Teams
- New Poll: Data Science Skills – Individual vs Team Approach
- Top 10 Data Analysis Tools for Business