I came upon this
popular Linked In post that attempts to define and draw distinctions
between these two roles. It is one of several I have read recently.
Upon reading this, my initial reaction was how much I
disagreed with these characterizations. My feeling is that, if we want to draw
such distinctions at all, they should be between business analysts and data
analysts. Data Scientist is just a newer title that combines some attributes of
both business and data analysis. It nearly always includes a mastery of big
data technologies and statistical methods, thus commanding higher compensation.
Then I realized that this is all beside the point. These
role definitions are more about recruiting, HR job descriptions, org charts,
and pay grades than what is actually required to succeed in an analytics
program. What matters is having the necessary skill sets on the analytics team,
regardless of what roles or organizations they come from.
As has always been the case in BI & Analytics, the
critical skill sets can be considered using the classic Input à Process à Output model:
|
Input
|
Process
|
Output
|
|
·
Data sourcing & extraction (ETL/ELT)
·
Data preparation
·
Data quality
·
Data governance
|
·
Data navigation & investigation
·
Data discovery
·
Business analysis
·
Modeling
·
Predictive analytics
|
·
Reporting
·
Dashboards & KPIs
·
Visualization
·
Operational applications
·
Presentation/storytelling
|
BI/Analytics technology no longer respects the walls between
these skill sets. The market has moved away from niche tools to suites that
address the entire analytics capability set. For example, what were once pure
visualization tools now offer data sourcing, transformation and modeling
features. The impact of this has been to democratize the entire data supply
chain in such a way that it has moved much closer to the business and
completely obscured the role distinctions between data analysts, scientists and
yes, decision makers. In fact, the overlap of these roles and the trend toward
self-service BI tends to create organizational redundancy within larger
organizations that can afford it.
The fact that the technology is available to many roles does
not mean that individuals should be expected to have all the necessary skills
to leverage it effectively. In fact, very few people do. Our trade has always
placed a high value on those who can navigate data, develop actionable information,
and present it effectively because they are still rare. This won’t last. The
generation that is now entering the workforce has a much higher level of data
skills than its predecessors and will value the ability to develop their own
stories and support its own decisions with data as it rises to executive
positions.
If the goal is to leverage data most effectively and
maximize decision support success, don’t look to your organization to create a
new role. Look to your team to fill any skills gaps, preferably by expanding
the roles already in place. The goal is to minimize the organizational distance,
handoffs, and filters between your sources of data and those who directly put
it to use in business processes.
