The following is the third in a series of four blog posts on the evolving roles, skills and functions played by business intelligence and data professionals.
In our first two posts on the changing role of BI professionals, we offered a glimpse of just how competitive the job market can be for managers looking to hire in this area.
We also learned that BI professionals are now, essentially, data professionals of various stripes.
Finally, we’ve also seen that data science is a complex, fast-moving and multi-disciplinary field with a relatively small but growing talent pipeline. And considering the number of organizations currently looking to add data professionals, it’s possible — perhaps even likely — that finding the perfect candidate to handle your BI needs is a next-to-impossible task.
The anatomy of the perfect data professional
So what skills, aptitudes and other traits make up the perfect data professional?
According to InfoWorld, the top five traits of a highly effective data scientist are:
- Analytical skills and quantitative reasoning
- Storytelling ability
- Being a team player
While CIO Magazine says the top skills required for data professionals in today’s job market are:
- Critical thinking
- Coding (including Python, C++, R, Scala, Clojure, Java and Octave)
- Machine learning/deep learning/AI
- Data architecture
- Risk analysis/process improvement/systems engineering
- Business intuition
Data scientists surveyed by O’Reilly Media in 2016 identified expertise in 17 programming languages, along with dozens of different relational databases, big data platforms, BI dashboards and visualization tools.
As the above illustrates, the top data scientists and BI professionals aren’t simply genius number-crunchers too shy or introverted to hold a conversation; rather, they have a potent mix of hard and soft skills that allow them to view the organization from a holistic perspective, while also effectively communicating their findings once the technical stuff has been done.
Indeed, according to an SAS survey, most data professionals have so-called “traditional” analytical, logical and technical traits, but a significant and growing number also bring softer skills to the table like project management, creativity and communications.
The survey went on to classify various types of data workers under the following broad classifications (from the largest group, Geeks, down to the smallest):
The geeks: Naturally technical with strong logic and analytical skills.
Potential roles: Defining system requirements, processes and programming.
The gurus: Strong scientific and technical disposition along with solid communications and social skills.
Potential roles: Liaising between the technical data science team and organizational decision-makers.
The drivers: Proactive introverts who are excellent at prioritizing, monitoring and driving projects.
Potential roles: Project managers and team leaders.
The crunchers: Reactive personality types who crave routine, they also have high technical competence.
Potential roles: Technical support roles, statistical analysis, data quality.
The deliverers: Proactive and similar to The Drivers in terms of project management ability, but with a strong proclivity towards technical skills as well.
Potential roles: Project managers and team leaders requiring an understanding of nuanced technical details.
The voices: Like The Gurus, The Voices are strong communicators, but have less technical aptitude.
Potential roles: Data science evangelist or communicator.
The survey goes on to say that to get the full value of big data and the new BI, organizations must have an awareness of these complementary skill sets when building their teams.
The importance of building a BI team
In reality, the perfect BI professional — especially today, in this era of multi-faceted skill sets — either doesn’t exist, or even if they come close, is in impossibly high demand. It’s not easy to finding someone with an analytical, statistical mind, who can code and build architecture, but who also excels at storytelling and can rub shoulders with executives.
This is why it’s important to have a team of people in your BI department, either internally or — if you don’t necessarily require these skills in-house — outsourced to a firm expert in data analytics.
And while Towards Data Science’s Chuong Do says there’s no one way to structure a data team, he lays out four key questions to keep in mind when building a BI/data team:
- How should data scientist roles be defined?
- Where should data scientists report?
- Where should the data science function live?
- What should your org do to set up data science for success?
Once these questions have been properly addressed and defined, you and your organization will be in a much better position to build your data team.