The Business Intelligence Skills “Wholebrain” Checklist

Business Intelligence is the latest buzzword meaning “analysis,” too often this means a focus on left-brain analytical business intelligence skills, and this is thé pitfall in understanding the complete set of business intelligence “wholebrain” skills. So what is a holistic set of skills in business intelligence to excel?

 

Business Intelligence Check List IntelligentHQ

Based on my own experience within very data-driven environments and research on the web, I encounter the following skill sets:

Interesting “wholebrain” checklist by the University of Michigan of types of skills and competencies needed to have effective business intelligence execution. It also shows litteraly the complete scope between left-brain and right brain skills.

I encountered a similar list by Search Business Analytics, dividing them into five key areas:

  1. Leadership and management
  2. Business analytics
  3. Data analysis and design
  4. Data integration
  5. Admin and technology

With leadership and management, it focuses in on integrating people, process and technology to deliver business value. It is a key success factor for BI and data warehousing programs and projects.

Mico Yuk on the SAP community wrote his five key competencies and skills from experience:

1. Superior Communication Skills… no longer a NTH (nice to have)
This may sound cliché, but it’s a well-known fact that most BI developers are not exactly socialites.  While the lack of communication within IT used to be a minor issue, as BI changes from being IT to 100% business led, the need for BI developers to have superior communication skills is now a requirement.  In order to gain the attention and buy-in of their business users, now more than ever, BI developers must understand that they are in the marketing business.

2. Think ‘Mobile First’ approach… or Don’t Build
Like it or not, the Iphone has changed the world and has had a huge impact on the Business Intelligence Industry.  Not only are business users expecting sexier reports/dashboards from their BI systems that resemble their apps, but they’ve raised the bar on their overall user experience thanks to Steve Jobs, the late founder and CEO of Apple, Inc.

3. Deployments are either Rapid… or a Waste of Time
Reality Check! If the thought of waiting for 6+ months to deliver a BI application does not bother you, you probably won’t be in the business of BI or even IT much longer.
Last year marked the end of long winded, overpriced and complex IT projects with no measurable ROI or value.  The reality is, if you cannot deliver visible results to your business users in the first 8-10 weeks of your BI project, they will simply find another way to do it. To overcome this growing epidemic of the new DOLAP “aka Desktop OLAPS”, as top SAP BI and HANA author, Dr. Bjarne Berg termed it, start by instilling a more Agile or SCRUM project approach to your team.

4. Make ‘User Adoption’ your ONLY KPI to Measure Success
It’s always amazing to see just how far removed BI teams are from the understanding that BI applications actually impact the business. This leads way to a very hands-off approach when focusing on ‘high user adoption’, which should be the only metric that matters in BI projects. Recent studies by TWDI and BIScorecard suggest that most BI deployments only experience a 24 -25% adoption rate.

5. Adopt Mobile, Cloud, and Big Data… they’re here to stay!
If you are still having issues digesting why user adoption is indeed your problem, when we add the words mobile, cloud, and big data to the mix, you may feel a bit overwhelmed. I’ve already covered mobile, but cloud and big data cannot be overlooked, as they are completely changing the BI self-service and BI value proposition.

My reflection: EQ & soft skills reign

From my own experience and having read how really successful people differentiate, which also can be applied on the business intelligence domain, are soft skills and Emotional Quotient (EQ). People that are really successful have a high EQ, most successful people do great on IQ, so that’s not the differentiatior. The differentiator is being able to communicate, to listen, to relate, empathy, understanding.

My checklist would have a good balance between soft and hard skills, such as the abovementioned research shows. Secondly, I think the best skill sets are being formed by a team, and not being requested by one person only. I’ve elaborated on this point in a previous article, the data-scientist was being characterized by the following aspects:

  • Analytical
  • Business savvyness
  • Communication
  • Engagement
  • Creativity

As Mico Yuk said, BI developers and data-smart people aren’t exactly socialities, can you expect to have all these traits? Perhaps, and training and coaching can help a lot. But Team is Key.

What skills and competencies would you add to the checklist?

What specific hard and soft skills would you add?

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