In Part I of this 2-part series, The Hammers, I made the case that attitude and aptitude are a business intelligence practitioner’s most critical tools. I’ve heard many folks suggest more technical skills (T-SQL, data visualization), as well as technical business skills (data modeling, business value/ROI calculations) as the hammer – or tool that becomes synonymous with the profession – of BI. And while technical skills are certainly critical, the pace of change in this space is just too rapid for any one technical skill to top the list. I’m intentionally omitting technology specific skills in the interest of making this more universal (across technical tool sets) as well as less given to the ceaseless march of time (every time you blink, a new technology comes out that changes how we interact with data!)
In addition to the right attitude and aptitude to be a successful BI practitioner, there are a handful of other critical skills that will make life easier, and help set up a candidate for success. These range from the fairly basic and teachable (effective, professional written and oral communication) to the more abstract and difficult to assess (the ability to tell stories with data). Here are some valuable skills that BlueGranite looks for and continues to develop:
Effective Communication – Effective business intelligence practice requires equal parts successful communication, translation and technical implementation. In order to succeed, one must be able to really listen, identify both overt and covert messages, and address these interactions with effective oral and written communication. The impact here goes beyond what common sense might supply – effective communication is likely more important than technical skill (though not by much).
Performance within a Deadline / Minimally Viable Solution – If you are not familiar with the Minimally Viable Solution or Product (a different take on MVP), it bears deeper understanding. The nature of BI projects requires flexibility and fluidity, and truly must embrace agile methodologies by allowing for continuous (or at least frequent) feedback from stakeholders, and working towards the MVS/MVPs allows for tighter development loops and a greater chance of ensuring that VALUE has been delivered within the time frame specified. Will it have every bell and whistle? Nope. But will it help ensure you don’t miss deadlines, foster tight, engaged feedback loops and prevent wasted time in features that sound good during requirements gathering, but don’t really deliver on the promise? More often than not.
Attention to Detail – Little things can make big differences, and given the nature of BI – essentially providing greater access and visibility into data and its integrations and correlations – these can really make or break the success of a business intelligence initiative. It is often small details in conversations or existing assets (reports, Excel workbooks, etc.) that help us identify the bits of encapsulated business logic that need to be considered in order to move forward. There are often things left unsaid that need to bubble to the top and expedite appropriate follow ups or research. The basics of attention to detail can be taught, but the nuance only comes with time and experience.
Analytical Feasibility – In other words, we can’t avoid the elegant truism of GIGO, aka garbage in garbage out. A BI practitioner needs to constantly assess the analytical feasibility of their current stakeholder’s expectations. Successful BI leads to more questions and increased appetite for consumable data. But because the visibility and appetite is new to the stakeholder, many associated domains have not had fully developed data quality assessments or programs associated with them. If the data hasn’t been accurately captured at the right grain, no amount of desire for a downstream metric is going to solve the problem. In most cases, we can take this new-found appreciation for data and do a better job collecting consistent, accurate data moving forward. In a few, rare cases, we can put in some significant effort to rebuild a portion of history with the right amount of data to compute a metric, but this is almost always more work than the stakeholders anticipate.
And there you have it. At BlueGranite, these are the types of tools we look for, support and foster in our consultants. While not every team member will pull top marks in every discipline or attribute, they represent a common pattern that denotes the right skills for a team to consistently deliver successful data and analytics projects. Our team members are the drivers of BlueGranite’s success, and our recipe can work for BI teams across different organizations. Of course, there is also the small issue of having the right amount of skill with the selected technologies (in our case those include SQL Server, Power BI, Hadoop, R, Azure, etc.), but those topics are explored in other articles.
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