In last week’s post and video we looked at how business intelligence tools have evolved, with each evolution solving one set of problems and introducing another set. The video described how self-service BI tools have enabled business users to work with data in ways that used to require specialized technical skills, freeing up IT to focus on other tasks – but at the same time introducing challenges around oversight, consistency, and control.
And that brings us to today’s video.
The video includes a few different graphics, but for this post I want to highlight this one, which I’ve taken from the Power BI Adoption Framework.
Successful cultures balance freedom and restriction in ways that benefit the culture as a whole. It’s a compromise – no one gets everything they want, but everyone gets the things they need the most.
For a data culture, this typically involves letting IT do the things they do best, and letting business get down to business.
When an organization adopts a managed self-service model for business intelligence, the central BI team in IT does the heavy lifting. This means they prepare, cleanse, warehouse, and deliver the biggest and most important data. They deliver the data that would be hard for a business user to create using a self-service tool, and which will be used by a large audience. They do the things that have broad reach, strategic impact, and strategic importance. They create things that need to be correct, consistent, and supported.
And business users do the rest. This is a broad bucket, but it’s a reasonable generalization as well. Business users create their own reports using the IT-managed data sources and data models. And they prepare and use their own data where there is no IT-managed data for a given purpose.
Over time a given application or a given data source may become more or less managed, as the culture adopts, adapts, and improves.
Managed self-service BI isn’t the only way to be successful, but in my experience working with many large Power BI customers it’s the best way and most predictable way. By having clearly defined roles and responsibilities – who does what – moving from either extreme can overcome the problems that that extreme creates, without going too far in the other direction.
Does your organization take this approach?
If it doesn’t today, what will it take to make this change a reality?
 Which I have shamelessly stolen from the Power BI Adoption Framework, because it is an awesome graphic and because I love to stand on the shoulders of giants.
 Which is the approach most likely to drive short-and long term efficiencies and successes.
 Please understand that this is a gross simplification. This “central BI team in IT” may be a single central team for the whole company, it may be a single central team for a specific business unit, or it may be one of dozens of BI teams that are designed to support a distributed global enterprise. This is less about the technology and the culture than it is about organizational structure, so I don’t expect to ever try to tackle this diversity of approaches in this series.
 Next week’s video will talk a little more about what data is likely to qualify for this distinction. And since the video is done already, I’m pretty confident to say “next week” this week.
7 thoughts on “Data Culture: Roles and Responsibilities”
Is there data that shows the chances of success of each of the adoption methods are?
What an awesome question!
The short answer is no, at least not that I have seen.
The longer answer is that each organization will have its own definition of success, and that this definition changes (often slowly) over time.
The only likely sources I can think of are industry analysts like Gartner or Forrester, and that data will be behind a paywall.
With that said, everything I know says that the managed self-service BI “middle column” approach is the method that will give you the greatest possibility of success, based on the most common definitions of success.
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Matthew – I *think* I’m seeing an evolving situation where IT are providing infrastructure, but effectively the project framework they are operating within devolves responsibility over wise use back to the business. “Here’s the infrastructure, business users. Fill your boots.”
This is made potentially more complex/painful by a very strong organisational preference for DirectQuery (possibly with aggregation tables made available in DQ mode) over import due to an organisational preference to have security handled by the data source.
The Power BI Adoption Roadmap is very clear on the huge investment required in the areas of governance and human capital. I’m worried that IT have completely underestimated how complex Power BI is for even well-seasoned IT professionals to get to grips with in order to produce performant, robust, planned, maintainable reporting, using a wise mix of import, DQ, composite models, and aggs as appropriate to each use case. No amount of training can turn all business users into career IT professionals…even if all business users had the time and desire.
Biting my nails. Trying to socialise that Power BI Adoption Roadmap and cultivate a suitable Exec Sponsor.