Last week’s post on migrating to Power BI was intended to be a stand-alone post, but this excellent comment from Matthew Choo via LinkedIn made me realize I had more to say.
I could not agree more, Matthew!
In my conversations with leaders from enterprise Power BI customers, if they mention to me that they’re standardizing on Power BI as their analytics tool and platform, I try to ask two questions:
- Why now?
The answer to the first question is almost always a combination of reasons from the previous post on migration and the fact that the customer sees Power BI as the best choice for the organization’s needs moving forward. There’s very little variation in the answers over dozens of conversations.
The answers to the second question are more diverse, but there are some common themes that I hear again and again.
One theme is that the selection of multiple tools happened organically. Sometimes a new BI tool is adopted through a merger or acquisition. Sometimes a new CIO or other senior leader mandates the adoption of their favorite tool, and as leaders change the old tools are left behind while the new tools are added to the stable. Sometimes a key data source only works well with the data source vendor’s reporting tool because the vendor refuses to open their APIs to 3rd parties. Often the plan has been to eliminate excess tools at some point, but the point hasn’t been now… until now.
Very often the factor that makes standardization a priority is pain, which brings me back to a point I made in the introductory post in the “building a data culture” series:
I strongly believe that pain is a fundamental precursor to significant change. If there is no pain, there is no motivation to change. Only when the pain of not changing exceeds the perceived pain of going through the change will most people and organizations consider giving up the status quo.
The most common general reasons for this “pain balance” shifting involve money. Organizations need to eliminate inefficiencies so they can invest in areas of strategic importance. A leader may proactively seek out inefficiencies to eliminate, but it’s more typical for me to hear about external market pressures necessitating a more reactive consolidation.
The other common theme in responses to the question “why now?” is that the organization has a new chief data officer, that the CDO is focused on building a data culture, and for the reasons listed in last week’s post has has made consolidation a priority.
What’s interesting about this theme is that the hiring of a CDO is part of a larger strategic shift in how the organization thinks about data. The C-level executives know that they need to start treating data as a strategic asset, and they realize that it’s too important to be rolled up into the responsibilities of the CIO or another existing leader. Very often, they already have a good idea of the types of changes that need to happen, but want to hire a senior leader who will make the final decisions and own the actions and their outcomes. In other words, the hiring of a CDO is often an early-but-lagging indicator that there’s executive support for a data culture. That’s a good thing.
Before making a big change always important to understand what you hope to achieve, but it’s also important to take a little time to examine how you got to the point where change is necessary, so you can better avoid the mistakes of the past…
 Usually when I say this I’m agreeing with myself so it’s nice to be referring to a different Matthew here.
 Not that i agree with myself very often, mind you.
 Yeah, you know who I’m talking about.
 I wasn’t expecting to coin this phrase, but as soon as I typed it, I loved it. I think I may quit my day job and start a new business as a consultant delivering expensive “Shifting the Pain Balance(tm)” workshops for chief data officers and other senior executives.
 Since it’s still 2020, I should point out that the COVID-19 has been at the root of many external market pressures. I’ve heard dozens of companies say that they’re increasing their investment in building a data culture because of pandemic-induced challenges.
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