Migrating to Power BI

One aspect of building a data culture is selecting the right tools for the job. If you want more people working with more data, giving the tools they need to do that work is an obvious[1] requirement. But how many tools do you need, and which tools are the right tools?

Migrating to the cloud

It should be equally obvious that the answer is “it depends.” This is the answer to practically every interesting question. The right tools for an organization depend on the data sources it uses, the people who work with that data, the history that has gotten the organization to the current decision point, and the goals the organization needs to achieve or enable with the tools it selects.

With that said, it’s increasingly common[2] to see large organizations actively working to reduce the number of BI tools they support[3]. The reasons for this move to standardization are often the same:

  • Reduce licensing costs
  • Reduce support costs
  • Reduce training costs
  • Reduce friction involved in driving the behaviors needed to build and grow a data culture

Other than reducing the licensing costs[4], most of these motivations revolve around simplification. Having fewer tools means learning and using fewer tools. It means everyone learning and using fewer tools, which often results in less time and money spent to get more value from the use of those tools.

One of the challenges in eliminating a BI tool is ensuring that the purpose that tool fulfilled is now effectively fulfilled by the tool that replaces it. This is where migration comes in.

The Power BI team at Microsoft has published a focused set of guidance articles focused specifically on migrating from other BI tools to Power BI.

This documentation was written by the inestimable Melissa Coates of Coates Data Strategies, with input and technical review by the Power BI customer advisory team. If you’re preparing to retire another BI tool and move its workload to Power BI – or if you’re wondering where to start – I can’t recommend it highly enough.


[1] If this isn’t obvious to a given organization or individual, I’m reasonably confident that they’re not actively trying to build a data culture, and not reading this blog.

[2] I’m not a market analyst but I do get to talk to BI, data, and analytics leaders at large companies around the world, and I suspect that my sample size is large and diverse enough to be meaningful.

[3] I’m using the word “support” here – and not “use” – deliberately. It’s also quite common to see companies remove internal IT support from deprecated BI tools, but also let individual business units continue to use them – but also to pay for the tools and support out of their own budgets. This is typically a way to allow reluctant “laggard” internal customer groups to align with the strategic direction, but to do it on their own schedules.

[4] I’m pretty consistent in saying I don’t know anything about licensing, but even I understand that paying for two things costs more than paying for one of those things.

Data Culture: Training for the Community of Practice

The last few posts and videos in this series have introduced the importance of a community where your data culture can grow, and ways to help motivate members of the community, so your data culture can thrive.

But what about training? How do we give people the skills, knowledge, and guidance that they need before they are able do work with data and participate in the data culture you need them to help build?

Training is a key aspect of any successful data culture, but it isn’t always recognized as a priority. In fact the opposite is often true.

I’ve worked in tech long enough, and have spent enough of that time close to training to know that training budgets are often among the first things cut during an economic downturn. These short-term savings often produce long-term costs that could be avoided, and more mature organizations are beginning to realize this.

In my conversations with enterprise Power BI customers this year, I’ve noticed a trend emerging. When I ask how the COVID-19 pandemic is affecting how they work with data, I hear “we’re accelerating our efforts around self-service BI and building a data culture because we know this is now more important than ever” a lot more than I hear “we’re cutting back on training to save money.” There’s also a clear correlation between the maturity of the organizations I’m talking with and the response I get. Successful data cultures understand the value of training.

I’ll let the video speak for itself, but I do want to call out a few key points:

  1. Training on tools is necessary, but it isn’t enough. Your users need to know how to use Power BI[1], but they also need to know how to follow organizational processes and work with organizational data sources.
  2. Training material should be located as close as possible to where learners are already working – the people who need it the most will not go out of their way to look for it or to change their daily habits.
  3. There is a wealth of free Power BI training available from Microsoft (link | link | link) as well as a broad ecosystem of free and paid training from partners.

The most successful customers I work with use all of the resources that are available. Typically they will develop internal online training courses that include links to Microsoft-developed training material, Microsoft product documentation, and community-developed content, in a format and structure[2] that they develop and maintain themselves, based on their understanding of the specific needs of their data culture.

Start as small as necessary, listen and grow, and iterate as necessary. There’s no time like the present.


[1] Or whatever your self-service BI tool of choice may be – if you’re reading this blog, odds are it’s Power BI.

[2] I’m tempted to use the term “curriculum” here, but this carries extra baggage that I don’t want to include. Your training solution can be simple or complex and still be successful – a lot of this will depend on your company culture, and the needs of the learners you’re targeting.

Sometimes culture is life or death

Back in January I shared a video that wasn’t technically about data culture, but which I believed was a near-perfect analogy for the evolution of a data culture. Now I’d like to share another one. It’s a short and thoughtful six minute video that I hope you’ll take the time to watch.

Consider this question from the video: “How much freedom is too much? How much is not enough?” Then consider the answer: it depends.

In the first post in my data culture series, I included this footnote:

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. There are occasional exceptions, but in my experience these are very rare.

Bermuda changed, because the pain of not changing was too great. They realized that the traditional, centralized approach[1] would not work for them, so they developed a distributed, decentralized approach that would work.

This change meant that individuals needed to do some of the things that most of us would expect the a government agency to do. This change meant that individuals gave up some freedom that most of us[2] have always taken for granted.

This change also kept those individuals from dying.

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If you skipped over the video and just read to this point, please go back up and watch it now. Go. Listen to the words about Bermuda, and think about how your organization uses data. Think about how hard change is – who accepts it, and who pushes back.

Evan Hadfield, the young man behind the Rare Earth channel on YouTube, touches on a lot of the nuance and balance and conflict that makes culture change so difficult. A lot of his videos touch on painful historical topics which he explores and questions, but often without answers to those questions. I love it[3], and watch every video he releases. If you like this blog for more than just the data stuff, odds are you’ll love it too.


[1] For them it was about water management. For you it might be about data. Work with me here.

[2] If you have a homeowners association that mandates and restricts the exterior of your home, you may be in the exception on this one.

[3] I first discovered the channel when YouTube recommended this video. I ignored it for weeks, but when I finally gave in and watched it the first time I was instantly hooked.

Data Culture: Motivation and Encouragement

The last post in our ongoing series on building a data culture focused on the importance of community, and on ways organizations can create and promote successful communities around data. But while a community is where the data culture can grow, how can you motivate people to participate, to contribute, and to be part of that growth?

Business intelligence is more about business than it is about technology, and business is really about people. Despite this, many BI professionals focus their learning largely on the technology – not the people.

Do you remember the first time you were involved in a performance tuning and optimization effort? The learning process involved looking at the different parts of the tech stack, and in understanding what each part does, how it does it, and how it relates to all of the other parts. Only when you understood these “internals” could you start looking at applying your knowledge to optimizing a specific query or workload.

You need to know how a system works before you can make it work for you. This is true of human systems too.

This video[1] looks at motivation in the workplace, and how you can motivate the citizen analysts in your data culture to help it – and them – grow and thrive. If you think about these techniques as “performance tuning and optimization” for the human components in a data culture, you’re on the right track.

This image makes a lot more sense after you’ve watched the video – I promise

People are motivated by extrinsic motivators (doing something to get rewards) and intrinsic motivators (doing something because doing it makes them happy)[2], and while it’s important to understand both types of motivators, it’s the intrinsic motivators that are more likely to be interesting – and that’s where we spend the most time in the video.

When you’re done with the video, you probably want to take a moment to read this Psychology Today article, and maybe not stop there. People are complicated, and if you’re working to build a data culture, you need to understand how you can make people more likely to want to participate. Even with an engaged executive sponsor, it can be difficult to drive personal change.

In my personal experience, task identity and task significance are the biggest success factors when motivating people to contribute in a data culture. If someone knows that their work is a vital part of an important strategic effort, and if they know that their work makes other people’s lives better, they’re more likely to go the extra mile, and to willingly change their daily habits. That’s a big deal.


[1] If you’re not old enough to recognize the opening line in the video, please take a moment to appreciate how awesome commercials were in the 1980s.

[2] Yes, I’m oversimplifying.

Data Culture: The Importance of Community

The last two videos  in our series on building a data culture covered different aspects of  how business and IT stakeholders can partner and collaborate to achieve the goals of the data culture. One video focused on the roles and responsibilities of each group, and one focused on the fact that you can’t treat all data as equal. Each of these videos builds on the series introduction, where we presented core concepts about cultures in general, and data culture in particular.

Today’s video takes a closer look at where much of that business/IT collaboration takes place – in a community.

Having a common community space – virtual, physical, or both – where your data culture can thrive is an important factor in determining success. In my work with global enterprise Power BI customers, when I hear about increasing usage and business value, I invariably hear about a vibrant, active community. When I hear about a central BI team or a business group that is struggling, and I ask about a community, I usually hear that this is something they want to do, but never seem to get around to prioritizing.

Community is important.[1]

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A successful data culture lets IT do what IT does well, and enables business to focus on solving their problems themselves… but sometimes folks on both sides of this partnership need help. Where do they find it, and who provides that help?

This is where the community comes in. A successful community brings together people with questions and people with the answer to these questions. A successful community recognizes and motivates people who share their knowledge, and encourages people to increase their own knowledge and to share it as well.

Unfortunately, many organizations overlook this vital aspect of the data culture. It’s not really something IT traditionally owns, and it’s not really something business can run on their own, and sometimes it falls through the cracks[2] because it’s not part of how organizations think about solving problems.

If you’re part of your organization’s journey to build and grow a data culture and you’re not making the progress you want, look more closely at how you’re running your community. If you look online you’ll find lots of resources that can give you inspiration and ideas, anything from community-building ideas for educators[3] to tips for creating a corporate community of practice.


[1] Really important. Really really.

[2] This is a pattern you will likely notice in other complex problem spaces as well: the most interesting challenges come not within a problem domain, but at the overlap or intersection of related problem domains. If you haven’t noticed it already, I suspect you’ll start to notice it now. That’s the value (or curse) of reading the footnotes.

[3] You may be surprised at how many of these tips are applicable to the workplace as well. Or you may not be surprised, since some workplaces feel a lot like middle school sometimes…

Data Culture: Picking Your Battles

Not all data is created equal.

One size does not fit all.

In addition to collaboration and partnership between business and IT, successful data cultures have something  else in common: they recognize the need for both discipline and flexibility, and have clear, consistent criteria and responsibilities that let all stakeholders know what controls apply to what data and applications.

2020-08-01-19-55-59-794--POWERPNT

Today’s video looks at this key fact, and emphasizes this important point: you need to pick your battles[1].

If you try to lock everything down and manage all data and applications rigorously, business users who need more agility will not be able to do their jobs – or more likely they will simply work around your controls. This approach puts you back into the bad old days before there were robust and flexible self-service BI tools – you don’t want this.

If you try to let every user do whatever they want with any data, you’ll quickly find yourself in the “wild west” days – you don’t want that either.

Instead, work with your executive sponsor and key stakeholders from business and IT to understand what requires discipline and control, and what supports flexibility and agility.

One approach will never work for all data – don’t try to make it fit.


[1] The original title of this post and video was “discipline and flexibility” but when the phrase “pick your battles” came out unscripted[2] as I was recording the video, I realized that no other title would be so on-brand for me. And here we are.

[2] In case you were wondering, it’s all unscripted. Every time I edit and watch a recording, I’m surprised. True story.

Data Culture: Roles and Responsibilities

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[1].

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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[2], the central BI team in IT[3] does the heavy lifting. This means they prepare, cleanse, warehouse, and deliver the biggest and most important[4] 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?


[1] 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.

[2] Which is the approach most likely to drive short-and long term efficiencies and successes.

[3] 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.

[4] 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.

Data Culture: A brief history of business intelligence

Why is a data culture important?

Why does building a data culture in the 2020s involve the challenges and goals that it does?

Why am I publishing in July a video recorded in January?

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BI Application Internals, Circa 1986

The first two questions are probably the easier ones to answer, so I’ll save the third one for last.

For the first question, I’ll refer you to the first post and video in this series, which introduces the key concepts for a data culture.

For the second question, you need to understand how we got to where we are today – how the data and BI technologies we use today evolved over the past decades. And that’s where today’s video[1] comes in. Check out a brief history of business intelligence for context on the problems that a successful data culture needs to solve, and what your starting point might be.

I won’t go into depth here (I spent six months on that video, so darned right I’m not going to spoil it!) but there are basically four phases in this brief history:

  1. The dark ages, also known as “back when I was your age” when there was no BI, and decisions were made based on human knowledge, instinct, and experience
  2. The good old days, when BI solutions were developed by highly skilled BI professionals using complex and difficult tools, and when ETL consultants could charge exorbitant rates[2] without clients blinking an eye
  3. The SSBI era where every business user got up to their elbows in data, and IT wasn’t slowing anyone down any more
  4. Nirvana[3], where IT and business work together as partners, each contributing based on their skills and priorities to common shared goals

And each phase brought (or brings) with it its own challenges:

  1. Dependence on specific individuals with specialized knowledge, slow decisions, and probably giant hair you spent hours sculpting with Aqua Net
  2. Expensive development efforts, long turnaround times for insights, IT becomes a bottleneck
  3. Inconsistent results, untrusted (and untrustworthy) insights, producing misinformed business decisions
  4. Difficulty in aligning business and IT and making the necessary cultural changes… also flannel?

And for that third question, please let me just say that 2020 is kicking my butt in ways I never expected. Between work and family and the global pandemic that’s ravaging the globe, I’ve needed to harshly triage all non-essential work. Back in January I was sure I would publish this video a week late, but still in January. Back on February 24th I made this optimistic comment:

In case you’ve been wondering why my blog and YouTube output has dried up this month, it’s because real life has been kicking my ass. I think I can finally see the light at the end of the tunnel, so hopefully we’ll be back with regular content before too long. Hopefully.

That was about a week before the coronavirus outbreak at the nursing home down the road from my house hit the news, and it’s all been downhill from there.

I’m tempted to say I again see the light at the end of the tunnel, but I’m not going to do that. The last time the light turned out to be a train. I will instead say that I’ll do my best to get more blog and video content created and published as consistently as possible and that I appreciate your patience and your support[4].


[1] Today’s video was recorded on January 19th as “take two” that delayed its publishing. Basically I was filming the video at the end of the day when I had planned on publishing it, so I figured it would end up being a week late. Yeah.

[2] This may not be how everyone remembers those times, but this is my blog and my story and I’m sticking with it.

[3] Or Pearl Jam. Maybe Soundgarden. I never really did keep up with grunge, so I’m honestly not sure.

[4] I don’t want to get anyone’s hopes up, but between Friday evening when I wrote this post and finished editing the video, I have recorded the next two videos in the data culture series, and edited the first of them. Only time will tell if August is kinder to me than recent months have been, but barring unforeseen circumstances you’ll see the next two videos on the next two Monday.