If you’ve worked in tech for 25 years, you’ve seen some stuff, and you’ve learned a lesson or two. On October 1st, I presented a new session at Data Saturday Atlanta, sharing the story of my unplanned career, and some of the lessons I’ve learned along the way. This was the first time I presented this very personal session; and I’m incredibly grateful for the full crowd that attended, and for their feedback after the session.
My primary goal for the session is to show that you don’t need a computer science degree to build a successful career in tech – and that our industry needs more people from more non-traditional backgrounds.
My secondary goal for the session is to share some of the sharp edges that are typically hidden when people talk about their careers. Everyone wants to share their highlights, but sharing your own pain and failures is harder. This is important, because too often we’re each comparing our own blooper reel against everyone else’s highlights… and nothing good comes from that.
The Data Saturday event was in-person only[1], but I’d had a bunch of people mention they wanted to attend, but couldn’t make it to Atlanta. So…I packed a camera and microphone and recorded the video on my own.
[1] Please understand that there is no criticism implied here. Organizing a hybrid event is significantly more difficult than organizing an in-person or online-only event, and focusing on in-person community produced wonderful results.
The recording for my “Unleashing Your Personal Superpower” session is now online:
I hope you’ll watch the recording[1], but here’s a summary just in case:
Growth often results from challenge
Mental health issues like anxiety and depression present real challenges that can produce “superpowers” – skills that most people don’t have, and which can grow from the day-to-day experience of living with constant challenge
Recognizing and using these “superpowers” isn’t always easy – you need to be honest with yourself and the people around you, which in turn depends on being in a place of trust and safety to do so
In the presentation I mainly use an X-Men metaphor, and suggest that my personal superpowers are:
Fear: Most social interactions[2] are deeply stressful for me, so to compensate I over-prepare and take effective notes for things I need to remember or actions I need to take
Confusion: I don’t really understand how other people’s brains work, or the relationship between my actions and their reactions – to compensate I have developed techniques for effective written and verbal communication to eliminate ambiguity and drive clarity
Chaos: My mind is made of chaos[3], which causes all sorts of challenges – to compensate I have developed a “process reflex” to understand complex problems and implement processes to address or mitigate them
I wrap up the session with a quick mention of the little-known years before Superman joined the Justice League, which he spent as a Kryptonite delivery guy, and absolutely hated his life. Once he found a team where he could use his strengths and not need to always fight to overcome his weaknesses, he was much happier and effective.
In related news, if I could only get these Swedes to return my calls, I’m thinking of forming a new superhero team…
As you may have noticed, life is complicated and keeps getting in the way of my plans to be a more active blogger and YouTuber[1]. I haven’t released a new dataflows video of my own in far too long[2], but my teammate Kasper is helping out by doing the hard work for me:
Last week I had an awesome conversation on “everything dataflows” with Kasper and the video is now available on his excellent Kasper On BI YouTube channel. In this hour-long video we talk about a lot of the “big picture” topics related to dataflows, including how dataflows fit into a larger data estate – and when you should use them, or avoid using them.
Please check it out, and let me know what you think!
[1] If this isn’t the name of a social media platform for potatoes, it should be.
[2] To add insult to the injury that is life in a global pandemic, my video editing PC died a few weeks ago. I now have it back up and running, but I lost all of my project templates and works in progress, which is likely to introduce more delays. FFS.
Important: This post was written and published in 2020, and the content below may no longer represent the current capabilities of Power BI. Please consider this post to be more of an historical record and less of a technical resource. All content on this site is the personal output of the author and not an official resource from Microsoft.
A lot of the questions I get about dataflows in Power BI boil down to the simplest[1] question: “Why dataflows?”
On Saturday November 7 I joined MVP Reid Havens for a YouTube live stream where we looked at this question and a bunch of other awesome dataflow questions from the 60+ folks who joined us.
The stream recording is now available for on-demand viewing. You should check it out!
[1] And therefore most difficult to answer concisely. That’s just how it goes.
Even though he lived 2,000 years ago, you’ve probably heard of the Chinese military strategist and general Sun Tzu. He’s known for a lot of things, but these days he’s best known for his work The Art of War[1], which captures military wisdom that is still studied and applied today
Even though Sun Tzu didn’t write about building a data culture[2], there’s still a lot we can learn from his writings. Perhaps the most relevant advice is this:
Building a data culture is hard. Keeping it going, and thriving, as the world and the organization change around you is harder. Perhaps the single most important thing[3] you can do to ensure long-term success is to define the strategic goals for your efforts.
Rather than doing all the other important and valuable tactical things, pause and think about why you’re doing them, and where you want to be once they’re done. This strategic reflection will prove invaluable, as it will help you prioritize, scope, and tune those tactical efforts.
Having a shared strategic vision makes everything else easier. At every step of the journey, any contributor can evaluate their actions against that strategic vision. When conflicts arise – as they inevitably will – your pre-defined strategic north star can help resolve them and to keep your efforts on track.
[3] I say “perhaps” because having an engaged executive sponsor is the other side of the strategy coin. Your executive sponsor will play a major role in defining your strategy, and in getting all necessary stakeholders on board with the strategy. Although I didn’t plan it this way, I’m quite pleased with the parallelism of having executive sponsorship be the first non-introductory video in the series, and having this one be the last non-summary video. It feels neat, and right, and satisfying.
Building a data culture is hard. It involves technology and people, each of which is complicated enough on its own. When you combine them[1] they get even harder together. Building a data culture takes time, effort, and money – and because it takes time, you don’t always know if the effort and money you’re investing will get you to where you need to go.
Measuring the success of your efforts can be as hard as the efforts themselves.
Very often the work involved in building a data culture doesn’t start neatly and cleanly with a clearly defined end state. It often starts messily, with organic bottom-up efforts combining with top-down efforts over a period of change that’s driven as much by external forces as by any single decision to begin. This means that measuring success – and defining what “success” means – happens while the work is being done.
Measuring usage is the easiest approach, but it’s not really measuring success. Does having more reports or more users actually produce more value?
For many organizations[2], success is measured in the bottom line – is the investment in building a data culture delivering the expected return from a financial perspective?
Having a data culture can make financial sense in two ways: it can reduce costs, and it can increase revenue.
Organizations often reduce costs by simplifying their data estate. This could involve standardizing on a single BI tool, or at least minimizing the number of tools used, migrating from older tools before they’re retired and decommissioned. This reduces costs directly by eliminating licensing expenses, and reduces costs indirectly by reducing the effort required for training, support, and related tasks. Measuring cost reduction can be straightforward – odds are someone is already tracking the IT budget – and measuring the reduction in the utilization of legacy tools can also take advantage of existing usage reporting.
Organizations can increase revenue by building more efficient, data-driven business processes. This is harder to measure. Typically this involves instrumenting the business processes in question, and proactively building the processes to correlate data culture efforts to business process outcomes.
In the video I mention the work of several enterprise Power BI customers who have build Power BI apps for their information workers and salespeople. These apps provide up-to-date data and insights for employees who would otherwise need to rely on days- or weeks-old batch data delivered via email or printout. By tracking which employees are using what aspects of the Power BI apps, the organizations can correlate this usage with the business outcomes of the employees’ work[3]. If a person or team’s efficiency increases as data usage increases, it’s hard to argue with that sort of success.
But.. this post and video assume that you have actually set explicit goals. Have you? If you haven’t defined that strategy, you definitely want to check out next week’s video…
[1] Especially since you usually have organizational politics thrown into the mix for good measure, and that never makes things any simpler.
[2] I originally typed “most organizations” but I don’t have the data to support that assertion. This is true of most of the mature enterprise organizations that I’ve worked with, but I suspect that for a broader population, most organizations don’t actually measure – they just cross their fingers and do what they can.
[3] Odds are someone is already tracking things like sales, so the “business outcomes” part of this approach might be simpler than you might otherwise assume. Getting access to the data and incorporating it in a reporting solution may not be straightforward, but it’s likely the data itself already exists for key business processes.
Power BI lets business users solve more and more problems without requiring deep BI and data expertise. This is what self-service business intelligence is all about, as we saw when we looked at a brief history of business intelligence.
Some BI solutions are too important to let grow organically through self-service development. Sometimes you need true BI experts who can design, implement, and support applications that will scale to significant data volumes and number of concurrent users.
In this video we look at a specific approach taken by the BI team at Microsoft that developed the analytic platform used by Microsoft finance[1].
This is one specific approach, but it demonstrates a few fundamental facts that can be overlooked too easily:
Building an enterprise BI solution is building enterprise software, and it requires the rigor and discipline that building enterprise software demands
Each delivery team has dedicated teams of experts responsible for their part of the whole
Each business group with data and BI functionality included in the solution pays for what they get, with both money and personnel
Organizations that choose to ignore the need for experts tend to build sub-optimal solutions that fail to deliver on stakeholder expectations. These solutions are often replaced much sooner than planned, and the people responsible for their implementation are often replaced at the same time[2].
This isn’t the right place to go into the details of what sort of expertise you’ll need, because there’s too much to cover, and because the details will depend on your goals and your starting point. In my opinion the best place to go for more information is the Power BI whitepaper on Planning a Power BI Enterprise Deployment. This free resources delivers 250+ pages of wisdom from authors Melissa Coates and Chris Webb. You probably don’t need to read all of it, but odds are you will probably want to once you get started…
Wow. Although I am generally a big fan of Ars Technica’s journalism, I need to object to the sub-headline: “Lost data was reportedly the result of Excel’s limit of 1,048,576 rows.”
Yeah, no. The lost data was not the result of a capability that has been well-known and documented for over a decade. The lost data was a result of using non-experts to do a job that experts should have done.
Choosing the wrong tool for a given job is often a symptom of not including experts and their hard-earned knowledge at the phases of a project where that expertise could have set everything up for success. This is just one example of many. Don’t let this happen to you.
[2] If you’ve been a consultant for very long, you’ve probably seen this pattern more than once. A client calls you in to replace or repair a system that never really worked, and all of the people who built it are no longer around.
Lost. The epic battle would be lost without champions.
Don’t let this happen to you battle to build a data culture. Instead, find your champions, recognize and thank them, and give them the tools they need to rally their forces and lead them to victory.
Let’s do this!!
Despite what the nice short video[1] may lead you to believe, it’s not absolutely necessary to provide your data culture champions with literal swords[2]. But it is vital that you arm[3] them with the resources and connections they need to be successful.
In any community there will be people who step up to go the extra mile, to learn more than they need to know, and to do more than they are asked. These people are your champions, but they can’t do it all on their own. In the long term champions will succeed or fail based on the support they get from the center of excellence.
With support from the BI COE, champions can help a small central team scale their reach and impact. Champions typically become the primary point of contact for their teams and business groups, sharing information and answering questions. They demonstrate the art of the possible, and put technical concepts into the context and language that their business peers understand.
This is just what they do – this is what makes them champions.
An organization that’s actively working to build a data culture will recognize and support these activities. And if an organization does not…
[1] This video is about 1/3 as long as the last video in the series. You’re very welcome.
[2] But why take chances, am I right?
[3] See what I did there? I shouldn’t be allowed to write blog posts this close to bedtime.
From a distance many of them look the same, and even up close they tend to look similar, but each one is unique – and you cannot treat them all the same.
This post and video take a closer[1] look at the topic introduced when we looked at picking your battles for a successful data culture. Where that post and video looked at more general concepts, this video looks at specific techniques and examples used by successful enterprise Power BI customers around the world.
I won’t attempt to reproduce here everything that’s in the video, but I do want to share two diagrams[2] that represent how one organization has structured their community of practice uses Power BI, and how their Power BI COE supports and enables it. I chose this example because it hews closely to the standard successful approach I see with dozens of large organizations building their data culture around Power BI, but also puts the generic approach into a specific and real context.
This first diagram shows the “rings” of BI within the organization, with personal BI at the outside and enterprise BI on the inside. Each ring represents a specific point on the more control / less control spectrum introduced in earlier videos, and demonstrates how one large organization thinks about the consistent and well-defined criteria and responsibilities represented by points on that spectrum.
This second diagram “explodes” the inner ring to show how a given application may mature. This organization has well-defined entry points for self-service BI authors to engage with the central BI team to promote and operationalize reports and data that originate with business authors, and a well-defined path for each app to follow… but they also understand that not every app will follow the path to the end. Some apps don’t need to be fully IT-supported solutions, because their usage, impact, and value doesn’t justify the up-front and ongoing work this would require. Some do, because they’re more important.
It depends.
And the key factor that this organization – and other successful organizations like them – realizes, is that they can put in place processes like the ones illustrated above that examine the factors on which it depends for them, and take appropriate action.
On a case by case, app by app basis.
Because one size will never fit all.
[1] And longer – at nearly 23 minutes, this is by far the longest video in the series.
[2] If you’re ever feeling impostor syndrome please remember that I created these diagrams using SmartArt in PowerPoint, looked at them, and exclaimed “that looks great!” before publishing them publicly where thousands of people would likely see them.
Important: This post was written and published in 2020, and the content below may no longer represent the current capabilities of Power BI. Please consider this post to be more of an historical record and less of a technical resource. All content on this site is the personal output of the author and not an official resource from Microsoft.
This morning I presented a new webinar for the Istanbul Power BI user group, covering one of my favorite subjects: common patterns for successfully using and adopting dataflows in Power BI.
This session represents an intersection of my data culture series in that it presents lessons learned from successful enterprise customers, and my dataflows series in that… in that it’s about dataflows. I probably didn’t need to point out that part.
The session recording is available for on-demand viewing. The presentation is around 50 minutes, with about 30 minutes of dataflows-centric Q&A at the end. Please check it out, and share it with your friends!
BI Polar 