Category: Metaphors

Simplifying the solution domain

On By Matthew RocheIn Communication, Community, Data Culture, Metaphors, Power BILeave a comment

My recent posts have focused on the importance of understanding both the problem domain and solution domain in which you’re working, the value of operating in both the problem domain and the solution domain, and some specific resources and techniques for solution-oriented practitioners to increase their fluency in understanding the problem domain. A lot of the message I’ve been trying to get across in these posts can be summarized as “the problem domain is really important, and we need to get better at appreciating and understanding the problems we work to solve.”

This message is predicated on two basic assumptions:

  1. You, the reader, are a technical professional of some sort[1], and you need to deliver technical solutions to people with problems
  2. The people who have problems can’t solve those problems on their own

Let’s look more closely at that second assumption, because it’s becoming increasingly less valid, and less true. And let’s look at it through the lens of… furniture.

Close your eyes and imagine for a moment that you need a table. Let’s say you need a bedside table. What do you do?

As you read through this list you were probably evaluating each option against your own requirements and preferences. Odds are you’ve chosen one or more options at some point in your life. Odds are the criteria you used to decide which options are viable or preferable have changed as your circumstances, skills, and budget have changed.

I couldn’t find Ikea instructions for a bedside table, but honestly I didn’t try very hard

Of course, the decision doesn’t need to be about a bedside table. It could be about a dining room table, or a bed, or a full set of kitchen cabinets. Each option will have different but similar choices, and you’ll likely use different criteria to evaluate and select the choice that’s right for you.

This is also true if the choice is about business intelligence solutions, which may not technically be considered furniture[2]. Close your eyes once more and imagine for a moment that you need a report, which may or may not include a table.  What do you do?

  • You could use an existing report
  • You could start with an existing report and customize it
  • You could start with an existing dataset use it to build a new report
  • You could start with an existing dataset, customize it using a composite model, and use it to build a new report
  • You could work with a center of excellence or BI team to create a dataset, customize a dataset, create a report, or customize a report to meet your needs
  • You could locate or create data sources and build a custom BI solution end-to-end, maybe getting some help along the way if you don’t already have the tools and expertise required for the project

The parallels are easy to see, and they hold up surprisingly well to closer inspection. Just as ready-to-build options make it possible for someone with no woodworking or cabinetry skills[3] to self-serve their own furniture, DIY BI tools[4] like Power BI make it increasingly easy for someone with no data preparation or data modeling skills to build their own BI solutions.

To step away from the furniture analogy, tools like Power BI simplify the solution domain and make it easy for problem domain experts to solve their own problems without needing to become true experts in the solution domain.

Any finance or supply chain professional can use Power BI Desktop to build reports that do what they need, without needing to ask for help. Their reports may not be the most beautiful or best optimized[5],  but maybe they don’t need to be. Maybe “good enough” is actually good enough. Just as it might make more sense to buy a $40 Ikea bedside table instead of spending hundreds or thousands of dollars on something fancier, it might make sense to stack a few milk cartons on top of each other and call it good, at least until you can save up for something better[6]. There is no one size that fits all.

If you are a Power BI professional, it may be difficult to think of the “boards and cinderblocks bookshelf” approach as acceptable, but in most circumstances it’s not up to you to decide. The tools for DIY BI are widely and freely available, and people will use them if they present the most attractive and accessible option to solve their problems. You can’t stop them from building DIY solutions, but you can help them build better ones.

This is where having an effective and engaged center of excellence comes in. Whether you think in terms of problem and solution domains, or in terms of enabling DIY through readily available tools and guidance[7], you can help make “good enough” better by meeting your problem domain experts where they are. They have the tools they need to get started building their own solutions, but they probably need your expertise and assistance to achieve their full potential.

You should help them.

[1] Probably a Power BI practitioner, but who knows?

[2] I have repeatedly seen Power BI referred to as a “platform” which is basically the same thing as a table, so I’m going to withhold judgment on this one.

[3] Someone like me! I married a carpenter and have never found the motivation to develop these skills myself.

[4] DIY BI has a ring to it that SSBI doesn’t have. Why don’t we start using DIY BI instead. DBIY? D(B)IY? D(BI)Y? Hmmm…

[5] They may indeed be the Power BI versions of my DIY home improvement projects.

[6] Please raise your hand if you too had bookshelves made of random boards and cinderblocks when you were in college, and were also happy with the results.

[7] This is the second place in this post where I’ve shared a link to this Marketplace story. If you run a Power BI COE for your organization, do yourself a favor and listen to the story – it will inspire you to think about your COE in different ways.

The Blind Men and the Data Elephant

On By Matthew RocheIn Communication, Data Culture, Diversity, Metaphors, Power BI1 Comment

Are you familiar with the parable of the blind men and the elephant?

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Would you have known this was an elephant, just from this picture?

Sometimes it seems to me that the world of business intelligence – and self-service business intelligence in particular – is made up of blind men feeling their way around a elephant made up of technology and people, each of them feeling something different but none of them able to effectively communicate to the others what they are experiencing.

This blog post was inspired by this email, sent by a technical job recruiter:

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What does this email tell you about the role and the company?

More specifically this blog post was inspired by a tweet[1] from Power BI consultant Jeff Weir, in which he shared this recruiter’s email:

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In this conversation Jeff and I each took the role of a blind man, with this data analytics manager position playing the elephant. Jeff and I each reached very different conclusions.

Jeff may have[2] concluded that Power BI as a tool wasn’t ready for the job, or that that this company wasn’t fully committed to doing what it takes to build a successful data culture, including the right training for their Power BI creators and consumers.

I reached a different conclusion, and I the more I read and re-read the recruiter’s email the more I believed that my initial conclusion is correct: this is an organization that is stepping back from solutions to ensure that they’re focused on the right problems. This is an organization that already has tactics, but is willing to admit that those tactics aren’t getting them where they need to go without the right strategy. This is an organization that seems ready to make significant, substantive change.

Why did I reach this conclusion?

The first sentence: “Ultimately Power BI while being a tool they are currently using it is not the only tool they will consider.”[3]

Wow. As an organization they are avoiding the sunk cost fallacy and explicitly stating they’re open to change, even though that change is likely to be disruptive and expensive. Switching BI tools is hard, and it is not a change that is taken lightly. The leaders at this organization are willing to take that hit and make that change, and they’re taking the steps to find the right leader to help them make it… if that leader decides the change is necessary.

The second sentence, with my emphasis added: “Here they are really looking for someone with some strong technical ability, but more so strong leadership and strategy skills.“[3]

Wow!  The recruiter is explicitly calling out the organization’s priorities: they need a strategic leader. They need someone to help them define the strategy, and to lead them in its execution. Yes, they need someone with the technical chops to help carry out

The third sentence: “there will be an element of helping there and mentoring the staff members currently in the team.”

Maybe not exactly wow on this one, but it’s clear that the organization knows that they need to build the skills of their current BI team, and they want someone able to help. Something missing here that I would prefer to see[4] is additional information about the size and skill levels of the team today, and what other learning resources the organization makes available.

The third sentence, again: “it is more about getting a grip on the current landscape of the data and what can be done with it, setting a vision and a strategy aligned with product development and then going from there…”

WOW! This sounds like an organization that knows their current path isn’t leading them to success. It sounds like an organization that acknowledges its challenges and is proactively looking for a strategic solution to root causes, not just a band-aid.

But… it also sounds like an organization that needs a Chief Data Officer, not just an Insights and Data Analytics Manager, and based on Jeff’s Twitter comment it sounds like they’re not willing to pay a competitive rate for the less senior position, much less the more senior one. They seem to want parts of a data culture, but don’t  appear willing to invest in what it will actually take to make it a reality…

I was originally planning to structure this post around the theme of 1 Corinthians 13:11, but the more I thought about it the more I realized that describing a technical solutions focus as being “childish ways” that you then grow out of really didn’t match up with what I was trying to say.[5] Although to be fair, this post has traveled far afield from what I intended when I started writing, and I’m honestly not certain I ended up making a particularly strong point despite all the words.

Maybe if I’d had more time I could have just said “different people see different things in shared situations because of differences in context, experience, and priorities, and it’s important to take multiple perspectives into consideration when making important decisions.” Maybe not. Brevity has never been my forte.

[1] Please feel encouraged to read the whole thread, as it went in a few different directions that this post doesn’t touch on directly. I also did explicitly ask Jeff’s permission to reference the conversation and image in a blog post… although I doubt either one of us expect it to arrive quite as late as it did.

[2] I qualify my summary of Jeff’s conclusion because I’m only working with the information available to me in the Twitter conversation, and I do not know that this is a complete or accurate summary. I suspect it is at least accurate, but only Jeff can say for sure.

[3] Please pause for a moment to reflect on how difficult it was to type in that sentence without editing it for clarity and grammar.

[4] Yes, I understand that this is an excerpt from one email that shows only the most narrow slice into what is likely a complex environment, and that it is not realistic to expect every aspect of that complexity to be included in three sentences – no matter how long that final sentence might be.

[5] Thanks to everyone who shared their thoughts on Twitter about the wisdom of using a Bible verse in a technical blog post.

Sometimes culture is life or death

On By Matthew RocheIn Data Culture, Metaphors1 Comment

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.

When does memory die?

On By Matthew RocheIn Data Culture, Data Governance, Metadata, MetaphorsLeave a comment

My recent post on metadata and Indian food has gotten more traffic than most posts on this blog, and it has also received more comments and discussion as well. One comment in particular, from Jessica Jolly, really resonated with me:

Love the analogy. My personal favorite analogy regards old family photos. If no one takes the time to write on the back of the photo the who/what/where/when/why (i.e. the metadata), that photo will get thrown away.

Not everyone agreed that this was a great analogy. Khürt Williams in particular called out the inherent value of some data independent of any metadata to give it context.

No one throws away old family photos because they lack who/what/where/when/why. In fact, I would argue that with family photos the metadata lives in the minds of the people in the photograph or some family member you haven’t yet spoken to.

Somethings have value way beyond their metadata.

These comments got me thinking, and made me ask myself: when does memory die?

I’ve seen many variations on this quote[1], but I don’t know who said it first:

You only live as long as the last person who remembers you.

This may be a Russian proverb or it may be a quote from Westworld, but I believe the principle applies as much to business data as it does to family photos, despite the obvious differences between the two.

Looking at the family photos context first I can clearly recall times in my life, in those dark days following a funeral or a divorce, when family photos were discarded and the lack of metadata was a contributing factor. The photos of close relatives were kept, but those of more distant relatives were at risk.

When you’re asking “should I keep this photo?” and the next question is “who are these people?” the answer to the second question is going to influence the answer to the first.

As a specific example, I’d like to share a photo that hangs above the one-handed swords[2] in my hallway.

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I don’t know who this is.

This photo was in the home of my wife’s grandmother, who passed away almost 20 years ago. We found it when we were cleaning out her house after her funeral; it was in the attic, not on display, and no one knew who this young man might be. A few relatives thought that he was a cousin or second cousin of my wife’s late grandmother who went to the great war and never returned – but no one was certain. There was writing on the paper backing the frame, but it was faded and smudged by the years, and by the time we discovered the photo the words were illegible.

By the time we discovered the data, the metadata was no longer usable, and any subject matter expert who could have shared the deeper context of the data had long since moved on.

And once you phrase it like that, it starts to sound familiar again.

In far too many business contexts the metadata lives only in the minds of the people who create and work with the data. It’s tribal knowledge – just like unlabeled family photographs. But as people move on to new jobs and the business changes over time, that tribal knowledge is lost. Even though the data may still be the same, and may still be valuable, when the people move on the tribal knowledge leaves with them. At this point it will either be organically rediscovered and recreated, or the data will stagnate because no one remembers anymore why it was important. Or, as is the case with the photo above, the data may be used and applied to a different purpose.

Tribal knowledge is a lousy metadata solution, no matter the context. Because tribal knowledge is inherently transitory and lossy, we should strive to capture metadata in a more systematic way, and to keep the metadata as close to the data as possible.

Because eventually memory will die. And some things are too important to forget.

[1] My favorite variation may be from Manowar, who remind us that only courage and heroism linger after death… but it would be a stretch even for me to incorporate this into the body of the post. This is why we have footnotes.

[2] I call out the fact that these are the one-handed swords because the two-handed swords hang in a different hallway, and there isn’t enough room for a photo above them.

Foster Kittens and Managed Self-Service BI

On By Matthew RocheIn Metaphors, Power BI, VideoLeave a comment

My family is a foster family for cats and kittens from the cat shelter where we’ve adopted some of our own cats. Usually a litter will stay with us for a month or two, but it depends on the kittens themselves, and on external factors.

While the kittens are with us, it’s our responsibility to help them grow, both physically and socially. The experts at the shelter are always available if we need help, but for the most part we have the knowledge and tools we need to be successful. In many cases we’re their first real exposure to humans, and we can prepare them to be loving and playful members of a family. Just not our family.

Once the kittens are ready to be adopted, we take them to the shelter, where they will be carefully matched with their forever family. This last part is important – it’s hard enough to let go, and knowing that each of them will find a good home is what makes it possible.

It’s really the best of both worlds – kind of like managed self-service BI with Power BI.[1]

 

Not unlike fostering kittens, managed self-service BI can be the best of both worlds. As an analyst working in Power BI, you can often pick up projects when the scope is still small and manageable, and when you can have fun playing around with the data and seeing what it’s likely to become.

I’m emphasizing the “managed” in managed self-service BI, because it’s best to not be completely on your own. Having someone backing you up, someone with the expertise and resources to get you through challenging spots with a helping hand, is just as important with BI as it is with kittens. An author on his own may make avoidable mistakes with long-term consequences, but a center of excellence or community of practice can provide training up front, and assistance along the way so the finished self-service solution is ready to grow up – and growing up is an important goal.

Just as my family includes our adult cats, that analyst working in Power BI has a day job. If we kept each litter of kittens we foster, things would soon become messy and unmanageable. If an analyst retained ownership of every Power BI solution he developed, he would struggle to stay on top of his core priorities.

Being able to hand off a self-service solution to a central BI team is what gives this story a happy ending. The BI team can give the analyst’s work the long-term home it deserves, and the analyst can get back to his job… while also keeping an eye open for the next self-service BI challenge to come along and steal his heart.

Of all the kittens I have loved, I miss Tiny the most.
Your head. I will bite it now.[2]
If you’re interested in learning more about the shelter where we volunteer, please visit the Meow Cat Rescue web site. Please also consider donating while you’re there – the global pandemic is making it harder for their awesome staff and volunteers to do what they do, and kitten season is upon us. If you appreciate the BI Polar blog and its companion YouTube channel, there’s no better way to say thank you than to donate to Meow. Even a small donation will help.

[1] I hope you saw that one coming

[2] This footage of Tiny attacking my head didn’t fit into the Power Kittens video, but I shared it on Twitter because it was just too cute to not share.

Metadata is not a “nice to have”

On By Matthew RocheIn Cooking and Baking, Data Culture, Data Governance, Metadata, Metaphors, Power BI15 Comments

I love to cook, and over the past few days I’ve made a few of my favorite Indian recipes that I haven’t made in a while. So, of course, this has me thinking about metadata.

IMG_20200319_193323
Homemade naan and chicken tikka masala

Going from Indian cooking to metadata isn’t as big a leap as you might think. The bridge is one of my favorite cookbooks: Julie Sahni’s Classic Indian Vegetarian and Grain Cooking.

If you’re just here for the food, you should immediately make this spectacularly delicious Bengal red lentil recipe taken from this book, because it is absolutely phenomenal. If you’re here for the metadata, remember the link but don’t click on it yet.

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Every recipe I’ve made from this cookbook has produced fantastic results. It’s one of those go-to cookbooks where I know that anything I try will be good. And yet, I almost never seek it out when I want to cook, except for the recipes I already know. The reason is metadata.

It doesn’t matter how good your data is – without effective and available metadata, your investment in quality data will be undermined.

Let’s look at the recipe for saag paneer. Say those words out loud (“saag paneer”) and images of that rich, vibrant green sauce will start running through your mind.

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I found this recipe easily because I have a bookmark. But let’s say I didn’t – it should still be easy to find, because cookbooks have indexes, and indexes are the perfect tool for finding recipes. Let’s find the recipe for saag paneer.

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Oh, there’s no entry for saag paneer, or for saag?
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There’s no entry for paneer? There are so many paneer recipes in this book!
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Ok, we’re making progress… I think.
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There it is. Maybe? It still doesn’t say saag paneer anywhere.

Literally the only place the phrase “saag paneer” exists in this book is below the recipe header. This means that the only way to find the saag paneer recipe is to flip through the book page by page, or to know the specific and arbitrary phrase the author uses to describe the recipe for Western readers. This is why my copy of the book looks like this[1]:

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This systemic problem is exacerbated by the book’s complete lack of photos; there’s also no way to skim through the book and quickly visually identify recipes of relevant interest. The reader is forced to carefully evaluate each recipe in turn, looking at ingredients and processes to decide if the recipe is worth making.

At this point you may be asking what this has to do with metadata[2] or you may see the connection already.

The reason I immediately thought of metadata may be related to a BI effort I’m working on. Without going into too much detail, I have built a small Power BI app that presents information from a program I run and makes that information available to other members of my extended team.

I’m currently at the point where my app needs to include data from other sources in order to increase its value. Fortunately, that data already exists, and to make it even easier to work with, it is available as a set of Power BI dataflows. I was able to email the owner to get access[3] and to learn which dataflows to look in, and I was off. But not for very far, or for very long.

Very quickly I was back where this post started: I was faced with the high-quality data I needed, and I lacked the metadata to efficiently use it. I needed to manually evaluate each dataflow and each entity to understand its contents and context and to decide if it was right for me. I made some early progress, but because of the lack of metadata the effort will likely take days not hours, and this means it probably won’t get done this month or next.

Let that sink in: because of a lack of effective metadata, quality curated data is going unused, and business insights are being delayed by weeks or months[4].

Just like these fantastic recipes sitting on my shelf, largely unused and unmade because a fantastic cookbook lacks a usable index, these fantastic dataflows are going largely unused, at least by me. All because metadata was treated as a “nice to have” rather than as a fundamental high-priority requirement.

Does your data have the metadata it needs, in a format and location that serves the needs of your users? How do you know? Remember that last picture of all the bookmarks[5]?

These bookmarks are a symptom of the underlying metadata problem. Bookmarks aren’t a problem themselves, but if you’re paying attention you can see that they’ve been implemented as a workaround to a problem that might not otherwise be apparent. If you’re familiar with the concept of “code smells”, you probably see where I’m going.

When your data lacks useful metadata to enable its effective use, people will start to take actions because of this lack. Things like emailing you to ask questions. Things like building their own ad hoc data dictionaries. Things like using alternate or derivative sources instead of using your authoritative data source – like the recipe link I shared above.

The more of these actions you identify, the more urgency you should feel about closing the metadata gap. Not every data source is a werewolf, but every data source requires metadata to be effectively and efficiently used.

Requires.

[1] Remember this picture. There will be a quiz later.

[2] You may also be asking if there’s anything in life that doesn’t make me think about metadata. This is a fair question.

[3] I knew the owner’s email because I had bookmarked it earlier.

[4] To be fair, my full schedule is also contributing to this delay – I’m not trying to say that the lack of metadata is independently costing months. But it is a key factor: my schedule could accommodate two or three hours for this work, but it doesn’t have room for two or three days until the end of April.

[5] I told you there would be a quiz.

Viral adoption: Self-service BI and COVID-19

On By Matthew RocheIn Data Culture, Data Governance, Generic Blather, Heavy Metal, Metaphors, Patterns, Power BI1 Comment

I live 2.6 miles (4.2 km) from the epicenter of the coronavirus outbreak in Washington state. You know, the nursing home that’s been in the news, where over 10 people have died, and dozens more are infected.[1]

As you can imagine, this has started me thinking about self-service BI.

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Where can I find information I can trust?[2]
When the news started to come out covering the US outbreak, there was something I immediately noticed: authoritative information was very difficult to find. Here’s a quote from that last link.

This escalation “raises our level of concern about the immediate threat of COVID-19 for certain communities,” Dr. Nancy Messonnier, director of the CDC’s National Center for Immunization and Respiratory Diseases, said in the briefing. Still, the risk to the general public not in these areas is considered to be low, she said.

That’s great, but what about the general public in these areas?

What about me and my family?

When most of what I saw on Twitter was people making jokes about Jira tickets[3], I was trying to figure out what was going on, and what I needed to do. What actions should I take to stay safe? What actions were unnecessary or unhelpful?

Before I could answer these questions, I needed to find sources of information. This was surprisingly difficult.

Specifically, I needed to find sources of information that I could trust. There was already a surge in misinformation, some of it presumably well-intentioned, and some from deliberately malicious actors. I needed to explore, validate, confirm, cross-check, act, and repeat. And I was doing this while everyone around me seemed to be treating the emerging pandemic as a joke or a curiosity.

I did this work and made my decisions because I was a highly-motivated stakeholder, while others in otherwise similar positions were farther away from the problem, and were naturally less motivated at the time.[4]

And this is what got me thinking about self-service BI.

In many organizations, self-service BI tools like Power BI will spread virally. A highly-motivated business user will find a tool, find some data, explore, iterate, refine, and repeat. They will work with untrusted – and sometimes untrustworthy – data sources to find the information they need to use, and to make the decisions they need to make. And they do it before people in similar positions are motivated enough to act.

But before long, scraping together whatever data is available isn’t enough anymore. As the number of users relying on the insights being produced increases – even if the insights are being produced by a self-service BI solution – the need for trusted data increases as well.

Where an individual might successfully use disparate unmanaged sources successfully, a population needs a trusted source of truth.

At some point a central authority needs to step up, to make available the data that can serve as that single source of truth. This is easier said than done[5], but it must be done. And this isn’t even the hard part.

The hard part is getting everyone to stop using the unofficial and untrusted sources that they’ve been using to make decisions, and to use the trusted source instead. This is difficult because these users are invested in their current sources, and believe that they are good enough. They may not be ideal, but they work, right? They got me this far, so why should I have to stop using them just because someone says so?

This brings me back to those malicious actors mentioned earlier. Why would someone deliberately share false information about public health issues when lies could potentially cost people their lives? They would do it when the lies would help forward an agenda they value more than they value other people’s lives.

In most business situations, lives aren’t at stake, but people still have their own agendas. I’ve often seen situations where the lack of a single source of truth allows stakeholders to present their own numbers, skewed to make their efforts look more successful than they actually are. Some people don’t want to have to rebuild their reports – but some people want to use falsified numbers so they can get a promotion, or a bonus, or a raise.

Regardless of the reason for using untrusted sources, their use is damaging and should be reduced and eliminated. This is true of business data and analytics, and it is true of the current global health crisis. In both arenas, let’s all be part of the solution, not part of the problem.

Let us be a part of the cure, never part of the plague – we’ll only be remembered for what we create.[6]

[1] Before you ask, yes, my family and I are healthy and well. I’ve been working from home for over a week now, which is a nice silver lining; I have a small but comfortable home office, and can avoid the obnoxious Seattle-area commute.

[2] This article is the best single source I know of. It’s not authoritative source for the subject, but it is aggregating and citing authoritative sources and presenting their information in a form closer to the solution domain than to the problem domain.

[3] This is why I’ve been practicing social media distancing.

[4] This is the where the “personal pandemic parable” part of the blog post ends. From here on it’s all about SSBI. If you’re actually curious, I erred on the side of caution and started working from home and avoiding crowds before it was recommended or mandated. I still don’t know if all of the actions I’ve taken were necessary, but I’m glad I took them and I hope you all stay safe as well.

[5] As anyone who has ever implemented a single source of truth for any non-trivial data domain can attest.

[6] You can enjoy the lyrics even if Kreator’s awesome music isn’t to your taste.

Data culture and the centerline

On By Matthew RocheIn Data Culture, Data Governance, Metaphors7 Comments

I’m running behind on my own YouTube publishing duties[1], but that doesn’t keep me from watching[2] the occasional data culture YouTube video produced by others.

Like this one:

Ok… you may be confused. You may believe this video is not actually about data culture. This is an easy mistake to make, and you can be forgiven for making it, but the content of the video make its true subject very clear:

A new technology is introduced that changes the way people work and live. This new technology replaces existing and established technologies; it lets people do what they used to do in a new way – easier, faster, and further. It also lets people do things they couldn’t do before, and opens up new horizons of possibility.

The technology also brings risk and challenge. Some of this is because of the new capabilities, and some is because of the collision[3] between the new way and the old way of doing things. The old way and the new way aren’t completely compatible, but they use shared resources and sometimes things go wrong.

At the root of these challenges is users moving faster than any relevant authorities. Increasing numbers of people are seeing the value of the new technology, assuming the inherent risk[4], and embracing its capabilities while hoping for the best.

Different groups see the rising costs and devise solutions for these challenges. Some solutions are tactical, some are strategic. And eventually some champions emerge to push for the creation of standard solutions. Or standards plural, because there always seems to be more than one of those darned things.

Not everyone buys into the standards at first, but over time the standards are refined and… actually standardized.

This process doesn’t slow down the technology adoption. The process and the standards instead provide the necessary shape and structure for adoption to take place as safely as possible.

With the passage of time, users take for granted the safety standards as much as they take for granted the capabilities of the technology… and can’t imagine using one without the other.

For the life of me I can’t imagine why they kept doubling down on the “lane markings” analogy, but I’m actually happy they did. This approach may get more people paying attention – I can’t find any other data culture videos on YouTube with 488K views…

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[1] Part of this is because my wife has been out of town, and my increased parental responsibilities have reduced the free time I would normally spend filming and editing… but it’s mainly because I’m finding that talking coherently about data culture is harder for me than writing about data culture. I’ll get better, I assume. I hope.

[2] In this case, I watched while I was folding laundry. As one does.

[3] Yes, pun intended. No, I’m not sorry.

[4] Either through knowledge or through ignorance.

Video: A most delicious analogy

On By Matthew RocheIn Cooking and Baking, Metaphors, Patterns, Power BI, Recipe, Video5 Comments

Every time I cook or bake something, I think about how the tasks and patterns present in making food have strong and significant parallels with building BI[1] solutions. At some point in the future I’m likely to write a “data mis en place” blog post, but for today I decided to take a more visual approach, starting with one of my favorite holiday recipes[2].

Check it out:

(Please forgive my clickbaitey title and thumbnail image. I was struggling to think of a meaningful title and image, and decided to have a little fun with this one.)

I won’t repeat all of the information from the video here, but I will share a view of what’s involved in making this self-service BI treat.

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When visualized like this, the parallels between data development and reuse are probably a bit more obvious. Please take a look at the video, and see what others jump out at you.

And please let me know what you think. Seriously.

[1] And other types of software, but mainly BI these days.

[2] I published this recipe almost exactly a year ago. The timing isn’t intentional, but it’s interesting to me to see this pattern emerging as well…

It all comes down to culture

On By Matthew RocheIn Data Culture, Diversity, Generic Blather, Metaphors, Power BILeave a comment

I talk about data culture a lot, and in my presentations I often emphasize how the most important success factor when adopting a tool like Power BI[1] is the culture of the organization, not the tool itself.

I talk about this a lot, but I think Caitie McCaffrey may have just had the final word.[2]

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I don’t think that Caitie was talking about the enterprise adoption of self-service business intelligence, but she could have been.

In my day job I get to talk to leaders from large companies around the world, and to see how they’re adopting and using Power BI, Azure. Before today I didn’t think of Moby Dick – I thought of Leo Tolstoy’s classic Anna Karenina, which starts with this classic line:

All happy families are alike; each unhappy family is unhappy in its own way.

Although the details vary, large companies that have successfully adopted managed self-service BI at scale have cultures with important aspects in common:

  • Leaders empower business users to work with data
  • Leaders trust business users to use data to make better decisions
  • IT supports business users with platforms and tools and with curated data sources
  • Business users work with the tools from IT and the guidance from leaders, and work within the guardrails and guidelines given to them for this use
  • Business and IT collaborate to deliver responsive solutions and mature/stable solutions, with clearly defined responsibilities between them

Companies that are successful with managed self-service BI do these things. Companies that are not successful do not. The details vary, but the pattern holds up again and again.

How do these roles and responsibilities relate to culture?

In many ways a culture is defined by the behaviors it rewards, the behaviors it allows, and the behaviors it punishes. A culture isn’t what you say – it’s what you do.

In the context of BI, having a culture with shared goals that enable business and IT to work together with the support from the company leaders is the key. If you have this culture, you can be successful with any tool. Some tools may be more helpful than others, and the culture will enable the selection of better tools over time, but the tool is not the most important factor. The culture – not the tool – inevitably determines success.

This is not to say that BI tools should not improve to be a bigger part of the solution. But to paraphrase Caitie… maybe you should let that white whale swim past.

 

[1] But definitely not only Power BI.

[2] He says unironically, before writing many more words.