Problem Domain, Solution Domain

I’ve been thinking about problem domains and solution domains a lot lately. I’ve posted on this topic before, but the more I think about it, the more I think I should explore it more. Starting now.

Image of interlocking puzzle piece hearts from https://pixabay.com/illustrations/puzzle-heart-love-two-hearts-1721619/
When the right problems meet the right solutions, it can be magical

Let’s begin by defining our terms.[1]

A problem domain is a subject area where people work.

If you’re a business intelligence or data professional, the problem domains of interest are often a business function like finance, supply chain or HR. The problem domain experts[2] are typically people who are work in one of these fields, and who might come to you looking for solutions, or for help building solutions.

A solution domain is also a subject area where people work.

If you’re a business intelligence or data professional, the solution domains of interest are often some combination of data visualization, data modeling, data transformation, and so on. They may also be DAX, Power Query, Power BI, or another specific set of tools and technologies.  The solution domain experts[3] are typically people who build data and BI applications and systems to solve problems in other problem domains.

On the other hand, if you’re a member of the Power BI product team[4], the solution domain you’re working in is commercial software development – and the problem domain of interest is building, deploying, monitoring, and/or administering Power BI solutions. Everything is relative, and whether a given subject area is a problem domain or a solution domain is a function of the context in which it is being evaluated.

Let’s pause to let that sink in for a minute. None of the information above is particularly new, and it may not seem profound at first glance, but these two terms are some of the most foundational concepts of building a data culture.

A successful and mature data culture is the product of the right people doing the right things with the right data as part of the right processes.[5] This means that a successful and mature data culture involves solution domain experts and problem domain experts having healthy partnerships and mutual respect… which is also a foundational concept that sounds simple until you look at it more closely.

If you think about the traditional relationship between business and IT, “partnership” probably isn’t the first word that leaps to mind. All too often this relationship is characterized by conflict and a lack of mutual respect that is in part a function of misaligned priorities. Like many entrenched conflicts it is also partly a function of history and  the mistrust produced by historical wrongs – actual or perceived.

Most interesting things – interesting conversations, interesting projects, interesting jobs and careers – exist at the intersection of the problem domain and the solution domain. Interesting things happen at the edges where one thing ends and another thing begins. This is where complexity is found, because multiple domains are involved and making sense requires experts in each domain.

Unfortunately this is also where things tend to go wrong. Too often things fall into the cracks between the problem domain and the solution domain. Experts in one domain don’t value the other part of the picture, or they don’t see it as their job, or they assume that someone else will figure that part out… or maybe it’s none of these things, and they just lack the vocabulary and the context to close that gap.

Please take a moment to think honestly and critically about the problem domains and solution domains in which you operate, and your relationships with the folks in other domains with whom you interact. This is all I have for today, and although I don’t know exactly where I’m going down this path, I know I’m not done – and I know a little introspection can go a long way.


[1] These are my personal definitions that I’m making up as I write this post. You’ll find other definitions if you’re willing to go looking, and although those definitions will align broadly with these, they will have different emphasis and context and nuance because they’re not mine.

[2] The subject matter experts in a given problem domain.

[3] The subject matter experts in a given solution domain.

[4] Or another team building commercial tools for data and analytics.

[5] And the state in which these things are the norm and the exception.

5 thoughts on “Problem Domain, Solution Domain

  1. Pingback: Between the problem domain and the solution domain – BI Polar

  2. Bob Kundrat

    “A successful and mature data culture is the product of the right people doing the right things with the right data as part of the right processes.[5] This means that a successful and mature data culture involves solution domain experts and problem domain experts having healthy partnerships and mutual respect… which is also a foundational concept that sounds simple until you look at it more closely.”

    On my 1st read of this post, I missed the link to the earlier April post, so I just read that one. All 3 combined (including the follow up to this one) are very intriguing. You note that in this post by stating that the information itself isn’t particularly new or profound but is foundational to building a data culture.

    Where I work, we’re on the road to building a better data culture and the quote I added above resonates with me. I’m going to take some time and think critically about the domains and relationships as I do think that will be helpful.

    At the same time, there’s a lot to unpack in the “right people”, “right things”, “right process” and “healthy relationships” statements. There’s a lot to think honestly and critically about.

    A simple question :), where should one start? Or maybe, once you’ve thought about these things a bit, what’s the next best step?

    Like

  3. Pingback: Simplifying the solution domain – BI Polar

  4. Pingback: On joining the product team – BI Polar

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