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In this episode, Scott interviewed Ryan Dolley, an Independent Business Intelligence (BI) Consultant.
Before we jump in, lots of things in the key takeaways are marked as potentially controversial. Because much of what Ryan covered hasn’t really been stated by a lot of people. So until we have more consensus, of course things _could_ be controversial.
Some key takeaways/thoughts from Ryan’s point of view:
- Begin with the end in mind. It’s easy to lose focus on what you are trying to accomplish instead of what steps you are taking. Focus on what the target outcome is and use that as a North Star to measure if you need to course correct.
- BI people need to brace themselves for a wave of innovation coming. There is so much – hopefully positive – change coming up the stack and BI people can embrace it or get washed over by the wave. Embrace and ride that wave and upskill!
- ?Controversial?: Data mesh – and just about every other paradigm – does not focus enough on the last mile of analytics, at least not explicitly. So we need to get a lot more specific about what is necessary to actually take advantage of upstream improvements in data to deliver better analytics.
- !Important!: We need to get more specific on who does cross-domain BI in a decentralized world. Otherwise, we have interoperable data but no one specifically leveraging that interoperability for improving our understanding of the overall organization.
- BI as a practice needs to be much better at understanding and implementing iterative feedback. It’s not been part of the playbook to date.
- ?Controversial?: If domains develop BI capabilities, how will they interact with a central BI team if one exists? Historically, they haven’t gotten along well…
- ?Controversial?: Your central BI / Data warehouse team should get converted into an insight generation engine team.
- We need to figure out if BI is a primary role inside the domain or if it is a skill people inside the domain need to develop as part of their roles. Which is preferable is likely dependent on the size, complexity, and data value of the domain.
- BI can’t be a data mesh afterthought – we’ll get to it when we get to it isn’t going to cut it. It might not be literally part of the data mesh implementation but it must be part of your overall data strategy. Otherwise, why are you doing data work if not to generate and take action on insights?
- !Controversial!: The semantic layer is likely to play an important role in data mesh. Scott note: depends what you mean by semantic layer. See Zhamak’s Corner #12 for more on the dangers of layers.
- BI teams must become part of the insight generation and resulting action conversations. That means implementing product thinking instead of simply responding to requests. But most are not ready for that mindset shift just yet.
- ?Controversial?: Domains should own an element of BI and not just share their data via data products but should generate and share their insights as part of data mesh.
- Product oriented thinking is not widely prevalent in the BI world – e.g. most BI teams do not understand the usefulness/usage of their BI assets or even have monitoring setup to track usage.
- !Controversial!: The BI analyst in the board room as merely a live question answer mechanism is a bad approach. It often leads to wrong answers with no meaningful quality control.
- BI people need to adopt product thinking and create meaningful relationships with users so they can evolve what they produce to continue to suit user needs. Blindly producing the same report for 4 years into the void needs to go the way of the dodo.
- !Important but Annoying!: We will probably have to meet some people where they are when it comes to sharing insights. Trying to upskill everyone to being an SQL whiz isn’t realistic. Allowing people to export into Excel for further manipulation isn’t going anywhere…
- Part of product thinking is designing to the user. That’s pretty rare in data. What are they – the data consumer – specifically trying to do and how can the producing team support that? Instead of what does the producing team want to share, which is the common current method.
- Dashboards aren’t dead, they are the best way to answer known questions in an oversight and management kind of way.
- ?Controversial?: BI is possibly the most important part of your data culture simply because for the vast majority of your organization, they will interact with data mostly at the BI level. But too many organizations leave BI as an afterthought in data culture because it will be where the most people need the most help and thus is the most difficult aspect. People change is hard 🙂
- ?Controversial?: In a successful data mesh implementation, there will be less work on the BI team because a lot more will be pushed down to the data product. The more the data product can share the insights inherently without an interpreter – the BI team – in the middle, the better.
Ryan started with a bit of inspiration and/or a call to action to fellow business intelligence people: data mesh and a lot of other movements in data are creating a massive wave of innovation further up the data stack / data and analytics process. So BI people can either get with the wave and ride the changes to greater value or get washed over by it. Ride that wave 🙂 Upskilling can be painful but it can mean a major upgrade in capabilities and value. And being highly tech-capable in BI is a great career move right now.
According to Ryan, the last mile of analytics where BI really fits has been somewhat ignored in most modern data approaches and technical innovations. Tableau defined an era of BI and while Tableau isn’t going away, that era is coming to a close. So he believes things like data mesh need to focus more on the last mile of analytics – or at least specifically call things out more often as to what changes and how in BI. Whether we call that self-serve or anything else. What does the upstream changes from data mesh mean for BI? How can we take advantage of these improvements in data processing, ownership, quality, etc. to deliver actual insights to those that matter?
Ryan asked how should we actually do corporate BI in a data mesh type setup? Right now pre data mesh, most organizations have centralized BI teams. And while domains will probably need some BI capabilities, a recurrent theme in the podcast is that most exec questions are not single domain questions. So we build this huge amount of interoperable data, who is in charge of bringing that together at the corporate level? Who actually owns creating and maintaining C-suite and board-level dashboards and reports? Are we expecting execs to build those themselves? 🤣 And historically, central BI / Data warehouse teams haven’t really loved embedded BI teams, often calling them “shadow IT”. Can they play nicely together? If yes, how?
There is often too much of a ‘we’ll cross that bridge when we come to it’ relative to BI as part of most modern data strategies according to Ryan. While BI may not actually be part of the exact data mesh implementation at some organizations – there’s all kinds of definitions and each org is extending the self-serve platform to encompass different aspects of analytics – BI definitely should be part of your data strategy. Otherwise, what is the point of doing data work if not to, you know, generate actionable insights? This is especially an issue when trying to embed insights and analytics in general into other applications.
An important aspect of a data mesh implementation for Ryan is what is left to the BI tool and what is pushed down into the data product*. There isn’t a lot of guidance or discussion happening exactly on what is a best practice or how people are implementing embedded insights, just that it’s the future. There needs to be far more conversation about who owns data throughout the data and analytics process lifecycle. Is it all on domains?
*Scott note: episode #40 w/ Xavier Gumara Rigol covers this well
For Ryan, there are a few reasons why the general data warehouse has not been as successful as it could be. To actually interact with the data warehouse, people need a decent level of technical and BI skill. So when it comes to data mesh, we don’t want there to be an even higher skill level required. So domains likely need to own some of their own BI, sharing insights from the domain in a consumable way to the rest of the organization. We can’t only have domains sharing data, we need to be explicit about sharing information to a purpose. Is that to get the data, the insight, or the action that should be taken based on the insight? Scott note: there will be people in your organization that are only information consumers – taking info from dashboards and reports made for them. It’s up to the organization to figure out if that is acceptable or to try to upskill those people against their will.
While product oriented thinking is picking up in more areas of data, it’s still not very common in the BI world according to Ryan. Most BI teams have no real idea of how useful any of what they produce is to the organization. Sometimes that is they don’t have the systems set up to even monitor it but if they do, they typically don’t understand how usage translates to value or honestly even look at the usage metrics. That means the BI teams lack the insights to make their BI assets as valuable as possible – what are the SLAs people need to make the most use of this data? How timely or accurate or complete does this need to be to drive the most value and be trustable? If BI people develop those relationships and that information flow, they can evolve what they produce to continue to meet needs. We need to stop producing work into the void; who is using this BI asset and why and how can we make it better and/or less costly to produce?
A really important aspect of designing usable BI is thinking about the actual user in Ryan’s view. It’s often overlooked too and is part of product thinking: what is the product market fit for the BI asset / data product you are developing? How do people want to interact with it? How far along the pathway from raw data to insight to “so what” – the action that should be taken – is it the producer’s responsibility to own and do the actual work? Does the consumer understand and agree? How can we generate things in data to create understanding and trust with the minimal amount of effort? Dashboards are great if you’re monitoring a known question but we also need to get far better at answering how do we persuade someone with data.
Ryan believes BI people need to move past the idea that the output is the end of the process. That dashboard or report or whatever is a communication mechanism about what is currently known. But it’s not as if there is a single answer that stays the same. How many customers do we have is not a one-time answer question. There needs to be a lot of work to bring more iteration into BI, especially collaborating with consumers as both sides learn more about questions they are trying to answer. So BI teams need to move past being order takers and move to being part of the productization process around data and insights. Iterating in BI is not common practice but needs to be!
BI is probably the most important aspect of your data culture according to Ryan. That’s simply because it’s the place where the vast majority of your organization will interact with your data, at the BI level. Whether that is in generating insights or merely consuming them, BI should not be left until the end of your data strategy. But it often is left to the end specifically because it is where there is the most human interaction and change management and that’s the hardest part of change management, dealing with those pesky humans. Don’t try to put it off until the end as it will just create more pain than is necessary.
Ryan said “begin with the end in mind.” It’s quite easy in data work to get lost in the how instead of the why. The how is the fun/interesting technical challenges part. What are you trying to achieve? BI at the end of the day is not about generating insights, it’s about supporting actions that help the business. So how do you build your BI strategy into your data mesh implementation strategy so you are not just making data available and trustable? How can you move past just generating insights? How do you think about the end – making smarter business decisions driven by trustable data – and work backwards? Don’t lose sight of what you are trying to achieve.
Ryan wrapped up the conversation by returning to the crucial points of integrating in product thinking to BI and upskilling. We can go further than we ever have before with data and BI but we have to embrace new ways of working, both on the strategy/data culture, and the technology. But just trying to advance your BI practice with technology is going to miss a whole lot of the value.
We have to figure out in the domain if BI is a primary role or is it part of the necessary skillsets involved but not a separate role. It will probably be domain dependent, not even organization dependent, based on the complexity, size, and data value of the domain.
Right now, most organizations want to embed insights into their general flow applications – e.g. the CRM for the sales team – but they aren’t doing the back-end work to make that possible.
Putting a business analyst in the boardroom simply as a vehicle to live answer questions usually causes more issues than it solves. It leads to a high pressure environment where you can’t be sure of quality. Scott note: fully agree. There’s a long rant I have about this…
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