Power BI Strategy - What an Honest BI Strategy Looks Like for an Australian Organisation

Most BI strategies I read are written for a company that doesn't exist. They assume a clean data estate, a unified leadership team, a stable budget, and a workforce that has time to learn new tools. The companies I actually work with have legacy ERPs that emit CSVs, a finance team that builds reports in Excel, an IT team that is stretched, and a CEO who wants insight on his phone by Tuesday. The gap between the strategy doc and the reality is where a lot of BI investment gets buried.

Microsoft's BI strategy guidance for Power BI is a useful document. It covers the right ground. But if you tried to implement all of it in one go, in a typical Australian mid-market business, you would never finish. So this post is about how I'd actually use that guidance, what I'd prioritise, and what I'd quietly skip on the first pass.

Start with the business question, not the tooling

Every strategy doc I write starts the same way. What decisions are people trying to make? Which decisions are being made badly today because the data isn't there or isn't trusted? Pick the five highest-stakes decisions that BI could improve. Those are your anchor cases.

Sounds obvious. It is not what happens in practice. What happens in practice is someone gets excited about Fabric, buys some capacity, and asks the data team to "migrate everything". Six months in, costs are up, nothing is materially better, and the exec who sponsored it has gone quiet.

The decisions framing avoids that. If you can write down "we need to know which products are losing margin within a week of the change, and right now it takes three weeks", you've got a target. Everything else either contributes to that or doesn't.

The three layers of a real strategy

Microsoft's framework talks about strategic, tactical, and operational layers. I think of it slightly differently. There are three things that have to line up.

Sponsorship. Someone senior, with budget, who wants BI to succeed for reasons they can articulate. Without this, you will fail. Not maybe, will. Every successful BI rollout I've been part of had an exec who would take phone calls about it on a Sunday because they cared.

Capability. People who can actually build and maintain things. This is a mix of central data team capability and self-service users with enough skill to do their own analysis. The Microsoft guidance has a lot to say about champions, communities of practice, and a centre of excellence. The labels matter less than the function. Are there people who know what they are doing and can help others?

Architecture. The technical setup. Workspaces, capacities, data sources, semantic models, governance rules. This is where the consultants tend to live, because it is the thing they understand best. But it is the least important of the three. A great architecture with no sponsorship and no capability is a museum.

When clients ask me where to invest first, the answer is almost always sponsorship and capability. The architecture can be fixed later. The other two can't.

What I'd put in your first six months

If I had to build a BI strategy from scratch for a mid-sized Australian organisation, here is what the first six months would look like.

Month one: pick the five anchor decisions. Map the data sources needed for each. Run a brutally honest data quality assessment. Most teams I've worked with skip this and pay for it for years afterwards. The data is always worse than people think.

Month two and three: build the first two anchor reports end to end. Not pretty. Just working. Get them into the hands of the decision-makers. Watch what they do with them. Find out what is wrong, what is missing, what is misunderstood. This is where the strategy meets reality.

Month four: based on what you learned, refine the data model. Establish the first version of a semantic model that you actually want to be the canonical version. Move the first two reports onto it. Start documenting decisions about naming, definitions of measures, what "active customer" means, all the small things that turn into religious wars later.

Month five: open up self-service. Pick a small group of people in the business who have shown interest. Give them access, give them training, give them the semantic model. Let them build their own things. Watch them.

Month six: review. What worked? What broke? Who is using it? Who isn't? Make a real decision about whether to expand, double down, or step back. Don't just keep going because you've already started.

That is a six-month plan that earns the right to a second six months. It is far less ambitious than most strategy decks. It is also more likely to still be running in two years.

Where Fabric fits

Microsoft has clearly bet on Fabric as the future of the data and BI stack. The licensing pushes you toward Fabric capacities for advanced features. The integration story across data engineering, data science, real-time, and BI is genuinely better than the old fragmented setup. If you are starting from a blank page in 2026, you'd build on Fabric.

But most Australian organisations don't have a blank page. They have a Synapse instance from 2022, a Power BI tenant that has been around since 2019, an Azure SQL data warehouse, maybe some Databricks, and a bunch of Excel models that finance refuses to let go of. The Fabric migration story for an existing stack is not as smooth as the marketing suggests.

So a realistic strategy treats Fabric as a destination, not the starting point. Some new workloads land on Fabric from day one. Some existing workloads stay where they are until there's a real reason to move them. Some never move because the cost-benefit doesn't justify it. The strategy document should be honest about which is which.

We help organisations work through these tradeoffs as part of Power BI consulting and Fabric implementation engagements. The honest answer is almost always "it depends on what you've already got". A pure Fabric strategy that ignores your existing estate will be expensive and slow.

The governance section nobody reads

Every BI strategy has a governance section. It is the bit that gets written, gets reviewed by legal, gets bolted to the front of the document, and then never gets opened again.

If you want governance to be real, write less of it and put more of it into the platform. Sensitivity labels enforced by the tenant. Workspace permissions that match the org chart. Certified datasets that are easy to find and trust. A single source of truth for measures, in the semantic model, not in a Word document.

Process governance only works for things you can't automate. Who can request a new workspace. Who decides what gets certified. How a report owner transitions when someone leaves. Keep that to one page. The rest should be enforced by the tools.

What to watch out for

A few things I see go wrong often enough that they are worth flagging.

The exec dashboard built once and never updated. Someone builds a beautiful executive dashboard. The exec loves it. Then the underlying data changes, the report breaks, and nobody owns fixing it. Decide who owns each report and what happens when it fails.

The shadow BI estate. While IT builds the official thing, the business builds its own using Excel and personal Power BI accounts. By the time the official thing is ready, the business has built workarounds and trusts those more. The strategy needs to either bring the shadow estate into the tent or make peace with its existence. Pretending it isn't there is the worst option.

The cost surprise. Fabric capacity is not cheap. Premium per user is not cheap. Pro licences at scale add up. Most strategies don't model the running cost properly. The deal you signed in year one might look very different in year three when your data volumes have grown. Build a real cost model. Revisit it every six months.

The vendor lock-in risk. Microsoft's stack is a good stack. It is also a deep commitment. If you build your entire data estate around Power BI, Fabric, and Azure, switching becomes expensive. That is not necessarily bad. It is a risk to be aware of and price accordingly.

My honest take

A BI strategy is more about discipline than about ideas. The ideas are out there. Microsoft has written them down. So have a dozen consulting firms. What separates the organisations that succeed from the ones that don't is the willingness to start small, measure what they're doing, kill things that aren't working, and resist the temptation to boil the ocean.

If you can do that, almost any reasonable strategy framework will get you there. If you can't, no strategy will save you.

When we help clients put together a strategic AI and data plan, this is the conversation we end up having more than any other. The framework is the easy bit. The hard bit is the organisational discipline to follow through.

Reference: Microsoft Learn - Power BI implementation planning: BI strategy overview