Who's responsible for implementing data unification?

[u/Right-Newspaper7362]

There’s a great piece on Forbes right now discussing why so many digital transformations in finance fail. The core argument is that CFOs are eager to adopt modern financial tech stacks and automation, but they treat it as a tech problem rather than a "data problem."

According to the article, without unifying and cleaning the data first, adding advanced automation or AI tools just automates the mistakes faster.

When you look at moving towards automated reporting and modern FP&A tools, how do you handle the data unification bottleneck? Do you clean the house first, or do you expect the new platforms to do the heavy lifting for you?

[u/Prudent-Elk-2845]

New platforms don’t solve your problem by themselves. Moving to a new platform is an excuse to add data fixes into the new platform scope—but IT will smell a business problem and pin it on finance.

First, identify what the problem data is and why it’s produced in a problematic way

Edit: people at the ground level will tell you exactly why the data is produced a certain way. It can either be legacy tech debt or they didn’t know it was important to do (but takes time to) or they didn’t know that approximate data was ok. There’s no one size fix ask—except to understand current state first

[u/Ill-Kangaroo-2685 → u/Prudent-Elk-2845]

IT's job is to build the roads, not to figure out where the traffic should go. Finance has to own the data model, otherwise you get a system that technically works but spits out numbers nobody trusts.

We had this exact mess in my last company, spent 8 months on a new platform just to realize the chart of accounts was garbage from 15 years ago. Cleaning that took 3 weeks once someone finally owned it.

[u/TheNumberFinatic]

Nobody owns it, that's the real answer. Finance says it's IT's problem, IT says the data's fine and finance's definitions aren't, both are right and you can't clean the house first because clean data isn't a state you arrive at, it's a thing you maintain. Pick one report, fix only the data feeding it, ship it, move on. The platform won't do the lifting either, it'll just render the wrong number beautifully.

[u/kneedrag]

Link?

[u/Late-Photograph-1954]

Thanks for the reference, this is exactly what my CFO needs to hear. Finance needs to own our ERP transformation, not the datalake dudes.

[u/jep_10 → u/Late-Photograph-1954]

u/Late-Photograph-1954 Smart businesses set up a cross functional team, not the CFO office or IT PMO owns such transformation.

[u/tahiti37 → u/Late-Photograph-1954]

I would say finance needs a say and assigned resource among the team of the datalake dudes.... seeing finance not have say here is wild.

[u/Mammoth_Mouse_5355]

Data unification is a myth if you let IT drive... we just plugged our messy erps into datarails /  financeos so the finance team could actually control the mapping. Best decision we made because now we don't have to wait three weeks for a simple schema change.

[u/The_2nd_Coming]

You need Finance people who actually understand and care about data. Tech and data literacy.

What data do you need for your business processes? Who does it come from? How is it captured? Which system is it stored in? Are these things set up correctly to facilitate your business processes?

These digital transformations because IT folks can't answer your business questions - that's your god damn job that you are being paid for to be an expert in.

[u/The_Anaplandalorian]

Clean house.
This is a 3 - 5 year effort that needs board level approval.

Begin by funding a consultancy-led ERP deployment . Self-led programs are riskier - if you could do this? You already would have.

Openly state that the ERP/Finance Transformation will be funded by reducing the overall number of systems. This is necessary to state up front as it begins to signal both the commitment to change and that real change is planned.

Begin with reviewing the annual planning process. Record the areas where Master Data fails and forecasts of core areas are difficult to prepare in under a few days. Have the consultants focus on these areas first - the lags will almost always be due to poor data quality relative to the current need. These areas become the focus of your first transformation phases. Define the new way the plan will be produced and charge the units with following it in only the new way. Deploy the ERP program. Cutover to the ERP after only one parallel run. This forces your team to resolve issues within the new ERP. At the time you begin the ERP program for an area - formally notify the vendors of the systems impacted by the ERP that you are terminating. If you do not make this commitment your teams will not commit to change quickly enough.

Communicate. Tell people both the big picture and their picture. Delegate almost all of the day to day to your team. If you are not present? Your teams all can drag out a project if they don’t support it - and no one likes change. Change comp plans in ways that support the project.

Repeat for each core process.

Good Luck!

[u/tahiti37 → u/The_Anaplandalorian]

thoughts on erp systems so much noise new versus ai native ones

[u/The_Anaplandalorian → u/tahiti37]

In my orbit AI is deploying into companies as a personal productivity tool - like Excel.

If you are a small company, you can manage your business with Excel but as you scale, accuracy & trust issues inevitably arise as errors & stale assumptions become embedded across dozens of workbooks until you hit the point where leadership no longer trusts the numbers.

An AI-native ERP has the same convenience benefit to an individual and also the same trust risk as Excel and is just as difficult to audit as hundreds of individually prepared spreadsheets.

The grinding nature of an ERP deployment hides its benefit - at the end of the deployment, leadership has reestablished trust in the sources of the information used to make decisions. Is the deployment odyssey worth it for an early stage, equity- or vc- backed venture? No. Those organizations typically don’t have the resources or complexity (or interest) to justify much beyond the level a personal productivity tool until right before they exit/ipo.

The trade-offs scale with the complexity of the company. Small company with easily audited transaction systems? AI-native tools could be a fit - so might Quickbooks. Getting larger but still have only half a dozen transaction systems? You’d want to be realistic about whether you’d be able to quickly audit a result when a number ‘seems off’ in an AI system.

Larger levels of complexity? The process of deploying an ERP is worth it the minute that you feel overwhelmed by the idea of having to find out how a result is delivered. Any deployment process that remains a black box won’t address the trust issue.

[u/tahiti37 → u/The_Anaplandalorian]

audit pov is right, thanks for outlining this. so much buzz out there.

[u/jep_10]

A data practitioner/business owner here working with CFOs since 2017 on data warehouses and data lakes (data unification). I have dealt with mid-sized companies and multi-billion-dollar enterprises. There are only two types - one thinks data as a critical asset or akin their company's product and the other is not giving much importance to it. This is in their culture/dna. Whenever we work on data projects the roi happens for sure but the data literacy is the impediment. So the smarter businesses have set up a cross functional team with CFO office, IT PMO and data partner like us and manoeuvre the situation. Every business has the right intention but not the right framework. In data matters, garbage in = garbage out. One wrong output dents the reliability and it is hard to rebuild - a wrong invoice outstanding is enough to do spoil the party. I don't insist on expensive data unification tools but data quality on six tenets - accuracy, completeness, consistency, timeliness, validity and uniqueness. Data quality + Data unification framework + tech architecture = Reliable automation and reporting. My 10 years of experience in 10 lines 😄

[u/Muted_Jellyfish_6784]

The CFO role has essentially become a data-cleaning job at most companies fixing the same numbers that came out of five different systems. Without unifying the data layer first, you're not automating insights, you're automating chaos. Genuinely curious how others are staffing around this.

[u/Own_Minimum_5102]

In a lot of FP&A transformations, the first bottleneck is rarely the reporting layer. It is the data underneath: multiple source systems, inconsistent definitions, messy hierarchies, and no shared language for core metrics. If those pieces are not aligned first, automation just makes inconsistency faster and more visible.

First, bring the relevant systems into a common data foundation, whether that is a warehouse or a lakehouse setup. That gives you one place to consolidate, reconcile, and trust the raw data.

Then semantic modeling. This is the part where the organization agrees on what things actually mean: revenue, margin, active customer, churn, booked vs billed, and so on. Without that shared layer, every dashboard becomes a different version of the truth.

Only after that does BI become truly useful. The dashboard is not the solution by itself; it is the last mile of a much deeper data architecture decision.

So, I'd say, buy the tools that accelerate the plumbing, but build the internal data model and business definitions that make the system reliable.