![[headshot] image of customer giving a testimonial (for a ai biotech company)](https://cdn.prod.website-files.com/690cdf62418694e59d87cccf/695d40f0cb2a2d98b5650aa7_cat-bw-outline-profile.png)
As marketing teams scale paid acquisition, one question comes up repeatedly: how do you connect ad platforms like Meta and Google Ads to BI tools in a way that is reliable, repeatable, and actually useful?
On the surface, the answer seems simple. Most modern BI tools advertise native integrations, and ad platforms all expose APIs. In practice, teams quickly discover that “connected” does not mean “usable,” and that getting to trustworthy dashboards requires more than toggling on a connector.
This article walks through what BI tools can and cannot do with ad data today, the real engineering and data lift required to make it work, how long reliable setups actually take, and why many growth and performance marketers still find themselves writing SQL or maintaining scripts. It also explains why newer approaches, like Nockpoint’s five-minute setup done in just a few clicks, are changing who can work with marketing data day to day.
Why teams want ad data in BI tools
The motivation is straightforward. Ad platforms are designed for campaign execution, not cross-channel analysis or long-term business reporting. Once a team is running ads across Meta, Google, LinkedIn, and other channels, comparing performance becomes difficult and time-consuming.
BI tools promise a single view of performance, where spend, clicks, conversions, and downstream metrics can be analyzed together. When implemented well, this enables better budget planning, clearer performance trends, and stronger alignment between marketing activity and business outcomes.
The key phrase here is “implemented well.”
Which BI tools actually support ad platform data
Several mainstream BI tools can be used to analyze ad data, but they differ significantly in how much work is required to get there.
Tools like Looker, Tableau, and Power BI are commonly used in organizations with established data teams. These tools are powerful and flexible, but they assume that clean, modeled data already exists in a warehouse. They do not handle ad platform ingestion or normalization on their own.
Open-source or lightweight tools such as Metabase lower the barrier to entry on cost, but the underlying requirements are the same. Ad data still needs to be extracted, transformed, and modeled before it becomes useful.
In all of these cases, the BI tool is the final layer, not the system that does the hard work of making ad data usable.
The real engineering lift behind “connecting” ad platforms
When teams say they have connected ad platforms to a BI tool, what they usually mean is that they have built and maintained a data pipeline.
At minimum, this involves extracting data from ad platform APIs, loading it into a warehouse, and transforming it into a schema that supports analysis. Even with managed connectors, this process requires ongoing engineering oversight. APIs change, fields are deprecated, and conversion logic evolves as campaigns change.
For many teams, this translates into a surprising amount of manual effort. It is common to see data engineers or analytics engineers writing scripts, debugging failed syncs, and re-processing historical data on a regular basis. This is not because BI tools are inadequate, but because ad platforms were not designed to be clean analytical data sources.
The data modeling lift most teams underestimate
Beyond engineering effort, the data lift is often the bigger challenge.
Ad platforms use different naming conventions, attribution windows, currencies, and timezones. A “conversion” in Meta does not mean the same thing as a “conversion” in Google Ads. Without deliberate normalization, cross-channel dashboards are misleading at best.
To make ad data usable in BI tools, teams typically need to define canonical metrics, standardize campaign hierarchies, and document assumptions clearly. This modeling work is essential, but it is also ongoing. As soon as campaigns change or new platforms are added, the model needs to be updated.
This is why some teams with dedicated data staff still spend hours every week maintaining marketing dashboards.
How long does it actually take to set up reliably?
Initial dashboards can often be built in a few weeks, especially if the scope is limited to spend and basic performance metrics. However, reaching a point where dashboards are trusted by marketing, finance, and leadership usually takes much longer.
For many organizations, it takes several months to stabilize pipelines, align definitions, and iron out discrepancies between platforms. Reliability is earned over time, through iteration and maintenance, not achieved on day one.
This timeline is one reason some teams hesitate to invest deeply in BI for marketing data, especially when campaign decisions need to be made quickly.
Who you need on the team to make this work
Traditional BI setups for ad data usually require at least one of the following roles:
A data engineer or analytics engineer to manage pipelines, connectors, and transformations.
A data analyst to write queries, validate metrics, and build dashboards.
A marketing operations or RevOps role to align definitions and ensure consistency across teams.
In smaller teams, these responsibilities are often shared or handled ad hoc, which increases the risk of errors and burnout. It is not uncommon for growth marketers to step in and write SQL themselves simply because they are closest to the questions being asked.
Why many performance marketers still need SQL
Despite the promise of self-serve BI, many performance and growth marketers still rely on SQL to answer basic questions. This is not because they want to be data engineers, but because traditional BI tools often expose raw tables without opinionated structure.
To answer questions like cohort performance, blended CAC, or channel-level efficiency over time, marketers frequently need to join tables, apply filters, and calculate metrics manually. SQL becomes the fastest way to get answers, even if it is not the most sustainable approach.
This reliance on SQL is a signal that the data layer has not been tailored to marketing workflows.
The costs of relying solely on traditional BI tools
Using traditional BI tools for ad data is not wrong, but it comes with tradeoffs.
There is the obvious cost of licenses and infrastructure, but the larger cost is often operational. Engineering time, analyst time, and opportunity cost add up quickly. When dashboards break or metrics are questioned, decisions slow down.
Over time, teams may end up with multiple versions of the truth, each maintained by a different person or tool. This undermines confidence and reduces the value of having centralized reporting in the first place.
How Nockpoint changes the equation
Nockpoint approaches ad data integration from a different angle. Instead of assuming a data team will handle ingestion and modeling, it focuses on making marketing data usable out of the box.
By handling connections, normalization, and modeling automatically, Nockpoint reduces both the engineering and data lift required to analyze ad performance. This makes it possible for growth and performance marketers to explore and understand their data without writing SQL or maintaining scripts.
The five-minute setup is not about oversimplifying analytics, but about removing unnecessary friction. Teams still benefit from BI-style analysis, but without needing to rebuild the same pipelines and models repeatedly.
TL;DR
Connecting ad platforms to BI tools is absolutely achievable, and many teams do it successfully. The key is understanding what the tools are designed to do, and where additional layers are required.
Traditional BI tools are powerful, but they assume clean, modeled data and ongoing technical support. For teams with the resources to invest, this approach can work well. For teams that need faster insights and less operational overhead, newer solutions like Nockpoint offer a more accessible path.
The best setups are the ones that match the organization’s needs, skills, and pace of decision-making. When ad data is structured thoughtfully and analyzed with the right tools, BI becomes a strategic asset rather than a maintenance burden.
