Data Preparation for Analytics: How Customer Analytics Requests Turn Into Infrastructure Projects

Here's how embedded analytics quietly fails. Product teams promise analytics features that customers actually want, but once development starts, most of the time disappears into preparing data. What gets delivered doesn’t hold up to the promise of the envisioned analytics roadmap. Instead, it becomes whatever the team had time to build.

As important as it is to fulfill customer requests, custom features meant to differentiate your product can easily turn into a support burden. The data layer is almost always the hidden reason. Here, we discuss data preparation for analytics and how customer requests can quickly turn into complex infrastructure projects.

Why Data Preparation Keeps Slowing Teams Down

ETL processes are stable by design. They define how data is prepared and transformed through structured pipelines that typically change infrequently. That stability is valuable and worth preserving.

The analytics layer inside a product behaves very differently, and customer questions evolve constantly once the feature is in their hands. Without the right layer in between, each of those requests turns into an engineering cycle. For example, a product manager hears the request, engineering evaluates the data model, someone modifies ETL, validation follows, and eventually the change ships. By that point, the customer conversation has often moved on.

Most organizations invest in data warehouses to make data more accessible. Warehouses centralize data and standardize schemas, which are genuinely valuable capabilities. The challenge is that warehouses operate at infrastructure speed while customer-facing products operate at feature speed.

When a customer asks a question the warehouse was not designed to answer, the work moves upstream. This is where a backlog ticket appears, pipeline logic changes, and data models are adjusted. By the time your team resolves the ticket, the turnaround time looks nothing like normal product iteration.

What embedded analytics teams need is an intermediary layer that sits between the stable ETL pipeline and the analytics delivered to customers. That layer handles the shaping and preparation work that changes frequently, without forcing teams to modify the infrastructure underneath. A new customer request stays a product decision rather than becoming an architecture project.

The Cloud Answer and Why It Doesn't Always Apply

When organizations encounter performance or preparation challenges, analytics vendors often recommend the same approach, which is to move to the cloud.

Cloud analytics platforms have built real advantages in that environment.

• Faster query engines

• Automated modeling

• AI-assisted preparation

All of these can reduce the effort required to shape data for analysis. The catch is that many of those capabilities exist only inside the vendor's own cloud infrastructure. When vendors recommend moving analytics workloads to the cloud, they are often recommending that customers move their data into the vendor's environment in order to use those tools.

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For organizations building embedded analytics into their products, this is where it becomes a product architecture decision. Where analytics runs is now tied directly to where the product itself can run. For many industries, that trade-off simply doesn’t work.

Healthcare organizations cannot route patient data through third party cloud infrastructure without facing red tape. Financial services firms operate under strict frameworks that restrict where data can be processed, while manufacturers and regulated technology companies often cannot move sensitive operational data outside their own environment.

In Europe, the regulatory landscape adds additional pressure. GDPR, DORA, the EU AI Act, and NIS2 introduce compliance requirements that directly influence where analytics systems are allowed to operate and where data is permitted to move.

This shift reflects a growing tension between the capabilities vendors build inside their own cloud platforms and the environments where many organizations are actually required to operate their software.

A Different Path Forward

Logi Symphony from insightsoftware Data + Analytics is built for organizations that want modern analytics capabilities without moving their product data onto someone else's infrastructure. It sits between existing data pipelines and the analytics layer delivered to customers, which means ETL can continue doing what it does best. The preparation work that evolves with customer questions happens in the analytics layer instead of inside the pipelines.

For product teams, that means a new analytics request doesn’t immediately become a data engineering project. The data can be reshaped and prepared within the analytics environment itself, allowing teams to respond to customer requests on a normal product timeline.

The preparation layer is visual for most users. When you need additional flexibility, programmable transforms are available in C#, Python, and R. Hierarchies including ragged and unbalanced structures that many platforms struggle to support can be modeled directly in the analytics layer rather than being hard coded upstream. Governance travels with the data through a security model that defines access once and applies it consistently across analytics experiences.

Logi Symphony handles performance through several approaches depending on the scenario:

• Direct connection works when the source system is already fast

• Results can be cached inside Logi Symphony when connectivity is unreliable or when data changes infrequently

• For complex self-service workloads, in-memory caching stores pre-calculated results in RAM on the organization's own servers

Two patented technologies support these capabilities:

ta Sharpening streams query results directly into visualizations so users can begin interacting before the full query finishes.

In-Memory Caching keeps frequently accessed results close to the user so filtering, drilling, and pivoting remain fast even under heavy usage.

ShapeThe capabilities people expect from modern analytics don’t have to live inside someone else's cloud environment. With Logi Symphony:

• Your ETL stays in place.

• Your infrastructure stays in place

• Your compliance posture stays intact

What changes is the speed at which product teams can respond to analytics requests from customers. Instead of waiting weeks for pipeline changes, teams can adapt analytics features in minutes.

Ready to learn more? Watch our on-demand webinar about achieving analytics-ready data without waiting on data prep.