Data Infrastructure
Data infrastructure is everything between your source systems and the people who act on the numbers: the connections that extract data from your CRM, ad platforms, finance, and product tools, the warehouse that stores it, and the transformations that make it comparable. Most companies build this layer to feed reporting. The endpoint is a dashboard, and the project is declared done when the dashboard loads. That is infrastructure built to display data, not to improve a decision, and the difference determines whether the spend pays back.
How it actually works
The standard build syncs each source into a warehouse through an integration tool, cleans and joins the tables in a transformation layer, and puts a dashboard tool on top. Every vendor in that chain bills whether or not a decision improves, so the stack grows by default: more sources, more storage, more seats. The default has a measured ceiling: at one client, a CIO was unintentionally spending $10 million on duplicate data and tools alone, half of the IT budget, because departments had each bought their own copy of the same layer and nothing in the stack reported the overlap. The chain includes no step where a number reaches the person who can act on it, in time to act.
Infrastructure built for decisions starts from the decision and works back to the data. The design begins with the decisions the business makes on a cycle, which budget to move, which accounts to work, which initiative to stop, and works backward to the data each decision needs, how fresh it has to be, and who has to see it. Most of what a reporting stack stores feeds no decision at all, which is why decision-first pipelines are usually smaller and cheaper than the reporting stacks they replace.
The single-point-metric risk is the sharpest failure this layer produces. When one number, a blended ROAS, a total pipeline figure, a topline conversion rate, becomes the input to every decision, every flaw upstream of that number propagates into every decision downstream of it. One broken connection, one changed definition, one platform's overreporting, and the company steers on a figure that is wrong everywhere at once. Sound infrastructure carries multiple independent measures so no single number can fail silently.
In practice
For Digital Realty and Equinix, the architecture work produced one pipeline from data sources to the decisions the go-to-market team made, with the people reading the numbers and the people acting on them working from the same data. The standing test for this layer: name the decision each pipeline feeds. The cost of a wrong number in this layer is documented. At a company where one of our principals worked in-house, a 30% repeat-buyer figure had sat in the reports for years, sourced from Google Analytics measuring return visits in a rolling 30-day window. The all-time repeat purchase rate in the actual purchase history was 8%. An entire growth strategy, 20 product launches that consumed the marketing team for the better part of a year, was built on that 22-point gap, and the company posted a $100,000 loss on its P&L in December 2017 ([single-point metrics](/insights/why-strategic-initiatives-fail#single-point-metrics)).
Where we come in
This is core Revenue Systems Architecture work, and we have built it for businesses from new startups to the Fortune 15, including Digital Realty and Equinix. We demonstrate ROI before each build, everything is scoped up front, and the systems are portable, so you are never locked to a single model, platform, or vendor.
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Related terms
- Data pipeline / ETL
- The process that extracts data from your systems, reshapes it, and loads it where it can be analyzed. Every report and model depends on it.
- Data warehouse
- The central store where data from every source lands. Storing the data costs little; deciding which decisions it has to feed is the harder problem.
- Single-point metric
- One number used as the input to many decisions. When it breaks or misleads, every decision built on it goes wrong together.
- Data freshness
- How current the data is when a decision gets made. A correct number that arrives after the budget meeting changes nothing.
- Reverse ETL
- Pushing warehouse data back into the operating tools, the CRM, the ad platforms, so the systems where work happens run on the same numbers as the reports.