Data Processing & Reporting

What this covers

Data processing and reporting is what turns a set of completed interviews into numbers someone can act on: cleaning and validation, open-end coding, weighting and rim balancing, banner and cross-tabulation, dashboard build, and the written readout that sits on top of all of it. It is available on data you collected yourself — through your own panel, your own fielding partner, or a study run before this became a service you used — not only on studies fielded through us. The raw file coming in does not have to have been produced to any particular standard for this to start.

When you need it

Reach for this when data exists but nobody has the time, the coding framework, or the weighting expertise to turn it into something reportable — a common gap when fieldwork was run quickly or through a supplier who delivers raw data and stops there. It is also worth using even when you ran the study yourself and could process it in-house, simply for a documented, auditable version of the cleaning and weighting decisions, which matters most on data that will get revisited or challenged later.

How an engagement runs

The banner plan and the weighting scheme are agreed before the data arrives, so processing is execution against a plan rather than a negotiation that starts once the file lands. Cleaning rules — which patterns of straightlining, speeding, or inconsistent answers get a respondent excluded, and on what threshold — are written down first and applied identically to every case, so which respondents were removed and why is a record you receive rather than a judgement call you have to take on trust. Open ends are coded to a frame you approve before coding starts, not to whatever the first coder happened to see, and a sample of coded output is checked against the frame for consistency before the file is considered final. Weighting follows the scheme agreed up front — rim balancing or another approach, chosen to match the study’s design — and every derived variable is documented at the point it is created, not reconstructed from memory once someone asks what it means.

What you get

A cleaned and weighted dataset, banner tables built against the agreed plan, and a readout that leads with the answer rather than the process behind it. The data map, the weighting report, and the definition of every derived variable ship with the delivery, so the numbers remain auditable long after the project closes — by your team, or by anyone else who needs to check the work later.