Expert analysts

Who they are

Someone who creates their own analysis from data. This user downloads spreadsheets into their own statistical models to create personal datasets.
Access to the data for analysis is more important to them than its presentation.

Likely to say

“Written reports give helpful context, but I would prefer to see the data. It has to be very easy to find what I want.”

What motivates them

It is part of their job to analyse data. They have a passion for data and need accurate statistics to provide confidence in their analysis. They are interested in the use of open data.

What they want

Expert analysts want to:

  • have clear links through to the latest available economic data
  • find specific data, such as historical data
  • create datasets to support their statistical models
  • access data quickly, within the hour, as they are time-pressured
  • avoid being distracted by similar-sounding data
  • find data in number format, not just percentage change
  • know when the next release of data is due
  • understand what impact changes in methodology have on data
  • see clear signposting for data revisions

Behaviour and preferences

They are more likely to be critical about mistakes and shortcomings in the provision of statistics, and will phone ONS to locate new data or to query changes in data.

They may use an application programming interface (API). An API allows users to automatically import data between computer software. An example of this would be Quandl’s Financial Data API, which can be used to access datasets from multiple publishers.

How they find information

These users prefer to access data via a desktop computer. They will bookmark browser pages. They know when information is due for release, so will sit on the site to wait for it, often on the release calendar page.

What they like

  • Datasets and previous releases being simple to find and re-find.
  • The impact of changes to methodology being made clear.
  • Data and analysis not being conflated – presentation is unimportant to them.

What they do not like

  • Delays to data releases, or changes to their frequency.
  • Datasets using different formats and layouts.
  • They get frustrated if they cannot find clear links to data quickly.
  • Overuse of zip files.