How can I configure my EXcelerate Dashboard?

EXcelerate requires a minimum of three pieces of information to run an analysis through
the pipeline:

Organisation: The group of people on whom the analysis is to be performed;
usually a whole business or business unit. As a rule of thumb, this group should
be 500 people or more to ensure sufficient data availability.
Industry: This is necessary to determine the relevant benchmark to score the
organisation’s data against. It also provides important context for the AI agents
used in the analysis.
Geography: Welliba’s benchmarks are region-specific to account for important
cultural differences across population groups. Specifying a geography is
therefore not only important to help define the analysis group but is also
important from a benchmarking and scoring perspective.

Competitors: You can add extra organisations to the base analysis, and
EXcelerate will weave them into the insights and recommendations, perfect for
side-by-side comparisons or a deeper dive into specific peers.
Population Sub-Groups: frequently, users will want to compare analysis across
sub-groups of a population e.g. role-types, tenure, demographic, etc. The same
data availability limitations as before apply here (n > 500).
Timeframe: EXcelerate lets you confine an analysis to a specific time window, but
a few caveats apply:
- Data freshness: Source fidelity is generally dependable only for the past
  18 months. Older records are often overwritten or archived by their host
  sites, which can degrade quality.
- Data availability: A very tight window may yield too little material to
  analyse.
- Date logic: EXcelerate classifies each data point by the time period it
  describes, not the timestamp of its creation. A comment posted in 2025
  that discusses a 2024 event, for instance, is treated as a 2024 data point.

To run an analysis, EXcelerate requires three key pieces of information: the organisation being analysed, the industry for benchmarking, and the geography. Optional details like competitors, population sub-groups, and a timeframe can further refine insights.