Data quality

Data quality is an important topic when looking at engineering intelligence tooling. Data needs to be accurate for trust in the decision-making, both for the decision-makers and the teams that may interact with that data.

Even within an organization different groups and teams may work differently. One team may use Scrum while another uses Kanban.

There’s a host of small pieces to align that can cause data quality issues; users may have different identities across tools, some teams may be more diligent about keeping their issue tracker up to date, and two teams might use the same label for two different things.

These are all important, and we’ve greatly emphasized data quality to ensure that what you see reflects reality as closely as possible.