EMPRIS
EMPRIS
Making complex energy market data easier to explore and digest.
EMPRIS is a web application developed by ElectraLink for energy professionals working with electricity-market data. It was created to replace dense spreadsheets and manual workflows with a self-serve interface that makes large, complex datasets easier to explore. The goal was not to simplify the data itself, but to reduce the effort required to reach trusted insight. The platform needed to work for expert analysts who live in SQL, as well as for non-technical users who still need reliable answers without wading through raw files.
Making complex energy market data easier to explore and digest.
EMPRIS is a web application developed by ElectraLink for energy professionals working with electricity-market data. It was created to replace dense spreadsheets and manual workflows with a self-serve interface that makes large, complex datasets easier to explore. The goal was not to simplify the data itself, but to reduce the effort required to reach trusted insight. The platform needed to work for expert analysts who live in SQL, as well as for non-technical users who still need reliable answers without wading through raw files.
Context & constraints
Energy-market data is high-volume, sensitive, and often used to inform decisions with real financial and regulatory consequences. Many users rely on spreadsheets and direct SQL access because they do not trust abstraction unless they can inspect the source themselves. At the same time, ElectraLink wanted EMPRIS to be usable beyond specialist teams. The core constraint was trust. The interface needed to make data more accessible without hiding complexity or undermining confidence in the results.
Context & constraints
Energy-market data is high-volume, sensitive, and often used to inform decisions with real financial and regulatory consequences. Many users rely on spreadsheets and direct SQL access because they do not trust abstraction unless they can inspect the source themselves. At the same time, ElectraLink wanted EMPRIS to be usable beyond specialist teams. The core constraint was trust. The interface needed to make data more accessible without hiding complexity or undermining confidence in the results.
Accessing the platform
Early versions of the dashboard tried to surface everything at once. While well intentioned, this created noise and made it harder for users to understand where to start. The dashboard was restructured around personal relevance. Users could pin key widgets for quick reference, rely on clear navigation to stay oriented, and return directly to recent SQL queries without retracing steps. The aim was to make the first screen feel useful rather than impressive.
Accessing the platform
Early versions of the dashboard tried to surface everything at once. While well intentioned, this created noise and made it harder for users to understand where to start. The dashboard was restructured around personal relevance. Users could pin key widgets for quick reference, rely on clear navigation to stay oriented, and return directly to recent SQL queries without retracing steps. The aim was to make the first screen feel useful rather than impressive.
Immediate orientation
Users understand where they are and what matters.
Personal relevance
Frequently used data is surfaced without clutter.
Immediate orientation
Users understand where they are and what matters.
Personal relevance
Frequently used data is surfaced without clutter.
Browsing datasets
Dataset discovery initially felt closer to procurement than exploration. Technical labels made sense to experts but created friction for newer or adjacent roles. A Marketplace view reframed discovery around clarity and context. Dataset cards combined plain-language summaries with practical details such as price and refresh cadence. Filters were shaped by user testing, and checkout was streamlined to reduce friction when committing to data.
Browsing datasets
Dataset discovery initially felt closer to procurement than exploration. Technical labels made sense to experts but created friction for newer or adjacent roles. A Marketplace view reframed discovery around clarity and context. Dataset cards combined plain-language summaries with practical details such as price and refresh cadence. Filters were shaped by user testing, and checkout was streamlined to reduce friction when committing to data.
Approachable discovery
Datasets are understandable without specialist knowledge.
Clear decision context
Key details are visible upfront.
Approachable discovery
Datasets are understandable without specialist knowledge.
Clear decision context
Key details are visible upfront.
Reduced friction
Selection and checkout feel predictable.
Shared language
Teams align around the same descriptions.
Reduced friction
Selection and checkout feel predictable.
Shared language
Teams align around the same descriptions.
Building queries
The SQL Builder needed to remain powerful without intimidating less experienced users. Early designs attempted to keep everything on one screen, which quickly became overwhelming. Separating query building, results, and visualisation into clear stages made the flow easier to follow. Raw data stays visible throughout, while charts and exports sit alongside it without interrupting the core task. This preserved flexibility for experts while making the process legible for everyone else.
Building queries
The SQL Builder needed to remain powerful without intimidating less experienced users. Early designs attempted to keep everything on one screen, which quickly became overwhelming. Separating query building, results, and visualisation into clear stages made the flow easier to follow. Raw data stays visible throughout, while charts and exports sit alongside it without interrupting the core task. This preserved flexibility for experts while making the process legible for everyone else.
Approachable discovery
Datasets are understandable without specialist knowledge.
Clear decision context
Key details are visible upfront.
Approachable discovery
Datasets are understandable without specialist knowledge.
Clear decision context
Key details are visible upfront.
Reduced friction
Selection and checkout feel predictable.
Shared language
Teams align around the same descriptions.
Reduced friction
Selection and checkout feel predictable.
Shared language
Teams align around the same descriptions.
