Analytics & BI Consulting
Dashboards your teams check daily, built on metrics everyone agrees on. Analytics only counts when people act on it.
The problems this solves
Reports without decisions
Dashboards exist, but the Monday meeting still runs on gut feel and a screenshot from last quarter.
Competing numbers
Every team reports a slightly different figure for the same metric, and reconciling them is someone’s job.
The analyst bottleneck
Leadership waits days for answers that require a person to assemble data by hand.
Adoption stalled
The BI rollout launched with fanfare, usage peaked in week two, and it never recovered.
One set of definitions, every way you ask
Dashboards come first, and plain-language questions can follow, both answering from the same semantic layer. When the numbers match everywhere they appear, trust follows, and so does usage.
Diagram showing a business intelligence dashboard as the primary surface, with a smaller panel for plain-language questions labelled as the next step. The question panel answers a question about net revenue with a governed figure and a link to view the generated SQL. Both draw from a single governed semantic layer of shared metric definitions.
What analytics and BI consulting includes
We build the reporting layer on one set of agreed definitions, so whatever you add later reads from the same place.
- KPI and metric definition workshops that end in agreement, recorded in code
- Semantic layer design, so definitions live in one governed place
- Dashboard development in Power BI, Tableau, Looker, or Sigma
- Verified queries for your highest-stakes questions
- Report rationalization: fewer reports, better ones, trusted
- Adoption support: training, office hours, and iteration on real usage
- Usage analytics on the analytics, so we know what is landing
- Conversational AI agents over governed data, with the SQL visibleOptional
How we work
Agree the metrics
The hard conversations first: what does revenue mean, and who owns the definition.
Model the semantics
Definitions, joins, and synonyms encoded once, governed like the code they are.
Build the dashboards
Dashboards for monitoring and self-serve first. Plain-language questions can follow, reading from the same layer.
Drive adoption
Launch is the midpoint. Training, feedback loops, and iteration on what people actually use.
Outcomes
80%
reduction in manual reporting effort for a manufacturing client after we centralized reporting on Snowflake and dbt
Time moved from preparing data to analyzing it. That is the trade every reporting team is trying to make.
DSC developed the ETL pipelines from Braintree, Google Sheets, and QuickBooks into a Snowflake data warehouse… a polished, finished product that updates in real-time as new data comes in.
Questions we hear most
Which BI tools do you work with?
Power BI, Tableau, Looker, and Sigma among others. Support for consuming governed semantic definitions varies by tool, and we will tell you candidly where each one stands before you commit a team to it.
What does adoption support actually look like?
Training sessions, office hours, a feedback loop into the backlog, and honest usage reporting. If a dashboard is not being used, we would rather find out why and fix or retire it than let it decay.
Do AI agents replace dashboards?
No, and most organizations will run both. Dashboards are better for monitoring the same view every day; agents are better for the question that did not exist yesterday. They should share one set of definitions.
How accurate are natural-language answers?
Honest answer: not perfect, especially on complex multi-step questions. We ground agents in semantic definitions and verified queries, keep the generated SQL visible, and set expectations with leadership accordingly. Teams that promise infallibility lose the room after one bad answer.
Where this leads
Answers your whole team can trust
If your reporting exists but decisions still run on gut feel, the gap is usually trust and adoption. Let’s close it.
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