Data Analytics FAQ

What data analytics services do you offer?

Our analytics practice covers: data strategy and roadmap development, data warehouse and lakehouse design and build (BigQuery, Snowflake, Redshift), ETL/ELT pipeline engineering, BI dashboard development (Looker, Power BI, Metabase, Tableau), advanced analytics and predictive modelling, and self-serve analytics enablement for business teams. We work across the full stack from raw data ingestion through to executive dashboards.

How do you handle poor data quality?

Data quality is almost always the first challenge we encounter. We start every engagement with a data audit — profiling your sources for completeness, consistency, accuracy, and timeliness. From there we design data quality rules and implement validation checks at ingestion so bad data is flagged before it reaches reports. We also document data lineage so analysts know exactly where each metric comes from and how it is calculated. Data quality is not a one-time fix; we set up ongoing monitoring with alerts when data quality scores drop below agreed thresholds.

Which BI tools do you work with?

We are tool-agnostic and experienced with the major platforms: Looker (and Looker Studio), Power BI, Tableau, Metabase, and Superset. Our recommendation depends on your team's technical level, budget, and existing tech stack. For engineering-led teams who want version-controlled metrics, Looker with LookML is excellent. For business analysts who need to self-serve without SQL, Power BI or Metabase are usually more practical. We build dashboards that non-technical stakeholders can use daily without calling the data team.

Do you help set up a data warehouse from scratch?

Yes, this is one of the most common starting points. We design the warehouse architecture, set up the cloud environment, build ingestion pipelines from your source systems (CRMs, ERPs, databases, APIs, spreadsheets), implement a transformation layer using dbt, and deliver a set of foundational data models representing your core business entities. The result is a single source of truth your whole organisation can query — replacing the chaos of disconnected spreadsheets and siloed system reports.

How do we make sure dashboards get used after the project ends?

Adoption is designed in from the start. We involve the end users of each dashboard during requirements gathering — not just the project sponsor — so the metrics are ones people actually care about. We run training sessions before handover and create short video walkthroughs for each dashboard. Post-launch, we track dashboard usage (views, active users, query counts) and check in at 30 and 90 days to address friction points. The dashboards that get used are the ones that answer real questions people have every day.