BI projects

A BI project is a one-off project (with possible support): we design the data model, set up collection and loading from sources (databases, analytics, ad accounts, CRM, sheets) into a warehouse (DWH/cloud), model data marts and unified metric definitions, build reporting and dashboards in the chosen BI, set up refresh, access and data security. Honestly about data, this is key: BI shows what is built into the model and sources — with dirty, incomplete or inconsistent data the reports will be pretty but wrong (garbage in, garbage out); a significant part of a BI project is exactly the modeling and putting data in order, not 'drawing charts'. Honestly about divergence: different sources count differently (attribution, time zones, definitions) — we fix unified definitions, but 'perfect convergence of everything with everything' does not exist. Honestly about the essence: this is infrastructure and visualization, NOT analysis-conclusions, decisions and strategy and NOT metric growth — BI gives reliable numbers, but what they mean and what to do is decided by you or your analyst; having BI does not raise the metrics. Honestly about the platform and resources: BI systems and the warehouse (Power BI, Tableau, cloud/DWH) are often paid — billed directly, separately; the project needs access to sources and time. Maintenance: when sources and needs change the model and reports need support. An important boundary: this is a BI project (warehouse+model+reporting), not a single Looker Studio dashboard (a smaller service), not analytics setup from scratch and not analytics consulting. If there are few sources and a simple dashboard suffices, a full BI project is overkill — we say so honestly. Picture this: instead of 'reports compiled manually, numbers across systems don't match, no trust' you have a single warehouse and reliable reporting on agreed metrics. The base price starts from 50,000 ₽ per project; it depends on the number of sources, model complexity and the chosen BI (licenses/warehouse — separate).

• Reports are compiled manually and slowly, data does not match.
• There is no single warehouse and agreed metric definitions.
• Numbers across systems contradict each other.
• Management needs reliable analytical reporting, not exports.

• Designing the data model and unified metrics
• Collection and loading from sources into a warehouse (DWH/cloud)
• Modeling data marts
• Reporting and dashboards in Power BI/Tableau/Looker/DataLens
• Refresh, access, data security
• Flagging gaps and dirty data (putting data in order — part of the project)
• Model and report documentation
• Handover and basic training

• A single warehouse and reliable reporting
• Agreed metric definitions that are trusted
• Less manual compilation and contradiction
• A base for analytics and decisions (conclusions — separately)

• We clarify metrics, sources, requirements and BI choice; collect access
• We design the model, set up loading, model marts, build reports
• We verify correctness, train, hand over documentation

• Will a BI project raise the metrics?

  No. BI gives reliable data and reporting, but by itself it does not raise sales or effectiveness. What the numbers mean and what to do is decided by you or your analyst; growth is separate work, not a consequence of having BI.

• Will the reports be trustworthy?

  As far as the sources and model are correct. A significant part of the project is modeling and putting data in order; with dirty or inconsistent data the reports will be distorted (garbage in, garbage out). Perfect convergence of all sources does not exist.

• Are BI licenses and the warehouse included in the price?

  No. BI systems (Power BI, Tableau) and the warehouse/cloud are often paid and billed directly, separate from our work. If there are few sources and a simple dashboard suffices, a full BI project may be overkill.