Data analytics and reporting.

At Meybrain Technologies we connect the data sources, agree metric definitions with you and build self-updating dashboards. The hours spent assembling a weekly report go into reading it instead.

Most reporting problems are about definitions, not technology. If sales and finance mean different things by "revenue", even a beautiful dashboard starts an argument. So we begin with a shared metric dictionary, not with charts.

• Companies building weekly or monthly reports by copying data into spreadsheets.
• Leadership teams with sales, stock and finance in different systems and no single trusted number.
• E-commerce businesses selling on several marketplaces and their own site that want channel-level profitability.
• Organisations comparing branches, dealers or regions on the same yardstick.
• Teams that want their data in order before attempting machine learning.

• 01

  Source inventory

  What data lives where, who owns it and how often it changes.

• 02

  Metric dictionary

  Each indicator's definition, formula, source and owner.

• 03

  Data pipelines

  That extract, clean and combine data automatically.

• 04

  Reporting database or data warehouse

  One historical source organised for analysis.

• 05

  Dashboards by role

  Summaries for the board, detail for operations.

• 06

  Scheduled reports

  By email, and alerts when thresholds are crossed.

• 07

  Access control

  Defining who sees which data.

• 08

  Training and documentation

  On reading dashboards and requesting new ones.

1. 01

   Decisions first

   Which meetings make which decisions, and what they need answered.

2. 02

   Source inventory

   Where the data sits and how to reach it.

3. 03

   Definitions

   The metric dictionary, written with the departments involved.

4. 04

   Pipelines

   Sources connected, data cleaned and combined.

5. 05

   First dashboard

   The single most-used view goes live first.

6. 06

   Feedback and expansion

   Refined by real use, then extended.

7. 07

   Handover

   Your team learns how to add new metrics.

For moderate volumes a PostgreSQL reporting database is usually enough; as data grows we look at BigQuery or similar cloud warehouses. Transformations are written as version-controlled SQL models (for example with dbt), so you can always trace how a number was calculated.

For visualisation there are three routes. Tools such as Power BI or Looker Studio suit teams that want to build their own reports. Open-source tools such as Metabase run in-house without licence fees. When dashboards are shown to your customers or dealers, we build embedded, custom dashboards inside your application. The choice depends on who builds reports, the licence budget and where the data must stay.

Good reporting starts with good records. In Kolay Sevkiyat we designed the prepaid wallet together with a transaction and activity ledger, so the label and payment flow can be traced step by step afterwards. Designing data to be reportable from day one is far more reliable than trying to extract reports later. See the product in our portfolio.

We apply the same rule on client work: accurate records first, polished dashboards second.

We usually price analytics in two stages: a set-up stage that connects sources and launches the first dashboards, followed by monthly or on-demand development as sources and metrics are added.

Main cost drivers:

• The number of sources and how easy they are to access.
• Data cleaning needs, and how hard it is to agree on definitions.
• The number of dashboards and metrics.
• Refresh frequency: daily, hourly or real-time.
• Users and permission structure.
• BI tool licences, which you pay directly.

Each project is priced on its own. After a short call about your sources and needs, you receive a written, itemised quote.