Services
Business intelligence built around the questions your leadership asks
One place where sales, finance, marketing and product numbers agree, pipelines that keep it current, and dashboards that answer named questions instead of showing every metric available.
Business intelligence services should get you to a Monday meeting where nobody argues about whose number is right. Meritbyte Technologies, a Nepal-based web and software development company, builds the warehouse, pipelines and dashboards behind that meeting, starting from the questions your leadership asks rather than from whatever data happens to be lying around.
The usual problem: five systems, three versions of revenue
Revenue lives in the accounting system, orders in Shopify or your ERP, leads in the CRM, usage in the product database, and marketing in GA4 and the ad platforms. Every Monday someone exports CSVs into a spreadsheet, and that spreadsheet has quietly become a business process. The totals disagree because "customer", "active" and "revenue" mean something slightly different in each system.
This service fits:
- Companies with three or more systems that leadership wants to see together.
- SaaS teams that need activation, retention and recurring revenue measured the same way every month.
- Online stores that want contribution margin by channel, not just revenue by channel.
- A founder who spends Sunday evenings rebuilding the same report.
Data analytics services are overkill for a business that runs on one system. If everything is in Shopify or Xero, their built-in reports may be enough, and we will say so.
Start with the questions, not the tools
Before choosing any software we write down the questions and agree what each number means. A few examples:
| Question leadership asks | Metric we would define | Sources |
|---|---|---|
| Which channels bring customers who stay? | Customers by first-touch channel, with 90-day retention | GA4, CRM, billing |
| Do we make money on each order? | Contribution margin after discounts, shipping, payment fees and returns | Store platform, accounting |
| Where do deals stall? | Median days spent in each pipeline stage | CRM |
| Is the new feature being used? | Share of weekly active accounts that used it | Product analytics |
The definitions go into a glossary, and every dashboard tile links to its definition. When someone asks why the number moved, the answer starts from a shared meaning.
What business intelligence services include
- Data warehouse. BigQuery, Snowflake or PostgreSQL, created in your own cloud account.
- Pipelines. Airbyte or a managed connector service for standard sources such as HubSpot, Shopify and Stripe; custom Python for the awkward ones. Every pipeline runs on a schedule and alerts when it fails or the data arrives late.
- Modelling in dbt. Raw data is cleaned and joined into business tables using version-controlled SQL, with tests for missing values, duplicates and unexpected categories.
- Dashboards. A small number, each for one audience: a weekly leadership view, then marketing, finance or product. Every tile answers a question from the list.
- Tracking. A GA4 event plan with consent mode and the BigQuery export, plus product analytics such as PostHog or Mixpanel for what users do inside your app.
- Data quality. Freshness and accuracy checks, so a broken pipeline shows up as a warning to us, not as a wrong number in the board pack.
Choosing the tools
| Tool | Good fit | Trade-off to know |
|---|---|---|
| Power BI | Microsoft 365 companies, finance teams, Excel users | Sharing reports generally needs a paid licence for each viewer unless you pay for capacity |
| Looker Studio | Google-centric and marketing reporting; free | Can get slow on large blended datasets; little room for complex modelling |
| Metabase | Product teams comfortable with SQL; open source | Self-hosted means you maintain it, or you pay for the cloud version |
| BigQuery | GA4 export, pay-per-query analytics | A careless query can scan a lot of data; we set quotas |
| Snowflake | Many sources, heavier workloads | More than a small company usually needs |
| PostgreSQL | Modest data volumes on a tight budget | Not built for heavy analytical queries at scale |
We pick based on the licences you already pay for and who will read the dashboards. If your company lives in Excel and Teams, Power BI dashboards will be opened; a separate tool with its own login may not be. For a wider comparison of cloud platforms, see AWS vs Azure vs Google Cloud.
From first call to a running system
- Free discovery conversation. We list the questions, the systems and who reads what.
- Fixed-price first milestone. One set of questions answered end to end: sources connected, warehouse, models and one leadership dashboard on real data. It proves the plumbing before the scope grows.
- Two-week blocks. Each adds sources or dashboards and ends with a demo on your real numbers. If a block does not earn its cost, you can stop there.
- Run. A retainer of set days a month for new questions, pipeline repairs when a source changes its API, and training the person on your side who will own it.
The cloud account, warehouse, BI workspace and dbt repository are all in your company's name, and the documentation includes the metric glossary. We choose the warehouse region with your privacy obligations in mind; clients in Canada, for instance, may need to consider Quebec's Law 25 before personal information leaves the province.
What drives the cost of a BI project?
- Number and state of sources. A clean, documented SaaS API is quick. An old on-premise accounting database with custom fields is not.
- History. Backfilling five years of data takes longer than starting from today.
- Freshness. Daily updates are simple; hourly or near real-time costs more to build and to run.
- Audiences. Each extra dashboard audience adds definitions, design and review.
- Row-level security. Letting each regional manager see only their region adds work.
- Running costs. Warehouse compute, connector fees and BI licences are paid by you directly to the vendors; we estimate them during discovery.
Where the data sits inside an ageing custom system, extracting it can be the biggest line item; legacy system modernization covers that decision. Marketing attribution work often pairs with our digital marketing service, and pipelines inside your product with custom software development.
How to judge a BI provider
Ask what decisions the first dashboard will support. If the answer is a list of charts, keep looking.
Warning signs:
- Dashboards with forty tiles and no stated audience.
- No written metric definitions.
- Extracts refreshed by hand from someone's laptop.
- Business logic locked inside a proprietary tool nobody else can read.
- Pipelines with no tests and no alerts.
- A warehouse in the provider's account rather than yours.
Frequently asked questions
Do we need a data warehouse, or can dashboards connect straight to our systems?
For one or two sources, a direct connection can be fine. Once you combine several systems, a warehouse pays for itself: joins happen once, history is kept even when a source deletes records, and heavy reporting queries do not slow down your live application. It also means changing BI tools later does not mean rebuilding every calculation.
Power BI or Looker Studio?
Power BI suits organisations on Microsoft 365, finance teams and anyone who lives in Excel; budget for per-viewer licences. Looker Studio is free, fits Google Ads and GA4 reporting well, and shares by link, but struggles with large or complex models. With a warehouse doing the heavy lifting, either tool works, so pick the one your team will actually open.
How up to date will the numbers be?
As fresh as the decisions need. Most leadership dashboards are fine with a daily refresh overnight. Operations teams watching stock or support queues may need hourly updates. Near real-time is possible but costs more to build and run, so we ask what someone would do differently with a number that is five minutes old rather than a day old.
Is GA4 enough for product analytics?
GA4 is built for websites and marketing: acquisition, landing pages and conversions. For behaviour inside a logged-in product, such as feature adoption, funnels by account and retention cohorts, a product analytics tool like PostHog or Mixpanel is usually a better fit. Both can feed the same warehouse, so marketing and product data can still be joined in one place.
Who maintains the pipelines after launch?
Either your team or us on a small retainer. Source systems change their APIs and fields, so pipelines need occasional repair; the data quality alerts tell whoever owns them when that happens. We train a named person on your side, document every pipeline and model, and keep everything in your accounts so you can switch to in-house support whenever you like.