See your whole business on one screen

A business intelligence dashboard puts the numbers your business runs on in one place, up to date and agreed by everyone. Sales, margin, spend, stock and cash stop living in separate tools and separate spreadsheets.

The point is not more charts. It is faster, better decisions: which product to push, which campaign to cut, what to reorder, which customer is slipping. A good dashboard answers the questions a team asks every week before anyone has to ask them, and makes the next question easy to answer too.

Dashboards also help the people who aren’t analysts. A store manager, a sales head or a founder on the move can see where things stand without asking anyone, on a laptop or a phone.

Numbers disagree because every tool counts in its own way. Your store counts revenue before refunds; your accounts count it after. Amazon reports in its own time zone. Google and Meta each claim credit for the same sale. The warehouse counts stock that the store has already sold.

So every Monday someone exports files and stitches them together, and the meeting starts with an argument about whose figure is right. A Denver home goods brand we built for had exactly this: its numbers lived in eight places, and they never agreed.

Adding another dashboard on top of that mess doesn’t help. The fix starts underneath.

We need view-only access to your tools, an hour or two with each team, and one person who owns the numbers.

Access means a view-only login or API key for each source: the store, marketplaces, ad accounts, analytics, the CRM, accounting and any warehouse or ERP system. The hours with each team are for the decisions they make and the questions they keep asking. One person on your side, often the finance head or a founder, settles definitions when two teams disagree, and signs them off.

It also helps to bring the spreadsheets people use today. They show which numbers matter, how they are worked out now, and where the gaps are.

Before connecting a single source, we agree what each number means and write it down. We talk to leadership and each team, list the decisions they make and the numbers behind them, and settle the details: is net revenue before or after returns, what counts as a new customer, which costs sit inside contribution margin.

Typical definitions include net revenue, contribution margin per order and per product, marketing efficiency, customer acquisition cost, lifetime value, days of stock cover and cash collected. Each gets one written definition that finance, marketing and operations sign off.

Definitions also settle the awkward edges: how a part-refunded order counts, which day a late-night order belongs to, how a bundle’s revenue splits across its products, and what happens to cash-on-delivery orders that come back undelivered.

This step takes judgement more than code, and it is the step that makes everything after it trusted.

Your data stays in the tools you already use; we copy it, on a schedule, into one data warehouse. Typical sources are online stores and marketplaces, Google and Meta ads, Google Analytics, your CRM, Tally or other accounting software, an ERP or warehouse system, payment gateways and the spreadsheets nobody has replaced yet.

Most sources sync daily, and fast-moving ones such as ad spend can sync every few hours during a sale. Time zones, currencies and product codes are matched on the way in, so a day, a rupee and a product mean the same thing in every source. Tools without a ready connector are reached through their API or a scheduled export.

We read with read-only access wherever a tool allows it, so reporting can never change your live systems.

We choose the tools to suit your size and budget. A typical setup is a sync tool such as Airbyte, a warehouse such as BigQuery, models written in dbt and dashboards in Metabase or Looker Studio. Smaller businesses can start lighter, and the definitions and tests stay yours whichever tools you use.

Every definition becomes a model in code, written once and used by every dashboard, report and AI answer. Each model has tests for missing, duplicated or late data, and when a source fails to sync or a figure looks wrong, the team is alerted before anyone reads it.

Before launch, every dashboard is checked against finance’s month-end figures until they match. That reconciliation is what turns a dashboard from something people glance at into something they act on. Every figure also links to its written definition, so anyone can see exactly what it includes.

Each team gets views built around what it decides, not a single page of everything:

• leadership: a daily snapshot of sales, margin and spend by channel, with this week against the last;
• sales: pipeline, orders and collections by salesperson, region and customer;
• marketing: spend and return by channel and campaign, new against returning customers, and acquisition cost;
• operations: stock cover, inbound shipments and products at risk of running out;
• finance: profit and loss by channel each month, reconciled to the accounts.

Each view starts with the question it answers, shows the few numbers that answer it, and lets people click through to the orders, products or campaigns behind any figure. A few clear views get opened every day; a wall of charts doesn’t.

Alerts sit beside the dashboards: low stock, spend that jumps, margin that slips, a source that stops sending data. They go to the person who can act, while there is still time.

The first wins usually come from margin and stock. Margin per product, after cost of goods, fees, shipping, returns and ad spend, can show that a best-seller earns far less than its revenue suggests, or nothing at all. Stock cover by product and location turns stock-outs from a surprise into a warning.

The third win is time. When the Monday spreadsheet retires, the person who built it gets their week back.

Operational views pay off the same way. A transport company we built for in Coimbatore runs its whole fleet from one live map, where delay alerts that know each route’s normal pace give dispatchers time to act. A supermarket chain we built for in Cork sees which offers were activated and used, store by store.

Not every question deserves a dashboard. For the rest, an AI analyst lets anyone ask in plain English, in Slack or in the dashboards: which products lost margin last month and why, or how this festive sale compared with last year’s.

It answers from the same defined numbers, with the figure, a chart and the query behind it, so every answer can be checked. It follows the same access rules as the dashboards, and when the data to answer doesn’t exist, it says so instead of guessing. Each Monday it can also write a short summary of the week for leadership: what moved, what to watch and where to look.

Dashboards drift if nobody looks after them. Tools change their exports, new products and channels arrive, and definitions need updating as the business grows.

We keep the data flowing and the tests passing, add sources and numbers as you need them, and review which dashboards people actually open. Views nobody uses are removed; questions people keep asking the AI become new views. At handover each team learns to filter, drill down and save its own views, and one person learns to add a new number the right way, with its definition and tests. We stay on hand on India time for the harder changes.