DEV Community: Linac

Google Sheets vs a Real Dashboard: When It Is Time to Graduate

Linac — Sat, 24 Oct 2026 00:03:15 +0000

Everyone starts in Google Sheets. A tab per channel, a pivot here, a QUERY there, and for a while it works. Then one day the weekly report takes two hours to assemble, three people ask 'is this number final?' and you realize the spreadsheet has quietly become critical infrastructure.

Here are the five signals that it is time to graduate from spreadsheets to a real dashboard.

Signal 1: Your file names tell a story

marketing_2026_Q3_final_v7_USE-THIS-ONE.xlsx. If that name looks familiar, you have a versioning problem. Spreadsheets copy themselves. Every copy is a fork of the truth, and nobody knows which fork the CEO opened this morning.

A dashboard has one source of truth. Everyone sees the same numbers at the same time, and there is no 'which file was this?' archaeology at the end of the month.

Signal 2: One person owns the spreadsheet

Every team has that person. The one who 'knows the formulas'. When they are on holiday, reporting stops. When they leave, nobody can update the tabs without breaking three things.

This is not a staffing problem. It is a tooling problem. Spreadsheets concentrate knowledge in one head; dashboards encode it once and let anyone query it safely.

Signal 3: Refresh takes longer than the meeting

If your data lives in Shopify, Stripe, Meta Ads or a Postgres database, updating the sheet is a manual ritual: export, paste, pray the columns still match. By the time the numbers land, half the meeting is over and the data is already stale.

Modern BI connects directly to your sources and refreshes on a schedule or on demand. The conversation moves from 'where did this number come from?' to 'what should we do about it?'.

Signal 4: Nobody can answer follow-up questions

Dashboards answer the questions you thought of in advance. Business happens in the follow-up: 'what about Germany?' 'only mobile?' 'last Tuesday?'. In Sheets, that means pivot surgery while the meeting waits.

The next generation of tools answers follow-ups with AI — but only if the AI is grounded in your actual data. An AI that guesses is worse than a slow spreadsheet; an AI that cites the exact rows behind every number turns the meeting into a conversation.

Signal 5: Sharing means giving away the keys

Sheets permissions are binary: full edit access or view-everything. There is no 'the sales team sees their rows, the manager sees the region, nobody exports the customer list'. No API keys, no revocation, no audit trail of who saw what.

For small teams this is fine. The moment you handle customer data, investor reports or payroll, it becomes a liability. Real dashboards have roles, row-level permissions and access logs.

So you want a dashboard. Now what?

A common fear is migration: 'we will lose the flexibility of Sheets'. You do not have to abandon spreadsheets. A practical path looks like this:

Keep Sheets as a scratchpad. Exploratory analysis still belongs there.
Connect your real sources once. Shopify, Stripe, your database, a CSV upload.
Rebuild the five reports everyone actually reads. Not the twenty nobody opens.
Set up roles. Founders see everything; teams see their slice.
Add an API layer. If your tool cannot expose numbers to other systems, you have just bought a fancier spreadsheet.

That last point matters more than it sounds. A dashboard that cannot answer 'give me this metric in JSON' forces you back to copy-paste within a year.

The graduation test

You have outgrown Sheets when reporting costs more time than the decisions it feeds. The fix is not more formulas. It is one shared, live, permissioned view of your numbers — with an AI that is allowed to look, not guess.

DataShowing was built exactly for that moment: secure multi-tenant dashboards, read-only grounded AI that cites your data, and a versioned REST API with keys and webhooks. It is freemium, so you can graduate one report at a time — start at https://datashowing.com.

How to Build a Shopify Sales Dashboard Without Hiring a Developer

Linac — Fri, 23 Oct 2026 23:05:31 +0000

Most Shopify store owners I talk to have the same routine: open the admin, check today orders, squint at last week numbers, export a CSV when something looks off. Then they do it again tomorrow.

The problem is not a lack of data. Shopify has plenty. The problem is that the data lives in a place built for operating the store, not for seeing it.

Here is the routine I recommend instead: one dashboard, five metrics, ten minutes of setup. No developer, no spreadsheet glue.

The five metrics that actually matter

Before touching any tool, decide what goes on the dashboard. After setting this up for several stores, the same five keep earning their place:

Revenue per day (with a 7-day average line, so one good day does not fool you)
Orders per day
Average order value
Top 5 products by revenue (not by units: cheap bestsellers hide weak margins)
Revenue by channel (organic, paid, social, email)

If a metric does not change a decision this week, it does not go on the first page.

Connect the store, do not export CSVs

CSV exports are a trap: they are stale the moment you download them, and nobody enjoys rebuilding pivot tables every Monday.

Instead, connect the store directly to a dashboard tool. With DataShowing the flow is: connect Shopify (OAuth, no app to install), pick the metrics above, and you get a live dashboard in about five minutes. CSV, Excel, Google Sheets and WooCommerce work the same way, which matters if you sell on more than one channel.

Add the question box (this is where AI helps)

Once the numbers are in one place, the next step is being able to ask instead of calculate. Two questions every store owner ends up asking in the first week:

"Which products had the biggest revenue drop last month, and what changed?"
"How does AOV differ between returning and first-time customers?"

The catch with most AI tools is that you cannot verify the answer. That is why the DataShowing AI is read-only and citation-based: every answer links back to the exact rows it used. If it says revenue dropped because a product went out of stock, you click through and see the rows. No vibes, no hallucinated numbers.

Share it, do not screenshot it

The last habit worth changing: stop screenshotting numbers into Slack or WhatsApp. A shared dashboard link means everyone sees the same live data, and you keep access control: row-level security sits at the database layer, so a warehouse team sees warehouse numbers, not payroll.

TL;DR

Pick five decision metrics, ignore the rest
Connect the store directly; CSV exports rot
Use an AI that cites its rows (you will need to verify answers eventually)
Share a link, not a screenshot

If you want to try the whole setup on your own store, DataShowing starts at EUR 2.99/month with a free trial and the dashboard above takes about as long to build as reading this post.

What metrics would you add or cut from the list of five? I am curious what works outside fashion and home and garden, which is mostly what I have seen.

I built a SaaS that turns messy company data into dashboards, cited AI answers and a secure API

Linac — Thu, 22 Oct 2026 20:17:08 +0000

Six months ago I kept hearing the same thing from small business owners: "My data is everywhere, and I can't answer a simple question about it."

Sales in Shopify. Payments in Stripe. Costs in Excel. Forecasts in Google Sheets. And no analyst on the payroll.

So I built DataShowing — and I want to share the three design decisions that made it trustworthy enough for real company data.

The problem with "just ask the AI"

Everyone is bolting AI onto products right now. But when the data is company data, a free-form AI is a liability:

It can hallucinate numbers that don't exist.
It can leak data across tenants if your isolation is sloppy.
Nobody can audit what it actually read.

So I inverted the model: the AI never generates facts, it only formulates queries. Every number you see in an answer comes from a real row in your database, and every answer links back to the exact query that produced it.

Decision 1: Row-level security at the database layer

Multi-tenancy is usually done in application code: WHERE tenant_id = ?. One forgotten filter and you have a breach.

DataShowing enforces isolation with Postgres row-level security policies. Even a bug in my application code cannot read another tenant's rows, because the database itself refuses. The AI runs under the same read-only role — it physically cannot write, delete, or cross tenants.

Decision 2: The AI is read-only and cited

The analyst endpoint (POST /v1/ai/analyze) takes a question like "What was my best-selling product in September and why?" and:

Generates a SQL query (never executed directly — it goes through a validator).
Executes it with a read-only, RLS-scoped role.
Returns the answer with the query, the rows used and a confidence note.

If the data doesn't support an answer, it says "I don't have data for that" instead of inventing a KPI. That single behavior is what makes founders actually trust the tool.

Decision 3: Everything is an API

Dashboards are for humans. But half of my users wanted their numbers in their own apps, Slack bots or weekly email reports. So every dataset is exposed through a versioned REST API with scoped keys:

curl https://datashowing.com/v1/datasets/42/profile \
  -H "x-api-key: $DS_API_KEY"

Versioned means when I change a response shape, v1 keeps working. Your integrations never break because I shipped a feature.

The stack (boring on purpose)

TanStack Start + React on the frontend.
Postgres with RLS for every tenant boundary.
CSV, Excel, Shopify, WooCommerce, Google Sheets and Stripe connectors for ingestion.
A read-only analyst layer on top of the same database your dashboards use — one source of truth.

Pricing that a solo founder can expense

Starter at €2.99, Business at €5.99 (no VAT, UK company). No sales call, no "contact us". You connect your data and you have a dashboard in ten minutes.

What I'm learning from building in public

The unsexy truth: the hard part was not the AI. It was making the ingestion boring and predictable — deduplicating rows, normalizing currencies, handling timezone chaos in Stripe exports. Garbage in, garbage dashboard out is the real law of analytics.

I'm documenting the whole journey — distribution, SEO, what works and what flops. If you're building something similar, I'd love to compare notes in the comments.

Try it free: datashowing.com — feedback is more valuable than money right now.

Posted by the founder. I read every comment.