Decide what to measure
We look at the decisions you make and the data you hold, then say which questions it can answer, what is missing and what to build first.
Data intelligence
Decisions from your own data. Analytics, pipelines and dashboards.
Most reporting problems are data problems first. We work out what you need to know, build the pipelines that collect and tidy the data, and put the answer on a dashboard people open.
- Your sources
Databases, spreadsheets, apps and files, each in its own format.
- Pipeline and model
Data is collected, cleaned and joined on a schedule, in one place.
- Dashboard or report
The few numbers each team needs, with the detail one click away.
A chart is only as good as the data behind it.
01 / Three kinds of data work
Consulting, engineering and visualisation.
They are usually bought together, but each can stand alone.
02 / Is this the one
Is this the work you need?
Data projects fail more often on unclear questions than on technology. This is a quick test of fit.
The right choice when
- Reports are built by hand in spreadsheets and take days each month.
- The same number comes out differently depending on who pulls it.
- Your data sits in several systems that do not talk to each other.
Look elsewhere when
- You want forecasts or an AI assistant on top of your data. Start with our AI services.
- One off-the-shelf tool already answers your questions. Keep it.
- There is very little data yet. Collecting it comes first.
03 / What the work covers
From an audit to a dashboard your team owns.
Work starts small: one question, one pipeline, one dashboard. Then it widens.
- 01
Data audit
What data you hold, where it lives, how clean it is and which questions it can answer.
- 02
Pipelines and storage
Scheduled jobs that extract, clean and load your sources into a warehouse or database you control.
- 03
Data model
Agreed definitions, so revenue or an active user means the same thing in every report.
- 04
Dashboards and reports
Interactive dashboards, scheduled reports and exports for the people who need them.
- 05
Handover
Code, documentation and access go to your team, with a short training session.
04 / Limits and risks
What a data project will not do on its own.
Bad data in, bad charts out
If a source system records things inconsistently, the pipeline can flag it but cannot invent the truth. Some fixes belong in the source.
A dashboard nobody opens
A dashboard changes nothing unless someone owns each number. We ask who that is before building.
Personal data needs your rules
We build to the access and privacy rules you set, with roles and audit logs. Which laws apply to your data is for you and your advisers to decide.
05 / What we can show
FocusPulse: a data pipeline we run every day.
Our own AI product reads 500+ articles a day from 30+ business and markets publishers and sends a short briefing on Telegram and Web Chat. Each story is linked to the listed company, commodity, index or currency it concerns. That is data work before it is anything else.
What this does not show
A client analytics project. FocusPulse is our own product and handles news, not business reporting. We have no published case study of a data warehouse or analytics project built for a client.
06 / What to do next
Price it, compare it or talk it through.
The first step is a free scoping call about one decision you want better data for. A written estimate follows.
A little clarity
Before data work begins.
How does NSDBytes approach data intelligence projects?
We start with the decisions you need to make, not the tools. Then we audit the data you hold, build the pipelines that bring it together, agree the definitions and put the result on dashboards or reports. Work begins with one question and one dashboard, and widens from there.
Can NSDBytes assist in setting up a data intelligence infrastructure?
Yes. We choose the storage and pipeline tools with you, build the ETL jobs, the warehouse and the reporting layer, and hand over the code and documentation. It runs in cloud accounts registered to you.
What is the difference between analytics consulting, data engineering and data visualisation?
Analytics consulting decides what to measure and whether your data can answer it. Data engineering collects and cleans the data so it can be trusted. Visualisation shows the result on dashboards and reports. This page covers all three, and you can buy one without the others.
How does NSDBytes address data security and privacy concerns in data intelligence projects?
We agree access rules with you before any data moves: who can see what, where it is stored and how long it is kept. We use encryption in transit and at rest, role-based access and audit logs where the platform supports them, and we sign an NDA on request. Which regulations apply to your data is for you and your advisers to decide. We build to the rules you set.
How much does a data project cost?
It depends on how many sources there are and how clean they are. First releases at NSDBytes start from $5,000. The project cost estimator gives a range for your scope, and a fixed quote follows a free scoping call. Hosting and data tools are billed by their providers and are separate.
Start wherever you are
Bring one question your data should answer.
We’ll tell you whether the data you hold can answer it, and what it would take to see it on a dashboard.