Business Analytics

Certified data science, applied to decisions your business actually makes.

Dashboards that show what's happening, and models that help predict what's next — built by a certified Data Scientist, not a generic reporting template.

📊 Power BI, Python & SQL 🎯 Built for businesses without a data team 🔬 Certified Data Scientist & ML practitioner
Raw numbers, turned into a picture you can act on.
The problem

Most businesses have data. Few can actually use it.

Sales records sit in spreadsheets. Customer information is scattered across WhatsApp, email, and a CRM nobody updates consistently. Decisions get made on instinct because pulling a clear answer out of the data would take hours nobody has.

You don't need a data science department to fix this — you need someone who can turn what you already have into something usable.

  • Sales and customer data scattered across spreadsheets and apps
  • No dashboard — someone manually compiles numbers for every meeting
  • Decisions made on instinct because the data takes too long to interpret
  • No visibility into which customers, products, or channels actually perform
  • Interest in AI or automation, but no clear starting point
Who this is for

Built for businesses making decisions without a data team.

Businesses drowning in spreadsheets

Companies with real data — sales, inventory, customers — but no clear way to see trends or performance at a glance.

Growing companies making bigger bets

Businesses at the point where a wrong decision costs real money, and gut instinct alone isn't enough anymore.

Businesses curious about AI

Companies that keep hearing about AI and automation and want a grounded, honest assessment of where it actually applies to them.

Our approach

Assess → Clean → Build → Explain.

Every project starts with what data actually exists, not an assumption of a perfect dataset.

01

Assess

Review what data already exists, where it lives, and what question you're actually trying to answer.

02

Clean

Fix inconsistencies, gaps, and errors — the unglamorous work that makes everything after it reliable.

03

Build

Build the dashboard, model, or analysis that answers the actual business question.

04

Explain

Walk through what it shows and how to use it — not just hand over a file and disappear.

What you get

Deliverables, not just activity.

Data auditA clear picture of what data you have, where it lives, and what's missing or unreliable.
Custom dashboardsPower BI dashboards built around the specific metrics that matter to your business.
Data cleaning & structuringScattered, inconsistent data turned into something reliable enough to build on.
Predictive modellingModels built for specific business questions — forecasting demand, or recommending what a customer is likely to want next.
Plain-language explanationA walkthrough of what the dashboard or model shows, so your team can actually use it.
AI & automation advisoryAn honest assessment of where AI genuinely helps your business first — scoped to reality, not hype.
Where it fits

Common situations this solves.

Reporting

Someone manually builds reports every month

A live dashboard replaces hours of manual spreadsheet work with numbers that update automatically.

Customer insight

You don't know which customers matter most

Analysis surfaces which customers, products, or regions are actually driving the business.

Forecasting

Stock and demand planning is guesswork

Predictive models based on historical patterns give a more grounded basis for planning ahead.

AI readiness

You want to explore AI but don't know where to start

An honest advisory conversation about where automation or AI genuinely fits your business, without the hype.

Technical depth

Not a reporting template — real applied data science.

1.06 → 0.86RMSE reduction on a real, sparse-data recommendation engine
Power BI · Python · SQLCore toolset, chosen to fit the problem
CertifiedData Scientist & Machine Learning practitioner (ALX Africa, Coursera)

See the underlying technical work on the data science portfolio →

Common questions

Business analytics FAQs.

Less than most businesses assume. Spreadsheets, CRM exports, sales records, or even paper logs can be a starting point. Part of the process is assessing what data already exists and what's realistic to start collecting going forward — you don't need a perfect data warehouse first.

A dashboard shows you what has already happened — sales trends, customer numbers, performance over time — in a clear, visual format. A data science project goes further, using models to predict what's likely to happen next or recommend a specific action, like the recommendation engine built for a real project.

No. This service is built specifically for businesses that don't have an in-house data team. YuzziTech handles the technical work — data cleaning, modelling, dashboard building — and delivers something your team can actually use without needing to know Python or SQL.

Power BI for dashboards and reporting, Python and SQL for deeper analysis and modelling. The tool is chosen based on what fits the problem and what your team can realistically maintain afterward, not a fixed package.

Ready when you are

Tell us what decision you're trying to make with data.

Book a short consultation — we'll look at what data you already have and what's realistic to build from it.