Data & Analytics

Power BI Analytics

Turn Data into Insight, Insight into Action

Interactive dashboards and reports that drive better decisions.

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Key Capabilities

What Power BI Analytics Delivers

Custom Dashboard Development

Data Modeling & Transformation

Real-Time Data Refresh

Mobile BI Apps

Natural Language Queries

Embedded Analytics

What a reporting and BI project costs

Clear ranges so you can plan a budget before the first call. Pick the tier closest to your situation.

Essential

A handful of dashboards from one or two sources.

$2,000 to $5,000

one-time project

Get a fixed quote
Best fit for most organizations

Standard

A reporting suite across departments with refresh and security.

$8,000 to $16,000

one-time project

Get a fixed quote

Enterprise

An enterprise data platform with governance.

From $72,000

one-time project

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  • A range becomes a fixed price after a short scoping call. What moves a price: users and departments, data to migrate, integrations, and how much is configured versus built.
  • Optional annual support and managed governance starts at 18% of the project value per year (minimum $1,800). Changes after go-live are $85 per hour, or a prepaid 20-hour block for $1,600.
  • Prices exclude VAT (16% in Kenya) and withholding tax.

Not sure which fits? Tell us your team size and what you use today and we will point you to the right option.

Who this is for

  • Organizations where the monthly management pack is assembled by hand in Excel and is out of date by the time it is presented.
  • Leadership teams arriving at meetings with different numbers for the same measure because each department calculated it separately.
  • Organizations with data in several systems, finance, CRM, operations, that has never been brought together.
  • Teams that have bought Power BI licences and produced a handful of reports nobody trusts.

What a Power BI engagement covers

The dashboard is the visible part and the smallest part. Agreeing what a measure means, and building a model that computes it consistently, is the work.

  • Definition of the measures that matter, agreed across departments rather than assumed.
  • Data source connection and modelling, including the transformation needed to make sources reconcile.
  • A semantic model, so everyone computing revenue computes it the same way.
  • Reports and dashboards by audience, executive, management and operational are three different products.
  • Refresh scheduling, row-level security, and the workspace and publishing model.
  • Training for your analysts, so new questions do not require a new engagement.

How the engagement runs

  1. 1

    Measure definition

    We get the disagreements out early, what counts as an active customer, when revenue is recognized, which headcount figure is official. This is a business conversation and it is the foundation of everything after.

  2. 2

    Data modelling

    Sources are connected, transformed and modelled, with data quality problems surfaced rather than silently smoothed over.

  3. 3

    Report build

    Dashboards built by audience and reviewed against questions those people actually ask.

  4. 4

    Deployment and enablement

    Publishing, security and refresh configured, and your analysts trained to extend what exists.

What you need in place

  • Power BI licensing appropriate to your user numbers and sharing needs.
  • Access to source systems, and the credentials and cooperation of whoever owns them.
  • Authority to settle definitional disputes. Where two departments define a measure differently, someone must decide.
  • Acceptance that data quality problems will surface. Reporting makes them visible; it does not create them.

Working with Sibasi on Power BI

Why do our existing dashboards get ignored?

Most commonly because people do not trust the numbers, and they do not trust them because the same measure computes differently in different places. That is a modelling and definition problem rather than a visualization problem, and no amount of chart redesign fixes it. We start with measure definition for exactly this reason, which is slower to show something pretty and far more likely to produce something used.

Our data is messy. Should we fix that first?

Not necessarily first, but you should expect reporting to expose it. Dashboards make data quality visible in a way that spreadsheets conceal, and the first version of a dashboard frequently prompts a round of source-system cleanup. We plan for that rather than treating it as a surprise, and where a quality problem is severe enough to make a measure meaningless we will say so rather than publishing it.

How is this different from Fabric?

Power BI is the reporting and analytics layer; Fabric is the broader data platform it can sit on, with storage, pipelines, warehousing and real-time analytics unified underneath. Many organizations start with Power BI against existing sources and move to Fabric when the number of sources, the volume, or the need for one governed copy of the data justifies it. Starting with Fabric is right where you already know the estate is the problem.

Can we build our own reports afterwards?

Yes, and that is the intended outcome. Once the semantic model exists with measures defined correctly, building a new report is a modest task your analysts can do. The expertise concentrates in the model; report building on top of a good model is accessible. Enablement is scoped so your team leaves able to do it.

New to this category? What is business intelligence reporting?, a buyer's guide to what the category is, what to look for and how to compare options, whoever you end up choosing.

Let's put Power BI Analytics to work for you

Talk to our specialists about your goals, timeline and budget.

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