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A finance analyst checks a printed report against a dashboard of charts on screen.

Business intelligence advisors for data your organisation can trust

Arke Technologies (Pty) Ltd provides business intelligence advisors and data engineers from Johannesburg. We help South African organisations, government bodies and schools turn scattered records into a governed data warehouse and reports that auditors can trace, on Microsoft Fabric, Power BI or Google BigQuery, built and supported under one SLA.

What data analytics means for an organisation

Data analytics is the work of collecting, cleaning and examining data to answer a question or support a decision. In plain terms, data analytics means using the records you already hold, such as invoices, meter readings, learner marks and stock movements, to see what happened, why, and what is likely to happen next.

Business intelligence (BI) is the part of data and analytics that most organisations need first: a data warehouse, agreed definitions, and the reports and dashboards managers read every week. Done well, business intelligence is a discipline rather than a tool: every figure in a board pack traces to its source record, and finance, operations and audit all accept it.

Data analysis for business intelligence therefore starts with plumbing rather than charts: connecting systems, reconciling records that disagree and agreeing the meaning of each measure. That is why this work sits beside our systems integration and custom software development practices. For a primer, read what is business intelligence, and what does Power BI do.

Two colleagues review charts pinned to a glass wall in a meeting room.

Business intelligence, business analytics and data science compared

Buyers often ask about the difference between data science and analytics, or between business intelligence and business analytics. They sit on one spectrum, from describing the past to predicting and automating the future, and differ in method more than in purpose.

DisciplineQuestion it answersTypical methodsTypical output
Business intelligence and reportingWhat happened, where and why?Data warehouse, agreed measures, drill-downManagement packs, dashboards, business intelligence reports, exception lists
Business analyticsWhat is driving the result?Trend, variance and cohort analysis; statisticsDriver analysis, scenarios, budget against actual
Predictive data analyticsWhat is likely to happen next?Forecasting, regression and classification modelsDemand and cash-flow forecasts, risk scores
Data science and AIWhat should we do, and can it be automated?Machine learning, Python and SQL, AI modelsRecommendations and automated steps, with a person approving
Big data analyticsWhat do very large or fast-moving data sets show?Distributed processing on cloud platforms such as Google BigQueryAnalysis of logs, sensor readings or transactions at volume

Most organisations gain more from reliable business intelligence first, and add prediction once the data is trusted.

An integration lead talks a team through a process drawn on a board.

What our business intelligence advisors do

We advise, build and run. Each service below is available on its own or as one programme under one contract, alongside our other IT services.

  • BI strategy, consulting and analytics

    Advice that starts from the decisions your board, council or governing body must make and maps them to measures, source systems and a costed, phased roadmap.

  • Data warehouse and pipelines

    Pipelines that extract data from accounting, ERP, CRM, billing and HR systems, clean and match it, and load it with its history into a warehouse.

  • Business intelligence and reporting

    BI reporting that replaces the month-end spreadsheet scramble: management accounts, KPI scorecards and exception reports, refreshed on a schedule.

  • Data quality and governance

    Data profiling, a data dictionary, a business owner for each measure, access rules and a change process, so the numbers stay right after go-live.

  • Support under an SLA

    Refresh monitoring, fixes, new measures and user support, with incidents prioritised P1 to P4 and reported at a monthly service review.

Data warehouse, data lake and the platforms we build on

What is a data warehouse? It is a database designed for reporting rather than transactions. Data from every source system is loaded, cleaned, matched and kept with its history, so reports never slow the finance system and last year's figures survive a change of software. A data lake stores raw files of any shape cheaply, for later processing; a lakehouse combines the two on one storage layer.

Data warehouseData lakeLakehouse
HoldsStructured, cleaned, modelled dataRaw files: tables, logs, documents, imagesRaw and modelled data on one storage layer
Best forTrusted reporting and BIData science, archives, large or unstructured dataOrganisations that need both without keeping two copies
GovernanceStrong: defined tables and measuresWeak unless actively managedStrong where the modelled layer is governed
ExamplesGoogle BigQuery, a Microsoft Fabric warehouse, Azure SQL, PostgreSQL, the Snowflake data warehouseCloud object storage, OneLakeA Microsoft Fabric lakehouse

The data lake vs data warehouse decision is rarely either-or. Most mid-sized organisations need a warehouse first, and add lake storage only once they hold large volumes of logs, documents or sensor data.

We build on Microsoft Fabric (Microsoft's data platform, which now includes Power BI), Google BigQuery (Google Cloud's serverless warehouse) and PostgreSQL, across Microsoft Azure and Google Cloud. Some large estates already run SAP Business Warehouse, SAP BusinessObjects or a Snowflake data warehouse. We assess any existing platform in discovery, and the proposal states in writing whether we would build on it or read from it into a governed model. Loading any warehouse is as much a data migration task as a reporting one.

BI tools and business intelligence systems compared

BI tools, BI software and data analytics software all do the same five jobs: connect to sources, prepare the data, store it, model it and present it. The right business intelligence system is the one that fits your licences, data and skills, not the one at the top of a ranking.

SuiteMain componentsUsually fits
Microsoft business intelligence suitePower BI, Microsoft Fabric, Excel, SQL Server Reporting ServicesOrganisations on Microsoft 365, and finance teams that live in Excel
Google data analyticsGoogle BigQuery, Looker Studio, Looker, Google SheetsOrganisations on Google Cloud or Google Workspace
SAP business intelligenceSAP BusinessObjects, SAP Analytics Cloud, SAP Business WarehouseLarge organisations whose core systems run on SAP
Specialist BI software toolsTableau, Qlik and similarAnalyst teams focused on visual exploration
Built-in reportingReports inside ERP, CRM and accounting softwareOperational reports from a single system

Microsoft BI is the natural fit where Microsoft 365 is already in place, because Power BI shares its sign-in, Teams and SharePoint. Data analytics in Excel is where most analysis starts, and it serves exploration well. The risk comes when a material figure depends on one person's workbook with no audit trail, so we move recurring, material reports onto a governed model.

Data analytics methodologies: how we deliver

Data analytics methods range from simple aggregation to machine learning; data analytics methodologies, of which CRISP-DM is the best known, describe the steps around them. Ours follows the same arc inside a governed engagement, so nothing is built before scope and cost are agreed.

  1. Discovery

    Which decisions the reports must support, who makes them, how often and at what level of detail.

  2. Assessment

    A source audit: every system, the quality of its data, who owns it and how we will read it with the least access needed.

  3. Proposal

    A phased plan with licences, platform running costs and build costs shown in writing before you commit.

  4. SLA

    Scope, P1 to P4 priority levels with response and resolution targets agreed to your needs, exclusions, reporting and term.

  5. Build in phases

    The warehouse and pipelines first; then the model, with each measure defined once and signed off by its business owner; then reports refined with the people who use them.

  6. Onboarding and handover

    Documentation of sources, pipelines, measures and refresh schedules, and training for report users and your own analysts.

  7. Monthly service review

    Refresh reliability, data-quality exceptions, report usage, changes made and decisions needed.

Business intelligence for your industry

BI by industry differs less in technology than in the questions and rules that apply:

Governance, compliance and risk

Reports help a board only if the board can rely on them. Every engagement includes:

  • A written SLA, set to your needs, with incidents prioritised P1 to P4 (P1 a critical failure, such as the board pack not refreshing before a meeting, through P4 a low-impact request), response and resolution targets matched to the business impact, and a defined escalation path.
  • Monthly service reviews with a written report for your steering committee, audit committee or governing body.
  • Change control for measures. A new or changed definition is requested, assessed, approved by its business owner and recorded before it reaches a report.
  • POPIA by design. Personal information stays out of reports that do not need it, row-level security limits each person to their region, school or cost centre, and access is logged. See POPIA compliance.
  • King IV Principle 12. Directors can see how key figures are produced and who can change them. See King IV and technology governance and IT governance, risk and compliance.
  • PFMA and MFMA. For departments, public entities and municipalities, a documented trail from each reported figure to its source record that internal and external auditors can follow.
  • Security and reliability. Controls from our cybersecurity practice. Servers that host data gateways and pipelines sit under our monitoring, which checks them every 15 minutes and logs faults as tickets automatically; refresh failures are tracked under the SLA.

Procurement and continuity

Arke Technologies (Pty) Ltd has been a registered company since 2017, trading first as Fibre Boyz, and is a Level 1 B-BBEE contributor, which gives buyers 135% procurement recognition (sworn affidavit available on request). Company documents, our CSD supplier number, SARS tax compliance status and a bank confirmation letter are available on request, and we complete standard bidding documents (SBD forms) when a bid requires them. You can buy through a proposal and SLA, a purchase order or a tender response. See company facts and procuring from Arke.

Continuity is designed in. A certified team delivers remote-first under one contract, so adding a data source or a subsidiary changes the SLA schedule, not the supplier. The warehouse, models and reports are built in your own tenant or cloud account, so you own them, with the data, credentials and documentation; if the work moves, we hand everything over in an orderly exit. Everyone with access to your data is bound by confidentiality and our security controls.

Two colleagues examine a printed report full of charts.

What a business intelligence solution costs

Last reviewed: September 2026.

A business intelligence solution has three cost lines: licences, the build and the upkeep. Licences are the most visible, but the build, and above all the data work inside it, is usually the largest.

  • Licences and platform. Power BI Desktop is free; sharing reports needs per-user or capacity licences, and warehouses are billed by capacity, storage or query volume.
  • Source data. The number and condition of source systems drives cost most: one old database full of duplicate customers can take longer than several modern cloud systems.
  • Reports and upkeep. How many reports, how finely access is controlled, and a yearly budget for support and new measures.

For market context, published hourly rates for IT project work in South Africa run from about R550 to R750 ex VAT (matnet.co.za, September 2026). We quote BI work per project, in phases, after a scoping session, and ongoing support under an SLA, with licence and platform running costs shown separately.

Two colleagues review charts pinned to a glass wall in a meeting room.

Data analytics skills, training and certifications for your team

Most organisations want their own people to own the numbers, so we plan the in-house roles alongside the build. An in-house team needs data analytics skills in SQL, data modelling, a BI tool such as Power BI and basic statistics, plus knowledge of the business; a short Python program helps with automation.

The roles differ: a BI developer (business intelligence developer) builds pipelines, models and reports; a business intelligence analyst turns managers' questions into measures; a data scientist builds predictive models. Routes into these roles include:

  • Free data analytics training online, such as Microsoft Learn's Power BI and data analytics learning paths.
  • Vendor certificates, such as the Google Data Analytics Certificate, or Microsoft's Power BI Data Analyst certification for teams on Microsoft BI.
  • Courses and boot camps, such as ALX's data analytics programme or another data analytics boot camp, suited to people at data analytics entry level.
  • University study, from postgraduate data analytics diplomas to a master's in data analytics, often a Master of Science (an MS or MSc); masters programmes suit people heading into data science.

A certification in data analytics shows study; experience on your own data makes it count. We work beside in-house analysts in a co-managed IT arrangement, document what we build and train your team at handover, so the capability stays with you.

A finance analyst checks a printed report against a dashboard of charts on screen.

How to choose business intelligence advisors

  • One accountable team. Advisors who only recommend leave you to find builders. Choose a team that advises, builds and supports.
  • Evidence of discipline. Ask for a sample data dictionary and service report, and how dirty data is fixed at the source.
  • Data residency. Microsoft and Google both run cloud regions in South Africa; ask where each component stores data, and record it for POPIA.
  • Governance you can audit. Ask how incidents are prioritised and escalated, what the monthly service report contains, and how a change to a measure is approved.
  • Ownership, exit and procurement fit. Confirm in writing that you own the warehouse, models, reports and credentials, and check B-BBEE status, company documents and tax compliance before you shortlist.

Frequently asked questions

What do business intelligence advisors do? Show answer

Business intelligence advisors help an organisation decide which figures it needs, where they come from and how to report them reliably. The work covers mapping decisions to measures, auditing source systems, designing the data warehouse and choosing BI tools. Good advisors also build and support what they recommend, or hand it to a team that can, so the advice turns into reports people use.

What is the difference between data science and data analytics? Show answer

Data analytics answers defined business questions from prepared data, while data science builds models that predict, classify or automate. An analyst might explain why collections fell last quarter; a data scientist might build a model that flags accounts likely to stop paying. Both need clean, well-governed data, which is why most organisations invest in business intelligence and reporting first.

What is a data warehouse, and do we need one? Show answer

A data warehouse is a database built for reporting, holding cleaned and matched data from all your source systems with its history. You need one when reports draw on several systems, when those systems disagree about the same customer or property, or when auditors ask how a figure was produced. With a handful of clean sources, Power BI's own storage can be enough to start.

How is AI used in data analytics? Show answer

AI is used in data analytics to forecast demand or cash flow, flag unusual transactions, classify documents and answer plain-language questions about trusted data. Artificial intelligence and data analytics work best together when the underlying data is governed, because AI repeats the errors in its sources. We build it on one rule: AI recommends, people decide, and every AI action is logged and reviewable.

What does a business intelligence analyst do? Show answer

A business intelligence analyst turns managers' questions into measures, reports and dashboards, and explains what the figures show. The work includes gathering requirements, checking data quality, writing queries in SQL or DAX, designing reports and presenting findings. The best analysts understand the business as well as the tools, so they can tell when a number is technically correct but misleading.

How much does a business intelligence solution cost? Show answer

A business intelligence solution costs licences plus the build and upkeep, and the data work in the build is usually the largest part. Power BI Desktop is free, and sharing needs per-user or capacity licences. Published hourly rates for IT project work in South Africa run from about R550 to R750 ex VAT (matnet.co.za, September 2026). We quote per phase after a scoping session.

Can you deliver business intelligence for municipalities and government departments? Show answer

Yes, we can scope and deliver business intelligence for municipalities, national and provincial departments and public entities, with the documented audit trail that MFMA and PFMA reporting expects. For a municipality, that usually starts with reconciling billing, metering and valuation records. We are a Level 1 B-BBEE contributor, complete SBD forms when a bid requires them, and provide our CSD number and tax compliance status on request.

Turn your reporting into figures the board relies on

Tell us which reports you produce today, where the data lives and which decisions they must support. We will scope a phased build, with licences and running costs shown before you commit. Talk to us.