Familiar Microsoft tools, applications and interfaces, with business intelligence tools built in, for self-service, collaborative BI.
You’re in experienced hands with our analytics team - we have delivered hundreds of business intelligence and data warehouse solutions using the Microsoft technology stack, and were a recent finalist in the Optimising Operations category at the Microsoft NZ Partner Awards 2019.
A high-performance and feature rich foundation for data warehouse and business intelligence solutions whatever your requirements.
Integration Services provides the ability to connect to cloud and on-premises data sources and apply a wide range of built-in tasks, transformations and tools for data to be extracted, transformed and loaded as required.
SSIS is included in Microsoft SQL server and ETL solutions are developed using the familiar Visual Studio IDE. Use SSIS to effectively and efficiently apply data driven designs, implement complex business logic to ETL and orchestrate data loads.
Analysis Services (SSAS)
An analytics data engine used for providing consistent business analytics and decision support, Analysis Services includes the tools to create and manage Online Analytical Processing (OLAP) and data mining.
Analysis services includes two models:
Analysis services is available on-premises and in the Azure cloud as PaaS (Platform As A Service).
Use Analysis Services to provide business users with consistent data and with the ability to do self-service data analysis, reporting and dashboarding using client tools such as Power BI, Microsoft Excel, Microsoft SQL Server Reporting Services.
The Azure cloud environment offers many services that can be used to develop analytical solutions or complement existing solutions in the cloud or on-premises.
Centrally manage, share and publish your business intelligence applications, dashboards, reports and more. More on SharePoint.
Visualise and report on data how you want using the right tools.
Self-service, pixel-perfect, paginated reporting that allows users to subscribe to reports and choose different delivery and output options.
Powerful interactive visualisation tools that help users easily analyse and find insights in their data. Power BI integrates easily with other Microsoft tools including Teams and Dynamics 365 Business Central.
Data analysis using the familiar Excel environment
Microsoft provides several ways to do advanced analytics:
Azure Machine Learning - a cloud service providing a self-contained design and modelling application with simple drag and drop interface. Developers and analysts are able to load, profile and cleanse data to develop models using predefined algorithms (as well as custom R or Python scripts). Interfacing with the model can be done using REST APIs making integration with the BI environment simple.
SQL Server 2016 provides R Services which enables R code to be executed in T-SQL over datasets in the SQL Server database.
Power BI Desktop allows R scripts to be run over datasets and also provides the R visualisation component.
Azure Data Catalog is an Azure cloud service that allows for enterprise-wide metadata discovery and data source access across an organisation's data stores. It’s a fully managed service and lets any user—from analyst to data scientist to developer—register, enrich, discover, understand, and consume data sources.
Supported objects that can be catalogued include:
Metadata captured in Azure Data Catalog includes the data source, asset definitions (tables, views, reports) and data profiles (types, row counts, max/min). Users can enrich the metadata by adding documentation, notes, tags or common terms to build up a business glossary.
Users can work with data in their tool of choice, eg Power BI, Reporting Services or Excel. Your data stays where you want it, and the Data Catalog user portal helps you discover it and work with it where you want. It’s also possible to integrate into existing tools and processes with open REST APIs.
Implementing Azure Data Catalog will enable users to get more value out of the BI environment and encourage self-service reporting and analysis.