Automated Reporting and Data Science

Data is key to implementing a successful Reporting or Data Science project. Our approach uses our previous experience on complex projects to architect an effective data model; this then forms the basis of automated management reports, it can then be built upon for more advance statistical analysis and data science.

1.       Data Architecture and capture

Using Microsoft Azure services we build out a secure flow of data from source systems through to a central Database or Data Lake. Our design is aligned to the needs of our clients, where high availability real-time (or near real-time) data is required then we scale up services accordingly. But if data is only relevant if refreshed daily, then we can design the infrastructure to save costs.

Data from source systems ranges from full cloud based platforms where published APIs allow easy data extractions; through to Excel sheets saved in a SharePoint Document Library. A published API is by far the more robust approach, but we recognise that Excel can provide an effective source if used correctly.

Building the data model is an iterative process, we have found that most clients are unsure of what they would like until they actually start to see some data. We therefore have a pragmatic approach and build a quick version to start with, which then gets refined as we work through the data.

Our tried and tested approach uses our library of templates to build upon, this ensures we can hit the ground running, and are continually improving our base standards. 

2.       Automated Reporting

Power BI is our tool of choice to present the data captured. We have many years of experience in building out dashboards and reports, but have found that often simplicity is key. Working with users we recommend appropriate charts and visuals to use, and where possible add narrative to the reports to add an element of validation.

As reports and data models mature, we provide training to advanced users who can then connect to their data model in Power BI and start to discover greater insight from the data. 

3.       Data Science

Once data is being regularly captured and is of high quality, then we are able to assist our clients with the use of various different data science tools to enable greater insight from their data. This could be using sentiment analysis, or fully trained machine learning models.