Overview

Revenite offers a range of project services based around implementing specific Data and Analytics solutions:

  • Visualisations and Dashboards

  • Predictive Analytics

  • Modernising Data Platforms

These engagements typically cover the full life-cycle of services including:

  • Scoping and Design

  • Solution Architecture

  • Build and Test

  • Deployment and Support

These are delivered using industry standard Agile methodologies under PRINCE2 project methodologies. 

Visualisations & Dashboards

In the past five years there has been a huge proliferation and maturing of reporting tools that facilitate the design and creation of visualisations and dashboards to support business performance management.  The tools have become readily accessible via cloud based platforms and non-traditional licensing models, and they are able to connect to many different data sources with minimal IT involvement.  

Benefits

The benefits of Visualisations and Dashboards are obvious – presenting information adopting best practice design principles enables businesses to monitor business performance, make better and more timely decisions, and more effectively manage business outcomes.

Typical engagement approaches:

  • Perform a two week engagement to demonstrate the power of dashboard and visualisations for a specific KPI or Metric;

  • Migrate specific standard reports to a new Visualisation Tool to facilitate basic slicing/dicing of information;

  • Conduct a short 5 day review of existing visualisation and dashboard tools with a view to recommending gaps and features/functions not being leveraged by the client.  


Predictive Analytics

The ability to mine data for patterns of behaviour is now driving a specialisation around predictive analytics.  With the right strategies in place, predictive analytics techniques can help grow business profitability, improve customer satisfaction and reduce operational risk. 

The key is identifying a business problem that can be solved using advanced analytical techniques, so that data science teams can prepare the data, ingest and transform that data to generate models for interrogation. 

Benefits

The benefits of Predictive Analytics are that they can help your organisation to analyse large volumes of data and suggest patterns of behaviour which are impossible to identify without machine assistance.  Predictive models can supplement understanding of how people and systems functions in the real world under different conditions and constraints. 

Typical Engagement approaches:

  • Identify business problems that could benefit from the ability to predict future outcomes where significant historic behaviour/history exists.  Develop a classification or regression model using Machine Learning techniques.

  • Where there are large volumes of data with non-obvious relationships, use Machine Learning to find patterns which can be used to “cluster” the data for further analysis (e.g. customer, product segmentation).  


Modernising Data Management Platforms

Most organisations today have made investments in Data Management platforms and with data driving many digital transformations, the rise of data science, the rapid expansion of vendor solutions and the move to cloud based platforms, many organisations are now faced with the need to modernise these platforms. 

The challenge for many organisations is understanding where to start.  Many components make up a modern data platform, including the database layer (eg. SQL Server), the business intelligence layer (eg. PowerBI), advanced analytics (eg. Machine Learning) and new applications such as Internet of Things.

The key is defining a data strategy that highlights your current and future state vision and provides a roadmap that will enable better insights and decision making.  

A modern data management platform should:

  • Enable a hybrid cloud environment to be leveraged (as organisations start their migration to the cloud in a controlled fashion)

  • Maintain the necessary security around data;

  • Provide the necessary performance and scalability;

  • Provide ease of integration and access to business intelligence, advanced analytics and other reporting/visualisation tools;

  • Enable artificial intelligence and machine learning concepts to be leveraged;

  • Enable big data workloads to be managed;

  Suggested engagement approaches:

  • Perform a review of the current data platform with a view to highlighting architectural, technological, operational or performance inefficiencies and table recommendations for improvement. 

  • Perform a Cloud-migration readiness assessment of the current Data Platform as well as a staged plan to migrate to the Cloud.