Experts in data warehousing and analytics (data visualisation) solutions.
We offer a wide range of services, from data mining and analysis to reporting and visualisation. We also provide several customisation options to ensure that our clients' needs are met. In addition, we are constantly updating our technology and processes to ensure that we provide the best possible service.
Contact us today to learn more about how we can help you get the most out of your data.
What We Will Do For You?
The deliverables of the analytical platform services can be grouped into the following categories
Management Information
The foundation layer of analytics and data reporting, where information is derived from the data to carry out day to day business effectively.
- For Finance projects, this would be an up-to-date General Ledger with current PnL information, balance sheet breakdowns, and spend management information.
- For Retail projects, this would be a picture of sales, costs, turnover and procurement or order/pipeline management.
- For HR projects, it would constitute a picture of departmental workforce, inflows & outflows, planned promotions and succession planning
Business Intelligence & Data Analytics
Once the MI information is working effectively across the business, the data can be mined to analyse past trends, understand the current snapshot of the company and gain insights for strategy/decision making. This is typically a smaller audience than the MI initiative where users who understand the company model best spend time analysing the data through the analytical tools provided. The information is shared with high level management.
Predictive Analytics
This forms the end state companies aim towards. The data can be used with a high degree of accuracy to plan future deliverables (or scenarios). Scenarios can be both in the short term or longer-term and can vary by business area
- For HR projects, an example is to forecast 'leavers' or people who are not satisfied and are likely to leave employment in the near future. This would highlight key personnel whose loss would have a significant impact to the company. This is analysed by looking at a number of variables in the data, such as compensation, annual reviews, trade specialisation, personal characteristics and projecting forward using mathematical models and levers.
- For Infrastructure projects this is forecasting how new projects would require their workforce to be adapted, or re-trained in order to fulfil future requirements.
- For Insurance, this is extensively used for gauging risk appetite and deciding if insurance should be given and what premiums should be charged based on past trends. Fraud analysis is also key looking at means and deviations in claims.
- For Banking, it helps define customer appetite for new products (referred to as segmentation) or customer sentiment analysis based on previous contact history to allow for retention. Banks often come into the space of 'Big Data' due to the volume of transactions processed in a single day.
