We deliver insight and impact for clients through a wide range of flexible models, providing ad hoc analytics architecture and solutions. We work with various technologies and able to work with our clients’ preferred technologies and platforms. Once tools and models are implemented, we focus on training, support and system transfer to ensure the system can be fully utilised by the clients.
A successful analytics implementation depends on technical capabilities and good understanding of the business goals. We typically start projects by understanding the end goal of the implementations.
Once the goal is well-defined, we utilise various tools techniques to answer the objectives ranging from data visualisation, machine learning, statistical models, experiment design, data integration and market surveys.
We use different type of statistical techniques to solve problems and have a deeper understanding of the data. Some of the common techniques we use:
When dealing with marketing issues, decision makers often rely on various data including survey data, sales or other marketing information. Marketing science is a field that utilises different scientific methods to answer marketing problems, usually it is associated with techniques like statistical modelling, mathematics, market simulation and optimisation. We apply use different techniques like:
We help you in utilising these techniques for your dataset / survey projects, including initial consultation, data collection framework, modelling and output interpretations.
As data grows more complex, bringing it to life through visualisation becomes critical to help make the results of data analyses digestible for decision makers. Visualisation is an important step in turning data into insights.
Real-time and near-real-time data are becoming more prevalent, and organizations and teams need dynamic dashboards rather than reports. Data is increasingly required for decision making through all parts of an organization, and good visualization supports that goal, bringing the information to life in a way that can be understood by those who are new to analytics. We use different software and solutions enable users to make clear and intuitive visualizations from simpler data. Combination of a strong understanding of data with user interface/user experience and graphic design skills can play a valuable role in most organizations.
With the implementation of real-time analytics, data engineering becomes an important part of the whole implementation. We cover
Data Integration - Processes used to combine data from disparate sources into meaningful and valuable information. A complete data integration solution delivers trusted data from a variety of sources. We cover the ETL process, data-warehousing
Data Architecture - Assemble data in a business-centric view in a robust platform, prepared for the next process – dashboard, data visualisation or machine learning
Data Quality Management - Ensure a process is in place reduce inconsistencies as well as help organisations established policies and determine roles within organisations to safeguard one of the most valuable corporate assets: information.
When dealing with big data sets, designing or building a customized architecture or application is required. We build system architectures and integrate them with new or existing setup.
This is a very crucial step in building analytical solutions for clients that otherwise will result in insufficient memory, slow computation, or slow overall access to the digital dashboard / interactive system.