Big Data Services

We have delivering big data solutions for forward-thinking clients, ranging from government and corporate entities:

  • A proven skill set and analytics framework to customize analytics solutions for any organization
  • End-to-end servicescomprise of discovery, delivery, implementation, administration and maintenance for every big data solution and services
  • Data scientists, data engineers and data analysts to ensure that your organization takes maximum advantage of big data to support better data driven business decisions, create new sources of competitive advantage and create new and sustainable revenue streams.

Big Data Solutions

From years of experience we have developed a complete big data analytics framework that delivers a new, deeper level of actionable information for business decisions consists of 3 layers, each with its own sub-layers of building blocks and decisions

  • Big Data Infrastructure: Big data usually requires massive computing power and data storage capacity. Organizations may choose to host internally by investing in additional equipment, or transition big data processing to our cloud services.
  • Big Data Management: Most big data infrastructures are built using an open source software project that enables the distributed processing of large data sets across clusters of commodity servers.
  • Business Analytics: To reap the value in the big data stack, business analytics play important role. We equipped our services with the data scientists, engineers and data analysts that ensure massive amount of data collected is ingested, organized, maintained and presented as actionable information for decision-making. Our niche is we have the proven expertise to turn mountains of data into valuable, data driven analysis for our customers.

Our Big Data Services Model

Our Big Data Services Model provides our clients with insightful analysis that will make their businesses grow, high quality insights to make them at the fore front before their competitors. Our data scientists, engineers, and analysts will provide end to end analytics solution, mapping out your data needs at the same time your hardware and software infrastructure is determined.

C – Analytics Methodology

Our data science methodology is build upon our own proprietary methodology called C – Analytics Methodology (C-AM) that we use efficiently for all our data science projects. C-AM is designed for ad-hoc analytics studies and used iteratively bu our data scientists to understand data, set up models, and design algorithms to achieve the desired insight outcomes. The methodology can be divided into 6 sub

Phases as shown below:
Figure 1: C-AM model. Phases are represented via the stated number circled on top of every process box. This model is a step by step analytics methodology model being widely used for all the implementation of our data science projects.

1. Business requirement gathering

At this phase our data consultant will gather the business requirement with the users. We start with defining the business problem statement and clearly translated it into an analytics requirement.

2. Understanding data structure

We will evaluate the key data set to address each analytics problem statement together with the business users. Data issues (e.g. missing values, outliers) will be highlighted and validated with the data owner to find options of remedial for the data availability. Once we complete this process, we will then proceed with the data preparation stage.

3. Data processing stage

The main purpose of this phase is to turn the raw data inputs into formatted data for the ease of analysis, this will be prepared by our competent data scientists, which involves specialized activities such as data table joins, data restructuring, missing value imputation, feature selection and features engineering.

4. Data modelling process

Our data team will propose relevant data modelling before passing it to the analytics team for dashboard enablement. The data correlation will generate respective business insights depending on the richness of data available for each analysis.

5. Analytics deployment

The final process involve analytics deployment, this is where the insight consumers will be able to visualize the analytics outcome in dashboard form or via digital reporting method. Our dashboards can be views either in desktop or mobile version which will suite to the customer’s preference





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