Data Analytics

The fastest way to grow your business with the leader in Technology

Radix Solutions is a Managed Service Provider providing hybrid IT-as-a-Service (ITaaS)

Data & Analytics Approach

Our Data Analytics team focus on gathering, analyzing, and storing digital information that was previously inaccessible to enable your business to make informed decisions. The process evaluates and transforms your digital information into useful business intelligence.


Get near-instant insights from your data. Our Analytics Platform has a performance engine built on top of a world-class relational engine in SQL Server and coupled with enterprise-class hardware.


Support any scale of data, workloads, and users. Bring together relational and non-relational data and add governance and federation across on-premises and cloud data lakes and data assets.

Decision Making

With Data Analytics you are can analyse information immediately to gauge client satisfaction and make decisions based on what you have learned to create new products to meet your customer needs.


Business\Data Requirement

Our Data Analysts and Architects support you in identifying the data that address specific business requirements. The emphasis is on ensuring accurate, clean, and managed data that is easily accessible. Therefore, ensuring business makes informed decisions and provides both a baseline to measure and a target to optimize operations.

Data obtained from various sources ranging from organizational databases to web pages may not be structured and may contain irrelevant information. Our team works with your business stakeholders to conduct Data Processing and Data Cleaning to achieve your business’s required view.

Data processing and cleaning

Our Data Analysts assist in working through the collected data to process and organize the data. The process includes structuring the data as required for the relevant business analysis. For example, they are placing the data into a Data Model or Statistical Application.

Additionally, the processed and organized data may be incomplete, contain duplicates, or contain errors. Data Cleaning is the process of preventing and correcting these errors. Several types of Data Cleaning depend on the type of data. For example, while cleaning the financial data, individual totals might be compared against reliable published numbers or defined thresholds. Likewise, quantitative data methods can be used for outlier detection and subsequently excluded in the analysis.


Data Analysis

Our team utilizes various data analysis techniques to understand, interpret, and derive conclusions based on the requirements. Data Visualization is used to examine the data in a graphical format to obtain additional insight into its messages.

Statistical Data Models such as correlation and regression analysis are used to identify the data variable’s relations. These models are descriptive of the data that help simplify analysis and communicate results.

The process might require additional Data Cleaning or other Data Collection, and hence these activities are iterative.

The team provides dashboards and reporting in a format required by the users to support their decisions and further action. The feedback from the users might result in additional analysis.

Our data analysts can choose data visualization techniques, such as tables and charts, to communicate the message clearly and efficiently to the users. The analysis tools provide the facility with the required information with color codes and formatting in tables and charts.