Data Analysis is the process of analyzing data sets in order to draw conclusions about the information they contain. This is done with the aid of specialized systems and software technology. Data analytics technologies and techniques are widely used in commercial industries. Additionally enabling organizations to make more-informed business decisions. Thus allowing scientists and researchers to verify or disprove scientific models, theories and hypotheses.




As a term, Data Analytics refers to a number of applications which can include:

  • basic business intelligence (BI);
  • reporting and online analytical processing (OLAP); and
  • various forms of advanced analytics.

Looking at it this way, it’s similar in nature to business analytics in its approaches to analyzing data.
However, data analytics has a broader focus.




Data analytics initiatives can help businesses:

  • increase revenues;
  • improve operational efficiency;
  • optimize marketing campaigns and customer service efforts;
  • respond more quickly to emerging market trends and gain a competitive edge over rivals

The ultimate goal is to boost business performance. Depending on the particular application, the data that’s analyzed consists of either historical data or new information that has been processed. This data is also gathered from a mix of internal systems and external data sources.





A data warehouse is a unified repository for all the data collected by an enterprise’s various operational systems. This information needn’t be physical or logical. Data warehousing relates to the capture of data from diverse sources for access and analysis rather than for transaction processing.

In fact, a data warehouse is a relational database which can be housed on an enterprise mainframe server or in the cloud. Data from various online transaction processing (OLTP) applications and other sources are selectively extracted for BI activities, decision support and to answer user inquiries.




The first thing to remember is data warehouses store data that is extracted from data stores and from other external sources. The data records within the warehouse must contain certain details in order to be easily searched and useful to business users. For that reason there are three main components of data warehousing:

  • data sources from operational systems, such as Excel, ERP, CRM or financial applications;
  • a data staging area where data is ‘cleaned’ and ordered; and
  • a presentation area where data is warehoused.





Data analysis tools, such as BI software, are then able to access the data within the warehouse. Data warehouses can also feed ‘data marts’, which are decentralized systems in which data from the warehouse is organized and made available to specific business groups, such as sales or inventory teams.

Data warehouses can benefit organizations from both an IT and a business perspective. Separating the analytical processes from the operational processes can enhance the operational systems. Thus enabling business users to access and query relevant data faster from multiple sources. In addition, data warehouses can offer enhanced data quality and consistency, thereby improving BI.

Additionally, cloud-based data warehouses such as Amazon Redshift and Microsoft Azure SQL Data Warehouse enable companies to quickly scale. Thereby eliminating the initial infrastructure investments and ongoing maintenance requirements.


Project Management

Project management systems’ uses established principles, procedures and policies to successfully guide a project from conception through completion

Data Center Solutions

Data centres are centralized locations where computing and networking equipment is concentrated.

Data Analysis and Warehousing

DA is the process of analysing data sets in order to draw conclusions about the information they contain.

IT Infrastructure

Infrastructure is the foundation or framework that supports a system or organization.

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