BIG DATA Platforms
To address the different specifications for conducting analysis on Big Data, COGNXT follows a step-by-step methodology to plan the activities and responsibilities involved with collecting, processing, analyzing, and repurposing data.
We begin with a well-defined business case that gives a fair knowledge of the support, motivation, and purposes of conducting the analysis.
The next step is committed to classifying the datasets needed for the analysis project and their sources. Identifying a wider class of data sources may enhance the possibility of discovering hidden patterns and correlations.
During the Data Acquisition and Filtering stage, the data is collected from all of the data sources that were classified during the previous stage. The acquired data is then subjected to automated filtering for the elimination of contaminated data or data that has been considered to have no significance to the analysis purposes.
The Data Extraction lifecycle stage is dedicated to extracting disparate data and converting it into a format that the underlying Big Data solution can use for the purpose of the data analysis.
Invalid data can skew and falsify analysis results.
sensible business decisions based on data.
The Data Validation and Cleansing stage are dedicated to establishing complex validation rules and removing any known invalid data.
Data may be spread across multiple datasets, requiring that datasets be joined together via common fields, for example, date or ID. A method of data reconciliation is required or the dataset representing the correct value needs to be determined.
The Data Visualization stage is dedicated to using data visualization techniques and tools to graphically communicate the analysis results for effective interpretation by business users.
Consequent to analysis results being made available to business users to support business decision-making, such as via dashboards, there may be further opportunities to utilize the analysis results. The utilization of analysis results is dedicated to determining how and where processed analysis data can be further leveraged.