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Steps of Big Data Life Cycle

BigData is the new science of understanding and predicting human behavior by studying large volumes of unstructured data. BigData is also known as predictive analytics. Studying security videos, traffic data, weather patterns, flight arrivals, cell phone tower logs, and heart rate trackers are other forms.

Steps of BigData Life Cycle

Business Case Evaluation The purpose of the business case is to outline the rationale for undertaking the project and to define the parameters and management factors involved in the project itself. The beginning of the Big Data Lifecycle starts with a sound evaluation of the business case. Before any Big Data project can be started, it needs to be clear what the business objectives and results of the data analysis should be. Begin with the end in mind and clearly define the objectives and desired results of the project.

Data Identification is the practice of matching anonymous data with publicly available information, or auxiliary data, in order to discover the individual to which the data belongs to.  it is important to know what the sources of the data will be. Especially if data is procured from external suppliers, it is necessary to clearly identify what the original source of the data is and how reliable the dataset is.

Data Acquisition and Filtering abbreviated by the acronyms DAS or DAQ, typically convert analog waveforms into digital values for processing. The components of data acquisition systems include Sensors, to convert physical parameters to electrical signals. it builds upon the previous stage op the Big Data Lifecycle. In this stage, the data is gathered from different sources, both from within the company and outside of the company. After the acquisition, the first step of filtering is conducted to filter out corrupt data. 

Data Extraction is the act or process of retrieving data out of data sources for further data processing or data storage. The complexity of the transformation and the extent to which is necessary to transform data is greatly dependent on the Big Data tool that has been selected. Most modern Big Data tools can read industry standard data formats of relational and non-relational data.

Data Validation and Cleansing is the process of detecting and correcting corrupt or inaccurate records from a record set, table, or database and refers to identifying incomplete, incorrect, inaccurate or irrelevant parts of the data and then replacing, modifying, or deleting the dirty or course data. data that is invalid leads to invalid results. In order to ensure only the appropriate data is analyzed, the Data Validation and Cleansing stage of the Big Data Lifecycle is required. During this stage, data is validated against a set of predetermined conditions and rules in order to ensure the data is not corrupt.

Data Aggregation and Representation is any process in which information is gathered and expressed in a summary form, for purposes such as statistical analysis. A common aggregation purpose is to get more information about particular groups based on specific variables such as age, profession, or income. it is dedicated to integrating multiple datasets to arrive at a unified view. Additionally, data aggregation will greatly speed up the analysis process of the Big Data tool, because the tool will not be required to join different tables from different datasets, greatly speeding up the process.

Data Analysis is a process of inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information, informing conclusions, and supporting decision-making.  Data Analysis can be simple or really complex, depending on the required analysis type. In this stage, the ‘actual value’ of the Big Data project will be generated. If all previous stages have been executed carefully, the results will be factual and correct.

Data Visualization is a general term that describes an effort to help people understand the significance of data by placing it in a visual context. Patterns, trends, and correlations that might go undetected in text-based data can be exposed and recognized easier with data visualization software. 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. Frequently this requires plotting data points in charts, graphs or heat maps.

Utilization of Analysis Results After the data analysis has been performed an the result have been presented, the final step of the BigData Lifecycle is to use the results in practice. The Utilisation of Analysis results is dedicated to determining how and where the processed data can be further utilized to leverage the result of the BigData Project.

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