Big Data Analytics - Methodology

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In terms of methodology, big data analytics differs significantly from the traditional statistical approach of experimental design. Analytics starts with data. Normally, we model the data in a way that able to answer the questions that a business professionals have. The objectives of this approach are to predict the response behavior or understand how the input variables relate to a response.

Typically, statistical experimental designs develop an experiment and then retrieve the resulting data. This enables the generation of data suitable for a statistical model, under the assumption of independence, normality, and randomization. Big data analytics methodology begins with problem identification, and once the business problem is defined, a research stage is required to design the methodology. However, general guidelines are relevant to mention and apply to almost all problems.

The following figure demonstrates the methodology often followed in Big Data Analytics −

Big Data Analytics Methodology

The following are the methodologies of big data analytics −

Define Objectives

Clearly outline the analysis's goals and objectives. What insights do you seek? What business difficulties are you attempting to solve? This stage is critical to steering the entire process.

Data Collection

Gather relevant data from a variety of sources. This includes structured data from databases, semi-structured data from logs or JSON files, and unstructured data from social media, emails, and papers.

Data Pre-processing

This step involves cleaning and pre-processing the data to ensure its quality and consistency. This includes addressing missing values, deleting duplicates, resolving inconsistencies, and transforming data into a useful format.

Data Storage and Management

Store the data in an appropriate storage system. This could include a typical relational database, a NoSQL database, a data lake, or a distributed file system such as Hadoop Distributed File System (HDFS).

Exploratory Data Analysis (EDA)

This phase includes the identification of data features, finding patterns, and detecting outliers. We often use visualization tools like histograms, scatter plots, and box plots.

Feature Engineering

Create new features or modify existing ones to improve the performance of machine learning models. This could include feature scaling, dimensionality reduction, or constructing composite features.

Model Selection and Training

Choose relevant machine learning algorithms based on the nature of the problem and the properties of the data. If labeled data is available, train the models.

Model Evaluation

Measure the trained models' performance using accuracy, precision, recall, F1-score, and ROC curves. This helps to determine the best-performing model for deployment.

Deployment

In a production environment, deploy the model for real-world use. This could include integrating the model with existing systems, creating APIs for model inference, and establishing monitoring tools.

Monitoring and Maintenance

Also, change the analytics pipeline as needed to reflect changing business requirements or data characteristics.

Iterate

Big Data analytics is an iterative process. Analyze the data, collect comments, and update the models or procedures as needed to increase accuracy and effectiveness over time.

One of the most important tasks in big data analytics is statistical modeling, meaning supervised and unsupervised classification or regression problems. After cleaning and pre-processing the data for modeling, carefully assess various models with appropriate loss metrics. After implementing the model, conduct additional evaluations and report the outcomes. A common pitfall in predictive modeling is to just implement the model and never measure its performance.