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Big Data Analytics

Ninety percent of the data generated has been generated in the last two years. That data is too large and complex to be handled by traditional data-processing systems and has been termed as ‘Big Data’ owing to its huge volume, heterogeneity (structured, unstructured, and semi-structured), the velocity with which it is generated, and accuracy.

Big Data Analytics refers to extracting meaningful insights by identifying and understanding the hidden patterns after studying Big Data. The process includes collecting, storing, processing, analyzing, and interpreting the data, hence helping organizations leverage the benefits of Big Data Analytics.

Types of Big Data Analytics

Descriptive Analytics

Descriptive analytics in simple terms, Descriptive Analytics assists with answering questions describing your business, such as “What was the top-selling product in the last year?”. It involves understanding and describing past events with the help of dashboards or reports and learning from the discovered patterns. The steps include cleaning data, relating the different entities/columns of data, summarizing the data (such as count or aggregate of a column of data), and presenting the summarized data in the form of a dashboard (visualization). Descriptive Analytics forms the basis for any other kind of Big Data Analytics.

Diagnostic Analytics

Diagnostic Analytics is more of an explorative phase and focuses on understanding why a certain event happened in the past , for example, “Why was a particular product a top seller in the last year?”. The basic steps include preparing a visualization, finding correlations between different features, identifying highly correlated features, and visualizing those correlations in a graphical format to get a better idea, hence drilling down to the cause of a particular event.

Predictive Analytics

Predictive Analytics deals with answering questions about future events by processing the historical data. It involves building a model by training it on the past data and evaluating it based on the actual and predicted values. This model can be iteratively improved if the previous predictions are incorrect. These analytics help with making better business decisions beforehand by providing actionable insights.

Prescriptive Analytics

Prescriptive Analytics provides the users with actions that can be taken to improve businesses based on predictive analytics. If the prescriptive and predictive analytics are seamlessly integrated, it can reduce human intervention from the decision-making process and free decision-makers to focus on other crucial tasks. For example, if the predicted sales for the next month consume more resources than are available, orders can be placed automatically to fulfill the need.

Big Data Analytics has helped redefine many industries such as sports, hospitality, government, and public sector services, energy, agriculture and farming, education, banking and securities, entertainment, communications and media, retail and wholesale trade, and transportation. In a nutshell, Big Data Analytics can help organizations understand their users better, find hidden opportunities, monitor brand performance, prevent fraud and optimize services.

DRVN intelligence uses Vertex AI, BigQuery, Composer, Dataflow, Dataproc, AI Platform, Recommendations AI, Document AI, Data Catalog, and other services to make advanced big data workflows possible on Google Cloud Platform

Why DRVN Intelligence?

Recognized by Google as a premier partner, DRVN Intelligence places significant focus on Big Data / Advanced Analytics / Marketing Analytics/ Machine Learning Systems including state-of-the-art continuous training and prediction pipelines from legacy computing environments, into Google Cloud specifically. We engage as flexibly as Google Cloud, offering several engagements and support models to choose from. Our team is there to help you along every step of the way towards reaching your goal. Let us help you build analytics workflows with confidence.

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