Big Data Analytics Training in Chennai

Big Data Analytics Training in Chennai

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Big data should not be seen as an independent “processing silo.” Rather, it is important that any big data analytics applications (and their results!) be properly incorporated within the organization’s general data warehousing, business intelligence, and reporting framework. Although big data will combine its own specialty aspects of hardware, data management, software, data models, and analytical models assembled with practitioner expertise, the results should be aligned with other reporting and analysis activities as well as directly integrated within the business environment. That is the best way to derive the actionable insight driving profitable business value.

The following list gives a brief description of the three stages depicted in the preceding diagram:

  • Data Preparation: This stage involves activities from data creation (ETL) to bringing data on to a common platform. In this stage, you will check the quality of the data, cleanse and condition it, and remove unwanted noise. The structure of the data will dictate which tools and analytic techniques can be used. For example, if it contains textual data, sentiment analysis should be used, while if it contains structured financial data, perhaps regression via R analytics platform is the right method. A few more analytical techniques are MapReduce, Natural language processing (NLP), clustering (k-means clustering), and graph theory (social network analysis).
  • Data Visualization: This is the next stage after preparation of data. Micro-level analytics will take place here, feeding this data to the reporting engine that supports various visualization plugins. Visualization is a rapidly expanding discipline that not only supports Big Data but can enable enterprises to collaborate more effectively, analyze real-time and historical data for faster trading, develop new models and theories, consolidate IT infrastructure, or demonstrate past, current, and future datacenter performance. This is very handy when you are observing a neatly composed dashboard by a business analyst team.
  • Data Discovery: This will be the final stage where data miners, statisticians, and data scientists will use enriched data and using visual analysis they can drill into data for greater insight. There are various visualization techniques to find patterns and anomalies, such as geo mapping, heat grids, and scatter/bubble charts. Predictive analysis based on the Predictive Modeling Markup Language (PMML) comes in handy here. Using standard analysis and reporting, data scientists and analysts can uncover meaningful patterns and correlations otherwise hidden. Sophisticated and advanced analytics such as time series forecasting help plan for future outcomes based on a better understanding of prior business performance.

Business integration goes beyond the methods discussed for soliciting requirements. Rather, asking questions such as these will highlight the business process interfaces necessary to fully integrate big data into the environment:

• Who are the participants in the process?

• What are the desired outcomes of the process?

• What information is available to the participants?

• What knowledge is provided by big data analytics?

• How are the different information assets linked together?

• How are actionable results delivered to the participants?

• What are the expectations for decisions to be made and actions to be taken?

• How are results of decisions monitored?

• What additional training and guidance are needed?

• How do business processes need to be adjusted to make best use of big data analytics?

Reviewing the answers to these questions will guide the technical teams in properly leveraging big data within the business context.

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