WHAT IS BIG DATA ANALYTICS AND WHY IT IS IMPORTANT?

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Today’s world is entirely powered by big data. These data are generating from every step of an individual’s online activity, be it browsing the internet or the use of advanced connected devices, particularly IoT devices. This exponential increase in data has given the rise of big data analytics. It is very complex process to analyze big data and discover information such as underlying patterns, market trends, customer preferences, and others. This will definitely help organizations to make strategically well calculated data-driven business decisions. As big data analytics enables businesses to draw useful insights from their data and use it to identify new opportunities, it leads to smarter business moves with efficient operations.

Today, people as well as businesses generate data at an exceptional rate from millions of sources while social channels being the largest sources of data available out there. Thus, utilizing the power of truly big data analytics tools and techniques at a broader scale, they provide a means to examine data sets and get actionable insights.

 

Types of Big Data Analytics

As big data is not a new concept for businesses and organizations they are using different types of data analytics tools and methods to extract meaningful information from their data.

Here are some well known types of big data analytics.

Descriptive analytics: This method provides insight into what has happened in the past and will provide businesses with trends to get in-depth detail. Descriptive analytics is a primary stage of data processing that creates a summary of historical data to extract meaningful information and possibly prepare the data for further stages of analysis.

 

Prescriptive Analytics: This data analytics concept suggests what action to take to eliminate future problems or capitalize on a promising trend. Prescriptive analytics basically provides an organization with a focus to answer a specific question. It also helps to determine the best solution for a future opportunity and to avoid any future risks.

 

Predictive analytics: It uses big data to identify patterns in the past data to predict the future outcome. Predictive analytics achieve its power from numerous methods and technologies, such as big data, data mining, statistical modeling, machine learning and assorted mathematical processes, among others. By utilizing this model, an organization can use past and current data to reliably forecast trends and behaviors in future.

 

Diagnostic Analytics: With the help of this analytics technique, historical data can be measured against other data to answer the question of why something happened. Essentially, data scientists turn to this technique when trying to determine “Why” behind something happened. Diagnostic analytics can be beneficial in the sales cycle, for instance, to categorize customers by their likely product preferences and sales cycle.

 

Applications of Big Data Analytics 

Adopting big data and analytics technologies provide considerable benefits to organizations across industries, including healthcare, education, governance, retail, manufacturing, to name a few. Companies of all sizes and types utilize big data analytics for their business growth.

 

Following are some of the most promising applications of big data analytics.

 

·     Enhanced Customer Experience

·     Cost optimization

·     Operational Efficiency

·     Real-Time Monitoring

To learn more about Big Data, Data Science and Data Analytics visit ifads.org

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