Big data analytics is the process of inspecting large and different data sets i.e., big data – to discover hidden patterns, market trends, unknown correlations, customer preferences and other useful information that can help organizations make more-informed business decisions.

Big data analytics benefits

As driven by specialized analytics systems and software, big data analytics can be fruitful to various business benefits, including new revenue opportunities, better customer service, improved operational efficiency, more effective marketing and competitive advantages over other competitors.

Big data analytics applications enable data scientists, statisticians and other analytics professionals to analyze largely growing volumes of structured transaction data, plus other forms of data that are often left uncovered by conventional business intelligence (BI) and analytics programs. That comprises of a mix of semi-structured and unstructured data, for example, web server logs, internet clickstream data, text from customer emails, social media content, text from customer emails and survey responses, mobile-phone call-detail records and machine data captured by sensors connected to the internet of things.

Emergence and growth of big data analytics        

The term big data was first used to increasing data volumes in the mid-1990s. In 2001, the notion of big data expanded to also include increases in the variety of data being generated by organizations and the velocity at which that data was being created and updated. Those three factors -- volume, velocity and variety -- became known as the 3Vs of big data.

Separately, the Hadoop distributed processing framework was launched as an Apache open source project in 2006, a clustered platform built on top of professional hardware and speed-up to run big data applications. By 2011, big data analytics began to take a firm hold in organizations and the public eye, along with Hadoop and various related big data technologies that had come up around it.

Big data analytics technologies and tools

Unstructured and semi-structured data types typically doesn't fit well in conventional data warehouses that are based on relational databases oriented to structured data sets. Furthermore, data warehouses may not be able to handle the processing demands posed by sets of big data that need to be updated frequently or even continually, as in the case of real-time data on stock trading, the online activities of website visitors or the performance of mobile applications.

As a result, many organizations that collect, process and analyze big data  turn to Hadoop and its companion tools, such as MapReduce, YARN, Spark, Hive, HBase, Pig and Kafka as well as NoSQL databases. In some cases, Hadoop clusters and NoSQL systems are being used primarily as landing pads and staging areas for data before it gets loaded into an analytical database or data warehouse for analysis, usually in a compact form that is more conducive to relational structures.

Once the data is ready, it can be analyzed with the software commonly used in advanced analytics processes. That includes tools for data mining, which filter through data sets in search of patterns and relationships; predictive analytics, which build models for forecasting customer’s behavior and other future developments; machine learning, which execute algorithms to analyze large data sets; and deep learning, a more advanced paradigm of machine learning.

Big data analytics uses and challenges         

Big data analytics applications often include data from both internal systems and external sources, such as weather data or demographic data on consumers compiled by third-party information services providers. In addition, streaming analytics applications are becoming common in big data environments, as users look to do real-time analytics on data fed into Hadoop systems through Spark's Spark Streaming module or other open source stream processing engines, such as Flink and Storm.

Potential pitfalls that can trip up organizations on big data analytics initiatives include a lack of internal analytics skills and the high cost of hiring experienced data scientists and data engineers to fill the gaps.

The amount of data that's typically involved, and its variety, can cause data management issues in areas including data quality, consistency and governance; also, data silos can result from the use of different platforms and data stores in a big data architecture. In addition, integrating Hadoop, Spark and other big data tools into a cohesive architecture that meets an organization's big data analytics needs is a challenging proposition for many IT and analytics teams, which have to identify the right mix of technologies and then put the pieces together.