Big Data Analytics

Big data analytics is the often complex process of examining big data to uncover information such as hidden patterns, correlations, market trends and customer preferences that can help organizations make informed business decisions.

On a broad scale, data analytics technologies and techniques provide a means to analyze data sets and take away new information which can help organizations make informed business decisions. Business intelligence (BI) queries answer basic questions about business operations and performance.

Big data analytics is a form of advanced analytics, which involve complex applications with elements such as predictive models, statistical algorithms and what-if analysis powered by analytics systems.

The importance of big data analytics

Big data analytics through specialized systems and software can lead to positive business-related outcomes:

  • New revenue opportunities
  • More effective marketing
  • Better customer service
  • Improved operational efficiency
  • Competitive advantages over rivals

Big data analytics applications allow data analysts, data scientists, predictive modelers, statisticians and other analytics professionals to analyze growing volumes of structured transaction data, plus other forms of data that are often left untapped by conventional BI and analytics programs. This includes a mix of semi-structured and unstructured data. For example, internet data, web server logs, social media content, text from customer emails and survey responses, mobile phone records, and machine data captured by sensors connected to the internet of things (IoT). 

Big data analytics is a form of advanced analytics, which has marked differences compared to traditional BI.

How big data analytics works

In some cases, Hadoop clusters and NoSQL systems are used primarily as landing pads and staging areas for data. This is before it gets loaded into a data warehouse or analytical database for analysis usually in a summarized form that is more conducive to relational structures.

More frequently, however, big data analytics users are adopting the concept of a Hadoop data lake that serves as the primary repository for incoming streams of raw data. In such architectures, data can be analyzed directly in a Hadoop cluster or run through a processing engine like Spark. As in data warehousing, sound data management is a crucial first step in the big data analytics process. Data being stored in the HDFS must be organized, configured and partitioned properly to get good performance out of both extract, transform and load (ETL) integration jobs and analytical queries. 

Once the data is ready, it can be analyzed with the software commonly used for advanced analytics processes. That includes tools for:

  • data mining, which sift through data sets in search of patterns and relationships; 
  • predictive analytics, which build models to forecast customer behavior and other future developments; 
  • machine learning, which taps algorithms to analyze large data sets; and 
  • deep learning, a more advanced offshoot of machine learning.

Text mining and statistical analysis software can also play a role in the big data analytics process, as can mainstream business intelligence software and data visualization tools. For both ETL and analytics applications, queries can be written in MapReduce, with programming languages such as R, Python, Scala, and SQL. These are the standard languages for relational databases that are supported via SQL-on-Hadoop technologies.

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 perform real-time analytics on data fed into Hadoop systems through stream processing engines, such as Spark, Flink and Storm.

Early big data systems were mostly deployed on premises, particularly in large organizations that collected, organized and analyzed massive amounts of data. But cloud platform vendors, such as Amazon Web Services (AWS) and Microsoft, have made it easier to set up and manage Hadoop clusters in the cloud. The same goes for Hadoop suppliers such as Cloudera-Hortonworks, which supports the distribution of the big data framework on the AWS and Microsoft Azure clouds. Users can now spin up clusters in the cloud, run them for as long as they need and then take them offline with usage-based pricing that doesn’t require ongoing software licenses.

Big data has become increasingly beneficial in supply chain analytics. Big supply chain analytics utilizes big data and quantitative methods to enhance decision making processes across the supply chain. Specifically, big supply chain analytics expands datasets for increased analysis that goes beyond the traditional internal data found on enterprise resource planning (ERP) and supply chain management (SCM) systems. Also, big supply chain analytics implements highly effective statistical methods on new and existing data sources. The insights gathered facilitate better informed and more effective decisions that benefit and improve the supply chain.

Potential pitfalls of 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.

Big data analytics involves analyzing structured and unstructured data.

Emergence and growth of big data analytics

The term big data was first used to refer to increasing data volumes in the mid-1990s. In 2001, Doug Laney, then an analyst at consultancy Meta Group Inc., expanded the notion of big data.  This encompassed 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, a concept Gartner popularized after acquiring Meta Group and hiring Laney in 2005.

Separately, the Hadoop distributed processing framework was launched as an Apache open source project in 2006. This planted the seeds for a clustered platform built on top of commodity hardware and geared 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 sprug up around it.

Initially, as the Hadoop ecosystem took shape and started to mature, big data applications were primarily the province of large internet and e-commerce companies such as Yahoo, Google and Facebook, as well as analytics and marketing services providers. In the ensuing years, though, big data analytics has increasingly been embraced by retailers, financial services firms, insurers, healthcare organizations, manufacturers, energy companies and other enterprises.

Logical Architectures for Big Data Analytics

If you check the reference architectures for big data analytics proposed by Forrester and Gartner, modern analytics need a plurality of systems: one or several Hadoop clusters, in-memory processing systems, streaming tools, NoSQL databases, analytical appliances and operational data stores, among others.

This is not surprising, since different data processing tasks need different tools. For instance: real-time queries have different requirements than batch jobs, and the optimal way to execute queries for reporting is very different from the way to execute a machine learning process.  Therefore, all these on-going big data analytics initiatives are actually building logical architectures, where data is distributed across several systems.

The Architecture of an Enterprise Big Data Analytics Platform

This will not change anytime soon. As Gartner’s Ted Friedmann said in a recent tweet, ‘the world is getting more distributed and it is never going back the other way’. The ‘all the data in the same place’ mantra of the big ‘data warehouse’ projects of the 90’s and 00’s never happened: even in those simpler times, fully replicating all relevant data for a large company in a single system proved unfeasible. The analytics projects of today will not succeed in such task in a much more complex world of big data and cloud.

That is why the aforementioned reference architectures for big data analytics include a ‘unifying’ component to act as the interface between the consuming applications and the different systems. This component should provide: data combination capabilities, a single entry point to apply security and data governance policies, and should isolate applications from the changes in the underlying infrastructure (which, in the case of big data analytics, is constantly evolving).