The new benefits that big data analytics brings to the table, however, are speed and efficiency. Whereas a few years ago a business would have gathered information, run analytics and unearthed information that could be used for future decisions, today that business can identify insights for immediate decisions. The ability to work faster – and stay agile – gives organizations a competitive edge they didn’t have before.
Why is big data analytics important?
Big data analytics helps organizations harness their data and use it to identify new opportunities. That, in turn, leads to smarter business moves, more efficient operations, higher profits and happier customers. In his report Big Data in Big Companies, IIA Director of Research Tom Davenport interviewed more than 50 businesses to understand how they used big data. He found they got value in the following ways:
- Cost reduction. Big data technologies such as Hadoop and cloud-based analytics bring significant cost advantages when it comes to storing large amounts of data – plus they can identify more efficient ways of doing business.
- Faster, better decision making. With the speed of Hadoop and in-memory analytics, combined with the ability to analyze new sources of data, businesses are able to analyze information immediately – and make decisions based on what they’ve learned.
- New products and services. With the ability to gauge customer needs and satisfaction through analytics comes the power to give customers what they want. Davenport points out that with big data analytics, more companies are creating new products to meet customers’ needs.
Big data analytics is the process of examining large and varied data sets — i.e., big data — to uncover hidden patterns, unknown correlations, market trends, customer preferences and other useful information that can help organizations make more-informed business decisions.
Big data analytics applications enable 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 business intelligence (BI) and analytics programs. That encompasses a mix of semi-structured and unstructured data — for example, internet clickstream data, web server logs, 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.
On a broad scale, data analytics technologies and techniques provide a means of analyzing data sets and drawing conclusions about them to help organizations make informed business decisions. BI queries answer basic questions about business operations and performance. Big data analytics is a form of advanced analytics, which involves complex applications with elements such as predictive models, statistical algorithms and what-if analyses powered by high-performance analytics systems.
es, Hadoop clusters and NoSQL systems are being used primarily as landing pads and staging areas for data 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 Hadoop Distributed File System must be organized, configured and partitioned properly to get good performance on 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 in 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 for forecasting customer behavior and other future developments; machine learning, which tap 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 BI software and data visualization tools. For both ETL and analytics applications, queries can be written in batch-mode MapReduce; programming languages, such as R, Python and Scala; and SQL, the standard language for relational databases that’s supported via SQL-on-Hadoop technologies.
