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Big data

Big Data

Never before has the world contained such massive amounts of digital data as today - data that is increasing fivefold very five years. Yet despite today's businesses having more access to potential insight than ever before, they often fail to extract meaningful knowledge out of the piling up gold mine. Data in an enterprise has become even more complex, because it includes not only traditional relational data, but also raw, semi-structured, and unstructured data from a rapidly expanding variety of sources.

Three characteristics define Big Data: volume, variety, and velocity.

Volume

More and more companies are amassing larger and larger amounts of data, and storing them in bigger and bigger databases. During a typical day alone, Twitter generates more than 900 gigabytes every three hours, Facebook 1.2 terabytes, and some enterprises generate terabytes of data every hour of every day of the year.

Variety

Besides the sheer growth in data volume, with the explosion of sensors, and smart devices, as well as social collaboration technologies, data in an enterprise has become complex because it includes not only traditional relational data, but also raw, semi-structured, and unstructured data from web pages, web log files (including click-stream data), search indexes, social media forums, e-mail, documents, sensor data from active and passive systems, and so on store everything: environmental data, financial data, medical data, surveillance data, and the list goes on and on.

Velocity

Sometimes gaining a competitive edge can mean identifying a trend, problem, or opportunity in only seconds, or even microseconds, before your competition. In addition, more and more of the data being produced today has a very short shelf-life, so organisations must be able to analyse this data in near real time if they hope to find insights in this data.

Big Data Analytics is what converts an interconnected mess of information into knowledge, yet most businesses don’t even attempt to use this data to their advantage. Imagine if you had a way to analyse that data?

The need for big data analytics is at the heart of the groundbreaking book “Drinking From the Fire Hose” by veteran business leaders Christopher J Frank and Paul Magnone. As described in the book, data itself is meaningless without a framework for analysis of the information and a business perspective for it to be acted upon. AsiaAnalytics offers powerful analytical solutions, applications and consulting services that will help you turn your fire hose of information into actionable, timely and comprehensive insights.

Storage

Big data storage is the crucial first step for enterprises looking to harness the value of their data to drive innovation and profitable growth.

Enterprises today are creating more data than ever before from a rapidly expanding variety of sources, but the potential for this data to reveal actionable information can not be harnessed without an effective storage system.

Management

Once a scalable system for data storage is in place, big data management is the next step in converting an interconnected mess of data into actionable, timely and comprehensive insights.

Big data management involves the organisation, administration and governance of large volumes of both structured and unstructured data. Effective management turns big data into an asset by providing enterprises with a high level of data quality and accessibility for a variety of business intelligence and analytics purposes.

Analytics

Big data analytics refers to the process of examining large amounts of data to uncover hidden patterns, unidentified correlations, as well as other useful information. This information can then be leveraged to provide companies with competitive advantages and business benefits, such as more effective marketing and increased revenue.

The main goal of big data analytics aims to help companies make better business decisions through the analysis of huge volumes of transaction data, as well as other data sources that may be left untapped by conventional business intelligence (BI) programs. These other data sources may include web server logs and internet clickstream data, social media activity reports, mobile-phone call records and information captured by sensors.

Big data analytics can be done with the software tools commonly used as part of advanced analytics disciplines such as predictive analytics and data mining. However, the unstructured data sources used for big data analytics may not fit in traditional data warehouses. Furthermore, traditional data warehouses may not be able to handle the processing demands posed by big data. As a result, a new class of big data technology has emerged and is being used in many big data analytics environments.

This application is commonly used in the following industries: