Big data is a term that describes the large volume of data – both structured and unstructured – that inundates a business on a day-to-day basis. But it’s not the amount of data that’s important. It’s what organizations do with the data that matters. Big data can be analyzed for insights that lead to better decisions and strategic business moves.
While the term “big data” is relatively new, the act of gathering and storing large amounts of information for eventual analysis is ages old. The concept gained momentum in the early 2000s when industry analyst Doug Laney articulated the now-mainstream definition of big data as the three Vs:
Volume: Organizations collect data from a variety of sources, including business transactions, social media and information from sensor or machine-to-machine data. In the past, storing it would’ve been a problem – but new technologies (such as Hadoop) have eased the burden.
Velocity: Data streams in at an unprecedented speed and must be dealt with in a timely manner. RFID tags, sensors and smart metering are driving the need to deal with torrents of data in near-real time.
Variety: Data comes in all types of formats – from structured, numeric data in traditional databases to unstructured text documents, email, video, audio, stock ticker data and financial transactions.
The importance of big data doesn’t revolve around how much data you have, but what you do with it. You can take data from any source and analyze it to find answers that enable: 1) cost reductions, 2) time reductions, 3) new product development and optimized offerings, and 4) smart decision making.
How Big Data Analytics work: Companies start by identifying significant business opportunities that may be enhanced by superior data and then determine whether Big Data Analytics solutions are needed. If they are, the business will need to develop the hardware, software and talent required to capitalize on Big Data Analytics. That often requires the addition of data scientists who are skilled in asking the right questions, identifying cost-effective information sources, finding true patterns of causality and translating analytic insights into actionable business information.
To apply Big Data Analytics, companies should:
- Select a pilot (a business unit or functional group) with meaningful opportunities to capitalize on Big Data Analytics
- Establish a leadership group and team of data scientists with the skills and resources necessary to drive the effort successfully
- Identify specific decisions and actions that can be improved
- Determine the most appropriate hardware and software solutions for the targeted decisions
- Decide whether to purchase or rent the system
- Establish guiding principles such as data privacy and security policies
- Test, learn, share and refine
- Develop repeatable models and expand applications to additional business areas
Companies use Big Data Analytics to:
- Improve internal processes, such as risk management, Customer Relationship Management, supply chain logistics or Web content optimization
- Improve existing products and services
- Develop new product and service offerings
- Better target their offerings to their customers
- Transform the overall business model to capitalize on real-time information and feedback
One of the tools for Managing Big Data is Hadoop is an open source, Java-based programming framework that supports the processing and storage of extremely large data sets in a distributed computing environment. It is part of the Apache project sponsored by the Apache Software Foundation.
Apache Hadoop: An open-source software that allows to store and process large amounts of data across clusters of computers, systems and files, Apache™ Hadoop® provides the tools for extracting intelligence from data through analysis and visualization.