What makes data big? Big data is the term commonly used today to describe the large, diverse sets of information that grow at ever-increasing rates.
Big data includes the volume of information, the velocity or speed at which it is created and collected, and the variety or scope of the data points being covered. It quite often comes from several sources and arrives in multiple formats.
Most big data authorities categorize big data as structured or unstructured. Structured data consists of information already managed by the organization in databases and spreadsheets. This type of information is often numeric in nature.
Unstructured data is information that is unorganized and does not fall into a pre-determined model or format. It includes data gathered from social media sources, which help institutions gather information on customer needs.
The concept of big data grew in popularity in the early 2000s when Doug Laney, an industry analyst, described 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.
Some would argue there’s also a fourth V:
Variability. This has to do with the highly inconsistent nature of data flows and periodic peaks. Daily, seasonal and event-triggered peak data loads such as occurs in social media can be challenging to manage.
Want to know more about Big data? Tonex offers several courses in Big Data Analytics and Data Science, including:
—Applied Data Science and Machine Learning Training: A 3-day class where participants learn how to apply data science methodologies to popular machine learning and deep learning by applying datasets and principles of training data, prediction algorithms using data.
—Big Data For Project and Program Mangers Training: A 3-day class that covers all the methods, cautions, and concerns of big data analytics in project management.
—Big Data Training: A 2-day class where participants learn how to store information for efficient processing and analysis when it comes to informed and intelligent business decision-making.
—Fundamentals of Data Analytics and Methods: A 3-day class that provides participants the knowledge and skills to understand the data analytics and associated methods.
For more information, questions, comments, contact us.