What is big data?
Big data is a combination of structured, semi-structured and unstructured data that organizations collect, analyze and mine for information and insights. It's used in machine learning projects, predictive modeling and other advanced analytics applications.
Systems that process and store big data have become a common component of data management architectures in organizations. They're combined with tools that support big data analytics uses. Big data is often characterized by the three V's:
Doug Lany first identified these three V's of big data in 2001 when he was an analyst at consulting firm Meta Group Inc. Gartner popularized them after it acquired Meta Group in 2005. More recently, several other V's have been added to different descriptions of big data, including veracity, value and variability.
Although big data doesn't equate to any specific volume of data, big data deployments often involve terabytes, petabytes and even exabytes of data points created and collected over time.
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The ultimate guide to big data for businesses
Why is big data important and how is it used?
Companies use big data in their systems to improve operational efficiency, provide better customer service, create personalized marketing campaigns and take other actions that can increase revenue and profits. Businesses that use big data effectively hold a potential competitive advantage over those that don't because they're able to make faster and more informed business decisions.
For example, big data provides valuable insights into customers that companies can use to refine their marketing, advertising and promotions to increase customer engagement and conversion rates. Both historical and real-time data can be analyzed to assess the evolving preferences of consumers or corporate buyers, enabling businesses to become more responsive to customer wants and needs.
Medical researchers use big data to identify disease signs and risk factors. Doctors use it to help diagnose illnesses and medical conditions in patients. In addition, a combination of data from electronic health records, social media sites, the web and other sources gives healthcare organizations and government agencies up-to-date information on infectious disease threats and outbreaks.
Here are some more examples of how organizations in various industries use big data:
What are examples of big data?
Big data comes from many sources, including transaction processing systems, customer databases, documents, emails, medical records, internet clickstream logs, mobile apps and social networks. It also includes machine-generated data, such as network and server log files and sensor data from manufacturing machines, industrial equipment and internet of things devices.
In addition to data from internal systems, big data environments often incorporate external data on consumers, financial markets, weather and traffic conditions, geographic information, scientific research and more. Images, videos and audio files are forms of big data, too, and many big data applications involve streaming data that's processed and collected continually.
Breaking down big data V's: Volume, variety and velocity
Volume is the most cited characteristic of big data. A big data environment doesn't have to contain a large amount of data, but most do because of the nature of the data being collected and stored in them. Clickstreams, system logs and stream processing systems are among the sources that typically produce massive volumes of data on an ongoing basis.
In terms of variety, big data encompasses several data types, including the following:
Various data types must be stored and managed in big data systems. In addition, big data applications often include multiple data sets that can't be integrated upfront. For example, a big data analytics project might attempt to forecast sales of a product by correlating data on past sales, returns, online reviews and customer service calls.
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Velocity refers to the speed at which data is generated and must be processed and analyzed. In many cases, big data sets are updated on a real- or near-real-time basis, instead of the daily, weekly or monthly updates made in many traditional data warehouses. Managing data velocity is becoming more important as big data analysis expands into machine learning and artificial intelligence (AI), where analytical processes automatically find patterns in data and use them to generate insights.
More characteristics of big data: Veracity, value and variability
Looking beyond the original three V's, other ones are often associated with big data. Including the following:
Some people ascribe even more V's to big data; various lists have been created ranging from seven to 10.
How is big data stored and processed?
Big data is often stored in a data lake. While data warehouses are commonly built on relational databases and contain only structured data, data lakes can support various data types and typically are based on Hadoop clusters, cloud object storage services, NoSQL databases or other big data platforms.
Many big data environments combine multiple systems in a distributed architecture. For example, a central data lake might be integrated with other platforms, including relational databases or a data warehouse. The data in big data systems might be left in its raw form and then filtered and organized as needed for particular analytics uses, such as business intelligence (BI). In other cases, it's preprocessed using data mining tools and data preparation software so it's ready for applications that are run regularly.
Big data processing places heavy demands on the underlying compute infrastructure. Clustered systems often provide the required computing power. They handle data flow, using technologies like Hadoop and the Spark processing engine to distribute processing workloads across hundreds or thousands of commodity servers.
Getting that kind of processing capacity in a cost-effective way is a challenge. As a result, the cloud is a popular location for big data systems. Organizations can deploy their own cloud-based systems or use managed big-data-as-a-service offerings from cloud providers. Cloud users can scale up the required number of servers just long enough to complete big data analytics projects. The business only pays for the data storage and compute time it uses, and the cloud instances can be turned off when they aren't needed.
How big data analytics works
To get valid and relevant results from big data analytics applications, data scientists and other data analysts must have a detailed understanding of the available data and a sense of what they're looking for in it. That makes data preparation a crucial first step in the analytics process. It includes profiling, cleansing, validation and transformation of data sets,
Once the data has been gathered and prepared for analysis, various data science and advanced analytics disciplines can be applied to run different applications, using tools that provide big data analytics features and capabilities. Those disciplines include machine learning and its deep learning subset, predictive modeling, data mining, statistical analysis, streaming analytics and text mining.
Using customer data as an example, the different branches of analytics that can be done with sets of big data include the following:
Big data management technologies
Hadoop, an open source distributed processing framework released in 2006, was initially at the center of most big data architectures. The development of Spark and other processing engines pushed MapReduce, the engine built into Hadoop, more to the side. The result is an ecosystem of big data technologies that can be used for different applications but often are deployed together.
IT vendors offer big data platforms and managed services that combine many of those technologies in a single package, primarily for use in the cloud. For organizations that want to deploy big data systems themselves, either on premises or in the cloud, various tools are available in addition to Hadoop and Spark. They include the following categories of tools:
Big data benefits
Organizations that use and manage large data volumes correctly can reap many benefits, such as the following:
Big data challenges
There are common challenges for data experts when dealing with big data. They include the following: