What is Big Data and Why is it Important?

What is big data?

Big data is a combination of structured, semi structured and unstructured data collected by organizations that can be mined for information and 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, combined with tools that support big data analytics uses. Big data is often characterized by the three V’s:

• the large volume of data in many environments;
• the wide variety of data types frequently stored in big data systems; and
• the velocity at which much of the data is generated, collected and processed.

Why is big data important?

Companies use big data in their systems to improve operations, provide better customer service, create personalized marketing campaigns and take other actions that, ultimately, can increase revenue and profits. Businesses that use it 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 in order 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.

Big data is also used by medical researchers to identify disease signs and risk factors and by doctors 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 or outbreaks.

Here are some more examples of how big data is used by organizations:

• In the energy industry, big data helps oil and gas companies identify potential drilling locations and monitor pipeline operations; likewise, utilities use it to track electrical grids.
• Financial services firms use big data systems for risk management and real-time analysis of market data.
• Manufacturers and transportation companies rely on big data to manage their supply chains and optimize delivery routes.
• Other government uses include emergency response, crime prevention and smart city initiatives.

What are examples of big data?

Big data comes from myriad sources — some examples are 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 data from sensors on manufacturing machines, industrial equipment and internet of things devices.

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 structured data only, 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 may be left in its raw form and then filtered and organized as needed for particular analytics uses. In other cases, it’s preprocessed using data mining tools and data preparation software so it’s ready for applications that are run regularly.

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, which includes profiling, cleansing, validation and transformation of data sets, a crucial first step in the analytics process.