Big Data is a phrase used to mean a massive volume of both structured and unstructured data that is so large it is difficult to process using traditional database and software techniques. In many enterprises, the volume of data is enormous and extremely fast-moving. Every transaction on a website, every piece of lead contact information collected by sales representatives, and every document that passes through company emails contributes to enterprise data, and all of that information adds up very quickly.
When captured, formatted, stored, and analyzed, this data gives a company insight to increase revenues, attract or retain customers, and improve operations.
Brief history of big data
The term “Big Data” was first used in the 1990s, but usage had an uptick in the 2000s. In the 90s, data management involved databases and analyzing structured data. In the 2000s, with heavier Internet usage, data collected from web traffic and e-commerce began multiplying. In the 2010s, mobile devices and sensors, along with existing sources of data, began producing so much information that data creation has been growing exponentially during the last decade.
Why is big data important?
Data, when used appropriately, makes companies money. Data about customers, products, marketing, offices, stores, and employees reveals details about every aspect of the company’s business operations. But big data is hard to sort through because there is so much of it. It’s also challenging to analyze: picking the important data from the unimportant makes the difference between useful insights and useless, harmful ones. To capitalize on that data, enterprises need tools to organize and understand it. Accurate, relevant, and organized data leads to better organizational decision making. These tools include:
- Big data analytics software (SAS, Cloudera, SiSense)
- Data governance tools (Talend, Collibra, IBM)
- Database management systems (MongoDB, MySQL, IBM Db2)
- Data lakes (HPE Greenlake, AWS)
Big data is subject to many legal requirements. Companies are required to comply with data protection regulations, which protect customers’ personal information from unintended use. Those regulations, including GDPR and CCPA, require businesses to strictly track who accesses data, limit that access, and tell customers how their data is used. Companies that fail to keep their data organized won’t be able to comply with regulations and face steep fines.
How is big data stored?
Storage solutions for sets of big data include:
- Databases
- Data warehouses
- Data lakes
- Public cloudÂ
- Private cloud
- Hybrid cloud (combination of public and private clouds)
- On-premises servers
- Disk arrays
- Solid state drive arrays
Some storage solutions are more organized than others. Data lakes are useful tools for storing unstructured data, but without being carefully managed and governed, they can turn into data swamps—which make processing and analyzing data very difficult. Some of the above solutions can also coexist: for example, HPE Greenlake is a data lake, but it also has cloud storage capabilities.Â
Who uses big data?
All enterprises that have large volumes of data stored and incoming can be said to use big data, though that data’s degree of usefulness depends on how it is managed.
Software companies and analytics providers themselves use big data to power their products and provide their services to other businesses.Â
Security providers must use big data to analyze networks and systems, notice any anomalies or threats, and record that information for improved threat intelligence.
Applications of big data
Common uses of big data and data analytics include:
- Artificial intelligence and machine learning. Providing large data sets to intelligent machines allows them to learn from a collection of information, such as statistics or images.Â
- Customer relationship management (CRM). The more data enterprises can access and successfully analyze, the more prepared they will be to interact with customers and provide them with ideal solutions.
- Risk management. When companies have massive amounts of data about multiple business processes, they’ll be able to learn from past risks and plan to mitigate future ones.
Recommended Reading: See Big Data Analytics.
This article was updated October 2021 by Jenna Phipps.
