Big data has become increasingly more important over the years and is used by companies within their own systems for a number of actions, such as:
- Creating personalized marketing campaigns
- Improving operations
- Providing better customer service
The end goal is always the same: profit and revenue increase. When businesses are able to use it effectively, they get possibly highly-effective leverage over the ones that don’t. This is primarily because business decisions can be more informed and much faster.
Medical researchers also get a lot of big data. They use it for the identification of signs of a certain disease as well as risk factors. Doctors also use it for the correct diagnosis of medical conditions and illnesses in patients. Combined data from the internet, electronic health records, social media sites, and other sources allows healthcare organizations and government agencies to have updated information on infectious disease threats and/or outbreaks.
Big Data: Some Concrete Examples
Essentially, there is a mixed bag of sources that lead to big data. Examples of this include customer databases, documents, transaction processing systems, emails, internet clickstream logs, medical records, social networks, and mobile apps. Machine-generated data is also included here, like server and network log files as well as data from sensors on manufacturing machines, industrial equipment, and even internet of things devices.
Veracity Is Key!
Veracity measures how accurate information is and how trustworthy it is. When raw data is collected from various sources willy-nilly, it can lead to data quality issues. When not properly fixed by way of data cleansing, bad data will bring analysis errors that can do more harm than good to the value of business analytics initiatives.
Volume Matters In Big Data!
Asked about big data characteristics, most people would mention volume. The big data environment doesn’t necessarily need big amounts of data: most do, though. This is reliant on the nature of the data being collected and stored. Clickstreams, stream processing systems, and system logs are among the top sources that produce huge volumes of data on a continuous basis.
Big data has a wide variety of data types, such as:
- Semistructured data (streaming data from sensors, web server logs, and more)
- Structured data (financial records, transactions, and more)
- Unstructured data (documents, multimedia files, text, and more)
Big data systems have a tendency to store and manage several data types. Big data applications often have multiple data sets included, which could have not been integrated from the get-go.
Don’t Forget Velocity!
In the context of big data, velocity refers to the speed at which data gets generated, processed, and analyzed. A lot of the time, sets of big data are updated on a real-time (or near-real-time) basis. This is a sharp contrast to traditional data warehouses, wherein updates are daily, weekly, or monthly.
Data velocity has to be managed, so that big data analysis expands into artificial intelligence (AI) and machine learning.
Big data is essentially a collection of data that goes a long way in terms of the systems within companies to help them along. Concrete examples include documents, customer databases, and even social networks. Key characteristics include velocity and volume.
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