Content & Social Media Manager
By 2020, 1.7 megabytes of data will be created every second, for every person on earth. By then, the Internet of Things and next generation technologies will imply 200 billion connected devices and the worldwide revenues for Analytics and Big Data will be set out in more than $203 billion.
Gathered from different sources, such as social media, search engines, and online platforms, Big Data has become the new oil of the digital era. But, what exactly does this term mean?
Big Data, a simple explanation for everyone
Big Data could be defined as a large amount of data that grows exponentially in time and, according to CrewMachine, that is often characterized by 4Vs:
- Volume. We talk about a huge size of data: we create 2.5 quintillion bytes of data every day.
- Variety. Variety refers to data of a different nature and drawn from heterogeneous sources: 90% of generated data is unstructured (video, images, documents, etc.).
- Velocity. Data is generated fast and continuously: 50,000 GB/second is the estimated rate of global Internet traffic in 2018.
- Veracity. This refers to the certainty of data: 1 in 3 business leaders don’t trust the information they use to make decisions.
Furthermore, data can be usually found in three forms:
- Structured. Structured data represents around 5% to 10% of the total data. When we talk about this type of data, we refer to information that can be stored in practically any database, in rows and columns. It can be easily mapped and processed by all existing data mining tools using the type of data and field names (alphabetic, numeric, date, etc.).
- Unstructured. Unstructured data represent around 80% of data, including here videos, photos, audio files, email messages, web pages, etc. It is considered to be unstructured because this is generally binary data without an identifiable internal structure and has no value until it is identified and organized. When it is organized, it can be searched and categorized to extract information
Unstructured data can be machine-generated (scientific data, photographs and video, satellite images) or human-generated (social media data, mobile data, business applications, website content).
- Semi-structured. As in the case of structured data, semi-structured data also represents a smaller portion of data (5 to 10%). It lies halfway between structured and unstructured data because this information does not have a defined structure but has some organizational properties to describe objects and its relations.
The problem is, as we have mentioned above, that such large amounts of data are normally unstructured and it could take years for humans to extract relevant information. Using the right tools has become essential to any business wanting to extract value from data and turn it into a competitive advantage.
As Gary King said “Big Data is not about the data”...
How do we unravel the strands of Big Data and pick out the relevant parts?
But… Big Data is not only about the data
As Gary King said, “Big Data is not about the data”. The crucial issue when talking about this is, how do we unravel the strands of big data and pick out the relevant parts? How do we know where to look? How do we access it? In other words, how do we turn all this information into knowledge?
Data is very easy to gather (despite the financial and energy costs resulting from its maintenance), but its real value is associated with its analysis. Many companies accumulate large amounts of data but they don’t know how to use it or make the most out of it. Not to mention its lifespan; in some cases, the accumulated information comes with an early expiry date, turning it into useless information at a later stage.
It is precisely for this reason that the use of artificial intelligence -including machine learning and deep learning– in business is booming. Therefore, more and more companies place value on these techniques due to the great potential that Artificial Intelligence is showing to generate greater and more efficient business.
You can find some of the most important advantages of using analytics in a business below:
- Cost savings and time reduction.
- Faster and more effective decision-making processes.
- Understanding the market conditions and anticipating needs.
- New product development, delivering the right products.
- Personalization and improvement of the Customer Experience.
We will talk in detail about these advantages later on. However, if you want to find out more about how this technology can help you make the most of your data, do not hesitate to contact us!
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