What is Big Data?
Big Data deals mainly with two types of data, structured and unstructured. In addition, it is often recognised by the large amount of data it deals with on a day-to-day basis. But it is not the amount of data that is important. What matters with Big Data is what organisations do with the data. Big Data can be analysed for insights that lead to better decisions and strategic business moves.
It is a complex technique as it deals with a large volume of data, complex and interrelated data and very changing data in a very short time, which makes it difficult to understand and work with the data.
Although the size used to determine whether a given dataset is considered Big Data is not firmly defined and continues to change over time, most experts currently refer to a volume of 30 to 50 terabytes.
As we have mentioned, Big Data is complex not only because of the large amount of data but also because of the data itself. It must be taken into account that unstructured data can work with frequencies or responses that could not be analysed before (movements, gestures, temperatures...).
Why is Big Data important?
This methodology or area allows companies to analyse data more quickly and accurately. It also allows them to eliminate problem areas before the problems kill their profits or reputation.
Big Data analytics helps organisations leverage their data and use it to identify new opportunities.
The three major benefits of BIG DATA:
- Cost reduction.
- It allows for faster and deeper decision-making.
- Increased knowledge for customer satisfaction.
Types of Big Data
- Structured: This is any data that can be stored, accessed and processed in a fixed way. In a way, it is the data that we usually find in databases, usually displayed in rows and columns. And the facility they have is that they are easily sorted and processed.
- Unstructured: This is usually binary data that has no identifiable internal structure. A typical example of unstructured data are heterogeneous data sources containing a combination of simple text files, images, videos, among others.
- Semi-structured: Semi-structured data can contain both types of data. It usually has a format that can be defined, but is not easily understood by the user and requires the use of complex rules to help determine how to read each piece of information. An example of semi-structured data is data represented in an XML file.
How does Big Data work?
Although it is a technical and somewhat complex process, it could be divided into 3 phases to understand what BIG DATA is about or how it behaves:
- Integrate: This section consists of trying to collect as much data and sources as possible. You want to have the prism of all parties. In addition, the data should be formatted and made available for use by people in the company.
- Manage: All this data needs to be stored, and with large amounts of data, tools such as the cloud itself must be used.
- Analyse: This last section consists of the actual working of the data. It is to give a practical and useful vision to the data that have been previously processed. The analysis carries out different practices such as the construction of models or other patterns with different tools such as machine learning or artificial intelligence.
Technologies
- Hadoop FrameWork
- Mongo DB
- Rainstar
- Hunt
- Presto
- RapidMiner
- ElasticSearch
- Apachekafka
- Splunk
- KNIME
- Spark
- Blockchain
- Tableau
- Ploty
- Power BI
Operations M&A Big Data
2021 was a very good year for the sector and 2022 seems to be a very good year for the sector, although it still does not reach the same high levels as the previous year. We should bear in mind that 2022 is already at the same levels of M&A transactions in the BIG Data sector in 2022.
