Mexico. Comstor's business unit of Westcon-Comstor and one of the leading wholesalers of Cisco technology, unveils the 16 important big data terms that every IT professional should know, where learning how to use it is immensely more relevant.
Knowing what each term related to Big Data means is essential to apply in the day to day of business. However, many of the terms that exist today do not have an exact definition, because they are transformed to the extent they are used.
Here are some essential words for those who work with this technology:
1. Algorithm: Logical sequence, finite and with instructions that form a mathematical or statistical formula to perform data analysis.
2. Analytics: It is the way to capture information, process it and analyze it so that it becomes insights.
3. BI (Business Intelligence): It is the method that transforms stored and analyzed information into data that is strategic for a company and that becomes profit for the business.
4. Data Scientist: It is the data analyst. The person who will capture the insights, the main information within a large volume of information.
5. Small Data: Much smaller than Big Data, it refers to the analysis that is done with few data sources.
6. Structured and unstructured data: Structured data have a logical organization, but with small possibilities of extracting information for Big Data. On the other hand, the unstructured are disorganized, like messages in emails and social networks, but allow a great possibility of extracting commercial insights.
7. Dark Data: Refers to unknown data that can be lost or stored, without the possibility of being accessed or analyzed in case the system is not configured for that.
8. Data Cleansing: It is the method that keeps databases free of inconsistent or irrelevant information.
9. Data Lake: It is a data lake in which information is stored in its natural state and in large volume, it is there where the Data Scientist must dive to find his main insights.
10. Data Mining: It is the process prior to Analytics, it is the mining of data, the way to discover relevant information.
11. Dirty Data: These are the records that have not been cleaned. Data that was captured, stored and will be used, but that needs to be worked on first.
12. Fast Data: Fast data is what loses value over time and, therefore, needs to be analyzed practically in real time to generate strategic responses for companies that need to give answers and make decisions instantly.
13. Slow Data: It is the opposite of Fast Data and refers to the information that can be captured in the Data Lake for further analysis. That data doesn't need real-time analysis, with less response time.
14. Medium Data: A term that defines an intermediary amount of data that is necessary for analysis and insights to be generated. It is a smaller size of information than that generated by Big Data.
15. Predictive Analytics: Predictive analytics is the use of data to predict future trends or events.
16. Sentiment Analysis: Sentiment analysis are techniques used to identify an individual's feeling about a certain issue. There are many terms that arise at all times, often created by tool providers and consulting analysts to try to offer a new service. Generally they are functions that already exist and that those who work with Big Data are already used to, but with a new name or definition.
It is important that all those words are known, but it is even more important that you focus on the way in which Big Data can be used to generate results that can transform a company.
