All You Need To Know About Big Data And Small Data
In the early 2000s, analyst Doug Laney defined Big Data and small data with three characteristics called “the three Vs”: Volume, Speed, and Variety of Data.
However, it has been shown that it is not only science and technology, but responds to a strategic vision of the business . In short, in the volume of data is not as important as the knowledge it provides us and allows us to make better decisions and make better strategic movements.
Until the emergence of it (Massive Data), Business Intelligence worked with what we now call Small Data .Today we are in a position to differentiate them:
- Small Data works with smaller volumes of data , while it works since 2012 with petabytes instead of Terabytes , since data is collected from sources as varied as commercial transactions, Social Media and sensors in machines. There is talk of it starting at 4 or 5 terabytes, but as we have said in recent years we are already talking about petabytes.
- Small Data works with processed and structured data and the management and analysis is made from it , while it manages and analyzes changing data practically in real time.
- Small data works with data from different sources, but always structured , while BigData works with varieties of multi structured data, not just numerical structured data; but also unstructured from social networks, e-mail, videos, audios or commercial transactions.
Small Data works with OLTP (Online Data Processing) and EDW (Enterprise Data Warehouse) software for the management and analysis of data on DBMS (Database Management Systems). The most used database management systems are MySQL, Microsoft Access, SQL Server, FileMaker, Oracle, RDBMS, dBASE, Clipper and FoxPro.
It uses Data Warehouse that manages structured data such as financial records, customer and sales data and combines it with Systems that store unstructured data. In addition, it incorporates emerging systems such as Hadoop , a free software framework prepared to work with NoSQL Database Management systems (unstructured data) and incorporates Stream Computing to integrate data in motion from different sources, guaranteeing a response in milliseconds.
In short, if until now our database systems were fed by large volumes of structured data, the complexity that has meant that the data comes from different platforms, added to the seasonality of the same and the data entry peaks; has required software that allows the management area of the company to manage all this information to be able to make better decisions and adopt a correct strategy in an ever-changing business environment to which it is necessary to react quickly.