Everyone is talking about Big Data analytics and it’s valuable role to drive growth and profitability for businesses. As a technology solution it is groping at unprecedented level. If on the one hand, the revolutionary nature of data centric technology is responsible for this growth, on the other hand the gigantic volume of data which is growing in leaps and bounds is also responsible for helping Big Data analytics grow as a technology for business data management and analytics.
Big Data solutions are first of all about huge volume of digital data which is measuring several terabytes and is almost on the verge of touching petabytes. This huge volume of digital data has the tremendous potential of garnering actionable insights for business decisions and strategy building. In a world where every one of us is digitally active, only digital data channeled through analytics can have the power to drive precision and relevance in decision making. With the potential of digital data proving huge and towering, Big Data analytics is likely to play most crucial role for businesses and any technology solutions company should have inevitable focus on data.
- Three Categories Of Data
Big Data today can be characterised by three categories of data sets including structured, unstructured and semi structured.
As the name suggests, structured data sets come ready for analytical purpose and can be utilised in their original state garnering actionable outputs. For instance salary records of employees, graphic and structured presentation of health data, etc can be considered ready for analytics.
Unstructured data as the name suggests, appear without any proper format or structure to understand them with logical categorisation. From human text messages to google search results to social media posts to most of the digital data come under this category.
Semi-Structured data combines both the characteristics of structured and unstructured data sets and requires less processing manoeuvres than the data sets devoid of any structure.
Any business are exposed to all these three types of data. Processing and restructuring of the digital data is required for garnering actionable analytical output. The output derived by processing the digital data through Big Data analytics tools help businesses understand their customers better and take market opportunities.
- Big Data Analytics, Combining Various Technologies To Derive Data Driven Insights
Big data analytics which is used for extracting relevant, useful and actionable insights from large volume of structured and unstructured data sets can inform a business about any upcoming trend, competitive insights, market opportunities and pain points that businesses otherwise cannot see. For doing this Big Data Analytics need to depend on few technologies, processes and tools. Let us have a look at few of them.
Data acquisition corresponds to two different actions, respectively as identification and collection of big data.
This is a type of database required for storing huge volume of data that can be accessed from time to time for scalable data solutions.
In-memory Database Systems
This database systems help circumventing the problem of accessing relational database for processing information and actionable insights. Thanks to the ease of access to data stored inside RAM of Big Data servers, data driven processing can be quicker.
Hybrid Data Storage
Hybrid data storage like Apache Hadoop allows mid and small level companies flexibility and scalability in respect of data storage and processing capacity. Hadoop Distributed File System (HDFS) allows storing data files across multiple systems.