Big Data Dimentions

The big story in data analytic and information management in 2011-12 was big data and in 2014, the trend is accelerating. It’s about managing huge amounts of novel and various sources of information. One can perceive this effect as a huge data-net which is growing in fastest ever pace and you are clueless about which data to consider and which not. Data are now woven into every sector and function in the global economy and like other essential factors of production such as hard assets and human capital. This ‘digitized data’ has become the business driver for almost all business function in today’s modern world economics. The use of Big Data - large pools of data - that can be brought together and analyzed to discern patterns and make better decisions — will become the basis of competition and growth for individual firms, enhancing productivity and creating significant value for the world economy by reducing waste and increasing the quality of products and services.

The three important dimensions of Data- Volume, Variety and Velocity – are making it really ‘Big’. The use of Big Data is becoming a crucial way for companies to outperform their competitors. This makes the data relevancy an utmost requirement. This brings another dimension ‘Veracity’ which eventually decides the accuracy of data. In most industries, established competitors and new entrants alike will leverage data-driven strategies to innovate, transform, and generate value. Big Data will help to create new growth opportunities and entirely new categories of business processes such as designing data requirement and aggregate and analyze industry data. These processes will be used to ingest large information flows which pour data about products and services, buyers and suppliers, consumer preferences and intent.

Different data mean differently to different organizations. There four different data stages which are coherent to four analytics stages as below

1. Information Data Stage: This is the basic data stage where data is recognized at information level. The business value of the outcome in this stage is not much but it is the simplest form of data. This will only provide numbers, facts etc. This is the starting phase of each organization in Big data journey. This stage mainly talks about ‘What’ part of it. The descriptive type of analysis tools are used in this stage to provide dashboards, reports etc.

2. Knowledge Data Stage: This data stage is a step ahead from information. In this stage organizations are trying to identify relationships between different types of information. The diagnostic techniques in this stage use search based or query based dynamic reporting tools. It mainly focuses on ‘Why’ part of it.

3. Intelligence Data Stage: This data stage tries to find out the patterns from the ‘knowledge’ that organizations have. Advanced algorithms, statistical techniques are used to mine the data and to identify typical patterns. This is used in predictive analysis to forecast ‘what will happen’.

4. Wisdom Data Stage: This stage is an ultimate data stage where organizations can make use

 of their information, knowledge and Intelligence to be the market leaders by setting Industry-best practices. The prescriptive analysis is used to formulate the strategy and achieve the business objectives.