Big Data Themes

Organizations evolve through the data stages as a continuous journey. Big data approach will be helpful in each of the stage as it’s a continuous approach to achieve the competitive advantage. It’s not the ‘One Time’ solution. Following themes will help organizations to leverage big data. Each one belongs to a least one dimension of big data theme.

Transparency: Today, still few organizations observe significant amount of information which is not digitized. Making this digitized, provide huge opportunity to capture this information and make it available in the mainstream data flows. It is being stored in papers, files, reports, tapes etc. Some form of information like processes, standards is not even captured. All such ‘missing’ information is a missed opportunity for making growth strategies in long run

Generation: Advanced instrumentation and embedded technologies are making each possible physical ‘thing’ intelligent. These are forming ‘Internet-of-Things’ more and more communicable and traceable. These internet objects interact with themselves and to the outside world to generate lots and lots of data. Advanced sensors and embedded devices are now able to gather unimaginable information in huge chunks like heart rate monitors, touch sensors, advanced weather forecasting systems etc. This is all new information which organizations never thought about two decades ago and today it’s making it really Big. 

Surfacing: There is ‘Big Data’ available outside the organizations, about the organizations which are currently out of organizations’ control. This is an excellent opportunity to understand what is being said about, over ‘Social Media’. Also, there is huge unstructured data residing on servers in terms of logs which can let you know service performance and anomalies. 

Integration: Companies have started to ask this question. “How data ‘in-silos’ can be integrated together to identify if any correlation between them, eventually between different business functions. Huge transactional data and such loosely controlled data can be integrated together by using advanced data architectures. Organizations need to create customer profiles with this integrated data in order to identify customer interactions to external world to open up cross selling opportunities. 

Discovery: Huge datasets are worth to be examined. Advanced algorithms for data mining and data science techniques can be used to scan through the data and identify data patterns. Relevant business information can be discovered by studying these patterns which can be used take necessary measures. 

Consumption: Data is available today at very fast pace. Every minute is adding huge data in this ‘data-net’. Hence its accessibility becomes equally important. CXO’s of today’s corporate want such information on their screen the way data is getting generated. Fast processing, dynamic reporting are important factors today for data analytics.