Assistant Professor, BIMS Kolkata
Nowadays we are living in a SMART world. ‘Big Data and Analytics’, internet of things (IoT) have brought evolutions in different industry sectors as well as in our daily life applications. The concept of ‘Big Data’ lies on leaving of digital trace (data) through our daily life activity on digital platform, which can be extracted followed by rigorous analysis. In fact, in every two years the size of our digital world is getting double and at this rate there will be as many bits of information in digital world as there are many number of stars in the physical universe1.
Big data and analytics deal with following type of data: structured data, unstructured data and semi-structured data. The structured data is either stored in table or in file. Such as: customer data, financial data etc. ‘Structured Query Language’ (SQL) is being used for managing the structured data sets in database management systems (DBMS) and relation database management systems (RDBMS). The unstructured and semi-structured data represent all the data that can’t be easily stored into table structures. Such as photos, videos, websites, text files etc. As the name suggests, semi-structured data is a hybrid of structured and unstructured data sets. For instance, a Facebook post is a good example of semi-structured data. It can be categorized by author, date like structured data set but the content is unstructured. There is a huge hype on unstructured and semi-structured data as 80% of business data is relevant under these categories.
The data rich companies like Facebook, Google, Amazon and Walmart deploy data scientists to extract the insights on their customer, user behaviors which in turn helping to expand their businesses and new strategies. These strategies are becoming fear to other business leaders due to lack of insights on how to deal with Big Data. According to reports, degradation of leadership quality has increased from 33% to 67% which implies that one of third of our leaders has failed in their roles 2,3. These poor leaderships are also leading to stress among employees and the amount is 75% of the working adults who have made their supervisors responsible for these 4. Hence, according to the experts, it is better to make a proper strategy rather proceeding by direct processing with data. It will be helpful for diving into data and find out the relevant features for business and industrial research requirements. This can be a SMART solution to deal with Big Data issue. For instance, analyzing the CCTV camera footages of shopping malls, the business analytics can get information about how customers are being attracted towards sales offers, what are the brands they look for mostly such that it can be productive to increase sales. Even asking SMART questions can shine light on new product developments. For example, every day there are 20% new things on Google searches which were never appeared in search engine before 5. This is a good indication that people are thinking in innovative ways which may be helpful for new product developments and can be fruitful to generate new business models.
Though there are so many issues regarding privacy and transparency as we have no idea about how much companies know about us and moreover how much government knows about us! There is no doubt regarding personal data, Facebook is richer than any other data giant companies like Google. Facebook knows everything of us through our likes on products, sports, movies besides our personal information of birthday, gender, place, job status etc 6. Hence we have no clue about how much of our personal data is existing in another format. Nowadays 25% of Facebook users are not aware of their privacy! As an individual it is our duty to be more cautious on online privacy settings.
Across the globe, there are more than 80% of people under Bottom of Pyramid (BOP) category. Revolution with ‘Big Data and Analytics’, it is now becoming possible to come up with smarter medical devices like low cost precancer detection software7,10, low cost eye disease detection software8 besides the health monitoring mobile apps like measurement of heart rate variation (HRV) which can predict ‘all-cause mortality’9. Not only in affordable healthcare segment, there are applications in sports for players’ performance improvements, in agriculture for soil and water testing, in home applications for children activity monitoring and so on. Throughout the world a lot of researchers, innovators and entrepreneurs are working to make more low cost IoT based devices in betterment of our lives. Hence, it is quite apparent that ‘Big Data and Analytics’ will dominate the world in more sophisticated way in near future. It will be better if we can adapt it ethically and apply in innovations for billions of lives.
1. IDC The Digital Universe Study (April 2014) Sponsored by EMC2.
2. Sorcher, M. (1985) Predicting Executive Success. New York: Wiley.
3. Hogan, R., and Hogan, J. (2001) Assessing leadership: A view of the dark side. International Journal of Selection and Assessment, 9, 40-51.
4. Off the Rails: Avoiding the High Cost of Failed Leadership.
5. Qualman, E. (2013) Social Media, 2013, You Tube, https://www.youtube.com/watch?v=zxpa4dNVd3c
6. Kosinski, M., Stillwell, D., Graepel, T. (2013) Private traits and attributes are predictable from digital records of human behavior. Proceedings of National Academy of Sciences U S A. doi: 10.1073/pnas.1218772110
7. Celi, L.A., Sarmenta, L., Rotberg, J., Marcelo, A., and Clifford, G., (2009) Mobile Care (Moca) for Remote Diagnosis and Screening. Journal of Health Informatics in Developing Countries 3(1).
8. Das, N., Mukhopadhyay, S., Ghosh, N., Chhablani, J., Richhariya, A., Rao, K.D., Sahoo, N.K. (2016) Investigation of Alterations in multifractality in Optical Coherence Tomographic Images of In Vivo Human Retina. Journal of Biomedical Optics 21(09).
9. Dekker, J.M., Schouten, E.G., Klootwijk, P., Pool, J., Swenne, C.A., and Kromhout, D. (1997) Heart rate variability from short electrocardiographic recording predicts mortality from all causes in Middle-aged and elderly men. The Zutphen Study, American Journal of Epidemiology 145 (10).
10. Mukhopadhyay,S., Das,N., Kurmi,I., Pradhan,A., Ghosh,N., Panigrahi,P.K. (2017) Tissue multifractality and hidden Markov model based integrated framework for optimum precancer dectection. Journal of Biomedical Optics 22(10).