SigmaWay Blog

Big Data, if utilized properly, is believed to become the historic driver of progress. It plays an important role in the fields of public security, healthcare, poverty, to name a few. Video surveillance and facial recognition using big data is far more effective than reviewing the footages manually, which can be erroneous. It also helps in avoiding cybersecurity threats. Predictive models using big data can predict for future attacks even before their occurrence. With the application of big data in healthcare sector, there has been a shift from treating illnesses to proactively maintaining our health and taking certain measure for preventive care. It plays an immense role in the education sector as well. By understanding the needs of each district, it gives schools the opportunity to build innovative educational techniques. Big data solves urban transportation problem by enabling government agencies develop alternate routes to ease traffic. It helps in alleviating the dangers of food scarcity. It is time to embrace big data as it opens up opportunities to encourage economic prosperity. Read more at: https://datafloq.com/read/5-applications-big-data-in-government/65

The development of mobile, IoT, and the cloud has increased the need of analytics to solve challenges in the customer, product, operations, and marketing domains. The established companies need to restructure their business and technology to increase their sales. Organizations need to involve cross-functional teams to establish data governance. Analytics 1.0 was data warehousing and business intelligence; Analytics 2.0 was big data, Hadoop, and NoSQL. Now in the era of Analytics 3.0, when tools make decisions and measure the impact. For more read the article written by chandramohan Kannusamy (Technical Architect) : http://data-informed.com/analytics-3-0-and-data-driven-transformation/

The best way to improve the probability of desirable outcomes is to predict the unknown future results. Predictive analytics help organizations to become forward looking and proactive. It uses a number of predictive modelling and analytical technique for the prediction of the future. One of the modelling techniques is a response model which doesn't predict influence. It only predicts the desirable outcomes of one method without making any prediction of alternative method. Healthcare organizations will be more successful if the predictions for treatment decision results in the desired outcome. For more read the article written by Eric Siegel (founder of the Predictive Analytics World Conference):

http://data-informed.com/drive-influence-with-uplift-modeling/

The use of predictive analytics in the Human Resource Management is the recent development. The HR team is taking advantage of the loads of data to keep a check on the employees. Predictive Analytics is changing the relationship between employee and enterprise. Predictive models can easily predict the upcoming trends. The performance of the employees can be judged in an unbiased way rather than gut feelings. The models identify meaningful insights by collecting data from the various platforms. The kind of posts that individuals have on the social networking platforms, LinkedIn profiles all lead to predictions. HR teams use these insights to decide the promotions, pay scale etc. This also acts as a disadvantage to the privacy of workers. Soon there will be a need to redefine the worker privacy laws which will take care of these new technologies. Read more about this in the article written by Rodd Wagner (A best- selling author) at: http://www.stevenspointjournal.com/story/opinion/columnists/2016/01/22/predictive-analytics-transforms-workplace/79116764/

Predictive models are used to predict future outcomes on the basis of data collected from past. Many organizations take their crucial decisions based on the foundations laid by the predictive models. But what if the model goes wrong? "BOOM"- crash of a significant part of the strategy! Though each predictive model has some scope of error. There are chances that the input variables considered for the model were not appropriate. But we need to find out what kind of error and to what extent it is acceptable. There is a need to work on the foundation of the predictive models to prevent failures.  Read more about it in the article written by John Bates(Senior Product Manager for Data Science & Predictive Marketing Solutions) at: http://blogs.adobe.com/digitalmarketing/analytics/what-to-do-when-your-predictive-marketing-is-wrong/

Those using Predictive Analytics easily outpace those who don't. There is a clear incremental sales lift in the marketing campaigns which consider predictive analytics. Every organization strives to achieve a higher return on investment (ROI) from that spend on marketing. Predictive analytics help creation of unique customer profiles by analyzing the data.  Read more about how Predictive Analytics can be useful in B2B marketing in the article written by Laura at: http://blogs.forrester.com/laura_ramos/15-07-02-the_power_to_predict_can_give_b2b_marketers_an_unfair_advantage