Overtime the demand for wearables, like Fitbit, are increasing. The owners of the product get the details about themselves. However, at a large scale this data is beneficial for various sectors like the insurance companies, the pharmaceutical companies, doctors, etc. But this aggregate data is available only with the brand owner. Considering the insurance company, it can come into an agreement with its insurers, wherein the insurers would use the Fitbit and report the data to the company, which in return would give special discounts on premium amount. This aggregate data, or the Big Data, can then be used for the cost-benefit analysis. Faced with the fear of being misreported, the insurance companies might also go for vertical integration with the Fitbit companies, ensuring a greater market for the same in return of the Big Data. This would ensure a substantial portion of the market to the firm. The insurance company on the other hand would be successful in maximizing its profits. Hence, vertical integration and Big Data can do wonders in these markets. Read more at http://bigdatatobigprofits.com/2015/12/04/lessons-on-big-data-risk-and-the-vertical-integration-of-wearables-and-startups-2/

Data science comes with a new era on IT industries. From AI, ML, big data, data analytics and many more data science is proving its importance. With the emerging business plans on big data the requirement and demands of data scientists are also getting higher. Here are the guidelines to the students who want to pursue data science as their career. 

 Education background should relate to computer science.

 Beginning of your career experience and work focus

 Learning opportunities and certification

 Mid-career experience and certification

 Data science expertise and professionalism

For more details, visit:

https://www.analyticsinsight.net/how-to-become-a-data-scientist-the-skills-certifications-and-education-required-for-the-trendy-job/

It’s a difficult approach when it comes to the adoption of analytics facing real time challenges. It’s tough to analyze big data and assemble it in a single row. There are times when the minutes and seconds count are very crucial and no delays can be accepted. With the demand of more precision and accuracy we just cannot avoid the risk presence while accessing the data. But real time analytics demand much of our efforts and hard work to overcome the challenges and get that precision.  

For more details, visit: 

https://www.analyticsinsight.net/planning-to-embrace-big-data-here-are-real-time-challenges-you-might-face/

There are lot of challenges faced by HR, from slowed hiring to restructuring, after the layoff  from IT industry. HR has considerably lagged behind in the use of big data and analytics in India. And according to a study, only 5% of big-data investments were in made in human resources. HR analytics is critical not just from talent acquisition and management perspective, but also cost optimisation. According to Arjun Pratap Singh, “AI and analytics are the driving force behind HR technology and this will drive the new employment economy. From talent acquisition and workforce optimisation to workforce transformation, AI be the strategic enabler to HR”. Data-backed decisions lend transparency to processes such as annual reviews. In case of retrenchment, data-driven decisions lend validity to the process. It is the absence of the data that HR analytics is lagging behind. Read more at: http://analyticsindiamag.com/can-hr-analytics-solve-major-talent-challenges-times-mass-layoffs/

Increasing technology brings up more data and allow big data to act and perform more efficiently. Self-driving cars with more advanced technology are generating more data, new advancement of the license used in cars for smartphones and tablets that gathering Wide scale data. It affects insurance industry, by the introduction of new technology self-driving cars largely affects them with automatic car drivers. The data are also magnetic from different fields like in places such where sensors are attached to track down illegal cars. The business marketers are also developed for knowing the consumer behaviour zones outside their stores by tracking the road system. The big players are working for big data on connected cars. Read more at: https://datafloq.com/read/connected-cars-big-datas-next-mining-ground/3007

How Big data help today's world on sustainability by developing a platform for scientists and researchers to make the world in the path of development. Data estimated in the year 2020 will be pooled up by huge information than current scenario. It has been known by every individual the change in climate is because of human's activity and can be improved by the action taken by each one. This issue can be seen on the rear image with different insights in the world by using big data application. Big data will help by collecting more and more data on the business activities and their impact on the environment. Big data is actively acted to make a change by mitigating cost and aiming towards the friendly environment, by understanding more on demand on energy. Read more at: https://datafloq.com/read/what-does-big-data-mean-for-sustainability/2412   

Data is an important tool which manages a lot of important activities in the business. And this is further enhanced by artificial intelligence and machine learning and by ease of collecting and storing data. Managers rely too much on data for the guidance which abdicates their knowledge and experience. In a big data project the manager connects the internal and external team to collect and process the data in order to solve the problem. Data must be in usable form and the algorithms must identify statistically significant patterns. The results are then presented to the manager through different visualizations. The main problem is the managers are not good with data science and the scientists are not good with the businesses.  Read more at: https://hbr.org/2017/06/the-best-approach-to-decision-making-combines-data-and-managers-expertise

Big data has now become old, organizations are very well familiar with it. But some of them are still struggling with data. Data lakes provides easy access of data and data mining. Due to management defaults data may turn into data swamps making analysis difficult. Data Lake has a lot of benefits, but the data growing in size becomes difficult to handle. To avoid this problem following steps are taken at the time of creation. 1) Too much data must not be collected at the beginning. 2) Data insighting cannot be done manually, so machine-learning capabilities should be enabled. 3) Businesses should keep an eye on changing data statistics and the employed models. To make it successful one needs to integrate it with business strategy and outcome. Read more at: https://www.readitquik.com/articles/elastic-computing/smart-ways-to-manage-your-big-data/