Internet of things is growing at a rapid speed, network of physical devices are growing everywhere. With the number of IoT devices increasing 31% year over year, it is supposed there were around 8.4 billion IoT devices in the world in the year 2017 and it is supposed to increase at an every faster pace. In the last two decades, more than six billion devices have come online. All those connected "things" generate more than 2.5 quintillion bytes of data daily. A Data Scientist in todayĆ¢??s digital has more data everyday. With increase in the quantity of data being hired as a data scientist can be a hectic job being the only one that is able to convert this data into business intelligence. As a data scientist it is hard to reach the goals there are number of stumbling blocks.
In this article we will look at some of the most challenging things a Data Scientist faces will starting an IoT project or projects that keep them on toe.
It happens at time that while doing predictive analytics one collects excessive of data which is has no link in completion of the goal. This excess of data sometimes forces you into developing predictive models is not able to give you the result due to various factors. If you track too many occurrences without robust validation procedures and statistical tests in place, rare events may seem more frequent than they actually are. In either circumstance, validation and testing routines are paramount. This can even lead to a false satisfaction, predicting business events that are wrong. This abundance of data can be a big problem in your actual progress.
There is plenty of data already more than most data scientists even want too, with this huge data comes the data that is sloppy. It takes a lot of time and effort of data scientists to organize it. It is critical to avoid manual data entry wherever possible. One way of avoid these errors from the data is use of application integration tools that help in reducing the typographical errors, alternate spelling and individual trait from the data. Another easy way is to carefully prepare data to maintain a good quality in data. This involves clear communication and documentation of placeholder values, calculation and association logic, and cross-dataset keys. Following the industries standards and continuous anomaly detection and statistical validation techniques is a way to prevent re doing of working.
It is IoT that made predictive analytics an exciting capability. It is becoming a priority for IoT stakeholders because of its perceived value to business. Predictive analytics is not possible or valuable in all instances. ItĆ¢??s essential to first establish a clear objective for your analytics program and follow that with research to ensure its viability and value upon completion. In advance of any IoT program, data scientists should consider seeking the help of an outsourced data scientist or data science services. Doing so can help avoid mistakes, conserve internal resources, and reduce time to value. It is because of data science that companies are able to overcome the gap in the education amongst the data scientists when applying the skills to a specific market. Working with a team familiar with your industry, a data scientist can quickly learn and apply best practices to their program for optimized results.