Data analytics has a lot of useful applications and predictive maintenance is one of them. Its achievements are ensuring maximum availability of machines while guaranteeing the production quality. It is important, especially in some industries where the downtime is among the costliest risks of all potential threats. Data analytics is capable of creating machine-learning models trained by real-time data flow that predicts when, where and why a particular machine will fail.
FinTech stands for Financial Technology. It is a common term used for companies offering added value or entirely new financial services via technology. Financial business is generating a huge amount of data also known as big data. This is where the point of intersection for Data Analytics and FinTech is located. We also have to keep in mind that FinTech covers a lot of financial business domains like asset management, insurance, lending, transferring money, to name a few.
Since we`re into all things big data & analytics we are nothing less than tempted to share our thoughts on big data myths. And smash them as hard as we can. This way we`ll feel like MythBusters, at least for a minute.
Imagine you are selling boots and when it gets cold, the boots demand is naturally skyrocketing. This is for sure but if you are a store manager you`d like to have simple answer of a simple question:
How to make proper demand planning that actually works for seasonal businesses and products?
The modern business world is on hype about data analytics and there is a reason. Digging deep into historical data strings is a way to valuable insights that might generate new revenue streams to boost efficiency and to discover improved ways of doing things. Does this mean data analytics cannot fail? Of course not.
In this blog post, we are highlighting 5 different cases where data analytics failed one or another way. Not matter it was because of wrong presumption, bad execution, missing variables or wrong numbers – the results were at least surprising and definitely not nice.
Web mining is the process within you are extracting insightful information from World Wide Web. It is possible at different scales and for different purposes. In general, web mining is the term used for data mining application within online collected information. Such technical achievement gives businesses pretty interesting opportunities for the e-commerce businesses and online entrepreneurs.
It is not a secret that there is a staggering amount of information located on the Internet. The annual internet traffic is rated as a 1 zettabyte per year. If you ever have wondered how much data is that, just imagine a cup of coffee on your desk is equal to 1 gigabyte. A zettabyte is the volume of the Great Wall of China.