There was a time when the banks were the sole legitimate lending providers but the rapid increase of Internet coverage, smart devices penetration and credit scoring automation made possible to get a fast loan with just a few clicks on your phone. Welcome to the FinTech world built by much more than cryptocurrencies and ICOs. Credit scoring automation is the tool that enabled loan access to the underfinanced population. Matching credit scoring with machine learning, AI and automation, in general, made this process a viable business case.
From the Facebook ability to curate information flow designed for you, to the ability to read the human genome, modern data analytics has a lot of applications in different forms and shapes. So does the energy sector.
Businesses can apply analytics for reaching insights into every process and to explore optimization opportunities. Starting with better-managed operations to the demand side of commercial channels but analytics also is opening a lot of new opportunities related to smart grids, smart homes, renewable energy, etc. Since at A4E we are tempted by all things analytics, in this blog post we share real-life application of energy data analytics.
Now-a-days huge amount of rough data is available. At the same time many businesses need of fast (real-time) decisions in order to survive and grow in the dynamic competitive environment.
To manage in time with such large dimensional problems automated (without human intervention) analytical solutions are needed. In many cases, like the two examples below, the data mining task and a subsequent decision making should be run as an automated workflow.