For just a decade, the telco sector is nothing like what it was like. Telecommunication companies faced transforming changes and data is the centerpiece. First, mobile telecom companies shifted their focus from voice vendors to data carriers. Fixed services became obsolete and experience downfall. Competition became more fierce than ever because a market maturity means a limited amount of end users even though IoT are percepted as a potential booster. So, such situation should be examined more as an opportunity than like a dangerous threat.
Big data and predictive analytics have a lot of applications, including sales forecasting, marketing and strategy optimization, machine maintenance, even sports and design. Police and security experts already know that big data and predictive analytics might be extremely helpful in fighting and preventing crimes. We are going to explain how they do it.
Believe it or not, big data is the new gold. Poured into the fuel tank of the automotive industry, it is transforming itself into growth booster, creating new services and users` benefits. The core focus of big data and cars is not just autonomous vehicles, it is about humans and how they use their cars on an amazingly granular level.
Strategy optimization (SO) is a key process, which provides a substantial growth for retail and utility companies.
Generally the application of SO is directed to campaign management – helping the organizations to interact in a better way with their customers. The most popular optimization goals are increase of the revenue, popularity or customer loyalty, reducing the taken risk and so on. The multi-strategy optimization is also an option, where a balance between contradictory objectives is investigated, like the fine tuning between profit and loss.
Predictive analytics is full of tools and approaches enabling it to reveal key insights in almost any area. We already discussed the impact of Data Analytics in HR and we are delivering further.
Recent blog post by Toshi Takegushi, part of MathWorks team reveal in an interesting and comprehensive way how a predictive analytics model can be triggered on job-related data sets for scoring which employee is planning to quit its position. He relied on machine learning algorithms for predicting future events by utilizing historical data.
In this blog post, our chief scientist Alexander Efremov PhD. is discussing the application of direct and recurrent artificial neural networks (ANN) in some methodologies for credit risk models.
Inputs of ANN could be the available individuals’ characteristics, like age, income, marital status, credit bureau data, etc. The outputs are the probability the applicants to have a good performance as loan-holders, the individuals’ response to some actions, etc.
Google just surprised the data savvy community by withdrawing the limitation of 5 reports created within the free trial in their Data Studio product. The change was announced by Nick Mihailovski, product manager for Google Data Studio, in a recent blog post and via the official Google Analytics Twitter account.
We removed Data Studio limits based on your feedback. We’d love to hear what new integrations you’d like to see: https://t.co/wRgjTfARYe
— Google Analytics (@googleanalytics) February 2, 2017
Well, 2016 is all history and it is time to focus on the near future. That’s why we decided to share our point of view on what is going to be hot in data analytics world in 2017.
The first contribution of an artificial intellect, known today as A.I. is the hacking of Enigma coding machine. The story of Alan Turing was intriguingly impersonated by Benedict Cumberbatch in The Imitation Game drama movie. It is acknowledged that Turing`s success with creating an algorithm and machine that managed to break the Enigma code has saved millions of lives during the WWII.
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.