In today’s data-driven business landscape, effective Master Data Management is key. The amount of data enterprises generate and collect is growing at unprecedented speed. Everyone agrees that data can deliver the edge a business needs to be successful. However, the …
You have a great website and now you have to make sure those who browse it can easily find their way around. You know that people don’t have the time to read each and every page in search of what …
When we started building our product, we focused on the Recommender engine and Segmentation. As a startup, we embraced agility and focus on the market. At the beginning of 2020, we decided that our product must become a reflection of …
In this text we will try to briefly explain IM applications integration with Visual Search. You can read more about Visual Search implementation in our last blog post. Instant Messaging (IM) and Chatbots Among all other benefits, The Internet has …
Bonuses and monetary incentives are touted as a cure-all solution for instilling a culture in a company. Fortunately, it will take a touch more than financial incentives to get the culture right. Giving monetary incentives can lead to poor results; …
@things-solver Segmentation, a method of categorizing customers based on shared traits, is a powerful tool in banking. What makes AI customer segmentation in banking particularly appealing is the wealth and diversity of available data. Here, it’s not just about dividing …
According to Gartner, only 15% to 20% of data science projects get completed. Of those projects that did complete, CEOs say that only about 8% of them generate value. Despite these facts, data science is still considered as an opportunity …
In the field of software development, different teams have different approaches to how things are being done. When the team is small, introducing processes may be overwhelming, and the biggest challenge is to determine when is the right time to …
I didn’t want to start this post by quoting the percentage of data science projects failing to deliver commercial value or business utilization. Many data science projects fail – and that’s a fact. What’s important is to make sure that …
This confirms Things Solver’s strategy to continuously enhance its solutions and offer the most advanced products and services to customers Joining forces with the established IT partner ASEE, together we will accelerate the development of existing and new products based …
Over the last few decades, recommender systems have gained prominence with the emergence of platforms like YouTube, Amazon, and Netflix. These algorithms play a pivotal role in suggesting relevant content for online users – from e-commerce product recommendations to personalized …
Main challenges Although EDA is often observed as an initial step which should be straightforward, there are some challenges that could slow down and make this process poor and painful. Some of the challenges I have encountered so far are …
Marketing is one of the most fertile fields for Data Science. There are many different techniques and directions which marketing teams are striving to realize, but the two most frequently discussed are – up sell and cross sell. For those …
We live in the era of information technology, when we generate more data in 10 minutes than the amount of total data generated from prehistoric times by the year of 2003. Having the processing power and memory resources to handle …
After participating in a meetup at the end of March, subjected “Data Science – what is it?”, a lot of people contacted me to send them some introductory materials to help them get started with learning. It took me a …
Retail industry. In the glory of Data Science, it’s all about the data and tailor-made targeting. If you want to brag about it, you would say – I’ve got the unique, omni-channel, 360-something, that can perfectly model customer’s behaviour and …
Pedro Domingos is a professor of computer science and engineering at the University of Washington. He is a winner of the SIGKDD Innovation Award, which FYI is the highest honor in Data Science. They say that approximately seven years is …
When dealing with anomalies in the data, there are lots of challenges that should be solved. We’ve already had a couple of articles regarding this topic, and if you haven’t already read it, I suggest you to take a look …
The problem of anomaly detection is a very challenging problem often faced in data analysis. Whether it is about clustering, classification or some other machine learning problem, it is of great importance to identify anomalies and handle them in some …
Hi, everyone. Although I planned for my next post to be about anomaly detection and their treatment, I faced some other type of problem that quickly escalated into huge issue affecting the modelling and results accuracy, and couldn’t resist to …
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