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Picture this: your business is booming, and you need to make informed decisions quickly to stay ahead of the competition. But forecasting can be a tedious and time-consuming process, leaving you with less time to focus on what’s important. That’s where Azure Databricks comes in! We used this powerful technology to automate our internal forecasting process and save precious time. In this blog post, we’ll show you the steps we took to streamline our workflow and make better decisions with confidence. So make yourself a coffee and enjoy the read!
Where should you start when looking for a Data Science & Machine Learning solution? What information is important? And especially which sources are the most reliable? Our expert goes over these questions in just half an hour. Sign up now!
Forecasting with Data Science can help your organization take the next step in data maturity. In this webinar, we’ll show you how to get even more out of your forecasts with AI.
Do you want to know how predictive models can help your company make better decisions? Then watch our webinar “Predict churn with artificial intelligence”. Lots of companies are focusing on getting new customers instead of keeping their existing customer base. Due to these strategies, a lot of customers are hopping from company to company.
There is one thing that’s just as important as finding new clients: keeping your current clients. This one-pager will give you some practical tips to increase your loyal customer group by making use of the science of data!
Data Science, Artificial Intelligence, and Machine Learning are becoming buzzwords more and more. The terms appear to be valuable according to the trendwatchers, but most often people do not even know what the terms mean, let alone that they use them.
We at Rockfeather help people and organizations to understand and retrieve value from the buzzwords.
In collaboration with Finext and The Hague University of Applied Sciences, Rockfeather conducted a survey among our customers and other data enthusiast. The survey consists of twenty questions that investigate the data maturity, the presence of a data-mindset and how they reach and plan to reach their data ambitions and goals.
What is the optimal set of processes to allocate our resources? What process and steps do we have? These questions seem like questions that every company should answer easily. Unfortunately, a lot of companies are not able to do so. Their processes are just too complex because of changing price constraints, or other variables. There is a solution: Process optimization.
This year’s Magic Quadrant for Data Science and Machine Learning Platforms scored six Data Science and Machine Learning (DSML) Platforms as leaders. The amount of leaders has remained the same, compared to last year’s number of leaders. But has the composition of the leading area changed?