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Get in touch

Contact us for all inquiries regarding services and general information






Get in touch

Contact us for all inquiries regarding services and general information







Get in touch

Contact us for all inquiries regarding services and general information






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Data scientists - Are they real or just a myth?

What comes to your mind when someone says that they are a data scientist?

Do you think that that person is some kind of data wizard? Do you think that they can single-handedly fix all of your data problems, that they can make them all go away?

To me, ‘data science’ and ‘data scientist’ are very limiting terms, terms that do not incorporate everything that goes into what people actually do.

When you look at the terms online, they do explain a lot and they do show that being a data scientist is not just one thing, it’s a lot of little pieces put together to form something new.

But when you talk to people, especially people who don’t work in the same or similar area, the term sounds as if one person can create a beautiful data lake with a single press of a button. No coding, no modelling, no development (sounds great, doesn’t work).

Data science is an extension of data warehouse and business intelligence that have been around for about 50 years (in one form or another starting with decision support systems). It is not something that has replaced DWH and BI concepts and technologies, but simply improved on them, gave them an opportunity to be faster, more precise and give better insights to make better decisions.

Make your favorite sports team even better

People tend to think that you don’t need to analyse your data, that you don’t need to clean it up or have a strong data model underneath ‘to do’ data science. That’s where they are wrong. Without data analysis, data cleansing and data modelling there is no foundation for data science to thrive, to become something that a lot of people want it to become. And that’s that magical approach that gives you what you want. By utilizing the whole lifecycle from data analysis to the creation of data warehouse and data marts (implemented on top of relational or distributed frameworks) to using machine learning algorithms and real time streaming to get almost instant warnings, alerts, predictions needed to make better overall business decisions and short and long term plans. Big data and data science can help you make your favorite sports team even better, it can help with climate change, it can help governments see cost saving trends or it could help predict the next financial or ecological disaster. Overall it can help us, but we need to know how to use it.

If you are not sure where to start, if you need help with your data or if you need someone to give you more insight into your data, why not call our Data Engineering department?

We are all interested to show what we know, from DWH/BI to data science, and help you with your conundrums concerning your data. We don’t discriminate, we like all kinds of data.

 

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