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Our most popular posts - looking back

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Virginie Marelli

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2022-09-05

Our most popular posts - looking back

Last September, we started to regularly write blog posts. Everyone within Dataroots has been participating to this endeavour. People write about their job, about a conference, a new technology or simply they write about their passion. Thus since September, we have been publishing one post a week, without failing, sometimes even 2 posts per week!

Creating the content is probably the hardest, you need to find an interesting topic, have a creative angle, a pinch of opinion and mash this into a story that appeals to the reader. This definitely requires a lot of time, skills and effort.

Still nowadays, content is created at the minute and people are overflown by information. It is becoming harder and harder to differentiate True news from Fake news (AI certainly plays a role in that matter). There are so many medias and so much information that you have to carefully choose which content you will read and where you will dedicate your time.

Thus, not only do you need to create the content, you also need to attract and keep the reader. On our own blog adventure, we've been thinking how we could improve our content display and links.

We've started tagging the posts with respect to their topic, so later on we can display them under different categories.  AI, Data & Cloud, Data strategy are the main high level topics, obvious since they represent the 3 core businesses of dataroots. If you drill down, comes, XAI, architecture, deep learning, NLP, data quality and many others. We have indeed already gathered a lot of content and it would be nice to have an overview of the main topics we write about. This would allow interested readers to read more about one topic and related articles. Why not one day implement our own recommender system to help the readers with a next blogpost?

In the meantime, we want to give a big shoutout to the 10 most read articles until now:

You'll notice a slight time bias, as most of the posts are from earlier this year, meaning that the ones from later on have definitely a chance to catch up and are really worth reading too so get these statistics up and go read a couple of our other posts!

Got an idea?

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