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5 Problems Every Data Scientist Encounters

Have you found these problems?

24Aug

Data Scientists across the world are all different. They work with individual databases, use unique combinations of software and try to find different trends.

However, there are some problems that every Data Scientist will find, how many of these have you found?

Explaining what you do

‘So you try to predict the future?’ not exactly.

The data scientist, despite being a vital cog in many of the world’s biggest organizations, is still an unknown to many. Your parents will still not quite know what it is you do and explaining it to relatives or friends becomes increasingly difficult.

You do a complex job that is often challenging, but one of the biggest challenges is explaining to people exactly what you do. ‘I take a load of data, then I find trends in it’ is a bit too basic, but trying to go into more detail can often be difficult, especially for those who don’t necessarily understand IT, let alone the complexities of data mining.

The Speed Of Change

One of the best things about data science is that it is constantly evolving. One of the worst things about it is that it is always evolving.

Constant updates to systems can be great when they are needed, but before you know it, the system you implemented for hundreds of thousands of dollars 6 months ago looks slow and un-intuitive compared the latest offerings. Try telling your finance department that you need another chunk of money to upgrade and you may get chased out of the room.

The Nerd Stereotype

Although many will have no idea what a data scientist is, those who day instantly presume that you are a nerd.

Sometimes this is the case, but for many the label doesn’t stick. No, you don’t wear glasses held together with tape. Yes, you do have a girlfriend, no you don’t spend all of your free time playing World of Warcraft.

Bosses Expect More

One of the most exciting things that bosses can do is to start something totally new having heard that it’s great from several Forbes articles. When they then come in and do their job, but don’t cause the company to instantly grow by 50%, they get disappointed.

This may not be the case in your current role, especially if it is in an established data team, but for everybody who has started a data science department themselves, they will be all too familiar with this.

‘XYZ Is Just What We Did in The 80s’

Whatever amazing piece of technology you have found or algorithm you have written or huge database created, somebody will always claim that it’s just a rehashing of something from the 80’s.

We have always had Big Data apparently, because dealing with the amount of data created by Facebook users is exactly the same as a database of 5,000 customers who hardly used the internet at all. Text analytics is essentially just reading a book and predictive analytics is basically just ‘red sky at night, shepherds delight’, so it has been happening for hundreds of years.

It is not the same, it has similarities in the same way that walking 100m is the same as running it. We all know that one foot goes in front of the other, but that’s where the similarity ends.  

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