Current Challenges in Data Science
One of the big challenges that faces companies getting on board with the data revolution is the sheer cost. We are talking about the cost of much more than just the infrastructure and software. In fact from a pure infra and application point of view, the costs remain much the same. The costs of course start to mount up when you have to actually set a strategy for how you leverage the massive amounts of data you have in your hands. The very first thing you will need to think about is the skills gap that you are highly likely to have in your organisation. There is one school of thought (and applications) that will require you to build out a team of Data modellers, data analysts, coders, business analysts. None of this is likely to come cheap, especially given the shortage of data science skills in the market currently. So in short even if you have the budget you are going to face difficulties in filling the position or have to pay a considerable premium.
Vendor Lock In
Once you start to train people, if you are going with the current market leaders, you will be essentially tying yourself into proprietary and old technology. Most of the established players have not built their application suites from the ground up (like PyCell have). You will also have to train your staff in proprietary technology and code – if you go with SAS, one of the market leaders, it will mean that you are tying yourself into using Base SAS for any coding that you want to do in that application suite. This poses some difficulties, namely:
- You are now tied into a software stack with a big cost of migration in the future if you wanted to do so – vendor lock-in!
- Being a proprietary language, the skills will be even scarcer to find and more expensive.
- Your staff are not learning transferrable skills which they would feel enthused to put all their efforts into
The PyCell Answer
In short we believe these are significant problems which are not easy for an organisation to navigate. This is why we at PyCell have put a lot of the thought into how to cut the costs – both from the price point for the software to the internal changes you have to make to start using your data better to grow your business ad serve your customers. We can help you all the way from setting a data science strategy to getting better insights. We are also able to help you get the best out of your data no matter what industry or sector you are in. The reason we are able to do this comes down to the following capabilities that are built into our application from the ground up :
- Cloud Native – we are a pure cloud application so we can serve your needs in the most cost effective and speed efficient way
- PyCell Marketplace – this is the key to our offering. We give you the ability to write or buy addins from our marketplace written in Python and run it with your application. You can also develop your own addins and use them and even sell them in the marketplace if you have developed something really cool!
- PyCell AI / ML – We have a host of AI / ML capabilities builtin to our application. This means you can get a whole range of insights and help to leverage your data better straight out of the box! An example is our SmartColumns technology which allows you to add new columns to your data by using algorithmic pattern matching. This should allow you to get a better insight from the dataset. Of course no one knows your data better than you, so we will let you edit the SmartPattern suggestions so you can tailor it to exactly what you need.
- PyBot – This is where you get to the really good stuff. We let you automate repetitive boring tasks so you can focus on the more interesting stuff that can take your business to the next level. Essentially we are using Robotic Process Automation or RPA to start automating wherever possible.
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