I have to admit – I am having a really hard time with AI services and sales pitches from vendors about AI. Currently, the term AI is a hype without limits – I hear people talking about AI without a clue what it actually is and how it works. I mean I don’t want to be mean, but sales people are currently calling things “AI” that is nothing more than a rules engine. As already stated in my post for Advanced Analytics predictions, I tend to call this “rules based AI”. A really smart one ;). So, is AI dangerous at all?

AI isn’t as smart as you might think

Now, but why is AI creating so much trouble for all of us? It is mainly the Sales people that promise us now the magic AI thing. I recently heard a sales pitch where the seller told me: “you know, AI is this thing where our magicians make impressive stuff with”. I was really overpowered and didn’t know how to react. The only thing that came into my mind was asking him if their AI is already “rule based”. He was really enlighten, looked at with a winning grin and told me: “Yes, we are having a world-class rules based AI”. I didn’t ask any further, since it would eventually lead nowhere. However, I was really honoured to be a magician now.

I basically don’t fall for such sales pitches since I can easily uncover real AI. There are only few that get it done. Most others renamed their rules-engine to an AI. But imagine what happens when you are frequently dealing with business units? They are not so deep into technology and sales people now promise them the swiss army knife. I constantly get confronted with questions and have to explain the mess that has been created there. This is creating a lot of work and overload to an analytics departments that should deliver business results.

One demand from my side: could we please end this bullshit bingo about “AI”?

As always, I am looking forward to your feedback and thoughts about this topic 🙂

This post is part of the “Big Data for Business” tutorial. In this tutorial, I explain various aspects of handling data right within a company. If you should remain sceptical, I wouldn’t recommend Terminator. There, the question remains: is AI dangerous at all?

This is the last post of my series about the topics I care most about. This time, I will focus on Analytics and AI. Especially the last topic (AI) has been a major buzz-word this year, so it is interesting to see what might happen in 2019. Therefore, my predictions for 2019 are:

1. Governance will be seen as major enabler – or blocker – for self-service analytics. Self-service Analytics will become a key goal for most companies

Let’s stay on the ground: a “deal-breaker” for Advanced Analytics and Data Science is often the inability to access data (fast) or bad data quality. Both topics can be handled well if data governance is treated with major investments within enterprises. I often see data scientists waiting for days or weeks to access data. Once they have access to data, they only figure out that the quality is very bad. Let’s face it: data governance wasn’t important in enterprises nor attractive. Nobody I know was stating that he applied a great data governance strategy. Other topics are more interesting to talk about. Nevertheless, if an enterprise continues to treat data governance as done till now, it will block data science from being successful. A lot of consulting companies currently market the term “self-service analytics” – but this is simply not achievable without data governance in place. Next year, more and more companies will figure this out and either apply a data governance strategy or risk to fail with their data driven efforts.

2. AI will continue to be a buzz-word, creating even more confusion in 2019 than it did before

I don’t know how you felt about AI the past year, but I had some really great “aha” moments. A lot of vendors approached me and wanted to talk about their great AI solutions. When I started to ask questions, the answer from (sales staff) was – “don’t worry, our AI takes care of it”. When looking under the hood of the technologies, it was often just a simple rules engine – no smart AI! I started to call this “rules based AI”, as there was no magic involved. When asking some vendors how they would explain AI, they simply said: “don’t worry, only the smartest people understand it”. I found this to be sort of offensive as they considered themselves as not smart enough – and even me :). I even asked if their AI is already rules based, and they said yes. So, one thing is very clear: AI is a buzz-word. Everyone is talking about it, but hardly anyone understands it. Same story as with the cloud, just some years ago. This trend will continue and finding real AI solutions (no, I won’t mention which I would consider as real – no ad placement in here) will be tricky. Many companies will buy “AI” solutions as it is trendy and they want to be part of it or simply don’t want to loose in this growing market. However, many of them will figure out that their AI isn’t as smart as they would have hoped for.

3. Google will use it’s advantage in AI to catch up in the Cloud

This basically reflects what I already wrote in the post some days ago in the Cloud. When it comes to the cloud, the #3 in the market is definitely Google. They entered the market somewhat later than AWS or Microsoft did. However, they offer a very interesting portfolio and competitive pricing. A key strength Google has is their AI and Analytics services, as the company itself is very data driven. Google really knows how to handle and analyse data much more than the two others do, so it is very likely that Google will use this advantage to gain shares from their competitors. I am exited about next Google I/O and what will be shown there in terms of Analytics and AI. 

4. Voice is the new Bacon

One of the many things AI should solve is voice recognition. It is one of the strength of mankind and one key development factor for us becoming what we are. With AI, we already see significant advances in intend recognition for written text (e.g. Chat, E-Mail, …). We carried out a project recently and could classify intent in e-mails with very little effort. However, voice is still an issue – especially if you are operating in a market with 4-6 million native speakers only. In order to go for significant automation of customer care, it is inevitable to go for voice recognition. But will it work? Ask yourself. Do you have Alexa or Google Home at your flat? Yes? I think we can answer this immediately. It only works poorly and is somewhat of an issue. However, next year, we will see significant improvements in this space, mainly driven by business demand. When we look at what Google presented during Google IO, this is the way to go. I believe that this year we will see much more of these services. Expect a lot to come in 2019 around Voice.

5. Rise of the python

Python is already the most popular language when it comes to data science. However, other languages like R are still in this space. Now, since many new data scientists come fresh from the universities with an IT background / major, python will continue to grow. This will also be reflected in new packages and add-ons for Python. Other languages won’t see so much effort and new and exciting tools will be available for Python only. Python still lacks capabilities for data visualisation when compared to R, but also this will change during 2019 and Python will continue to grow for this as well.

So, this are my 5 predictions for Data Science and AI. What do you think? Where do you agree or disagree? I am looking forward to our discussion!