How the production analytic platform will evolve driven by ai, iot and analytics by ron powell – beyenetwork data recovery bad hard drive

Barry devlin: wisdom – I don’t know about that, ron. It’s a tough one. But to be honest, I’d say from my point of view as an old architect, it is really the architect’s job to try to predict the future because if you do a good job of predicting the future, then you actually get to develop an architecture that has legs and is going to last for some time. I suppose I can’t resist at this moment just mentioning that this year – 2018 – is the thirtieth anniversary of the first publication of the data warehouse architecture by me and paul murphy back in 1988. We’re 30 years doing that architecture so I must have done a reasonably good job of predicting the future back then.Production analytic platform so let’s see how I do now in my advanced years!

Barry devlin: I think that artificial intelligence is really evolving so fast in the world at the moment that it’s going to impact everything.

The whole area of autonomous vehicles is an example of what’s going on in artificial intelligence. If you think about the impact that autonomous vehicles are going to have on the economy, on the social structures, and on the way we design and build our cities, it is going to be huge. And underneath the covers is artificial intelligence – image recognition, natural language processing and event detection and so on. All of those things are observing the world and deciding what an event means, such as is a child on the curb going to run out in front of the car or not.Artificial intelligence all of those issues are about making decisions and acting upon those decisions. If we can do it in real time – which we have to do with autonomous vehicles – then it’s surely going to also become extremely important in enterprise computing.

So we’re now looking at a production analytic platform that is based on data coming from the internet of things and also from other sources and talking about analytics in the current environment. Analytics is where we started with the production analytic platform, and I see a very thin line between analytics and AI going forward. Analytics is also about finding patterns, the meanings of patterns, and what decisions should come out of those patterns.Artificial intelligence and, again, if you push it to the extreme, making decisions and taking action – as we used to do in operational BI and now we’re doing with operational analytics.

But the AI folks have now started to move away from this idea of being dependent on huge amounts of data because it’s a very expensive and very slow process that they go through in training. So there is a lot of work going on in a field called reinforcement learning, which is essentially getting AI systems to learn from the results they get from experiments without having large training data sets. They’re given a goal, they keep playing at that goal, and they see how they move toward it.Production analytic platform it’s how children learn. If you really want to explore that, read some of the material on the internet over the last couple of months about alphago zero, which is a game playing machine. In its previous inception, the system learned from literally thousands of pre-loaded games from experts in go. This current incarnation of alphago takes no data from experts. It just goes and plays against itself and learns. It handily beat the original alphago without any expert input.

But the other area that’s really important is that we see more and more of this artificial intelligence being pushed out to the edge of the network. Put it on your smart phone so it can answer your questions without going off to the internet to figure out your natural language processing.Production analytic platform put it in the car so we don’t have to spend so much time downloading and uploading data. All of those things say to me that we’re looking at a much more distributed and decentralized approach in the longer term.

And I see that coming from teradata as well. If you look at their teradata everywhere approach, what you see is that we still need a large piece of iron with a lot of processing power and a lot of data, but we also need to be able to move our processing out into different devices – smaller machines, out into open source systems and so on. That idea that we’re really pushing everything we can into a much more distributed and decentralized environment is essentially where we’re going to go in the future.Analytic platform

Barry devlin: I can try. This is a very big topic. It reminds me of data warehousing in the early days because there are so many different things going on. But I think like the data warehouse, we have to think what are the key technologies and environments that we need to support. Call me old-fashioned, if you like, but I still see that the heart of the production analytic platform is going to be a relational database technology. Reliability, availability, scalability, maintainability and performance levels that span from operational to analytic use – all those things require a relational database at the core, but with the built-in non-relational support for all sorts of formats and processing approaches from analytics through AI, linking it in via SQL as well as native languages.Artificial intelligence and then we have data in all sorts of places, so data virtualization – what some people call logical data warehousing – provides access to and use of the data stored remotely. And distributed and decentralized operation increasingly, as I’ve just said, is another aspect. And integration in terms of making all these technologies work together is going to be key. That’s something that teradata does very well. And, also integration in terms of providing an experience for the users so that we move away from this old idea of saying there’s an operational world, an informational world, an analytic world and they don’t really talk to one another but saying instead that they all need to meet together in one place so that we can make it work together.Analytic platform those are the things that are going to be really important as we roll out this production analytic platform concept.

Barry devlin: yes, ron, that’s a good point and thank you for bringing it up. I think it is absolutely essential to keep that in mind. Over my many years of working in this industry, one of the things that I’ve often seen that drives many failures is the attempt to rip and replace because the new technology looks better or, as is often the case, is cheaper. I think that’s a dangerous strategy, and I think you were right in saying that building on what you have is generally a safer place to start. For sure, the relational database with 40 years of history behind it has a lot of things going for it.Production analytic I think we would be putting ourselves in danger by moving too far away from that as a core technology.

Ron is an independent analyst, consultant and editorial expert with extensive knowledge and experience in business intelligence, big data, analytics and data warehousing. Currently president of powell interactive media, which specializes in consulting and podcast services, he is also executive producer of the world transformed fast forward show. In 2004, ron founded the beyenetwork, which was acquired by tech target in 2010. Prior to the founding of the beyenetwork, ron was cofounder, publisher and editorial director of DM review (now information management).Production analytic he maintains an expert channel and blog on the beyenetwork and may be contacted by email at rpowell@powellinteractivemedia.Com .