SAG_Twitter_MEME_The_Philosophy_AI_May17.jpgArtificial intelligence (AI) and the Internet of Things (IoT) go together like philosophy goes with Greece.

It was AI, the IoT and a shared philosophy of openness that brought Software AG and Zementis together; first into a productive partnership and then into a merger of the two companies.

Michael Zeller, co-founder of Zementis and now senior vice president of AI Strategy & Innovation at Software AG, said: “What we had in common was an open platform philosophy; that openness led us both to customer success during our partnership.”

The synergies were obvious, said Zeller. “As part of Software AG, we could do so much more; with Apama for streaming analytics and us for ML we can go way beyond what each could do separately.”

AI, along with machine learning (ML), offers organizations the ability to turn data into business value. And no one can fail to have missed how important the IoT is becoming. Combined, AI, ML and the IoT are the most important innovation trend of the future.

Bart Schouw said in a blog last year: “The next generation IoT platforms will probably be called AIOT, Artificial Intelligence of Things; it’s the IoT – but enhanced by AI.”

Making sense of fast-moving IoT data and using it to make intelligent decisions, generate new business and revenues involves using many different technologies. The ability to be agile and slot in with the systems and software that customers already use is essential.

Zementis big data predictive analytics solutions are based on open standards, drawing on the technical expertise and innovation capabilities of a vast community of data scientists, statisticians, IT professionals and others. Open standards also facilitate interoperability of predictive models across different applications and vendors, helping organizations expand both their data mining capacity and predictive modeling capabilities.

And Software AG’s Digital Business Platform (DBP) is the embodiment of openness. We believe that the solution for organizations wanting to digitalize and take advantage of technologies like IoT and AI is a set of best-in-class, well-integrated IoT capabilities around analytics, integration, process modeling and portfolio management. These platform services enable organizations to build their own innovative solutions by leveraging many technologies to speed up the process. That is the DBD.

Predictive analytics is a key part of our IoT and streaming analytics propositions, enabling the DBP to execute predictive models. Zementis delivers the ability for organizations to take predictive models and put them into production without having to write code. These predictive models look for patterns of behavior in past data such as when a component is likely to fail in order to predict that event happening again in the future.

These models are built using commercial data mining tools from organizations such as SAS, Knime, IBM SPSS or open source tools like R and Python - in fact anything that can export PMML. Typically, companies have had to spend months re-coding the models in something like C++. Zementis completely eliminates this hurdle, enabling error-free predictive models to be put into production in minutes rather than months.

PMML (Predictive Model Markup Language) is the de facto standard used to represent and share predictive analytic solutions between applications. It enables data mining scientists and users alike to easily build, visualize, and deploy their solutions using different platforms and systems.

Zeller said: “Customers want more automated intelligent decision-making, not more reports and more dashboards. We offer them the tools of the trade that they can deploy their models in, high-end streaming services such as Apama, mainframes or a massively parallel Hadoop system.”

Zementis will be embed AI capabilities into every part of the DBP technology “stack,” making AI and ML for the IoT reliable, scalable and available 24/7 – as well as completely open. Now that is a philosophy to go to market with.

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Digital Transformation, IoT


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