Are data science platforms a good idea?

According to Karl Valentin: Platforms are beautiful and take a lot of work off your neck. The idea of platforms for automatic data analysis comes at just the right time. In line with this, Gartner has now published a “Magic Quadrant for Data Science and Machine Learning Platforms“. The document itself can only be viewed behind a paywall, but on the net some of the companies mentioned in the report offer access to the document by entering the address.

Gartner particularly emphasizes that such a platform should provide everything you need from a single source, unlike various individual components that are not directly coordinated with each other.

Sounds good to me! However, data science is not an area where you can magically get ahead with a tool or even a platform. The development of solutions – for example, for predictive maintenance of the machines offered by a company – goes through various phases, with cleaning/wrangling and preprocessing accounting for most of the work. In this area, ETL (Extract, Transform, Load) and visualization tools such as Tableau can be ranked. And beyond the imaginary comfort zone of platforms that managers imagine, database queries and scripts for transformation and aggregation in Python or R are simply the means of choice. A look at data science online tutorials from top providers like Coursera underlines the importance of these – well – down-to-earth tools. “Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, machine learning with stats models and scikit-learn, deep learning with TensorFlow” is one of Udemy’s course programs.

In addition, the projects often get stuck in this preliminary stage or are cancelled. There are many reasons for this:

  • no analytical/statistical approach can be found
  • the original idea proves to be unfeasible
  • the data is not available in the quantity or quality you need
  • simple analyses and visualizations are enough and everything else would be “oversized”.

This is no big deal, as it only means that the automated use of Machine Learning and AI does not make a data treasure out of every data set. If, however, the productive benefit becomes apparent, it is necessary to prepare for the production pipeline and time or resource constraints. Usually you start from scratch and reproduce everything again, e.g. in Tensorflow for neural networks or in custom libraries.

The misunderstanding is that a) Data Science can be driven up to productive use without a trace and b) a one-stop-shop for Data Science (here “platform”) is needed that does everything in one go. That will never happen.

This is really good news, because it means that organizations can achieve their first goals without having to resort to large platforms. The reasonably careful selection of suitable tools (many of them open source) helps to achieve this.

Also interesting:
In my video “AI Needs Strategy” I explain which steps companies can take to to use AI technology in a successful way.

Die digitale Transformation transformiert auch PLM

Die Unternehmen der Automobilindustrie beschäftigen sich intensiv mit der digitalen Transformation, und dabei geht es nicht mehr nur um Industrie 4.0 und die intelligente Vernetzung der Fertigung, sondern um die Umgestaltung der Unternehmensprozesse und Geschäftsmodelle. Wie intensiv, das wurde auf dem diesjährigen ProSTEP iViP-Symposium in Stuttgart deutlich, an den über 660 Besucher aus 19 Ländern teilnahmen. Ein neuer Rekord, und sicher nicht der letzte. Viele Vorträge spiegelten die Sorge wider, dass disruptive Technologien wie das Internet of Things (IoT) zur Verdrängung der alten Platzhirsche durch neue Herausforderer führen könnten. Der Erfolg von Tesla hat die Branche aufgeschreckt und der Name Nokia steht wie ein Menetekel an der Wand. Continue reading “Die digitale Transformation transformiert auch PLM”

Vernetzung bedeutet Autonomieverlust

Digital Twin oder digitaler Zwilling ist das jüngste Ferkelchen, das die PLM-Hersteller durchs Dorf treiben. Wer den Begriff erfunden hat, weiß ich nicht, aber sicher nicht Bundespräsident Joachim Gauck, der ihn im Jahr 2013 in seiner vielgeachteten Rede zum Tag der Deutschen Einheit erwähnte. Er spielte damit auf die Abbildung unseres realen Ichs in den sozialen Netzwerken an und warnte vor den Gefahren, die daraus für unser Privatsphäre erwachsen können. Continue reading “Vernetzung bedeutet Autonomieverlust”