Digital Transformation

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.

The 14 Top Success Patterns of Digital Business Models

Let’s get digital – The Internet of Things (IoT) has an outstanding influence on the relationship between companies and their customers. Companies now face the challenge of placing attractive digital offerings so as not to fall behind. The white paper identifies the central mechanisms of digital offerings and identifies the 14 most important patterns and blueprints for IoT-driven business models.

Market pressure and a new terrain. The markets are becoming digital and smart. Hardly any industry or offer that is not networked and/or in the cloud – at least that’s how it seems. This is undoubtedly a trend that is massively promoted by market-determining players, especially from Silicon Valley. Today, we are all influenced by the use of smartphones and home automation solutions, and we transfer corresponding expectations to other areas as well. The question of “whether” no longer arises, but rather of “how”. According to McKinsey the sales potential for digitized products in the B2B environment is even twice as high as in the B2C sector! Certainly, some phenomena on the market can be accepted as hypes. However, it is also certain that concrete developments and sometimes existential challenges also arise in supposedly firmly established markets:

Innovative and established competitors place an offer as “first mover”, attracting attention to themselves from customers for whom digitisation is not yet an issue.
New players are breaking into existing markets and placing previously unknown offers on the basis of digitized services.
Previously specialized providers (non-providers or providers of secondary services) are expanding their offerings digitally and thus attacking providers in the core market.

The Internet of Things (“IoT”) as a vehicle for digitized product offerings is virtually universal and knows no industry or process boundaries. According to Gartner, this is reflected in “ambitious IoT plans” in a wide variety of industries. Many companies are therefore being forced to confront the potential erosion of their markets by new suppliers.

The challenge lies not only in the high market dynamics, but also in the technical and sales challenges in a partly unknown territory. Many, especially medium-sized companies, lack software know-how, especially if it goes beyond the embedded area. In particular, this includes networked and distributed product architectures or analytics.

Another complicating factor is the fact that suitable personnel is not actually available on the market today. In addition, it is not only about recruiting new employees, but also about building up new business areas. In order to be able to act, companies must invest in completely new alliances and partner models.

The following white paper focuses on the second area of customer service improvement and uses the term “IoT”. The analysis of IoT projects shows that the majority of projects are based on the expansion of a market position in existing markets, i. e. the expansion of the existing product range. Only a few companies approach new markets. In other words, companies generally take a very cautious approach to new business options and try to avoid risks.

Continue reading “The 14 Top Success Patterns of Digital Business Models”

Hat PLM eine Zukunft und wie sieht die aus?

In meinem letzten Blog-Beitrag habe ich darzulegen versucht, warum es aus Anwendersicht so wichtig ist, PLM aus seinem Engineering-Nischendasein zu befreien. Ebenso wichtig ist es aber auch aus Anbietersicht, wenn die PLM-Hersteller sich auf Dauer am Markt behaupten wollen. Darauf hat vor ein paar Monaten Oleg Shilovitsky in einem Gastblog hingewiesen, den ich jetzt erst entdeckt habe. Continue reading “Hat PLM eine Zukunft und wie sieht die aus?”