Posted in Data Leadership, Inclusion, Society

Why is it important to talk about toxic leadership?

Kasia Musur is a Berlin-based founder of an early-stage startup dealing with toxic leadership through preventive, protective and accountability measures.

„A job is a job. A boss is a boss “. For centuries people complain about their work-lives and yet the world goes round. Why is it suddenly such a big deal how we feel about our jobs, colleagues, and bosses?

Posted in Analytics, CRM & Omnichannel, Data education, Data-driven marketing

Nurturing the customer relationship with data – an interview with Sarah Carr

Contextualizing our world with data, part 2: Customer Relationship Management (CRM). Sarah Carr is a recovering Marketer who has gone on to become a CRM systems nerd. Aside from core CRM, Sarah also works on data governance, data quality, and privacy. Looking back at her journey, Sarah talks about how CRM went from email marketing to automated omni-channel orchestration of the customer experience. Sarah also gives her insight on how data teams and stakeholders can utilize self-service and data education to drive business forward together. Moreover, she discusses how Arts degrees can be good breeding grounds for analytical minds.

Posted in AI use case, Data careers, Data science, NLP

Why creatives in advertising should embrace data science and data mining – an interview with Les Guessing

Contextualizing our world with data, part 1: Advertising. Les Guessing has a high school degree (barely) but has managed to find great success as an Emmy Winning Copywriter / Creative Director in Los Angeles (and beyond) in advertising – the marketing arm of Capitalism. Over the last 10 years, he has become hellbent on using data/Data Science/Machine Learning and aspects of Artificial Intelligence (especially NLP, Natural Language Processing) to make advertising creative more insightful, more efficient, more impactful, and funnier. He explains why creatives should work with data because. Among other reasons, the creative mindset enables them to look at data and see something from an entirely different perspective than data people.

Posted in AI use case, Analytics, Current events, Data careers, Data Leadership, Data strategy, Data-driven marketing

Contextualizing our world with data, a D3M Labs Series

Contextualizing our world with data. A four part D3M Labs series about how communications professionals use data. Writing and other forms of communications might be art, however, technology is the means by which thoughts, news, images, etc. are conveyed, stored, measured and iterated. The impact can range from branding and connecting with customers and prospects, to reporting about world events.

Posted in AI use case, Current events, Data pipelines

Launching a human understandable data pipeline in times of war – and interview with Dmytro Zhuk

Automating the ETL process using deep learning and semantic data type detection is never easy, especially in the midst of war. One year after Russia’s invasion of Ukraine, Dmytro Zhuk, founder and CTO of talked to Elizabeth Press from D3M Labs about his experiences as a family man and an entrepreneur in Kharkiv. He also shares his vision and hopes for the future.

Posted in AI Strategy, Change management, Data as an asset, Data Leadership, Data product management, Data Products, Data science, Deutsch

Das Produktlebenszyklusmanagement im Zeitalter der intelligenten Geräte – ein Interview mit Eric JoAchim Liese

Wie managt man ein Data Science Produkt, Teil 2: Da die Geräte immer intelligenter werden, muss sich das Produktlebenszyklusmanagement weiterentwickeln, um die Daten als langfristigen Wert und Teil der Kundenbeziehung zu betrachten. Eric Joachim Liese spricht über Edge Computing und Geräteautonomie als Voraussetzung für ein gutes Kundenerlebnis. Er erklärt auch, wie traditionelle Hardware-Hersteller ihre Betriebsabläufe weiterentwickeln und Fachkräfte einstellen können, um diesen Weg erfolgreich zu beschreiten

Posted in AI Strategy, Change management, Data as an asset, Data Leadership, Data product management, Data Products, Data science

Product lifecycle management in the era of smart devices – an Interview with Eric JoAchim Liese

How to Manage the Data Science Product, Part 2: As devices get smart, product lifecycle management for hardware needs to evolve to encompass the view of data as a long-term asset and as an active, even pro-active part of the customer relationship. Eric JoAchim Liese talks about edge computing and device autonomy as being requisite to providing a good customer experience. He also explains how traditional hardware manufacturers can evolve their operations and hire in expertise to successfully navigate the journey.

Posted in Data governance, Data Leadership, Data Products, DataOps, Deutsch, Technical debt

Data Mesh – Wie man verhindert, dass es sich in ein geldverschlingendes Chaos verwandelt – ein Podcast

Data Mesh ist eine analytische Datenarchitektur und ein Betriebsmodell, bei dem Daten wie ein Produkt behandelt werden und den Teams gehören, die sie produzieren, d. h. den Geschäftsbereichen. Wie können sich Unternehmen auf den Weg zu Data Mesh machen, ohne ihre Budgets zu sprengen und letztlich einen großen, unübersichtlichen und teuren Datensumpf zu schaffen? Höre dir den Podcast an. Lese den Blog.

Posted in Data governance, Data Leadership, Data Products, DataOps, Technical debt

Data Mesh – How to prevent it from turning into a money draining mess – A podcast

Data Mesh is an analytical data architecture and operating model where data is treated like a product and owned by teams who produce it, i.e the busness domains. How can organizations embark on their data mesh journeys without exploding their budgets and ultimately creating a big, mess, expensive data swamp? Listen to the podcast. Read the blog.

Posted in AI Strategy, Data as an asset, Data Leadership, Data Products, Data science, Deutsch

Das Management des Data Science Produktes – ein Interview mit Anna Hannemann, PhD

Wie managt man ein Data Science Produkt, Teil 1: Algorithmen sind Produkte, die gemanagt werden müssen, um geschäftliche Ergebnisse zu erzielen. Anna Hannemann, PhD von erzählt, was sie als Pionierin im Produktmanagement für Datenwissenschaft gelernt hat. Sie spricht auch über den organisatorischen Aufbau, die Kompetenzen, die vorhanden sein müssen, und darüber, wie neue Tools das Management von Data-Science-Produkten beeinflussen.