Kategorie: Data Leadership

After more than a decade of leading data teams at various organizations, including startups and enterprises, I’ve seen centralized functional organizations, centers of excellence, start-up one woman shows and agile, zombie agile, pods, squads, the federated, the „data as a product“ model. But after all the pivots, restructures, and retrospectives, I’ve landed on a conclusion that might raise eyebrows: most data teams should function as service teams—with the critical caveat that they adopt ITIL principles.

Image was generated by ChatGPT.

The artisanal approach to SQL and the masterpiece culture in querying are not-so silent killer of many data teams. The self-view of many analysts and even data leaders is that writing long queries is an intellectual and technical pure form of analysis. However, if crafting a 200-something line SQL query takes hours or even days (a scenario all too familiar), it’s a sign that your data team is operating like artistic masters. Let’s face it, data teams don’t have royal sponsors to create masterpieces. Thinking Bauhaus is more fitting for cash-burn budgets that most data teams run on.

Analytics Fear of Missing Out (AFOMO) might be the reason your data strategy resembles a chaotic spaghetti-filled pasta bar, with many options and free-form combinations. This pervasive apprehension compels individuals and businesses alike to scramble in fear of being left behind, driving them to relentlessly try new KPIs and keep options open with any data set they could recall being in their organization. This blog is inspired by my interview „Data should be like a plate of hummus,“ a conversation with Lior Barak and Elizabeth Press (myself) from D3M Labs.

Dieser Blog enthält einige meiner wichtigsten Eindrücke von der kürzlich stattgefundenen niederländisch-bayerischen Konferenz ‚Zusammenarbeit in der Cybersicherheit: Die wichtigsten Aufgaben für die Unternehmensführung‘ in München, organisiert von InnovationQuarter. Ich hatte das Privileg, von der Recruiting-Firma GCS zu der Veranstaltung eingeladen zu werden. Die Welt wird immer digitaler und gefährlicher, was den Gedankenaustausch über Cybersicherheit zwischen Freunden und Geschäftspartnern unerlässlich macht. Ich verbrachte einen Tag damit, brillanten Köpfen aus den Niederlanden, Bayern und darüber hinaus zuzuhören, die darüber sprachen, wie man Cybersicherheit zu einem C-Suite-Thema machen kann.

This blog entails a few of my high-level takeaways from the recent Dutch-Bavarian „Collaboration in Cybersecurity: The most important tasks for business leaders“ conference in Munich, Organized by InnovationQuarter. I was privileged to get invited by the recruiting firm GCS to the event. The world gets increasingly digital and dangerous, making the exchange of ideas about cybersecurity between friends and trading partners essential. I spent a day listening to brilliant minds from the Netherlands, Bavaria and beyond talk about what how to make Cybersecurity a C-Suite issue.

From higher ROI to easier use, operational analytics answers many contemporary challenges facing data teams. I (Elizabeth Press), spoke with Dani Solà  Senior Vice President of Data and Analytics at Clark about Operational Analytics. Success, however, necessitates a well-governed data platform and solid security concepts. When your stakeholders come back with questions, it’s a sign of engagement and thus relevance of the system you built.

Kasia Musur from VENT conducted research about experiences of toxic leadership amongst data professionals in cooperation with Elizabeth Press from D3M Labs. The blog is a high level summary of a couple of insights with a link to the full interview on the D3M Labs YouTube channel.

Ingenieure und Customer Success Teams sind die unbesungenen Helden des IT-Betriebs.
Hast du dich schon einmal gefragt, was sie über deine Kaufgewohnheiten denken? Vor allem in virtuellen Organisationen können Ingenieure und Kundenerfolgsteams bei Anbietern in ihrer Programmierhöhle leben, hart arbeiten und für andere unsichtbar sein. Wenn man impulsive coole Tools kauft, ohne die Ingenieure mit einzubeziehen, kann das nicht nur diese schwer zu rekrutierenden Fachleute in den Wahnsinn treiben und die Beziehungen zu den Anbietern belasten, sondern auch die Entwicklung Ihrer Plattform und wichtige Projekte wie die Datenmigration zum Scheitern bringen. Das könnte Ihre Ingenieure so frustrieren, dass sie kündigen.

Engineers and customer success teams are unsung heroes of IT Operations.
Have you wondered what your they think of your purchasing habits? Especially in virtual organizations, engineers and customer success counterparts at vendors can live in their coding-cave, working hard, invisible to others. Impulsive cool tool purchasing without including engineers might not only be driving those difficult to recruit professionals crazy and burn vendor relationships, it could derail your platform development and important projects such as data migration. It might frustrate your engineers enough to quit.

Coole Tools sind oft die heiße Hookup des Data Leaders (oder Stakeholders), um am nächsten Morgen mit den Klamotten von gestern in einer kalten Gasse zu landen. Coole Tools werden gekauft, installiert, ausprobiert und für den nächsten vielversprechenden Anbieter aufgegeben. Und das bedroht ernsthaft die finanzielle und operative Überlebensfähigkeit des Data Teams. Warum sind Data Tools so verführerisch? 

Wie können wir verhindern, dass die Beschaffung zu einem Boulevard der geplatzten Träume und nicht realisierten Projekte wird?