With a long-lasting (and quite obsessive) passion for data, Olivier Penel strives to help organizations make the most of data, comply with data-driven regulations, fuel innovation with analytics, and create value from their most valuable asset: data. As a global leader at SAS for everything data management and privacy-related, Penel enjoys providing strategic guidance, and sharing best practices and experiences in using data governance and analytics as a catalyst for digital transformation.
The concept of sustainability has evolved significantly over the past few years. It is no longer just a trendy buzzword but has become an essential element of business models. Major multinational companies such as IKEA, PepsiCo and Amazon lead sustainability transformation by setting ambitious goals and implementing new initiatives. IKEA
Change is the only constant, and it doesn’t happen overnight. This is particularly true in the world of data analytics. As organizations are looking to become more digital, resilient and profitable, executives are going back to the whiteboard to reconsider how they’re using data and analytics to transform their business.
In my previous article, “The Vital Ingredients of Responsible AI,” I described the principles that underpin the need to develop AI systems that factor in the human factor, not only contribute to business outcomes but also protect individuals, society and the environment. While it’s difficult to argue with those principles,
In my previous article, “The Business Imperative for Responsible AI," I covered the main business drivers for responsible AI. Beyond the greater good and social responsibility, responsible AI is emerging as a key factor for successful AI adoption. In this article, I will describe the main ingredients of responsible AI:
With the steep rise of artificial intelligence (AI) adoption across all facets of society, ethics is proving to be the new frontier of technology. Public awareness, press scrutiny and upcoming regulations are forcing organizations and the data science community to consider the ethical implications of using AI. The need for
By Sarah Gates, Analytics Platform Strategist at SAS, and Olivier Penel, Data & Analytics Strategic Adviser at SAS Today, organizations of all types are having to change their perspective – of how to do business, of how to collaborate with remote employees, of how to best engage with customers and
Das ist der dritte Beitrag zur Blog-Serie Big Data Governance. Bisher sind erschienen Wie Big Data Unternehmen durcheinanderwirbelt und Mit Data Governance gegen den Datensumpf. Zum Abschluss möchte ich 5 Tipps abgeben, die ich selbst von Pionieren in Sachen Big Data übernommen habe. Meine erste Empfehlung lautet, Data Governance bereits zu Beginn der
Dies ist der zweite Teil der Blog-Serie zu Big Data Governance. Beginnen Sie vorher am besten mit „Teil 1: Wie Big Data Unternehmen durcheinanderwirbelt”, wenn sie ihn noch nicht gelesen haben.
Ob der Big Data-Hype schon vorbei ist oder nicht, sei dahingestellt: Big Data ist es jedenfalls nicht – und gibt immer noch genügend Gesprächspunkte für eine Blog-Serie. Im ersten Beitrag zum Thema Big Data Governance möchte ich auf den disruptiven Effekt von Big Data auf Unternehmen eingehen. Hier sind meiner
With big data, data governance challenges escalate in many ways: The diversity of data sources means that there are minimal standards for data structure, definition, semantics and content. The lack of control over data production means that you can’t enforce data quality at the source as you can do with
