As we entered the 2010s, corporate America underwent a seismic shift. The relentless pursuit of efficiency that characterized the 1990s and early 2000s gave way to a new paradigm—one that prioritized purpose and profit. While addressing crucial issues of employee burnout and societal expectations, this transformation inadvertently set in motion a chain of events that would profoundly impact data culture and leadership across organizations.
Read MoreDigital Transformation in Insurance: Overcoming Legacy Challenges
The insurance industry is experiencing a digital revolution. As customer expectations evolve and new technologies emerge, insurers are under increasing pressure to undergo digital transformation. However, legacy systems and outdated processes present significant hurdles for many companies. This blog post will explore the challenges of digital transformation in insurance, highlighting real-world cases and offering strategies to overcome common obstacles.
Read MoreMastering Data Governance in Insurance: Balancing Innovation with Compliance
Mastering data governance has become a critical challenge for insurers in today's rapidly evolving insurance landscape. Many companies struggle to balance fostering innovation and maintaining regulatory compliance. This blog post will explore the complexities of insurance data governance, highlighting the pitfalls and best practices.
Read MoreBuilding a Data-Literate Insurance Workforce: Strategies for CDOs
Data literacy has become a critical skill for insurance professionals at all levels. As Chief Data Officers (CDOs) in the insurance industry, one of the most crucial challenges is fostering a data-literate workforce capable of leveraging data for better decision-making and innovation. This blog post explores strategies for CDOs to build and maintain a data-literate insurance workforce, highlighting real-world examples and addressing common challenges.
Read MoreAll Technology Projects are Data Projects
One of the biggest ideas in Driving Data Projects (the book) is that "all technology projects are data projects." Yet data is still an afterthought in many organizations—even with AI on the horizon (or now, in many firms' backyards).
Author of Data Quality: The Field Guide, Tom Redman, popularized the idea that the most important moments in a piece of data's lifetime are the moment it is created and the moment it is used. These moments often occur outside of IT. The business consumes vast amounts of data, emphasizing the importance of business involvement in data quality management. Those who have provisioned and consumed data know from experience that bad data dies hard. It will get rid of you if you don't get rid of it.
Read More6 Myths and Misconceptions about Data Projects
As I considered how to promote my new book on driving data projects, I wanted to include myths and misconceptions that reinforce their value. I have experienced many of these in teams I’ve worked on or with. Data projects are not a static set of routines. It's a constantly evolving, open-to-innovation process.
Only 54 percent of organizations fully understand the value of project management, according to PMI's Pulse of the Profession™ report. That might explain, in part, why project success rates are so low: Less than two-thirds meet their original business intents.
Read MoreFrom Insights to Infrastructure: KPI Reports as Your Strategic Bridge
The synergy between analytics and Information Technology (IT) is more crucial than ever. As organizations strive for digital transformation, understanding the complex dynamic between these domains is critical to achieving strategic objectives. However, this relationship is not static; it's evolving in response to new tools and methodologies, governance requirements, and ethical considerations.
Understanding the tools facilitating this translation is critical to driving successful digital transformations and achieving strategic objectives. Key Performance Indicator (KPI) reports are often a misunderstood yet critical bridge between analytics vision and IT execution.
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