Just as beverage companies once managed their packaging waste, tech companies were once more straightforward about their data practices. But now, we’re seeing a troubling pattern of companies quietly expanding their data collection through obscure settings and opt-out mechanisms buried in lengthy terms of service.
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 MoreData Quality: Plan for Resistance
As organizations rush headlong into digital transformation initiatives, a critical factor often gets overlooked: data quality and the resistance to support ongoing data quality efforts. In the race to implement cutting-edge technologies and overhaul business processes, many companies fail to recognize that the success of these efforts hinges on the accuracy, completeness, and reliability of their underlying data. This oversight can lead to disastrous consequences, undermining the very goals that digital transformation aims to achieve.
Read MoreData Literacy: The $100 Million Insurance Policy You're Probably Ignoring
In boardrooms across the globe, executives are gleefully signing off on multi-million-dollar investments in data infrastructure. Big Data! AI! Machine Learning! But here’s the inconvenient truth they’re overlooking. Without a data-literate workforce, these shiny new toys are as useful as a Ferrari in a traffic jam.
Read MoreWhere Business Meets Technology in the Marketplace of Information
Real estate focuses on governance and value derivation from assets. The Marketplace facilitates value-driven transactions while maintaining order, quality, and trust throughout the ecosystem. BOTH must work TOGETHER.
Read MoreCultivating a Data Ecosystem: A Fresh Approach to Organizational Data Management
Many organizations have recognized the need to enhance their data management capabilities. The traditional response has been to centralize these efforts, often by appointing a Chief Data Officer (CDO). However, this well-intentioned approach often leads to challenges and resistance within the organization.
Read MoreData Storytelling: Transforming Insights into Action With 2 Case Studies
The ability to craft compelling narratives from complex information is a superpower. Working with graduate students across various sectors, I help communicate how effective storytelling can bridge the gap between data teams and business leaders. Let's explore how to master this art and avoid common pitfalls.
Read MoreAnalytics Challenge: Lack of Data Literacy Among Stakeholders
The symbiotic relationship between technical analytics development and business utilization underscores the heightened emphasis on data literacy skills. Recognizing that literacy demands effort from technical and business domains, analysts must simplify and convey insights while business teams must effectively apply them.
Read MoreThe Fragmentation of Psychological Science
The phenomenon of repackaging old ideas, as seen across different eras in psychology (but exists in every discipline), raises questions about the practices of pop psychologists like (dare I suggest) Brené Brown, who often employs grounded theory—a methodology typically reserved for less understood phenomena—to restate established concepts under the guise of novelty. For example, Brown's claim of inventing the idea that individuals adapt their identities within group settings restates theories already well-explored by Smith and Berg in the late '90s, illustrating a tendency among some in the field to "invent" rather than build on existing knowledge. This practice not only misrepresents the originality of an idea but also contributes to the unnecessary fragmentation of psychological science. We must become more discerning. https://lnkd.in/g3BwXRAF
The excessive creation of new constructs and measures leads to fragmentation, complicating the generation of insights and creating barriers to knowledge transfer. This fragmentation makes it difficult to compare results across experiments, limiting the development of a cohesive understanding of phenomena and impeding progress.
To address the challenges of fragmentation, there needs to be a shift toward greater hashtag#methodologicalrigor. We must improve, reuse, and validate existing constructs and hashtag#measures rather than continuously inventing new ones. This approach would make applying insights effectively in hashtag#decisionmaking processes easier and foster a more standardized and accessible body of knowledge.
🎯THERE IS A BETTER WAY
Improve methodological rigor and data quality processes. Apply operational excellence. Leverage data quality and senior stakeholder management to streamline data transformation processes. For example, by identifying and eliminating redundancies in data collection and processing, data-driven decisions' timeliness significantly improves and becomes more accurate.
Key Performance Indicators (KPIs) include efficiency, productivity, and quality outcomes. Focusing on DQ and meta data management and developing or refining KPIs that accurately track the performance of data transformation processes can ensure they align with organizational goals for operational excellence.
🎯WHY PRIOTIZE DQ KPIs?
They ensure accurate, reliable metrics for decision-making. High-quality data underpins effective KPIs, enabling organizations to track performance accurately and make informed strategic decisions, which is essential for operational excellence.
🎯WHY DOES STAKEHOLDER MANAGEMENT MATTER?
It nurtures beneficial relationships between a business and its stakeholders, creating shared value. It helps avoid or resolve conflicts, secure support, communicate effectively, and manage expectations, which is essential for operational excellence.
All 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.
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