Christine Haskell, Ph.D.

This article on essential skills for Chief Data Officers (CDOs) in 2024 highlights a shift I've been seeing for a while: technical expertise is no longer enough in data leadership. Business acumen is now a must-have. Why? Because data is only as valuable as the business outcomes it drives. CDOs who deeply understand their organization's strategy, financials, and market can truly translate data insights into actionable strategies that move the needle. In my experience working with top-tier companies around the world, I've seen firsthand how business acumen can transform data leaders into strategic partners. They can not only identify opportunities but also articulate the ROI of their initiatives in a way that resonates with C-suite executives. This shift is a challenge, but also an exciting opportunity for data leaders to step up and become even more valuable to their organizations.

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We often say that we slice data to the point of transparency, and so it is with our analysis of all non-Early Decision application outcomes. We link you to a viz on our Tableau Public page. Spoiler: the regular decision landscape is treacherous. https://ow.ly/1LnV50TsJK4

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I work with DEI consultants,  unlocking insights using 5 analysis types (Is the 3rd one scary for you?) Data analysis isn't just numbers.  It's the insight behind impactful,  measurable change. Here are five methods to take DEI ROI analysis 1) Comparative Analysis  2) Correlation Analysis 3) Cost-benefit analysis (CBA) 4) Sentiment Analysis 5) Predictive Modeling Most DEI Consultants focus on  Cost-benefit analysis when they hear about the ROI of DEI. ROI Analysis is more than that. ◉ ◉ ◉ ◉ ◉    Let's quickly learn each of the analyses. ✦ Comparative Analysis measures DEI metrics before & after initiatives. Insight Example: A company noticed a 25% improvement in employee retention after launching inclusive leadership training — proof that investing in culture can reduce costly turnover. ✦ Correlation Analysis links DEI variables to business performance (e.g., diversity vs. innovation). Insight Example: A diverse team at a tech firm saw a 30% spike in product innovation, underscoring how different perspectives drive creativity and customer alignment. ✦ Cost-benefit analysis assesses DEI costs vs. financial returns, such as improved retention. Insight Example: By investing $100K in DEI initiatives, an organization saved $300K in recruitment costs due to higher retention — an ROI of 3x! ✦ Sentiment Analysis analyses employee feedback and engagement surveys for DEI sentiment. Insight Example: After adding an inclusive hiring process, employee feedback went from mixed to overwhelmingly positive, signalling that employees felt more valued and seen. ✦ Predictive modelling uses historical data to predict DEI's impact on future KPIs, such as turnover or revenue. Insight Example: Forecasting showed a projected 10% reduction in turnover when implementing inclusive growth paths, guiding the company's DEI budget toward high-impact initiatives.    ⋮⋮⋮ ⋮⋮⋮ ⋮⋮⋮ ⋮⋮⋮ ⋮⋮⋮ ⋮⋮⋮ 📊 Each method brings clarity to the tangible ROI of DEI,  proving that inclusive practices aren't just "good-to-have"  but a competitive advantage that can be 🞴 measured 🞴 optimized 🞴 celebrated Which one you have used in the past? Put the number in the comments below. #Diversity #Business #Leadership --- 👋 Hi, I'm Vikram  🎯 My mission is to help 100 DEI consultants generate 10,000 total leads in the next twelve months. ROI of DEI - White paper Show corporate buyers that their investment in DEI will pay for itself. Like this post? Hit 👍 Love it? Leave a comment ✍️ Think others should read it? Repost ♻️

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📊 People analytics is more than just numbers—it's about understanding the story behind the data. While metrics and insights can inform decision-making, it's essential to approach them with empathy and an awareness of context. 👨‍🔧 Every data point represents an individual’s experience, and without considering the human element, the solutions we create can miss the mark. By digging deeper into the "why" behind the trends and listening to the people involved, we can craft strategies that truly resonate and drive meaningful change. 🌱 As we leverage analytics to shape better workplaces, let’s remember: empathy isn’t just a nice-to-have, it’s the key to unlocking real, impactful insights. . . . . . #PeopleAnalytics #Empathy #EmployeeExperience #DataDrivenHR #InclusiveWorkplaces

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What is arguably the new currency of the business world? Data literacy. In my personal side project, I've enjoyed reading Piyanka Jain's work in advocating the importance of building data literacy within organizations. It corresponds with what I see in my consulting. As an I/O psychologist, we are expected to be data experts—we are the "scientists of the workplace" afterall. As someone highly trained in all things data and advanced analysis, it's wonderful to work with companies trying to make use of their data to drive decision making. Yet I often see the same mistakes which keep companies from getting the most of their—often vast lakes of—data. Below are the top 10 insights I've observed so far in my time. Anything I'm missing? Now back to my reading on the FDA's guidance on patient-reported outcome measures... i.e., my continued learning in how data skills transfer so beautifully across fields 🔥. #dataliteracy #dataanalytics #datastorytelling #workplacedata #iopsychology #occupationalhealth

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I just discovered that Tableau calculations can use fields from different tables without needing explicit relationships or joins. This is incredibly handy for visualizations using a data template, such as a waffle grid table. Check it out to see this in action! https://lnkd.in/ghNz324T Tableau #viz

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The greatest opportunity for most organizations to expand and upskill their talent pool for data science and AI is through upskilling CURRENT staff. My colleague T. Scott Clendaniel has just lead his first piece of Gartner research which helps data and analytics leaders execute UPSKILLING initiatives for Data Scientists and AI experts. 🔵 What I hear is, many Gartner clients still report difficulty finding and attracting talent. 🔵 Evidence from our Gartner’s CDAO Agenda Survey for 2024 indicating that skills and staff shortages are a top-three challenge for 42% of respondents. 🔵 Data science and AI leaders struggle to meet the needs of employees simply by hiring more data scientists. 🔵 Therefore, upskilling paths must broaden AI skills across experience levels, from experts to novices. This research provides recommendations for creating and implementing those initiatives. "How to Lead Upskilling Initiatives for Data Scientists and AI Experts" https://lnkd.in/eFXPMgm9 (Gartner client login required) [Published 19 November 2024] by Scott and Aura Popa, CStat, Peter Krensky, Afraz Jaffri, Kurt Schlegel. Let me know in the comments if you are or have led a #DataScience or #AI upskilling initiative, or if you are a: - data scientist - PhD level or equivalent experience - looking to join a great company - keen to do research in #AI - in Europe ...you can drop me a direct message for a quick chat. Gartner is hiring! https://lnkd.in/eyNmvBU5

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Following up my previous post on a Semantic Web Layer Cake aligned to knowledge graphs...the below is an Ontology Framework for knowledge graphs. The Foundation Ontologies provide the base reusable entities for the Business Domain Ontologies (which is the key enterprise semantics) and the Vocabularies. These are all based on and refuse/map from Open Standards across the sectors. The business Shape rules are formed from the Ontologies which validates the Data instances. #knowledgegraphs #semantics #ontology #openstandards

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Leaders have poor visibility into data, analytics & AI expenditure. That's a problem when they get asked how much more they need (Agentic AI & GraphRAG?), or a threat to teams and leadership when asked to cut spending in 2025 (LLM prototypes? BI reports?). ⚠️ Leaders blind to their spending allowed inefficiency and lack of strategic alignment to creep in. But how would they know what they don't know? 💡 I've learned from Gartner clients in objectively leading organizations, they regularly examine and BENCHMARK spending across multiple categories of #data, #analytics & #AI work and that this one activity facilitates smarter spending and better biz outcomes. Why? Without this it's hard to identify performance improvements, justify investments, optimize resources and shape strategy and operating model against your PEERS. BENCHMARKING empowers chief data and analytics officers, those at the head of data, analytics and AI teams to compare their spending, staffing and organizational design with real peers, of a similar size and industry, not just theoretical or hypothetical ratios and models. So... Jishnu Mohan, JC Martel, PhD, Alan D. Duncan and our team of quants have gone and built something for our clients to fix this. 🛠️ It's an interactive tool. It guides you to enter some key metrics then it automagically creates informative, comparative views of your org.'s D&A function against your peers. Out pops Gartner insights! This benchmark answers: 1️⃣ How does my D&A spend compare to my peers? - D&A total spend (CapEx & OpEx) - Spend on resources (personnel, software, PaaS & external services) - Spend on functional activities (data curation/enhancement, data governance, analytical data mgt. & platform dev, transactional data mgt.) - Spend on mgt. & emerging capabilities (D&A mgt., AI tech, spend supporting AI) - Strategic spend (run, grow, transform) 2️⃣ How does my funding model and org. structure compare to my peers? - Decision making for funding (model, ownership, budget) - D&A org. structure (hybrid, centralized, federated) - Job title of the head of D&A and primary responsibilities 3️⃣ How does my staffing plan compare to my peers? - Total D&A employees (FTEs and contractors) - Staffing by functional activities and emerging capabilities - Staffing by roles (current vs. next 12 months, by FTEs & contractors)  4️⃣ How do my peers measure value from D&A use cases? - Metrics for measuring value from D&A use cases We've made this exclusively for clients who are CDAOs (see link in comments). If that's not you 😢 don't fret, we're publishing a summary of findings this week 🙂 for other clients, and I'll summarize highlights in another post - for everyone. Thank you to my colleagues who contributed: Mark Beyer, Francisco Javier De los Santos Ramos, Jorgen Heizenberg, Soren Krohn, Timothy Kropp, Brian Foster, Rita Sallam & Eric Stegman. 👍 Like if you think this sort of tool is useful. For CDAO clients ➡️ link in comments.

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The future workforce has spoken: AI excites 65% of under-55s, but only 39% of their older colleagues. This divide is just the tip of the iceberg in Korn Ferry's 2024 Workforce Survey. As I looked over the results, it’s younger professionals rewriting the rules and demanding work arrangements that fit their lives - not the other way around. But, despite generational gaps and cultural differences, workers worldwide share similar priorities. And it’s a global shift that savvy companies can't ignore. My takeaway? Adapt or get left behind. The organizations that will thrive are those brave enough to reimagine their workplace cultures, bridge the tech enthusiasm gap, and craft employee experiences that resonate across generations. The talent game is changing. Are you ready to play by the new rules? 📊🧐🔗: https://lnkd.in/e9kSVztg #FutureOfWork #EmployeeEngagement #WorkplaceCulture

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It’s great to learn how reliable data is for analytics and decision-making. This is definitely worth to read for anyone interested in how data is influencing higher-ed!

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Only two spots left for my upcoming dashboard design workshop! I’m Steve Wexler, founder, principal, and chief chart looker-atter at Data Revelations. I am one of three authors of The Big Book of Dashboards and the author of The Big Picture. I’m also a Tableau Iron Viz Champion and Tableau Visionary (Hall of Famer). I will be presenting my dashboard design workshop online on December 3 and 5. Learn more at https://buff.ly/3Bm0wnA PS: Don’t worry if you don’t use Tableau as the workshop is tool-agnostic. #dashboards #sigma #tableau #powerbi #excel

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Give infoFluency's new data strategy scorecard a try! It's designed to highlight areas of business where analytics could most move the needle for your organization.

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Great conversation today about data leadership! Big thanks to Celina Wong for thought-provoking questions. My biggest take-aways: 1) Data leaders in companies at every size and level of maturity have really similar issues - one of the biggest being the organization of data. Analytics Engineers/Data Architects are increasingly important to data success everywhere. 2) Leading with transparency and providing context and exposure are essential for retaining and developing talent - in any function, but in data especially. 3) Maintaining a balance between short-term priorities and longer-term infrastructure work is one of the most important skills for a data leader to develop

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In human systems Ontology is the overall methodology that describes a system of relationships and processes. The processes are described in workflows. Without the ontology the processes are less likely to perform optimally because roles and responsibilities are ambiguous. At the same time a rigid ontology gets in the way because it inhibits adaptability.

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