Ryan Flannelly’s Post

Data leaders are set up to fail. 🚫 Job descriptions for data leaders emphasize technical skills. This is problematic! Instead, JDs should focus on business, interpersonal and conceptual skills. In these days where the CDO is tasked with delivering tangible value, technical skills won't cut it anymore. What's needed is the ability to: *Understand business goals/objectives. *Help formulate business strategy. *Ability to translate business strategies into data & AI initiatives. *Communication/interpersonal skills to build consensus and influence. *Transformational leadership to motivate the data/AI team to contribute to business goals. *Big picture mindset to understand how data/AI can aid in threat mitigation and market opportunity exploitation. *Adequate knowledge of crucial data/AI technologies (& products) to achieve business goals. A data leader doesn't have any business with python, scala, and R. You have scientists, engineers, and analysts getting paid to do that. Your job is to provide strategic leadership for them. #data #business #cdo #skills --------------------- Looking to become a business-savvy data expert, to earn the trust of your business stakeholders? Check out the link in the comment section.

A successful CDO in 2025 will be vast in people and business judgement skills. Also, they will be as effective in data analytics initiatives as they are in data management initiatives. Data leaders must be able to easily identify and focus on the data initiatives that will drive business outcomes.

The Data Engineer's role, once the backbone of modern data systems, has expanded so much that it’s now at a tipping point. The rise of this role, often seen as a catchall for companies with low data maturity, has turned it into a double-edged sword. 👉 On one edge, Data Engineers are crucial for transforming raw data into actionable insights—fueling analytics, machine learning, and strategic decisions. 👉 On the other, the role has become overburdened, with expectations spanning infrastructure, security, data governance, and beyond. So, what does this mean for us as leaders and data professionals? 1️⃣ 𝗥𝗲𝗱𝗲𝗳𝗶𝗻𝗲 𝘁𝗵𝗲 𝗿𝗼𝗹𝗲: To avoid burnout and inefficiency, we must clearly delineate the responsibilities of Data Engineers—allowing them to focus on their core strengths. 2️⃣ 𝗘𝗺𝗯𝗿𝗮𝗰𝗲 𝘀𝗽𝗲𝗰𝗶𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻: Roles like Analytics Engineers, Data Architects, DataOps Engineers, and Data Strategists aren’t just buzzwords—they are critical to creating a sustainable and scalable data ecosystem. No one person can or should carry the weight of all these roles. 3️⃣ 𝗟𝗲𝗮𝗱 𝘄𝗶𝘁𝗵 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝘆: Leadership investment is non-negotiable. A strong data strategy and a clear roadmap for role specialization can eliminate chaos and set the stage for scalable success. Assigning all data challenges to Data Engineers is not a strategy; it’s a shortcut to inefficiency. While it’s natural for a small company to rely on a single data professional to handle everything initially, it's crucial to evolve as the team grows. Splitting roles and responsibilities and fostering a culture of awareness about different data positions is essential for long-term success. 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗶𝘀 𝗰𝗿𝗶𝘁𝗶𝗰𝗮𝗹 — 𝗯𝘂𝘁 𝗯𝗲𝗶𝗻𝗴 𝗰𝗿𝗶𝘁𝗶𝗰𝗮𝗹 𝗱𝗼𝗲𝘀𝗻'𝘁 𝗺𝗲𝗮𝗻 𝗱𝗼𝗶𝗻𝗴 𝗲𝘃𝗲𝗿𝘆𝘁𝗵𝗶𝗻𝗴. Image inspiration: https://lnkd.in/dkwdDZvA #dataengineering

'You can't mix operations and data science.' But guess what? My operations background is secretly helping me get through this Big Data degree @ Simon Fraser University. Let me break it down: 1/ Problem Framing In operations, success is all about understanding the problem before jumping to solutions. You don’t just throw resources at an issue and hope it works. You dissect it, analyze the workflow, and understand what’s causing the bottleneck. This is a mindset that naturally transfers to data science. When tackling complex data projects, I’ve found myself zooming out first—understanding the bigger picture before I dive into code or run a single analysis. 2/ Context Awareness I know which data actually matters (and why). Working in operations, you develop a strong sense of what’s relevant and what’s just noise. Similarly, not all data is useful; some of it is just fluff that can sidetrack decision-making. 3/ Implementation Over Theory My solutions don't just look good on paper. This is where my operations chops really shine. Data science is exciting when you uncover patterns or create a model with high accuracy, but that’s not where the value stops. If you can’t implement your insights in a way that makes sense operationally, you’ve only done half the job. My background ensures that my solutions are not just theoretical—they’re actionable. Lesson? Your "unrelated" experience might be your biggest asset. The next time you think one part of your career doesn’t mesh with the other, take a step back. You might just be surprised how much it can complement and enhance your new path.

Confident person Vs Talented person In the field of data science, a harmonious blend of confidence and talent is ideal. #Confident_Data_Scientist****** #Adaptability: A confident data scientist can quickly adapt to new tools, technologies, and methodologies, ensuring they stay ahead in the rapidly evolving field of data science. #Leadership: Confidence is often associated with strong leadership qualities. Confident individuals can inspire and motivate others, driving teams towards success. #Communication: Confidence enables data scientists to effectively communicate complex findings and insights to diverse stakeholders, bridging the gap between technical analysis and actionable business decisions. #Problem_Solving: Confident data scientists are more likely to tackle challenging problems with a positive mindset, exploring various approaches and experimenting with different algorithms to find optimal solutions. #Risk_taking: Confidence encourages risk-taking, which can lead to innovative solutions and breakthroughs. Confident individuals are more likely to step out of their comfort zones and explore new opportunities. #Talented_Data_Scientist****** #Technical_Proficiency: Talented data scientists possess deep technical expertise in programming, statistical analysis, machine learning, and data visualization, enabling them to tackle complex data problems effectively. #Innovation: Talented individuals often bring innovative ideas and approaches to data science projects, leveraging their creativity to develop novel algorithms, models, or data-driven strategies. #Consistency_Efficiency: Talented data scientists consistently produce high-quality work, ensuring accuracy, reliability, and reproducibility in their data analyses and predictive models.

The data science life cycle involves a series of steps that data scientists follow to solve problems using large amounts of data and various tools. While there might be variations, the core steps remain consistent. Let’s explore the six main stages of the data science life cycle: 📄 Identifying a Problem: Clearly state the problem and describe why it needs solving. Assess the value associated with finding a solution. Determine necessary resources and staff. Identify risks and stakeholders involved in the process. 🗒 Data Collection and Preparation: Gather relevant data from various sources. Clean and preprocess the data to ensure its quality and consistency. Handle missing values, outliers, and other anomalies. 🗒 Exploratory Data Analysis (EDA): Explore the data to understand its characteristics. Visualize data distributions, correlations, and patterns. Identify potential insights or trends. 🗒 Model Building and Selection: Choose appropriate algorithms and techniques. Train machine learning models using the prepared data. Evaluate model performance and select the best one. 🗒 Model Evaluation and Fine-Tuning: Assess the model’s performance on validation data. Optimize hyperparameters and fine-tune the model. Address overfitting or underfitting issues. 🗒 Deployment and Communication: Deploy the model in a production environment. Communicate the results to stakeholders and business leaders. Monitor the model’s performance and update as needed123. Data science plays a crucial role across industries, helping organizations make informed decisions and solve complex problems. If you’re interested in this field, consider exploring data science careers and building expertise in analytics, programming, and machine learning! As a BI Manager / Project Manager and MKT Operation, I can help you develop and lead high performance BI teams always looking for teamwork and motivating the unit creating healthy bonds, maintaining constant communication and thus achieve the teams achieve the objectives thus driving growth and long-term success.    Contact me and let's decide together how to lead your company to success!!!!! Aliel Perez Project Manager/BusinessIntelligence/ IT Manager 📧 alielperez2004@gmail.com 5516962259 #projectmanager#Business Intelligence#MKT Manager#SCRUM#itmanager

🌟 From #DataAnalyst to Data Leader: Navigating the Path to Strategic Influence 🌟 The leap from being a data analyst or scientist to stepping into a data leadership role is both significant and transformative. It’s not just about gaining deeper technical skills; it’s about evolving into a strategic influencer who shapes data culture and aligns data initiatives with business objectives. Shifting from Technical Expertise to Strategic Vision In technical roles, you focus on data analysis, modeling, and generating insights. But data leaders elevate this work by asking why and what—why are we pursuing a particular initiative and what impact will it have on business goals? Data leadership means thinking beyond algorithms and diving into data strategy, advocacy, and holistic impact. Overcoming Challenges as a Data Leader The path to data leadership is filled with hurdles: siloed data, poor data quality, and a lack of cohesive strategy are common pain points. #Dataleaders must bridge these gaps by championing data governance, breaking down silos, and driving a clear, unified data vision. It’s about influencing stakeholders, even those who may not see the immediate value of investing in data infrastructure. Mastering New Skills and Mindsets Effective data leaders are exceptional communicators who can tell compelling data stories that inspire action. They combine technical expertise with business acumen, understanding the nuances of organisational goals and how data can drive success. Building a high-performing data team and advocating for necessary resources also become crucial responsibilities. Transitioning to a data leadership role is challenging but immensely rewarding. It’s a journey of continuous learning, strategic influence, and driving meaningful change. Are you ready to take the next step in your data career and make a lasting impact? 👉 Read the full article to dive deeper into the journey from data analyst to strategic data leader! https://lnkd.in/e2fqfJGv Jane Smith #Leadership #Professionaldevelopment #Dataleaders #Newskills

📊  𝐃𝐚𝐭𝐚 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫'𝐬 𝐅𝐚𝐯𝐨𝐫𝐢𝐭𝐞 𝐓𝐢𝐦𝐞-𝐂𝐨𝐧𝐬𝐮𝐦𝐢𝐧𝐠 𝐓𝐚𝐬𝐤: 𝐃𝐚𝐭𝐚 𝐁𝐚𝐜𝐤𝐟𝐢𝐥𝐥𝐬 🤔 What is Data Backfill? 👉 Data backfill is the process of filling historical data that was missed or incorrect during initial ingestion. It ensures your data ecosystem is complete, enabling accurate analytics and insights that drive smarter decisions. ⏳ 𝐖𝐡𝐲 𝐢𝐬 𝐃𝐚𝐭𝐚 𝐁𝐚𝐜𝐤𝐟𝐢𝐥𝐥 𝐓𝐢𝐦𝐞-𝐂𝐨𝐧𝐬𝐮𝐦𝐢𝐧𝐠? 👉 Backfilling can be resource-intensive because it requires: 1. Detecting gaps in historical data 2. Sourcing accurate records 3. Transforming data to match current schemas 4. Rigorous validation to ensure data quality and consistency. These steps are vital but can quickly become complex and time-consuming! 💡 𝐈𝐦𝐩𝐨𝐫𝐭𝐚𝐧𝐜𝐞 𝐨𝐟 𝐚 𝐒𝐭𝐫𝐨𝐧𝐠 𝐁𝐚𝐜𝐤𝐟𝐢𝐥𝐥 𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐲 👉 A well-planned backfill strategy means: 1. Better insights from comprehensive historical data 2. Enhanced data integrity for more reliable reporting 3. Compliance and audit readiness 🔧 𝐓𝐢𝐩𝐬 𝐟𝐨𝐫 𝐄𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐭 𝐃𝐚𝐭𝐚 𝐁𝐚𝐜𝐤𝐟𝐢𝐥𝐥𝐢𝐧𝐠: 1. Identify & Prioritize Gaps: Regularly assess datasets for missing data. 2. Source Reliable Data: Use validated, consistent sources. 3. Transform & Validate: Ensure data matches schema and is quality-checked. 4. Document & Monitor: Track the backfill process to keep data consistent and accurate. With the right strategy, data backfill can turn your raw data into a fully unified resource, supporting powerful analytics and sound decision-making. 📈 Learning should be exciting, not intimidating! If you're looking to master complex data engineering concepts with a touch of humor, stay tuned. #DataEngineering #DataStrategy #DataIntegrity #Analytics #learning