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.

'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.

Preach, Eddie, Preach!! You are 💯 that the world of data management must transform dramatically. Data Management must transition from "managing" data to "monetizing" or "deriving value" from data. Yes, it must be an economics conversation about how we leverage these marvelous AI tools (plural, not singular) to derive and drive new sources of customer, product, service, operational, environmental, and societal value. Data Management leadership that starts the conversation talking about data has already lost. Send them packin'!! Modern data management leadership must first understand how their organization defines and measures value creation effectiveness. Then data management leadership has the framework for determining where and how data supports those value-creation processes and initiatives. "If we want to change the game, change the frame!" "Why Data Management is Today’s Most Important Business Discipline" https://lnkd.in/g4n8UWeK #DataStrategist #DataScience #IOT #BigData #AI #ML #DataTransformation #DataManagement #DataEconomics #DesignThinking #GenAI #AILiteracy #DataLiteracy #IWork4Dell #AI4IA 

🥅 Data Leaders/ Data professionals - 2025 - it's time for promotion or relegation.... there isn't going to be a status quo! 💣 ...if you're still spending more money managing it (#data) than you are driving value from it, it's time for the Pink Slip, P45, Reduction in Force (other options for termination are available!) ⚡ Data Governance - remains critical, but it's less the data and more the 'Governance'. 💣 Data Engineers - you've had a good run, but AI will eat your breakfast, lunch and dinner... 💣 Data Management - it's going to be automated, but not by the providers you're used to (see Data Engineers).... 🎯 Data Quality - remains important for structured data, but the real action is #unstructured data, and there the requirement is data... just data (quality notsomuch)! To be valuable (and successful) you need to be in the business of bringing together Data and Analytics (which includes AI, because what is #AI, but #Analytics, sitting on top of structured and unstructured data!....) to drive #Value for the Business!!! BUT don't get too comfortable, because the really smart AI, generates it's own synthetic data, as it really just needs the 'rules of the game'... #DAMA #EDMCouncil please wake up and smell the coffee ☕ there isn't any value in being the world's most expensive librarians! It's got to be about driving value from data, not storing and managing it! #observability not just #lineage #CDO / #CDAO - the real #VALUE is not in the Data or the Analytics #TECHNOLOGY it's in driving action and change in the business, so take a leaf from the #dean Bill Schmarzo (and learn some Economics) and focus on #DECISONING. That's the job of a leader - leave the Technology to the #CIO/#CTO. Jon Cooke Daniel Rolles Mark Stouse Dan French Kyle Winterbottom Tim Ellis David Pidsley Malcolm Hawker Randy Bean

Expanding on Eddie Short's commentary, here's our current perspective for data leaders and professionals in 2025 regarding promotion or relegation: 𝗗𝗮𝘁𝗮 𝗟𝗲𝗮𝗱𝗲𝗿𝘀/𝗗𝗮𝘁𝗮 𝗣𝗿𝗼𝗳𝗲𝘀𝘀𝗶𝗼𝗻𝗮𝗹𝘀: There's an ongoing discussion that the traditional roles might face significant changes. Consensus seems to be that if data professionals are not adding value to the business through their work, they might be at risk of being let go. The focus is shifting from merely managing data to leveraging it to drive business outcomes. 𝗗𝗮𝘁𝗮 𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲: While still critical, the emphasis is moving towards the governance aspect rather than just data management. This reflects a broader understanding that how data is used and governed is more crucial than the data itself for business success. 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝘀: There's a strong sentiment that AI and automation could replace many traditional data engineering tasks, suggesting a potential decline in demand for roles that focus on data integration and management without adding strategic value. 𝗗𝗮𝘁𝗮 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁: Automation is expected to take over, but not necessarily by the traditional data management providers. This implies a shift towards new technologies or methodologies that might bypass conventional data management practices. 𝗗𝗮𝘁𝗮 𝗤𝘂𝗮𝗹𝗶𝘁𝘆: For structured data, quality remains a priority, but there's increasing interest in unstructured data where the volume and availability might be more important than traditional quality metrics. The real challenge and opportunity lie in managing and deriving insights from unstructured data. 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻 𝗼𝗳 𝗗𝗮𝘁𝗮 𝗮𝗻𝗱 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀: Value proposition for data professionals is increasingly about integrating data with analytics (including AI) to drive business value. This convergence is seen as the key to relevance and success in the field. Being able to use data to inform decision-making processes is crucial. 𝗔𝗜 𝗮𝗻𝗱 𝗦𝘆𝗻𝘁𝗵𝗲𝘁𝗶𝗰 𝗗𝗮𝘁𝗮: There's a recognition that advanced AI can generate synthetic data, reducing the need for vast amounts of real data. This means understanding the "rules of the game" (i.e., the logic or algorithms behind data) could become more valuable than managing large datasets. 𝗟𝗲𝗮𝗱𝗲𝗿𝘀𝗵𝗶𝗽 𝗥𝗼𝗹𝗲𝘀 (𝗖𝗗𝗢/𝗖𝗗𝗔𝗢): Role of Chief Data Officers or Chief Data & Analytics Officers is underscored as being about driving decisions and business change rather than focusing solely on technology or data management. The advice is to focus on decision-making and economic value, with technology leadership left to CIOs or CTOs. For data professionals to thrive in 2025, they must pivot from being custodians of data to being architects of business value through data-driven insights and decisions. The era of status quo in data roles appears to be over, with a push towards efficiency, automation, and a strategic focus on what data can achieve for the business.

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

I always felt, if done right, the Chief Data and Analytics Officer role, should be a natural stepping stone to the CEO role.   However, it wasn’t done right in most cases and it is now a junior CIO role (at best) in many companies.    What happened?   There is a false belief that it is a technical role. While there are parts that are technical and at the IC level it very much is. And I would argue that if someone is running just a team like just data engineering, it is a technical role.  That changes when at the level of what would be a person running data engineering, analytics and data science, combined. Now you are running a business.   There in lies the big issue. People go from a technical role to a business role and often don’t make the transition needed.    When I worked at GE, I was a product manager. To even be considered, you needed 10 years of experience and a master degree, that’s just to get an interview. I was told on day one, I am the CEO of my products and I needed to act accordingly. I’m not the technical expert, I’m not the tech guru, I am the CEO.    That’s a very big mind shift that I don’t think takes place in data and analytics for all but a few. Nor is the leadership role treated like a business leader, but it should.   Last month I was at a data analytics leadership event and everyone there had a decade or more of experience and we talked business, not tech. As one person put it. “You know who has done this for a while by what they focus on. If they have true leadership experience, they talk business and customers issues. If they are junior, they talk technology.”    Be the CEO of data and analytics. When you adopt that mindset, you realize the tech is such a minor part. And this coming from someone who over a decade ago would spend time and money learning models, tools and being able to describe the difference between the tools in my sleep. Do I still pay attention to tech, yes, but only as a tool to help me craft a better customer experience.   I spend way more time on understanding how to replicate successful system that I can adapt at new companies to make them profitable. I spend way more time on building relationships. That’s how I got a major vendor to give me $100k in free services at a start up I worked at, building relationship. This was after that startup treated them poorly. A good CDAO knows how to grow a business more than anything else. That’s how you get a real seat at the table and be seen as real CEO material. You are acting like one when you run your team like your business.   Are you growing a business or just managing tech? #data #analytics #hiring

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