A-Team Insight, from A-Team Group’s Post

Take aways from CDO panel at Data Management Summit New York: Business / Data Governance alignment comes first. Solve DQ problems where they are causing real issues, don’t try to solve for everything at once. Education: Make sure that everyone knows that there is a process and data is an essential part of it. Use scorecards: Ownership of the data is with the part of the business that creates it. Who holds responsibility for it? Cultural shift. #DMSNYC #datamanagement #AI #GenAI #dataquality #datagovernance Julia Bardmesser Yinghua Michelle Zhou, MBA, CMA Jennifer Klimko Courant Eric Chacon Doris Brophy

🧼 The Impact of Data Cleaning: Unlocking the True Power of Data 🧹📊 Did you know that data cleaning is often the most time-consuming but critical step in any data project? Studies show that 80% of a data scientist’s work often revolves around preparing and cleaning data before meaningful insights can be derived. But the benefits are well worth the effort. 💡 Why Data Cleaning Matters: Improves Accuracy: Cleaning removes inconsistencies and errors, ensuring the insights and decisions based on the data are reliable. Enhances Efficiency: By eliminating duplicate and irrelevant data, analysts can work faster, focusing only on valuable data points. Enables Better Decision-Making: Clean data leads to better models and more accurate predictions, which means better strategies and business outcomes. 🔍 Real-World Impact: In finance, clean data reduces risk by improving model accuracy. In healthcare, it ensures patient data accuracy, which is crucial for diagnoses. Across sectors, data cleaning is the silent force driving successful data-driven decisions. 💡 My Take: Having worked with raw and messy datasets, I’ve seen firsthand how data cleaning can transform the quality of insights. A well-cleaned dataset is a foundation for impactful data science. What are your go-to data cleaning practices? #DataScience #DataCleaning #AI #DataQuality #GetDaily #DataDriven

The workplace is becoming increasingly data-driven, including in defense and intelligence environments. All roles will require at least a basic level of data literacy. How is your data science knowledge? Don't become obsolete in the new world of work. Enroll in the FedLearn course, Basic Data Management (AIDATA108)—part of our artificial intelligence and data science catalog Gain a solid understanding of the data management process, how to prepare data for use and how to interpret outputs for more informed decision making Learn more and register today: https://lnkd.in/eWHtCASW #fedlearn #departmentofdefense #dod #intelligencecommunity #govcon #trainingprovider #traininganddevelopment #training #onlinelearning #onlinetraining #onlinecourses #asynchronous #artificialintelligence #ai #datamanagement #datascience #datasciencetraining

76th course completion & certification on "RESPONSIBLE AI DATA MANAGEMENT" FROM DataCamp NY,USA.I AM GRATEFUL TO MY COURSE INSTRUCTOR Maria Prokofieva for guiding me.This course enabled me to learn: 1-Introduction to Responsible AI Data Management 2-Regulation Compliance and Licensing 3-Data Acquisition 4-Data Validation and Bias Mitigation Strategies #ai

The Growing Role of Automation in Data Science In the rapidly evolving landscape of data science, automation is increasingly becoming a game-changer. From automating data collection and cleaning processes to streamlining model training and deployment, automation tools are empowering data scientists to focus more on innovation and less on repetitive tasks. Automated Machine Learning (AutoML) platforms are a great example, making it easier for teams to build and optimize models without needing to dive deep into every detail of the algorithmic process. This not only accelerates the workflow but also democratizes access to advanced data science, enabling professionals from various backgrounds to contribute effectively. However, it's essential to strike a balance. While automation can enhance productivity, human oversight remains crucial to ensure the ethical use of data and the validity of results. As we embrace these tools, we must remain vigilant about the quality and bias of the data being used. Automation is not about replacing data scientists; it’s about augmenting their capabilities. By leveraging automation, we can push the boundaries of what’s possible in data science and drive innovation faster than ever before. #DataScience #Automation #AutoML #Innovation #AI #MachineLearning #DataDriven

Best practices in AI governance. Panel discussion by Keerthika Melissa Subramanian and Matt DeVoe. l Thank you to Keerthika Melissa Subramanian, partner at.Winston & Strawn LLP, and Matt DeVoe, Chief Information security officer at Zoetis, for sharing their insights and learnings with the students at the #CDAIO program at Executive Education at Carnegie Mellon University’s Heinz College. In a wide ranging discussion, these leaders shared their perspectives on - The need for AI governance - How to integrate AI governance into a broader digital and data risk management - Emerging AI regulations - Achieving the balance between unlocking value from innovation while minimizing risk - The ways to improve the legal.and compliance fluency of AI and Data leaders https://lnkd.in/eUWq83EY

For all my MBA students over the years.  This is what you need right now to stay ahead in AI and Big Data Strategy.

Data plays a huge role to facilitate business operations. Over the years, Data has emerged as a science that influences business reshaping across the globe. So the issues related to data protection, data integrity, data based valuations etc have emerged as a new standard for business strategy. And now with the arrival of AI, Data Analytics & Data Visualization has become a real game changer leading the whole business world to a new evolution. Here’s your chance to get the up close insight of the Data Analytics Experts about the world of AI. SIGN UP FOR THE FREE WEBINAR ON 11th May at 7 PM Join us to explore new-age business transformation from a global perspective. Link to sign up https://lnkd.in/gkjbV7Dm #neotiaskilldevelopmentacademy #ambujaneotia #nsda #learn #training #opportunities #employment #jobupdates #upskill #careergrowth #skilldevelopment #chatgpt #openai #dataanalysis #project #datascience #data #deeplearning #aitutor #powerbionlinetraining #powerbi #powerbicourse #CorporateTraining #EnterpriseLearning #ProfessionalDevelopment #DataAnalytics #DataAnalyticsusingAI #AI #webinar #webseminar

𝐃𝐚𝐭𝐚 𝐪𝐮𝐚𝐥𝐢𝐭𝐲 𝐢𝐬 𝐞𝐯𝐞𝐫𝐲𝐭𝐡𝐢𝐧𝐠. It determines the success of your work. The best way to ensure high data quality is to follow these steps: 1. Define clear data standards 2. Regularly clean your data 3. Use reliable data sources 4. Validate data accuracy 5. Train your team on data management 6. Implement data governance policies 7. Monitor data quality continuously 8. Use technology to automate checks It's not complex. But it is crucial. The top companies prioritize data quality. They understand that poor data leads to poor decisions. They invest in tools and training to maintain high standards. You can start improving your data quality today. P.S. Found this helpful? Feel free to share with your network. 🔁 #DataAnalytics #AI #BusinessIntelligence #DataScience #DigitalTransformation