The best way to train your team in data analytics is by breaking down the most complex concepts into simple, relatable examples. Provide hands-on workshops and real-life case studies relevant to their roles. Support them continuously through mentoring, accessible resources, and tools that make analytics tasks easier, helping them build confidence and practical skills over time.

When I was hiring entry level associates for various roles, one of the questions I would ask was “What was your least favorite subject in school and why?” I maintained a tick sheet of the data. In over 15 years of asking this question, over 80% gave an answer that sounded like Math, Business Math, Stats, Calculus, and Algebra. Before you teach / train colleagues on data and analysis techniques, it is important to assess their comfort level with the topic and their willingness to learn. Simultaneously, it is important to illustrate data and analytics doesn’t have to be complicated. It can be simple like my tick sheet. Start with individual / small group dialogue that focus on simple data and stories from a familiar news and sports stories.

To effectively train a team lacking understanding of data analytics: 1. Assess Skills: Identify knowledge gaps. 2. Set Goals: Define learning objectives. 3. Relate to Tasks: Use real-world examples. 4. Start Simple: Cover basics before advanced tools. 5. Hands-On Practice: Use sample datasets. 6. Online Training: Leverage courses like Coursera. 7. Team Projects: Encourage collaboration. 8. Track Progress: Use quizzes or projects. 9. Promote Culture: Discuss data in meetings. 10. Offer Support: Provide mentors and resources.

I definitely agree that the first step for and in every training is to pick up the participants and gain an understanding of where they stand with their knowledge. Which media is used at the point or whether it should take place in an appointment is initially secondary. It is important to collect the level of knowledge and associated expectations at the beginning, of course after presenting the objectives of an effective workshop.It must be ensured that the same language is spoken, for example "Incidents Response" a term that is understood differently... After you reached an common understanding of the topic, start your analyze of the knowledge gaps... and I don't want to say anything more about it for now 😉

To creat a enhancing data analytics skills for networking engineers can be a game-changer. Here my five : 1. **Leverage Industry-Specific Tools: Introduce your team to analytics tools tailored for networking. These tools provide hands-on experience. 2. **Host Workshops and Webinars: Regularly organize workshops and webinars led by analytics expert. 3. **Promote Certification Programs: Encourage your team to pursue certifications. 4. **Create a Knowledge-Sharing Culture: Foster an environment where team members can share insights and learnings from their projects. 5. **Implement Data-Driven Decision-Making: Encourage your team to use data analytics in their daily decision-making processes. Create KPI data for our team. Hope this help

To train a team with limited data analytics knowledge, start with a skills assessment to gauge their baseline understanding. Begin with foundational concepts like data types, cleaning, and visualization, using plain language and real-world examples relevant to their roles. Introduce intuitive tools like Excel or Google Sheets for basic tasks, gradually progressing to tools like Power BI or Tableau. Incorporate hands-on exercises with real datasets and encourage self-paced learning through tutorials or online courses. Provide mentorship, regular feedback, and collaborative projects to reinforce skills. Monitor progress, adapt the training as needed, and celebrate milestones to build confidence and engagement.

I find in less analytical team set ups, data makes more sense when one understands the process flow and the data inputs. The following steps can be used: 1. Recreate the process flow - through visual simulation of the real life processes. 2. Encourage collaboration - identify key performance areas and possible bottlenecks. 3. Make analytics relevant - Apply data analytics to each key performance area in an understandable format. 4. Continuous improvement - constinuosly improve on data collection and analysis thereof