🚨⚠️People issues are the biggest risk to funded startups.
55% of startups fail because of people problems, according to a study by Harvard, Stanford, and University of Chicago researchers.
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1. Minimize unnecessary micromanagement
Micromanaging can be helpful in certain situations, the most effective leaders aim to delegate work in order to scale both themselves and their businesses. Our data suggests that micromanaging can be a fatal flaw for CEOs.
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Treat people like volunteers
The best people are like volunteers—they’ll work passionately for a hard, meaningful mission. They
also have options. As the adage goes, people leave bosses, not companies.
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Invite disagreement
You want a conflict of ideas, not personalities. Our data suggests that founders consistently undervalue
giving teams an opportunity to voice their opinions, while employees value it highly. Encourage open team
dialogues early and often.
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Protect the team from distractions
While founders are typically seen as distracted by new ideas, the best ones create focus and clarity. Set clear goals and priorities to build momentum for your team, which in turn fuels better performance and morale.
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Keep pace with expertise
Leaders need to know enough about each role to hire the right people and help develop their team. 93% of the most effective founders have the technical expertise to effectively manage the work.
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Preserve interpersonal equity
Violated expectations are the main source of conflict. The most effective cofounders openly discuss and document what they expect from each other and constantly check for interpersonal equity—do both of you feel that expectations are fair?
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Overcome discouragement
While most would expect self-confidence to grow with time, our data suggests that the most effective founders are not nearly as confident as the least effective. Build a support system and know how to ask for help in order to overcome doubt.
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I tweet about my journey to the data world and some resources along the way. Follow @thecodemancer_ for more!
Tracking your Uber Rides and Uber Eats expenses through a data engineering process
Technologies and skills:
Python, Docker, Apache Airflow, AWS Redshift, Power BI, data modelling, Task schedulling, ETL and ELT processes, Data warehousing, Cloud
☁ You will likely encounter pushback when moving to the cloud. Moving to something new may seem risky and unnecessary to the developers. This requires a cultural shift.
💎 Here are some tips on how to tackle this problem.
1. Sync with cross-functional teams early and often. Train them so they understand the benefits of the cloud and are comfortable and knowledgeable using it.
2. Help teams understand the benefits, the project's processes, the desired goals and outcomes.
1. Was the database schema migrated correctly? 2. Has all the data been migrated? 3. How about user logins? 4. Can all of the users still connect and can users only access the data they're permitted to access?
💡 Developers can focus on code and logic. They do not need to manage clusters or tune infrastructure. They submit #Spark jobs from their interface of choice, and processing is auto-scaled to match the needs of the job.
💡 Data engineering teams do not need to manage and monitor infrastructure for their end users. They are freed up to work on higher value #dataengineering functions.
💡 Pay only for the job duration, vs paying for infrastructure time.