Are you looking for a way to identify top talent in data workers? Look no further than university graduates! A degree from a reputable institution is a strong indicator of an individuals ability to commit to long-term projects learn new skills and apply knowledge effectively. While a STEM background can be beneficial, it’s not the only path to success. What matters most is how well a candidate can think critically problem-solve and adapt to new situations – skills that are all transferable from academia to data work. To become a successful data worker, some additional training is necessary to learn the specifics of data work such as tech stacks, methodologies, and the mindset required. Are you ready to uncover the hidden gems in your next hire?
1. Where Can You Find (Good) Data Workers?
1.1 Introduction
1.2 Among university graduates
1.3 Consultants / Freelancers
1.4 Startups
1.5 In-house (after proper training)
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2. What Tools Do You Need for Data Work?
2.1 Introduction
2.2 Hardware
2.3 Basic programming stack
2.4 Cloud platforms
2.5 Big Data tools
2.6 Other tools
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3. Where Can You Apply Data Projects in an Org?
3.1 Introduction
3.2 Strategic decisions
3.3 Customer insights
3.4 Market analysis
3.5 Marketing campaigns
3.6 Specific KPIs based on data-rich processes
3.7 Anywhere else where there is good data
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4. When Is a Data Project Successful?
4.1 Introduction
4.2 When it meets its goals and adds value through them
4.3 When the integrity of the data is preserved
4.4 When everyone involved learns something new
4.5 When there is the potential of reproducibility
4.6 When a new iteration of the project is a worthy possibility
4.7 When it advances your and your team's know-how
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5. When Should You Use A.I.?
5.1 Introduction
5.2 When you have ample data
5.3 When other methods fail to deliver adequate value
5.4 When you need to brainstorm solutions, get text summaries, etc.
5.5 When you need to work with text data
5.6 When you need to interact with users or potential clients automatically
5.7 When it's part of your data strategy
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6. Who Does What in a Data Project?
6.1 Introduction
6.2 Data Lead
6.3 Data Scientist
6.4 Data Analyst
6.5 Data Engineer
6.6 Data Visualizer / Storyteller
6.7 Other, as needed
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7. Who Else Needs to Be Involved (and When)?
7.1 Introduction
7.2 Consultant for Privacy Matters
7.3 Consultant for Data Matters
7.4 Cybersecurity Expert
7.5 Subject Matter Expert (SME)
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8. Why Do Some Data Projects Fail?
8.1 Introduction
8.2 Poor Planning
8.3 Poor Management
8.4 Poor Implementation
8.5 Insufficient Data
8.6 Stale / Bad Data
8.7 Inadequate Data Workers
8.8 Infrastructure Issues
8.9 Other Reasons
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9. Why Data Culture is Key for Any Data-oriented Org?
9.1 Introduction
9.2 Better collaboration among stakeholders and other members of the org
9.3 Smoother implementation of data projects
9.4 Better handling of data, esp. sensitive data
9.5 Data-driven decision-making
9.6 Easier identification of new opportunities in data work
9.7 Growth of everyone involved
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10. What’s Next in Your Data Journey?
10.1 Introduction
10.2 Proof of Concept (PoC) project
10.3 Learn more about data work
10.4 Put together a Data Strategy
10.5 Empower data workers in your org & help them learn too, about your side of things
10.6 Ask good questions about data and relevant processes
10.7 Check out my latest book
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Evaluation Tests
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