This blog is written by Data3 Founder and CEO, Helen Tanner.
You’ve created your data strategy…
- you’ve got a data plan…
- you know where to extract your data from…
- you know what your users require…
- you know what tools you’re going to use…
- you have some ideas for data visualisation…
- you’re ready to get started…
So now you’ll need some help.
We recommend working with a multi-skilled team on any data project as no one person can do it all. For any data project, you need skills ranging from consultancy and client management to coding and engineering experience, through to data visualisation and insight generation. Rarely, if ever, do you find one person who’s experienced, capable and motivated in all of these areas. So, a team approach, using individual skills and experience, is the right approach for most data projects.
There are six key roles in any data project…
The Data Consultant’s role is to translate between the technical team and the business. They need to have enough understanding of data engineering to make it simple for the business. They need to make data simple. They need to act as a go-between with data engineers/architects and business users. They need to translate business requirements into technical requirements. And they need to be prepared to answer questions from either side. These people are rare, as most people are either technical or business orientated.
The skills required for a Data Consultant are:
- Commercial – they understand how a business makes money
- New Product Development – they understand how to turn data into business-facing solutions
- Data Governance – they know that data processes need to be robust, secure, and compliant
- Leadership – they’re confident being the person accountable for a data project’s success
The Data Architect’s role is to design the right solution so it’s robust, secure & scalable. This person needs to use the business requirements to design the right data architecture for a given project. They need to consider known and defined requirements today, as well as future, undefined and unknown requirements. They need to apply their learnings from past projects, to ensure the data architecture is flexible, scalable and future-proofed.
The skills required for a Data Architect are:
- Engineering – they have tons of experience designing, building, testing, launching, and managing data architectures
- Data warehouse design – they understand what good data warehouse solutions look like, how they vary and the pros and cons
- Data workflow design – they know how to plan and build data pipelines so data is stored and transferred successfully
The Data Wrangler’s role is to extract, transform & load data. This could involve multiple data sources, in different formats, from different locations. This person needs to be hands-on and happy working down on the detail of getting data into one place. They need to be a problem-solver, a trouble-shooter, and relish finding solutions to a variety of challenges that come up along the way.
The skills required for a Data Wrangler are:
- ETL – they’re confident extracting, transforming and loading data
- Python/SQL – they can use code to change the format of data, apply business logic and get data into the format required
- APIs – they know how to connect to data from different third-party platforms
The Data Analyst’s role is to analyse the data and find new patterns and trends. This person needs to be naturally curious, always asking questions, and keen to find out more. They need to be customer-orientated and focused on how data analysis can help solve a business problem or support a new business opportunity.
The skills required for a Data Analyst are:
- Requirement gathering – they need to dive into the details of the business requirements to ensure they fully understand the business value of potential data insight
- Analytics – they need to be able to slice and dice data in different ways to show different trends
- Statistics – they know how to use models to test, validate and prove the patterns found in the data
The Data Scientist’s role is to create advanced AI & predictive models. This person needs to build on the knowledge gained through standard data analytics and go deeper into the data. This person needs to be an expert in statistics and data modeling. And know how to apply these tools to specific business problems, so the insight gained is meaningful and actionable.
The skills required for a Data Scientist are:
- Statistics – they need to know how and when to create statistical data models
- AI – they need to be able to apply artificial intelligence methodologies to data
- ML – they have experience of applying machine learning approaches to create predictive models
- NLP – they are used to applying natural language processing to unstructured data
- R – they are experienced in using coding languages, such as R, to perform data science
The Data Visualiser’s role is to create intuitive user interfaces. This person needs to be someone who is both highly visual and user-orientated. They need to be able to make complex data simple. They are focused on how a user can benefit from seeing the data and give them exactly the tools they need. The end result is a simple, intuitive, sometimes beautiful dashboard, report or graph.
The skills required for a Data Visualiser are:
- Data storytelling – they are great at making data simple for their users
- Design – they love making data look easy-to-understand and stylish
- User interface development – they plan, build & test data tools for their users
- Insight – they are experienced at deriving the ‘so what’ and value from data
- Power BI/Tableau/Qlik – they are confident using data visualisation tools
What type of data person are you?
Most people usually put themselves into one of these six types of data roles. Sometimes two. But never, in our experience, would a data expert put themselves into all six roles. Think about what you’re good at, what your experience has included, and what you love to do.
No single person can do everything when it comes to data
So, we recommend that you bring together a team of data experts who can all play their specific role in your data project. Every project we work on has multiple data experts involved, at different stages, for different time periods, as no single data expert is the best person for every aspect of a data project. It’s a team effort.