BD #18 - A Template Data and Analytics Graduate Programme

This week I want to share a template for a data and analytics graduate programme that I've deployed with great success. This should give the organisation real results that it can leverage value from and give the graduate a broad experience across core data roles - allowing them to choose a path to further development.

A word of caution: I wrote this to be general and aimed at business stakeholders and leaders so that any data professional could share it with them, as is, to build the business case for a graduate programme in their organisation. This means some of the wording and definitions is high-level and vague. If you don't agree with them, change them! And be sure to let me know. This is an abstraction built from running several graduate programmes and a template that has worked for me - I hope it, and the accompanying fluff, serves you just as well.

The case for grow-your-own

The data and analytics space is complex and growing fast, with an increasing awareness of the requirements and hurdles for organisations to get the most from their data. New roles are emerging and existing roles have changed frequently over recent years.

With this in mind, bringing in aspiring data professionals to fill niche and specific roles is less than optimal for the following main reasons:

1. They might not fully understand what they would like to do day-to-day. What type of work best suits their skills and most fulfills them is hard to gauge fresh out of university.

2. The organisation’s requirements and needs will change over time. Many organisations are feeling the pain of having brought in bespoke data science teams, only to find they jumped the gun and are now trying to get these scarce, expensive individuals to perform tasks tangential to their primary strengths.

With this in mind the following, simple but effective approach to a graduate program allows participants to experience the types of work required for each role before picking a specialism to focus on more deeply and grow into.

The 24-month programme

This model follows a typical approach of rotating the main role in six-month blocks. In this program, however, the sequence of skill and experience acquisition is important as they build on one another (moving from general to niche in applicability).

A note on certifications: each of the rotations mentions training. I think it's good to arm graduates with structured learning where possible, whether that's self-paced or instructor lead. For some organisations certification makes sense (consultancies love badges!). This can also be a compelling selling point to attract more graduates in the future. Not all qualifications are made equal though, only you will know what makes sense for your organisation.

Fundamentals (months 1-3): this first three month period is essential for new graduates to attain the requisite skills to contribute to projects throughout the organisation. In this period:

• they will go through the normal onboarding

• learn about the company - its departments, culture, teams, tools, and revenue centres

• learn about the market and wider context basics

• take training courses in SQL, Python, and infrastructure/Cloud Solution Provider (CSP) essentials

Rotation 1 - Data Engineering (months 4-9): all data roles require datasets. Managing, accessing, moving, and curating these datasets is the primary role of the data engineer.

In this rotation they will:

• contribute to ongoing data engineering and data platform projects

• get exposure to a data ingestion project - bringing data in from a source (could be internal or external depending on size/complexity of your organisation)

• work with downstream data users and stakeholders to achieve their goals by adapting or creating data pipelines and storage systems

• Work with the data quality, catalogue, lineage, and reporting tooling to understand where data is and how to use it

• Build solutions using the CSP

• support this work with further training in the CSP and infrastructure you use

Rotation 2 - Data Analytics (months 10-15): data analysts answer the question “what does the data tell us, what happened, and why?”. This role requires taking business questions and turning them into either one off reports or dashboards for ongoing decision support.

In this rotation they will:

• be introduced to the visualisation and reporting tools and development processes used in the organisation - Tableau, Looker, Power BI, etc.

• contribute to the maintenance on development of BI reports

• take a project from business question, through to data exploration, and report delivery to answer questions from a business sponsor

• Work with the data engineering and data science teams to uncover data and insights relevant to the end users

• they will undergo training in the specific BI tooling used by your organisation

• (they might benefit from further training in SQL or statistics if these are weak points and/or especially pertinent to the work they'll be doing in your organisation)

Rotation 3 - Data Science (months 16-21): data scientists work with advanced analytics, statistics, and computational resources to uncover patterns in datasets. These insights can then be used to generate enhanced predictive capabilities or massively scale decision making processes using automation and machine learning.

In this rotation they will:

• take a project from business case and question formation, through exploration, hypothesis testing, all the way to model development and presenting their findings

• where applicable they will get hands on with their own model deployment, supported by data engineering and development teams

• perform analysis on existing data science efforts and contribute to them

• they will undergo basic data science and machine learning training

Rotation 4 - Specialisation (months 22-24): this final three month period allows the graduate to continue some of the work from the previous rotations, selecting that as their specialisation. This will be a strong indicator for the type of role they should move into once they’ve completed the program.

They should in this time:

• complete another self-guided project, lead and owned by them (with support from the wider data and analytics team)

• outline their continual development plan, identifying and taking ownership of their training as they grow (in agreement with their manager/technical mentor)

Outcomes:

• four projects spread across the three roles with presentations and deliverables shared

• hands-on experience with the necessary tools for all three roles (including formal training and certifications where applicable)

• extensive experience in developing their own data pipelines for themselves and others, allowing self-sufficiency

• in-depth understanding of the organisation and how data and analytics drives it forwards

The 36-month+ programme

Should you think a longer programme would be more beneficial I would simply extend the specialisation part to fill the remainder of the time - adding in any further, more specific training they may require as they advance. Having a longer period of time should afford them the ability to shadow more experienced members of the team and start to take more ownership of their contributions.

Final thoughts

I strongly believe expecting ready made data professionals to come and contribute is a frustrating and expensive way to build a data team. Much of the value any data professional brings is centred on business and context knowledge, for which there are few shortcuts. Investing in early career professionals gives you the chance to shape their ability to contribute and give them the opportunity to break this great industry.

Whatever you decide, I hope this has been helpful. Any feedback, discussion, or examples you may have of other structures that work would be greatly appreciated.

All the best,Adam

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