🤞🏼Two Truths and a Lie

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Ok the ‘lie’ is that there is no lie.

But there are 2 truth bombs coming your way!

💣 The hardest skills to acquire for data analysts are non technical.

The core skills for successful data professionals are not what you may think.

They are:

• Communication

• Business literacy

• Stakeholder management

These skills take years of experience to acquire. Of course, technical skills are required in these roles as well, but these are far easier and quicker to acquire than the engagement skills listed above.

💣 People coming to data from other domains bring tremendous perspective and experience that ‘pure’ data people won’t have.

These individuals will bring with them an innate ability to ask questions and observe the way we do things from a wholly new angle. They are the ones who will effectively challenge the “we’ve always done it his way” mindset.

For data analytics roles in particular, hiring individuals from non-data disciplines can unlock a host of benefits for your team and your business.

These professionals often bring fresh perspectives, deeper domain knowledge, and exceptional engagement skills that ‘pure’ data professionals may lack. By diversifying your team’s skillset in this way, you could see significant gains in creativity, collaboration, and business impact.

(Remember to check out the results from the last poll - down below👇🏻)

☄️ The Impact of only hiring ‘pure’ data people.

By focusing solely on hiring traditional data candidates, you risk:

🙊 Missing out on essential engagement skills
The technical side of data roles: SQL, Python, and data visualisation, etc., can be learned relatively quickly. (Advanced level of SQL with 5-10 hours/week in 3 months and Python in 6 months.)

However, effective communication, stakeholder management, and business literacy often take years of experience to develop. Without these skills, data professionals may struggle (and be reluctant) to translate insights into meaningful action - which is a key responsibility of data professionals.

😫 Failing to fully understand business needs
At its core, data is a communication discipline. It’s about interpreting information and communicating it in ways that resonate with and can be understood by decision makers.

Without a strong grasp of the business landscape, data professionals will fail miserably at this. This failure has knock-on effects and massively impacts how data teams are seen around the business (i.e. value add vs cost centre).

👩‍🔬 Losing the chance to innovate
People who come to data from other disciplines or areas of business bring with them a whole host of knowledge and skills that pure data professionals won’t have. These individuals will poses an innate ability to ask questions and observe the way things are done from a wholly new angle. They are the ones who will effectively challenge the “we’ve always done it his way” mindset.

🐢 Slowing down decision-making
Data professionals without domain knowledge may spend excessive time trying to understand which metrics matter most. Conversely, someone with industry expertise already knows the levers that drive performance and can focus on delivering actionable insights, saving the business money, and increasing the speed from insight to action.

You can address these challenges by embracing candidates with non-data backgrounds and equipping them with the technical skills they need to thrive in data roles once in roll - if you’re brave enough to.

♥️ Sharing is caring

💪🏻 Building a stronger data team

The first step to fixing this problem is recognising that technical skills, while important, are not the hardest part of a data role. The ability to engage effectively with stakeholders and understand the business context is far more critical to success.

Here’s how you can approach hiring and developing non-data professionals for data roles:

1. Prioritise engagement skills during the hiring process
Look for candidates with strong communication, storytelling, and stakeholder management skills. These are the qualities that will allow them to bridge the gap between data insights and business action.

2. Provide targeted technical training
Technical skills like SQL and Python can be taught relatively quickly. A candidate with basic spreadsheet skills can become proficient in SQL within three months, dedicating just 5-10 hours per week. Python may take slightly longer but is equally achievable with the right support.

3. Leverage domain knowledge as a superpower
Candidates from non-data backgrounds often bring deep expertise in specific industries or functions. Pair this with data skills, and you’ll create professionals who know exactly which levers to pull for better outcomes. It’s a bonus, not a negative - trust me on that.

4. Create a culture of learning
Support your team with ongoing learning opportunities and mentorship. This will enable both pure data and new to data professionals to continuously grow and share knowledge. Centres of Excellence are often a great approach.

5. Rethink your hiring criteria
Expand your candidate search to include professionals from other business disciplines. Their combination of domain knowledge and engagement skills could be exactly what your team needs to excel.

By opening the door to non-data professionals, you can build a team that’s not only technically proficient but also highly effective in driving business outcomes.

Domain knowledge + data skills = unstoppable.

🌟 DIY Coaching For Data Careers 🌟

If you’re interested in data leadership coaching, but not yet ready to commit to the full fat version, then my DIY Coaching could be for you.

DIY Coaching helps data professionals unlock and maximise the potential of their data careers in their own time and at their own speed.

This self-paced email series and accompanying exercises provides actionable tools to help data professionals navigate career transitions, set goals, and achieve success.

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To learn more about this hugely popular product and to purchase, click here!

⚡️ Previous poll results

Last week I asked you whether or not your company operates a formalised intake process for data requests.

Turns out it was an equal split!! Who’d a thunk it…

Here are a couple of comments on this shared by subscribers:

Tristan Burns

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