The demand for top data talent is getting increasingly higher as organisations start to realise the benefit from investment in data, AI and ML.
However, if they wish to attract the right people, they need to invest in building the right culture for them to thrive. And this means putting as much focus on people and processes as on technology.
In this blog, I’m going to outline five key areas of focus to enable your organisation to build a world-class data culture that attracts world-class engineers.
The data industry is seeing a revolution that has happened before in other areas of the tech industry.
The Agile Manifesto was penned 21 years ago, promoting cross-functional teams, decentralisation, user-centricity and so on, representing a profound willingness to rethink traditional roles and break down barriers between teams and departments.
This pattern then expanded into new territory a decade later with the arrival of DevOps culture.
And now, we are seeing the same thing in the data domain.
If we can learn anything from the past it’s that Rome wasn’t built in a day. But looking back we can see that the people who launched successful Agile and DevOps transformations were prepared to deeply rethink how they worked.
If you are looking to reorient your company culture around data, you will need to have a similar willingness to break the mould of how things have always been done. Risks will have to be taken and there will be plenty of trial and error involved.
But big impacts need big changes!
Read more about how this approach put data at the core of innovation for a Financial Services Organisation here.
In order to secure long-term investment for your data strategy it will have to contribute obviously and measurably to business goals.
There’s no point building a world-class data team unless you have business backing and it aligns to the business strategy.
Far too many businesses have expensive data experts that are spending vast amounts of time on projects that are of interest to them but that don’t align with the business.
In order to secure the buy-in, investment and adoption that is required to make your data strategy work, your data teams must have key business objectives at front-of-mind.
Often, there is an expectation on business folk to be data literate. But, as data teams become federated throughout the business and closer to business domains, it’s imperative that they become business literate so that their work is contributing to the business.
Teams learn more when we remove siloes and break down barriers by moving towards cross-functional teams.
Moving away from hyper specialised teams immediately reduces what we perceive as a skills gap given that teams can be balanced more effectively. The added advantage of cross collaboration and pollination means we intrinsically upskill each other, almost by osmosis.
Zhamak Deghani, who coined the term ‘data mesh’, makes a keen observation that summarises this well: “some data engineers, while competent in using the tools of their trade, lack software engineering standard practises, such as continuous delivery and automated testing, when it comes to building data assets. Similarly software engineers who are building operational systems often have no experience utilising data engineering tool sets. Removing the skillset silos will lead to creation of a larger and deeper pool of data engineering skill sets available to the organisation. We have observed the same cross-skill pollination with the DevOps movement, and the birth of new types of engineers such as SREs.”
The UK is very much on the front foot with a bullish national AI strategy that plans to make the UK an AI superpower in the next 10 years.
There is departmental spend and support committed to building the workforce of tomorrow.
The commercially savvy will be able to find the funding to ask someone other than the CEO to approve their upskilling budget for 2022.
The government website has a list of skills bootcamps and I can connect anyone who’s interested with bootcamps, accelerators and upskilling for both tenured and also early career individuals.
This is a great way of future proofing your workforce, retaining teams and ensuring your company has the skills set up for future success.
Humans thrive on connectivity and our success as a species boils down to our ability to learn from others.
Get plugged into communities through Slack channels, Meetups and industry events that give you exposure to those who are on similar journeys to you.
Here’s a few good ones to name but a few:
- Data Mesh Learning (Slack & now a podcast)
- MLOps (Meetup)
- GTA Data Tech (Slack & meetup)
Start following thought leaders - Francois Nguyen has a great piece on data team topologies.
If you’re curious on why this all matters I’d encourage you to read:
- “The Art of Business Value” by Mark Schwartz.
- “Building event-driven microservices” by Adam Bellemare
- “Designing Data-Intensive Applications” by Martin Kleppmann
In the event the above whets the appetite but you want to learn more I’m happy to open up a world of insight & community. You need only ask.
The above tips might seem a little trite, but unless you’re putting them all into action you may struggle to see the results you want.
Having done it in our own organisation and with many of our clients, we are happy to talk and guide anyone through this shift.
The movement is an amalgamation of people, process and technology; if you can balance that vortex you’ll find massive business value.
If you’re interested in joining the Mesh-AI team - we’re hiring! Get in touch with us at careers@mesh-ai.com or find our open roles here.
If you want to be competitive, you need to sort your data constraints, and that's where Mesh-AI can help. Identify the areas in your organisation that require the most attention and solve your most crucial data bottlenecks. Download our Data Maturity Assessment & Strategy Accelerator eBook.
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