The war for talent is a perennial and ongoing quandary, limiting organisations across every sector in their quest to become more data-driven. Often it can seem as though there’s little end in sight.

Demand for data-focused roles is growing at an incredible rate, and an already stretched talent market is struggling to provide the necessary supply. The latest IT Skills Report by developer screening and interview platform DevSkiller reported a 295% rise in the number of data science-related competencies sought by employers in recruitment processes during 2021. Clearly, organisations are spending a great deal of money and time in bolstering their data teams, but the talent pipeline simply isn’t flowing quickly enough.

Retention is deepening this quandary further, however. Our research showed 87% of data decision-makers believe their organisation struggles to retain talent. And with more than two-thirds of data users considering leaving their job in the next two years, the concern has never been more pressing. With recruitment demand outstripping supply at such a rate, many data professionals are being easily tempted with better offers from elsewhere, a trend that needs reversing quickly.

Acknowledging – not ignoring – the data skills gap

Bucking the trend starts with recognising that the data skills gap is a reality to navigate, rather than a problem to solve. In a period of such talent churn, solid frameworks that allow you to be productive with data, even when you inevitably lose talent and have to train new people, are an absolute necessity.

Part of that comes from embedding data productivity at every opportunity. Right now that’s a common challenge, with many organisations bogged down with migration and maintenance tasks. Two-thirds of those we surveyed (66%) believe their business is wasting time on data preparation, while 81% are seeing poor or incomplete data migration cost them opportunities and revenue, to the tune of up to $43.5 million annually.

This is where having a data philosophy comes in; a robust, process-driven approach to data within your organisation that transcends people can help you stay competitive and make informed decisions amidst constant internal and external change. But what are the main elements that drive a strong data philosophy?

Encoding your own data philosophy

The first step is to take stock of your existing data architecture and examine the current state of play. How are different teams using data within your organisation? What problems are you trying to solve with data? Where is the data coming from and in what form? Do you have any specific security or compliance requirements? Knowing exactly why and how your business currently uses data will help you to make technology-centric decisions based on your unique requirements.

Having a tech stack in place that allows you to apply that knowledge, and be as productive as possible with the data at your disposal, is the next step. Giving data teams access to tools that are user-friendly and taking proactive steps to ensure they don’t become burnt out or disengaged also helps to mitigate the issue of talent churn. After all, organisations that reduce toil for their data teams will be more successful at retaining their best people.

Implementing the right data catalogue and data management tools is also an integral part of bridging the gap between data expectations and data productivity. Historically, data teams have relied on outdated Extract, Transform and Load (ETL) or point solution ELT technology that is unsuitable for working in the cloud, and cannot sufficiently scale with the influx of data being generated.

The modern data estate is spread over an array of applications in a variety of different formats that often don’t communicate well with each other. Deploying a platform that ingests and integrates data from any source into any cloud, regardless of underlying infrastructure, is the way to get a competitive advantage.

Prioritising people to promote data productivity

Aside from technology considerations, having a robust data philosophy is also about ensuring as many people as possible have access to the tools and knowledge that give them the right insights at the right time. Being a genuinely data-driven organisation means that data isn’t merely the preserve of siloed teams of professionals, but a resource that any employee in any department can tap into to improve efficiency and solve problems.

On top of that, visualisation and integration tools are becoming more intuitive and beginner-friendly, allowing for a simpler way for less data-savvy employees to work with complex assets. The emergence of low-code and no-code tools also underlines the demand from organisations for streamlined, automated data integration, which puts valuable skills within reach of many instead of few.

The war for talent in the data world clearly isn’t a problem that is going to be solved in the short-term. So a guiding data philosophy is a must; it can be an oasis in the desert, helping to maintain continuity in the wake of the turbulent job market, and the many other uncertainties in the world right now.

Don’t be fooled into thinking it’s a quick fix, though. It‘s a strategy that needs to be constructed in collaboration with many stakeholders and take into account the specific needs and goals of the organisation. So make sure you put the building blocks in place now to kickstart your organisation’s journey toward data productivity.