Most executives agree data-driven operations across lines of business is key to a winning strategy. Illustrating that point is the 85% increased investment in digital capabilities and 77% increased investment in IT, as reported in the 2022 Gartner CEO and Senior Business Executive Survey.

Giving your employees the ability to access and make sense of their data, whether they sit within technical teams or not, is therefore crucial to your success. Your data needs to be democratised across the business, although this is often harder than it would seem.

According to New Vantage Partners’ Data and AI Leadership Executive Survey 2022, only 27% of organisations have managed to nail this, with another 19% struggling to establish a data culture. Through 2025, 80% of organisations seeking to scale digital business will fail because they don’t take a modern approach to data and analytics governance, as stated by Gartner’s State of Data and Analytics Governance.

Unfortunately, modernising tech stacks and migrating to the cloud are not enough to put the right data in the right hands of everyone across the business. Organisations must modernise their governance practices to fully uphold their efforts.

So, what should business leaders think about before they hand over the key to the treasure trove of data to the rest of the organisation? My top six considerations would be:

Take an end-to-end perspective: Successful data governance needs to be implemented from end to end, encompassing your entire data landscape from your data warehouse to your analytics solution. It’s like any process: if it’s not governed all the way, then you cannot control the end result. On the whole, data governance is about ensuring that the KPIs on which you are basing your business decisions are correct and trusted - having a process in place that ensures secure data is delivered to end-users who have the confidence to use it to make real-time decisions.

Consider the power of synthetic data: Data that is artificially created enables organisations to model innovatively for things that have never happened before while jumping over some of the privacy, copyright and ethical hurdles associated with the real world. It holds great potential for highly regulated industries like healthcare and financial services. And for anyone questioning its validity, research suggests that models trained on synthetic data can be more accurate than others. This is why synthetic data is rising in popularity and looks to completely overshadow real data in AI models by 2030.

Automate data delivery: Solutions are now available to help businesses move away from manual data delivery with automation. This approach enhances control in a number of ways. Automated solutions allow governance to be embedded into the process with rules and policies. They can also inject bespoke data-quality improvements based on the individual and/or workflow. And when you automate data property identification along the pipeline, you prevent users from seeing what they shouldn’t.

Take a case-based approach to cataloging: Giving more access to data and analytics increases risk with the complexity of managing and securing more users. But democratising access to data is vital for any organisation to reap its true benefits. That’s why establishing a data and analytics catalog will help mitigate the risk. In terms of the data, everyone within the organisation can see which data is securely available to them in one simplified view, and the IT team knows the catalog is secured by identifying and masking the data on user types and access rights. From an analytics perspective, the notion of a business glossary and re-usable assets, along with data lineage and impact analysis, provides additional context to the data, thus driving more consistency, understanding and faster, actionable insights.

Data lineage for full visibility: More users also mean more risk of errors. Data lineage gives you the ability to understand and visualise data flows from source to current location. With the ability to discover, track, and correct data process anomalies, businesses can meet data governance goals and lower the cost of regulatory compliance, increase trust and reliance on data across your organization, and improve data analysis and, thereby, business performance.

Take it step-by-step: As with many large-scale, high-risk digital transformation projects, taking a ‘think big, start small and scale fast’ approach to data governance is a sensible one. Keeping an outside-in perspective is also helpful, particularly if you use self-service analytics. By this we mean starting your data governance journey by getting an overview of your entire data landscape, identifying which inconsistencies, objectives and errors are most important, and building your efforts from there.

There’s no doubt that every business could benefit from data democratisation, and that appetite to invest in data and analytics continues to grow, with 93% of companies indicating they plan to continue to increase budgets in these areas. But, rapidly shifting rules and regulations around privacy, as well as the distribution, diversity and dynamics of data, can make it a daunting process. By taking an end-to-end perspective, a step-by-step approach, considering the power of synthetic data and investing in automated data delivery or case-based cataloging, there’s no reason why even the most risk-averse organisations can’t put the power of data into every single worker’s hands.