In the world of technology, generative AI will dominate 2023’s entry in the history books.
McKinsey estimates that generative AI may add up to $4.4 trillion annually to the global economy over the next decade – and it won’t be the only technological innovation to watch out for. With even more developments on the horizon, the race is on to capitalise on the data opportunity in 2024.
To start with, many businesses are empowering their data teams to attack this exciting challenge head-on. Whether it’s bringing in new talent, upskilling the existing team members or introducing no-code tools to lower the technical barrier to entry, data leaders are lining up investment to better leverage innovations.
The question is: how can they ensure profitable returns for their data initiatives? The answer lies in both robust technological capabilities and the right organisational approach.
Aligning data programmes to business goals
An efficient data strategy in 2024 will consider a business’ current capabilities, future goals and strategic assets required for growth. Applying data-driven initiatives only sporadically across the organisation will result in poor efficiency, low productivity and slowing growth. On the other hand, a holistic approach can lead to impressive sales growth of 15 to 25 percent in business-to-business lines.
A robust data strategy is particularly important as the lure of generative AI continues to gain momentum. Generative AI doesn’t have a solution to every business problem. In fact, common organisational issues can often be solved using much cheaper and simpler predictive modelling approaches, for example, around financial forecasting. Given the complex nature of generative AI, a good rule of thumb is to exhaust other options before resorting to it.
For success in 2024, data leaders must avoid the pitfall of rushed adoption. Indeed, of organisations already leveraging AI, an average of five per cent of annual revenue was lost due to inaccurate or low quality data – a direct symptom of poor data strategies. AI outputs are only as reliable as the data fed into models, so organisations must make robust data management a top priority.
The best place to start is addressing any prevailing data quality issues through automation and better data governance. Automation exists not only to free data talent up from manual, time-consuming jobs but also to create the shortest possible path for raw data to reach the hands of decision-makers in the form of accurate, analysis-ready insight. When this is coupled with effective data governance practices – such as masking Personally Identifiable Information and applying the right access controls – the result is secure and reliable data.
Create data leaders to catalyse change
Demonstrating trustworthiness in an organisation’s data is the first step to securing buy-in for more advanced analytic or ML use cases. However, it’s not the only prerequisite – businesses must also ensure commitment at all organisational levels.
While data leaders like Chief Data Officers play a crucial role, collaboration with department heads in finance, technology, marketing and human resources is essential. After all, the transformative impact of data extends across various departments, from finance optimisation and lean inventory management to gaining crucial insights into customer journeys and buyer behaviour.
Every department should take an active role in using data to further business objectives, which is only possible in an environment where they can securely and reliably access, use and interpret the data relevant to them. Automation and data governance processes also support this process of ‘data democratisation’ by establishing a clear data framework for all employees within an organisation. These guardrails for data use also ensure regulatory compliance – helping businesses weather uncertainty and scale without costly surprises.
The year of data opportunity
Leading organisations grasp that data is not just about short-term efficiency gains; it is a potent tool driving continuous business improvement year-on-year. In 2024, data-driven insights will be pivotal to laying the foundations to AI use, meeting evolving customer expectations and fostering a competitive edge.
While ambitions are running high, organisations will do well to ensure they can walk before they can run. Experimentation and innovation should never compromise data privacy and business continuity. For the data leaders of tomorrow, the key is to focus on making data accessible, governed and trustworthy – the rest will follow.
About the Author
Guro Bakkeng Bergan is VP GM EMEA at Fivetran. Fivetran, the global leader in data movement, helps customers use their data to power everything from AI applications and ML models, to predictive analytics and operational workloads. The Fivetran platform reliably and securely centralizes data from hundreds of SaaS applications and databases into any cloud destination — whether deployed on-premises, in the cloud or in a hybrid environment. Thousands of global brands, including Autodesk, Condé Nast, JetBlue and Morgan Stanley, trust Fivetran to move their most valuable data assets to fuel analytics, drive operational efficiencies and power innovation.
