4 Top-most Priorities and Challenges for Data Leaders in 2024

Advancement and acceleration in AI capabilities (specifically Generative AI) in the last twelve months will make the role of data leaders and professionals even more critical in the coming months. The next year will bring exciting opportunities for data leaders who simultaneously have risks and challenges that will need an ethical and measured approach.

This surge in capabilities will have a profound impact on various sectors within any organization.

For example, generative AI (GenAI) significantly impacts marketing by automating and enhancing various tasks such as media mix modeling, attribution, media scenario planning, and customer segmentation. The integration of GenAI in content creation for marketing campaigns and personalized customer interactions is becoming prevalent too.

Similarly, the demand for real-time data analytics has become more pronounced than ever. The advent of technologies facilitating real-time sales tracking and analysis empowers businesses to adapt swiftly to market dynamics.

Data leaders must not only embrace these developments but also explore novel approaches with AI. The focus on driving value to business will remain center stage in 2024 but these will need to be backed by doubling down on foundational capabilities of data culture, governance, and accountability.

Here are the top four priorities and challenges for data leaders in the upcoming year:

1. Data Leadership for driving organizational success

Ensuring data initiatives remain aligned with broader business objectives should be central to data leadership. The impetus for data teams to continue to be the driving force behind change and decision-making will lead to increased demand for their expertise which leaders need to prepare for.

Effective leadership is at the core of successful data management and analytics. Over the next year, data leaders will need to guide and inspire innovation and create a culture of data-driven decision-making within their organizations. Individual managers setting strategic direction will need to build forums and practices that foster cross-functional collaboration, broad problem-solving, and strategy development.

The current environment could be the best where data leaders clearly establish their and the team’s position as those who are accountable and the driving force behind the organization's data journey.

2. Unlocking business value

Translating data into programs that drive value will need renewed focus and belief. In my view, medium to large organizations should nominate and create a data board of senior leadership/executives who are engaged as partners and co-creators of the programs. This joined and collaborative approach will remove friction and provide a platform that inculcates innovation, ownership, and execution.

Data leaders should prioritize strategies that demonstrate how data and analytics directly contribute to growth, cost efficiency, and overall business performance. Embedding a data mindset that monitors its investment in data and the tangible outcomes would be an ideal way to ensure that data priorities always mirror business strategy.

3. Harnessing the power of artificial intelligence

Data leadership teams need to harness the power of Artificial Intelligence (AI) as it advances and creates new possibilities in data and analytics. Development in large language models (LLMs), computer vision, and predictive analytics will continue to dominate the data landscape.

We will start seeing low to moderate adoption/use of generative AI in Data teams. Generative AI will be increasingly used to convert old programming scripts to current languages during platform migrations thus delivering trust and security.

Also, its application in Master Data Management and data governance will start emerging which will have a very strong and positive impact on the projects worked on by the analytics teams. Data leaders must however remain cautious and focussed to ensure AI-based decisions remain unbiased and transparent.

The U.K. Information Commissioner's Office’s (ICO) guidance on AI and data protection offers insights into building ethical principles to avoid bias in AI models. It will be vital that the ethical impact of AI is kept in mind when building use cases and projects to win the trust and maintain engagement with the business stakeholders.

The rapid advancement will continue into the next few years and bring with it an additional challenge for the data teams to stay abreast of changes and developments in the field at large.

4. The evolution of self-service analytics

Self-service analytics has evolved significantly in the last decade but these have not necessarily kept pace with changes in the broader developments and improvements in the IT (particularly cloud) infrastructure.

Developments in data visualization tools and platforms present a good opportunity to refocus on self-service which will lead to business efficiency and quicker decision-making. Building a data culture will be pivotal to greater adoption and engagement with self-service. This can be driven by building a community of key self-service analytics users who help the data teams drive the agenda.

Conclusion

Data leaders face a changing landscape that demands bold choices, decisiveness, and strong partnering skills to drive business value, exploit the power of AI, and build high-performing teams. They must also navigate the complex and essential privacy, data security, and protection environment to promote the ethical and balanced use of AI.

About the Author:

Amitabh Seli is CDO and Head of Data and Analytics at Danone (UK). He is a commercial-minded Chief Data and Analytics Officer with extensive experience in building enterprise data capability. Seli has led large-scale transformations that have helped organizations leap-frog in becoming data-led. He has led large cross-functional internal and external teams to create products and platforms that empower teams to drive performance and productivity sustainably.

Seli is a trusted advisor to boards and adept at influencing investment and customer decisions. He has in-depth knowledge of building customer and consumer strategies and measurement frameworks by leveraging analytical tools and methodologies.