In 2024, organizations must embrace a good Data Strategy, a reliable touchstone created by an organization for businesspeople in their data-related endeavors and support its evolution. Executives face mounting pressure to swiftly adapt to a dynamic marketplace and demonstrate tangible impacts of their data planning. Particularly, business stakeholders want to see value from their artificial intelligence (AI) initiatives.

Meanwhile, leaders face a fundamental disconnect on data maturity, where 94% think their corporation is either on par with the industry standard or the best in class. However, empirical evidence tells a different story. 

The resolution to each data incident rose by 15 hours between 2022 and 2023. Moreover, 92% of technical executives emphasize that trustworthy data is needed more than ever, underscoring the urgent call for better Data Quality.

Achieving adequate Data Quality requires fresh thinking about data strategies. JPMorgan Chase has proactively embraced this approach to land $1.5 billion in business value from AI and machine learning (ML) programs. Moreover, other enterprises are also leaping ahead with successful outcomes, thanks, in part, to a good Data Strategy.

Companies must change their Data Management significantly to remain competitive, necessitating updated strategies for guidance. Fortunately, 2024 Data Strategy trends promise that carefully strategized data acquisition, Metadata Management, guidance around data roles and responsibilities, tactical alignment, and an adaptive mindset will offer companies the benefits of AI and analytics.

Strategizing Data Acquisition

Storing and managing lots of business data as a single-company operation carries increased costs and risks, including additional cloud usage, security vulnerabilities, and compliance with ever-expanding data and AI regulations. Therefore, senior managers will decide strategically on how much data they wish to manage, what to outsource, and why. 

Leaders will choose how much to prioritize business optimization to improve efficiency and grow revenue as compared to business transformation, developing data as a sellable product. A higher priority on business optimization means exploring Data as a Service (DaaS) to access the benefits of big data without the expensive overhead of building an extensive data collection to support AI projects. 

Already, nearly 40% of IT professionals use an as-a-service platform to store and back up their data. This cloud computing trend is expected to continue with Data Strategy implementation, as vertical cloud platforms offer industry-specific solutions. These business capabilities will likely expand to include DaaS, potentially enhancing and reinforcing existing data sets.

Clarifying what data to collect and manage will prove crucial to senior leaders to better allocate resources around Data Quality instead of data quantity. This approach ensures when organizations gather data through surveys or other sources, they have optimized their business processes or have a better data product to sell through their business transformation.

Paying Attention to Metadata Management 

When businesspeople think about a Data Strategy in 2024, they will need to consider how to manage metadata, the tagging that provides context around their datasets, and additional information. Metadata can no longer be overlooked when executing any Data Strategy.

As of 2022, nearly 50% of retailers and wholesalers used location data, which provides valuable context about customers and their proximity to physical stores. LLCBuddy shows that at least 83% of marketers claim that knowing proximity allows them to conduct more effective campaigns and that location-based marketing is expected to expand by 14%.

In its research, Gartner found that 65% of decisions implemented had more complexity than two years ago. So, accurate and continuous context through metadata will be essential in 2024 for organizations to reframe what is essential to the business. 

AI can help achieve these requirements by spotting patterns and making quicker recommendations.  However, even AI needs Data Quality, which includes accurate and relevant metadata to deliver recommendations. Therefore, expect data strategies to incorporate guidance on metadata management.

Guiding Data Roles and Responsibilities

Senior leaders must clarify the roles and responsibilities needed to support the data strategy in 2024, including its data acquisition and metadata management directives. As organizations face resource constraints, leaders must make smart investments in operational efficiency, prioritizing automated data processes and outsourcing data capabilities. Expect discussions about business logistics intertwined with strategy conversations, emphasizing measured progress toward goals.

In 2024, corporations will strive to deliver business value from their data investments. As a step in this direction, 48.1% of organizations have made the corporate Data Strategy a primary mandate of the chief data officer (CDO) or chief data analytics officer (CDAO). While this charge is a positive start, implementing such a valuable Data Strategy across the organization effectively will require an executive to have strong communication and social skills.

Executives must revisit their Data Strategy guidance around Data Governance programming and activities to get effective organization-wide responses and maintain credibility in handling operational challenges. This support must be flexible, scalable, and highly responsive to marketplace volatility.

Additionally, data strategies and their guidance around Data Governance must align with finance departments and their activities. Data teams function as a business unit with profit-and-loss responsibilities. Therefore, data strategies, along with their roadmaps, the strategies’ step-by-step guides, will need to synchronize with the CFO’s responsibilities.

Aligning Data Tactics and Activities

In addition to business value and Data Governance, data strategies and their roadmaps are crucial in steering data tactics, such as data modeling, toward unification across corporations. With the enforcement of data regulations and restrictions around AI usage, business leaders must understand data strategy implications from end-to-end across the company. These factors will impact how executives develop their data strategies.  

Successful organizational alignment through data strategies will rely on senior managers who clearly define organizational structures and show integration among roles, responsibilities, processes, and technologies. Data strategies will serve as a holistic vision from these outcomes with a shared understanding across different business units.

However, achieving this synchronization without interrupting or interfering with business units’ processes will remain a significant challenge. Therefore, coordinating data tactics and activities across internal teams will require leaders to ensure data strategies and their roadmaps agree with any implemented business strategies and change management plans.

Fortunately, demonstrated results from broad data policy implementations, through metrics and subjective feedback, will encourage businesspeople to notice the benefits of organizational alignment around data. The increasing use of data observability, a method of monitoring and analyzing the health of a company’s data and data systems, will inform how well data strategies and their roadmaps synchronize data to serve the entire business.

Evolving Strategies to Service Machine Customers and Other Developments

In addition to synchronizing tactics, data strategies and their roadmaps will need to adapt to keep pace with fast-moving technical innovations. For example, Gartner has identified a new trend where machines, like the Internet of Things (IoT), will act as customers or custobots, becoming active participants in transactions. 

By 2030, CEOs believe custobots will account for 20% or more of their organization’s revenue. Looking even further ahead, by 2036, custobots are expected to pay for goods as humans do today. If that forecast becomes a reality, Data Strategies will need to evolve accordingly.

Strategies will need to acknowledge that machine customers have distinct data interaction patterns compared to humans. Custobots primarily engage through application programming interfaces APIs, potentially requiring digital storefronts to be revamped. Furthermore, the increasing use of AI creates exponentially more algorithms, demanding scalability.

While the long-term impact of AI-driven machine customers and other newer trends on specific data strategies and their roadmaps may not be apparent, strategists and executives should closely monitor further technological developments in 2024 and beyond. Leaders will listen to business and technical feedback to identify when updates to data strategies are necessary to accommodate machine customers and related changes. Additionally, managers will consider evolving their data strategies to adapt to these transformations effectively.

Conclusion

Data strategies and their roadmaps will undergo significant adaptations to meet business stakeholder demands and to keep pace with data innovations. To do so, executives and leaders will have to create and implement thoughtful strategies around data acquisition, metadata management, data roles and responsibilities, and aligning data tactics and activities. Organizations will need to anticipate disruptions to their strategy in the next decade from emerging technologies like machine customers and plan to evolve their strategy accordingly.

Decisions around business optimization and transformation will inform how to update data strategies. The closer tie of data to revenue and the CFO position will impact how data strategies are implemented more. By embracing these strategic considerations, organizations can better leverage data for business success in 2024 and beyond.