Data is everywhere. It seems to be the most urgent and at the same time elusive theme of our time. Trendwatchers are tripping over each other to interpret how organizations should exploit the 'new gold.' The trends and developments are flying at us from all directions. It is a challenge to distinguish fashion fads from data concepts that are truly relevant to the development of a future-proof organization.  

As trends are often relevant for more than 1 year, the trends of 2023 (see earlier blogs here, here and here) formed the starting point for the trend analysis of 2024. This ensures a consistent level of alignment between the trends. In this blog, we guide you through the sense and nonsense of the data trends of 2024. What are these trends, how do they differ from those in 2023, and what is needed to make them a success? Looking at trends that are new, trends that have changed and trends that stayed the same will bring you up to speed for 2024. 

New trends in 2024 

In 2024, new advancements unfolded in the realm of data. In this section, we will provide an overview of the emerging trends that surfaced this year.  

  • Ethical uses of data 
    With the rise of AI, organisations need to take a strategic stance on if, how and where to deploy this and provide an ethical framework for how the organisation handles data. The rise of generative AI has pushed the ethical use of data into the boardroom making it one of the hot topics for 2024. 
  • Data-centric modelling 
    Data-centric modelling refers to a framework to develop, iterate, and maintain data for modelling purposes. This approach emphasizes the importance of data quality and organization, which is crucial for accurate and reliable modelling results, leading to better insights and decision-making.  
  • Hybrid AI 
    Hybrid AI can be seen as the perfect combination/integration of machine learning and machine reasoning (causal artificial intelligence). By integrating these two approaches, Hybrid AI can achieve a powerful synergy, enabling AI systems to not only recognize patterns but also understand why those patterns occur. This deeper level of understanding allows for more accurate and context-aware decision-making, leading to more efficient and effective AI applications across various industries and use cases. 
  • Enterprise AI integration 
    Enterprise AI integration describes the trend of companies becoming more AI mature, streamlining the process of developing and deploying data models and ensuring the business can use these models. It is a shift of focus on quality models and streamlining the development process, not on the quantity of solutions that are being used. Note that a company's data maturity plays a large role in this. 
  • Generative Artificial Intelligence (AI) 
    Generative AI is a type of AI that can create new and original content, using complex algorithms and methods of learning inspired by the human brain. With the correct controls in place, generative AI can provide more time for creativity, demonstrate the boundaries of knowledge, and act as a sparring partner to challenge conventional thinking. Still in its infancy it is known mostly for generating text and images (ChatGPT, MidJourney, Copilot, etc.) in large quantities, but could expand to other fields such as the generation of new medicines, vehicle design, and many more impactful areas. We must be mindful of its risks, ensuring we understand the consequences of the generated results.  
  • Edge AI 
    Edge AI is intelligence that runs at the edge of the network, close to the end user, instead of in a central location. This improves real-time on-site insights, increased availability, and increased privacy. The availability of many IoT devices and advances in computational power in small chips both are important enablers for Edge AI. 
  • Digital Identity Architecture 
    A digital identity architecture is a framework that ensures data storage, data governance, and automation operate in a decentralized, transparent, and secure manner. It enhances explainability, minimizes bias, mitigates risks, and enforces privacy. By employing zero-trust architectures, digital identity systems, and privacy engineering, it empowers users and assets with greater control over their data and its usage. This trend was shaped by the technological advancements in blockchain technology and the metaverse but add the need for a transparency- and privacy-focused mindset (e.g. the AI Act). Therefore, this trend encompasses these and other types of technologies, without having a specific technology as a focus. 

Changed trends from 2023 that are still relevant in 2024 

Some trends that were identified in 2023 have changed during the year. In this section, we will give you an overview of 2023 trends that remain relevant for 2024 although with some changes. 

  • Skills and literacy shortfall 
    Through 2024, it is expected that most organizations will fail to foster the necessary data literacy within the workforce to achieve their stated strategic data-driven business goals. Despite economic slowdown, data skills and literacy remain in high demand and people with these skills can still demand a premium as cost-cutting projects often look for automation and date-driven solutions.  
  • Complying to laws and regulations 
    With the introduction of new legislation that is either directly related to data or impacts how data is managed, organisations are starting to face new legislation that require their data management practice to be, either directly or indirectly, at a minimum standard.  With new laws coming into effect, no longer legislators and regulators that only have an interest in legislation/regulation with a strong data component. 
  • Connected governance 
    Organizations need effective governance at all levels. Connected governance establishes a virtual D&A governance layer across and aligned with other business functions and geographies. Setting-up effective governance across organisational units & -geographies remains a challenge for many organisations to focus on in 2024. However, alignment with other functions (e.g., IT and security teams) are essential to make them successful. 
  • Intercloud data management 
    With data ecosystems becoming more distributed data management must adapt accordingly by spanning cloud services from multiple vendors. Finding solutions for governance challenges across cloud platforms remains a challenge and relevant for organisations in 2024. 
  • Data ecosystem management 
    With the increasing use of data made available by third parties the need to expand the data management capability across the value chain becomes more and more apparent. Finding solutions for governance challenges across the value chain remains a challenge and relevant for organisations in 2024.  
  • Augmented data quality 
    As the need for good data quality is increasing due to analytics becoming ever more prevalent, new ways need to be found to improve data quality of different data types. Augmentation of data quality by using AI is seen to improve data quality. With AI becoming mainstream using AI solutions to improve data quality will be propelled forward. An increased range of data types (e.g., hologram &VR data) increases the need for automated data quality augmentation. 
  • Data democratisation 
    Clients and users demand their data is always available when they need it. This requires a democratisation of data across different platforms using a range of technologies. To succeed in promoting data sharing and increasing access to the right data aligned to the business case, collaborate across business and industry lines is needed.  The term data on demand / data as a service has evolved into data democratisation. The underlying concept of the right data being available to everyone at the right time however has remained the same. One thing that changed though is the shift from the concept being focussed on technology (e.g., data fabric or data mesh) to a more outcome-oriented discussing with technology only providing an enabling role. 
  • Automated decision making 
    Automated decision-making means making decisions based on the output of algorithms that are automatically produced. This can also be seen as data-driven decision making with a high-level maturity. Technical areas already are working with this principle, for example in factories or autonomous driving, but business decision making still has a lot of grow potential in this area. 
  • Democratising analytics 
    Democratising analytics concerns enabling a larger part of the business to interact with data and unlock insights, using simple data analytics tools. This allows for more data-driven decision making across the business. Key to realising the democratisation of analytics is for the users to have an adequate level of data literacy and good understanding of the concerning data. Generative AI could play a big role in this. 
  • Composable Ecosystems 
    Composable ecosystems function cohesively through seamless integrations, governance and technical interoperability and apply the core principles of composability in assembling and deploying configurable applications and services. We see that the drive towards data centralisation is diminishing, and that a singular architecture is also being reduced. Instead, this trend encompasses the steps to use existing systems and embed them and use them as-is, for example using data mesh as an approach. 
  • Practical Data Fabric 
    Practical data fabrics leverage individual data domains or specific types of metadata to enable business use cases like data sharing while creating a foundation for comprehensive data pipeline designs. Within a practical data fabric, focus is not purely on the metadata only that drives a fabric, but also the technological combination of data sources and its use, resulting in higher data values. 
  • Intelligent Applications 
    Intelligent applications have the capability to adapt to user context and intent appropriately and autonomously. They focus on user experience and have the goal of recommending, augmenting or automating work instead of providing analysis. This trend expands on the large steps taken in personalisation of applications, but also looks towards the capabilities of AI to tailor applications to individuals. With intelligent applications companies will be able to really deliver value to everyone. 
  • Platform engineering 
    This trend focuses on the discipline of building and operating self-service data, analytics and developer platforms. Each platform is a layer designed to support the needs of its users by interfacing with tools and processes. While the original platform engineering trend mainly focused on (application) developers, we see that self-service platforms are applicable to other fields, especially data and analytics. A data platform, for example, can be an interface to the insights and raw data that a company generates, making it available internally and externally. This approach will improve the reuse of both data and algorithms. 
  • Hyperautomation 
    The orchestrated use of multiple technologies, tools or platforms to identify, vet and automate as many business and IT processes as possible. There where automation is possible, automation should be investigated. One should, however, evaluate the quality of the automation and ensure monitoring of the automated result. Through automation results will be achieved quicker and objectively. 

Unchanged trends from 2023 that remain relevant in 2024 

As trends tend to stay relevant over a longer period, some of the trends that were identified in 2023 are still relevant in 2024 in their original form. In this section, we will give you an overview of these trends. 

  • Becoming a data driven business 
    Where data management once was the responsibility of a separate data management department, it is becoming fully intertwined with business departments. Becoming (more) data driven across remains a challenge and relevant for organisations in 2024. Despite economic slowdown, being data driven is now also seen to cut costs.  
  • Insights narrative 
    Insights Narrative combines interactive data visualization with narrative techniques to deliver insights in compelling, easily assimilated forms and are used to inform and educate decision makers. This is still relevant as a great way to present insights to all kinds of different stakeholders. 
  • Industry cloud platforms 
    A combination of SaaS, PaaS and IaaS along with tailored, industry-specific functionality to offer the agility needed to respond to continuous disruptions. This trend is still valid, and links very well to the data ecosystem management (by using multiple cloud platforms) and platform engineering (making data and insights readily available). 
  • Decision intelligence 
    Improve decision making by explicitly understanding and engineering how decisions are made and how outcomes are evaluated, managed and improved by feedback. The executional aspect of this trend also requires both a data engineering and tool development focus. 

Urgency and complexity of trends 

The trends mentioned above differ from each other in terms of their urgency and complexity. To make the relationship between them more transparent, the trends were compared on these two axes (see the figure below). In addition, the extent to which these trends are mandatory or contribute to the differentiation of an organization has been considered. This has been done by classifying the trends into three categories: 

  • Stay in business: following the trend is necessary to ensure the survival of the organization. This can be due to regulations or because all organizations in a sector have already achieved this. 
  • Grow up: following a trend demonstrates the maturity of an organization in the field of data management. 
  • Ahead of the curve: following the trend provides a differentiation for the organization compared to similar organizations. 

Figure 1: data trends for 2024

In this blog we gave an overview of the data trends that we think will be the most relevant for 2024. If you would like to know more about how these trends can be applied to your organization in the meantime, we welcome you to reach out to discuss this further! 

Contact

Joris Schut
Manager
joris.schut@bearingpoint.com

Joost Kuckartz
Senior Consultant
joost.kuckartz@bearingpoint.com

Joost van der Ploeg
Senior Consultant
joost.vanderploeg@bearingpoint.com