Published: 24 January 2024

Reading time: About 5 minutes

As we enter 2024, it is clear that the increasing growth and use of data is impossible to ignore both in our everyday lives and as technology advances, data is certainly the lifeblood of any business. Organisations have access to exponentially growing volumes of data in more diverse formats than ever before. To gain a competitive edge, companies must implement robust data strategies that allow them to harness the power of their data.  

So, what should we expect to see in 2024? Several key data management trends and predictions will define best practices in the year ahead. In our latest blog, we look at these trends focusing on data privacy and governance, expanding the use of artificial intelligence (AI), leveraging unstructured data, and emerging areas such as ESG, natural language processing (NLP), hybrid cloud security, and implementing automated data tools.

1. Data Privacy and Governance Take Center Stage 

With regulations like GDPR in full effect, organisations must ensure they collect, store, and use customer data in a responsible manner. Data governance frameworks provide guidelines for ensuring data quality, security, compliance and minimising risk. Not only are organisations at risk of hefty regulatory and legal fines but protecting their reputation is a huge factor to consider.  

Leading organisations are investing in data cataloguing and lineage mapping tools. These tools allow companies to discover what data they have and how it flows through their systems. This metadata aids privacy and compliance efforts while enabling better data insights. 

Furthermore, with the increase in LLM (Large Language Model) and Gen AI models, robust governance practices are a must within an organisations data strategy to remain compliant and ethical in their handling of data, this is where the complexity of these models and data sensitivity becomes a focus.   


2. Artificial Intelligence Moves Mainstream / Pitfalls of Gen AI & LLMs 

Following the explosion of AI in 2023, the continued use and adoption of AI and machine learning will become mainstream in 2024 for garnering insights, reshaping data and analytics, predicting outcomes, and automating decisions. Managing the pipelines that feed data to AI systems will be crucial. Companies must also rigorously monitor AI outputs for accuracy, fairness, and explainability. 

As organisations depend more on AI systems, implementing AI governance becomes pivotal. There is no doubt AI is revolutionising often time-consuming processes and complex data management and preparation tasks; however, organisations are also encountering pitfalls when they attempt to implement Gen AI and LLMs into their data strategy. These include issues with ethical compliance, data quality, perpetuating harmful biases, security and privacy vulnerabilities and the need for continuous training.  


3. Unlocking Value from Unstructured Data 

Text, images, videos, and other unstructured data types are proliferating. Indeed, the unstructured data market is said to grow to a staggering 181 zettabytes by 2025. Unstructured data comes with numerous challenges such as its lack of structure, making it difficult to discover and classify information. Additionally, data security risks loom large, as sensitive information can easily be exposed if not adequately protected. However, this unstructured data holds huge untapped business value. Big data solutions allow for ingesting, processing, and analysing unstructured data at scale such as Datalift. 

When working with unstructured data, organisations must balance processing power with data governance, security, and privacy.  With thoughtful strategy, unstructured data can provide valuable insights and remove data chaos.  

At Automated Intelligence, we have seen first-hand how implementing data management tools can benefit our customers, reducing storage costs, increasing staff capacity and productivity, and improving business decisions such as the work we are doing with the Cabinet Office. 


4. ESG Data Enables Tracking Sustainability Goals 

ESG (environmental, social, governance) metrics are becoming essential for companies looking to track sustainability initiatives. Data strategies are crucial for monitoring key emissions, energy usage, diversity goals, and other ESG KPIs. 

Specialised tools for collecting ESG data, analysing it for insights, and creating reports are gaining popularity. Companies focused on “green tech” are also using data to develop better batteries, solar panels, EV chargers and develop the smart grid. 


5. NLP Unlocks Value in Text  

Natural language processing allows organisations to extract value and insights from human language data at unprecedented scale. NLP powers chatbots, sentiment analysis, document summarisation, and other applications. Allows users to interact with analytics using plain language (reword).  

NLP adoption requires managing training data pipelines and continuously monitoring NLP models for bias. Like AI, NLP governance ensures these models align with business goals and ethics. 


6. Managing security challenges with hybrid cloud 

Hybrid cloud architectures with assets spread across on-prem and cloud environments create added data security challenges. Organisations must implement robust access controls, encryption key management strategies, and network security to protect data across hybrid environments.  

Automating identity and access management and standardising security policies across cloud vendors is imperative for managing hybrid cloud risk. 


7. Automation Boosts Data Management Efficiency 

More organisations are using automation to expedite data processes. Automated data cataloguing, pipeline monitoring, metadata management, and model monitoring improve efficiency while enhancing data quality and model performance. 

Governance frameworks remain crucial for automated systems. As organisations implement more automated data tools, they must ensure sufficient human oversight of these technologies. 


The Road Ahead  

Data management in 2024 will require holistic data strategies that encompass emerging trends in AI, NLP, unstructured data, hybrid cloud architectures, ESG metrics, automation, and more. With careful investment in people, processes and technologies, companies can realise substantial competitive advantage from their data. 

If you would like further information on how Automated Intelligence can help you with your unstructured data, contact us at or visit our website at