It is a fact that data increasingly governs our lives and dominates industries, processes, and companies. There is almost no intelligent decision that can be made if it is not based on data and platform management implies data management and a data-based leadership strategy. Organizations will experience a critical moment this year, becoming aware of the urgent need to exploit data for their business. Throughout this post, we review what will be the main data trends for 2024.
Without a doubt, traditional repositories and spreadsheets cannot handle the enormous volume of data and the variety of data that companies require today, in the era of AI, machine learning, and the IOT. It is time for companies to jump on the bandwagon of the key trends associated with new data strategies, it is the main way to revive their business strategies and pave the way to success.
8 Key Data Trends for 2024
These trends are not the only ones, but they will surely be the most important and the ones that will require the most attention at the corporate level.
- Democratization of data: Data democratization is the practice of making data accessible and consumable to everyone in a company, regardless of their technical skills. In this sense, a data fabric is a data management architecture that integrates data from multiple sources into a unified view. This makes it easier for users to find and access the necessary data. Similarly, a data mesh gives domain experts more control over their data and can also help improve data quality and governance. Both architectures are equally essential to ensure data democratization. In addition to improving data access, they make it easier for users to find and access data according to their needs. This leads to faster decision-making and insights.
- Industry 4.0 technology to extract insights from data: Industry 4.0 is called the “fourth industrial revolution.” It heavily uses automation, data analytics, and artificial intelligence to create smart factories. In Industry 4.0 data analytics can improve efficiency, productivity, and quality by identifying areas where processes can be optimized or defects prevented. For example, data analytics tracks machine performance and identifies patterns that indicate a machine or device is about to fail. Additionally, use this information to schedule maintenance before failure occurs, which can prevent downtime and lost productivity. What matters is that manufacturing technology is growing at a CAGR of 16.3% between 2023 and 2030. This could revalue the industry by $377 billion.
- AI and ML Integration: Artificial intelligence (AI) and machine learning (ML) are not just buzzwords, but the backbone of the advancement of business intelligence systems. They add a layer of automation by analyzing data, predicting trends, and making sound, informed decisions. One current use case is Amazon, which has recently focused on what it calls “regionalization”: shipping products to customers from warehouses closest to them, rather than from another part of the country. This requires technology that is capable of analyzing data and order patterns to predict which products will be in demand and where. In that case, AI plays a central role: if a product is closer to customers, Amazon will be able to deliver same-day or next-day delivery.
- Augmented Analysis: By bridging the gap between data scientists and engineers, augmented analytics is democratizing the understanding of data, making it accessible to people from all walks of life. Augmented analytics leverages AI to automate data preparation, discovery, and knowledge sharing that helps you explore and understand your data, such as intuitive recommendations and intelligent visualizations.
- Governance, compliance, and data security: As data becomes the new oil, its governance, and security are critical to preventing breaches and ensuring compliance with evolving regulations. The casino giant, MGM, data breach compliance incident, which is expected to result in losses of $100 million, reminds us that even large organizations are vulnerable to cyber threats. Software companies subject to strict data privacy regulations, such as GDPR and CCPA, must implement robust data security and governance procedures. Companies must protect user data and follow these standards to avoid high fines and reputational damage. At the same time, by masking sensitive data, companies can protect the privacy of their customers and employees, and can also reduce the risk of data breaches. Masking hides sensitive data from data sets that are used for analytics and machine learning. This ensures that sensitive data is not used in ways that could violate the GDPR, such as profiling or segmenting people.
- Natural language processing and large language models: NLP, and its milestones based on large language models (LLMs), are ushering in a new era in which computers interpret human language and unlock valuable information from unstructured data. It is a subset of Artificial Intelligence that allows computers to understand, interpret, and generate human language, a critical component of data analysis. This way, organizations can extract valuable insights from unstructured data sources, such as social media posts, customer comments, and support requests. They can also use applications such as ChatGPT and combine them with their database, to obtain more reliable answers to questions of interest about the company itself.
- Ubiquitous AI and its application in health: The pervasiveness of AI in our daily lives points to a future where AI-powered analysis will be the norm, opening up new perspectives in data interpretation. AI is poised to revolutionize healthcare by 2024. AI-powered diagnoses and treatment recommendations will become more accurate and widely adopted. Machine learning algorithms will improve drug discovery processes, making them faster and more cost-effective. Telemedicine will continue to grow and provide accessible healthcare services around the world. Additionally, AI will power personalized medicine by analyzing patients’ genetic data to tailor treatments to their unique genetic makeup.
- Growing demand for data scientists and engineers: The growing generation of data is simultaneously driving demand for expert data scientists and engineers, heralding a bright future for data analytics. Currently, data science is one of the professions with the most future of the 21st century, reaching the point that the great demand is not usually satisfied with enough professionals, and any professional with this profile is capable of adding value in all productive sectors and industries.
These trends are not mere predictions, but rather a beacon that guides us into the future. It is a fact that organizations must prepare for the future data revolution. With the relentless growth and importance of data, companies that proactively adapt and capitalize on emerging trends will gain a significant competitive advantage. Furthermore, the advances in AI, machine learning, and large language models mean that many companies must invest to a greater extent in projects linked to innovation with data.