These trends are driving innovation, shaping industries across the board, from medical and manufacturing to finance, retail, and beyond.

In today’s world, data trends are guiding the way we advance with technology. But what exactly are ‘data trends’? Put simply, they show us where technology-driven behaviour and situations are heading. These trends are driving innovation, shaping industries across the board, from medical and manufacturing to finance, retail, and beyond. Data has become invaluable, providing insights into how our world operates. Using cutting-edge technology for data insights isn’t just important; it’s essential for staying competitive and driving progress. It’s changing our everyday lives in ways we might not even realize.

But knowing how important data is, and how it may help us improve our day-to-day lives, how exactly are we moving towards that goal?

In 2024, three key data trends are shaping the future of technology. Firstly, companies are making data more accessible to everyone, regardless of their technical expertise. This fosters collaboration and innovation. Secondly, tailored cloud solutions are revolutionizing how businesses manage data. These flexible platforms help organizations optimize their operations efficiently. Lastly, advancements in technology are allowing organizations to analyse data more deeply than ever before. This means better insights for smarter decision-making. These trends are set to become crucial elements of a data-driven world, fuelling innovation and efficiency across industries.

Data Democratisation

We begin with a trend that can impact a company across all levels and benefit from it too! We are talking about Data Democratisation.

But what exactly is it? In short, Data Democratisation is a sort of policy implemented as an architecture with the use of tools and proper methodologies; it opens a company to innovation through diversification, meaning that by allowing employees access to the right, curated data they can not only come up with solutions to problems, but also create a data-driven culture that facilitates a company’s decision-making process.

It is important, however, that the data available is accessible to users and is easy to comprehend and use. In turn, users across the company must be data literate, meaning that they know how to access the data, understand the data itself and how to apply it to solve business problems, and have the skills to determine whether the data is accurate or not. Moreover, the concern of data security is always present, and employees need to ensure it, thus it’s important to provide the necessary guidance on how, where, and when to use the data.

Furthermore, data is stored not only on-premises but on the cloud as well, which allows employees a real-time access from across locations. Businesses are more dynamic nowadays, and a well-designed infrastructure for easy, real-time access is key. This is where architecture plays a crucial role. It determines how data is stored and accessed, such is the case of two architectures, which are not mutually exclusive and can be beneficial to have both, we are talking about data fabric and data mesh architectures.

Data Fabric

Data fabrics allow for a simplified interaction between users and data through applications. By using APIs and data services, a data fabric can retrieve data from a variety of sources, it is defined by using metadata and it can even be used in unstructured data environments. This greatly helps on analytics and machine learning applications as it can give you a broader picture of the data available.

This is particularly useful when managing large amounts of data, makes it more manageable and allows for real-time information being accessible nearly on demand. As such, industries that have large and complex datasets are moving towards this method of data management (including the following architecture: data mesh).

Data Mesh

Data mesh is slightly different on its own, it is a decentralised architecture that sorts the data by business domain. It puts together, via a variety of methods that include graphs, AI/ML, or similar technologies, all the data under a specific domain, which in turn facilitates their management. It uses parameters around the data to simplify the access to the latter so that any team under any specific domain can get a direct access to the data, whilst still allowing other teams in the organisation access to it. If you want to delve deeper into the concept, you can read more about it in these insightful articles on data mesh architecture: Introduction to Data Mesh Architecture and Creating and Managing a Data Mesh in AWS with Lake Formation. Also, take a look at the shirt video about the topic: DataMesh.

In summary, data meshes are an efficient way to manage large amounts of data. It is flexible and scalable, which allows for new ways of grouping data and organise it in a more dynamic manner.

Industry Cloud Platform

Next up, Industry Cloud Platforms (ICPs), which can be defined as Cloud Platforms (CPs) oriented towards industry-specific solutions, are cloud platforms that can manage a business’ data whilst providing industry-specific solutions to said business.

Many CPs we see out there are more general when it comes to building solutions. Take for instance AWS’ or Google Cloud Platform’s general-purpose platform, they provide a myriad of tools and services that are rarely used to their full potential by any given industry sector.

Sometimes less is more; when a business invests its resources in a specific industry platform, it gives way to a more tailored solution. As such, ICPs are intended to be specialised, scalable, and flexible. By giving businesses, the capability to use industry-specific solutions – including tools and technologies –, they can invest resources where needed and focus on growing. The scalability and flexibility are somewhat similar to what general-purpose cloud platforms offer, only difference is that they are based on a business‘ activity rather than speculation and static market indicators.

Some examples from larger cloud platform providers include IBM’s Watson Health and AWS Healthcare for healthcare solutions, AWS Retail and IBM Sterling Supply Chain Suite for retail and logistics solutions, Microsoft’s Azure IoT Suite for Internet of Things applications, and more!

It’s a matter of understanding your market segment and finding an ICP that best suits your needs.

Augmented Analytics

Simply put, let’s look at the analytics cycle:

Bonthu, Sridevi & Bindu, Hima. (2018). Review of Leading Data Analytics Tools. International Journal of Engineering and Technology(UAE). 7. 10.14419/ijet.v7i3.31.18190.

By understanding the processes behind all the steps in the cycle, we can see how complex, and a growing amount of data can complicate the whole thing! Let’s not only think about the processes, but also the people involved behind each step. From data scientists to even business users, we can see that lots of people with different expertise participate in the whole cycle and coordinating them all can become a hassle.

But what if we can work alongside computers to make the analytics process more enjoyable and simpler? That is the main point in augmented analytics; by employing machine learning techniques or similar within the analytics cycle, we can even accelerate the process and have more insightful information from the data being analysed.

For instance, if we have a cluster of data and we need to identify a pattern to a problem we can use classification machine learning algorithms to identify a problem or new opportunities. It is, however, important to denote the limitations of augmented analytics.

In the end deciding is really at the hands of those who manage the business, although arguably a machine can make decisions on its own – with the help of machine learning models, which is common in repeated actions that have a higher certainty rate of success. But not everything can become „augmented” just as easily, we must be careful on where we use this ML models in the cycle, such is the case of data preparation, which still require careful data selection and cannot be necessarily automated.

The more behavioural phases of the analytics cycle can really be done by human actors, besides, who can understand human behaviour more than another human? Augmented analytics is a very disruptive methodology in any business that requires analysis on data, it can shift how we perceive the activities of each actor in the cycle; such as data scientists, analysts, business agents, etc. their roles may evolve altogether and as we move towards more technology-oriented businesses, it is important to define where we stand in the analytics cycle and where do we need machine intervention. For further insights into data science trends and its impact on analytics, you can explore this article: Top Data Science Trends in 2020.

Data Security

Data Security, in simple terms, means to maintain the data’s confidentiality and integrity. Many industries, nowadays, hold important, confidential data that needs to be protected from external actors and possible manipulation and/or damages to the data itself.

With data security, under these trends, it is imperative to keep the data safe. As a system grows and more data is collected, so does the vulnerabilities, and as such, it is important to not only have the infrastructure to keep it all protected, but it is also important for people to be aware of the risks that can compromise vulnerable information.

Data security and data governance go hand in hand, but it is important to understand that while data governance focuses on best practices, processes and guidelines to keep data safe, data security covers even data governance, as well as the frameworks and policies that keeps access to the data limited and ensures its integrity. For further insights into data security practices such as data encryption, you can explore this resource: Data Encryption.

Generative AI

Generative AI must be one of the most relevant topics in the last couple of years, and 2024 is no exception. Generative AI takes large amounts of data, learns from it and makes the best statistical prediction to what is needed. This can be extrapolated to so many different areas, from simple mathematics to artwork-level image generation, Natural Language Processing (NLP), and much more.

In the realm of Data Analytics, Generative AI can make accurate predictions from large datasets. It provides us with relevant information based on what the models have been trained to identify. If you’re interested in exploring more about Generative AI and its applications, you can check out this resource on Generative AI by Elixirr.

Final Thoughts

Technology will continue to find ways to evolve and change how we perceive our world. Data is right now one of the biggest drivers of technology itself, and it may be for more years to come. These trends might not be something entirely new, these topics have been discussed some time ago, however it is now that we see their potential and have made great advancements in technology, enough to consider them a must-go in today’s world, and as we build more and more on top of these advancements, more trends will come.

We must remain relevant to succeed in a constantly changing world; understanding what surrounds us is essential, and from these trends data is something to keep ourselves on the lookout and exploit such a valuable resource. In the end, we are all information waiting to be discovered.

Okay… one more final thought…

What do these trends mean for data scientists, data engineers, developers, business and tech analysts, management, and everyone else involved in various industries? Will they replace our jobs? The answer is both yes and no. While technologies like AI can streamline processes, they still rely on human input for gathering and interpreting data. Understanding what to look for and transforming that data into useful information requires human expertise. Machines can assist us significantly in this process, but they cannot completely replace the human element—at least not yet.

Thus, we must remember that these are tools, which may take some things off our plate, but as with any tool, they are as useful if someone needs them and knows how to use them. So, for now, we must keep ourselves in the loop and relevant, and just keep on (machine) learning. If you need advice or the best approach for your data requirements, feel free to contact us.