Data Technology Trends 2023 and How They Impact Organizations

Csanád Bánhegyi
Csanád Bánhegyi
02 May 2023 · 7 min read

With an economic downturn looming, organizations in 2023 are called to make bold moves. So far, they have been met with varying degrees of success. 

Information is power, however, and staying informed can pay in spades – as can harnessing the power of data. By understanding how changes in data technology affect business processes, you can make better decisions. Let’s take a closer look.

What Are the Data Trends of 2023?

From buzzworthy artificial intelligence to data decentralization and democratization, let’s take a look at the data trends of 2023 and beyond. 

  1. Machine Learning
  2. Decentralized Analytics 
  3. Geospatial Analytics
  4. Cloud Migration
  5. Democratization and Governance
5 data trends for 2023: Machine Learning, Decentralized Analytics, Geospatial Analytics, Cloud Migration, Democratization and Governance.

1. Machine Learning: For and of Data

You would need to be living under a rock to have missed the rise of artificial intelligence in popular consciousness, which, in 2023, spans every industry and nearly every consumer. Sometimes, for good reason; others, as more of a proof of concept exercise that caught the eye of the public.

In data science, though, machine learning finds diverse applications. Stumped by the sheer size of data and impressed by the democratization and perhaps overpromotion of artificial intelligence, more businesses are now seeking to apply artificial intelligence to their data.

Use cases span far and wide, from improved production efficiency to image analysis, marketing analytics and personalized customer experiences. 

So, what form does the marriage of data and machine learning take? There are several very real avenues to leverage machine learning in data tech:

  1. Utilizing data to build ML models: Once cleaned and appropriately labeled, data gathered by companies of all types can be used to train and re-train machine learning and deep learning models. The obvious opportunity here is to ingest data from source systems for deep learning, machine learning, and AI applications. Sometimes, this data will have to be pre-processed or otherwise manipulated through data engineering services – but more and more customers are seeing the benefits and taking advantage.
  2. ML models sifting through data: There’s also the inverse: An efficiently built AI module can help make better sense of unstructured data, efficiently helping data management scale without the need for additional human resources. ML models are ideal as the next step to data lake building, as they are well-equipped to make the most of the data lake. In fact, nRoad analysts expect 80% of all data to be unstructured by 2025. Also called operationalizing ML, machine learning inference runs data points into machine learning models with the goal of calculating outputs. These can be conditional or unconditional, depending on the architecture and goals.
  3. Explaining data in natural language: Already making waves in the wider public consciousness, natural language processing engines such as ChatGPT (in reality, a more accessible implementation of GPT3.5, released in March 2022) can help phrase takeaways from huge swathes of data in a way that is widely understood not just by specialists, but by anyone. Efficiently, this would make it immensely easier to explain results, metrics, and opportunities to stakeholders, no matter how familiar they are with analytics – as well as showcase the vast value of data analytics and data tech in general.  



2. Decentralized Analytics: Moving to the Edge

Reflecting upon the wider trend for the Internet of Things (IoT) and edge computing, edge analytics come in to draw data for decision intelligence. In fact, a key reason for the rise of edge computing is the vast amounts of data generated from traditional architectures. 

According to Grand View Research, the edge computing market in the US alone is worth approximately $5 billion in 2023 and is expected to skyrocket to $43.4 billion by 2027. There’s no doubt that this will impact the analytics market, too, with companies already making use of gathered data.

So, what are the benefits of decentralized data analytics? 

  • Faster, with reduced latency
  • More reliable
  • Improved oversight and safety/privacy
  • Better collaboration by domain experts
  • Increased visibility of results

Simply put, edge computing allows you to analyze data more quickly and efficiently because it does so on the spot, without sending it off to another location. It’s thus no wonder that edge analytics are trending in 2023, with an increased focus on the intelligent edge.

3. The Rise of Geospatial Analytics, Far and Wide

Who cares about geospatial data? Well, the answer is more and more companies. 

According to the Geospatial Analytics Market Report 2023, the geospatial analytics market will reach $40.5 billion by 2027, expanding by 14.67% annually.

Why? Because this type of analytics is the best way to estimate (rather than guesstimate) the ideal location to do business, build neighborhoods, or set up services. In fact, it can also help streamline the movement of people or goods, assess the value of a contract, and boost manufacturing yields.

The economy of scale is at work here, with tools that can detect location and stream data in real-time: Satellites, GPS, and all manner of sensors gather everything from health bulletins to stream gauges, traffic data, and beyond to form not just context but actually map the fabric of human and natural activity across the globe – a “nervous system of the planet,” according to one industry insider, the CEO of Esri.

And to make sense of this nervous system, we need analytics. There is no question that geospatial applications will become increasingly adopted and proliferated moving into the future.



4. Cloud Data Migration: More Flexible and Versatile 

In late 2022, Google found that 32.8% of decision-makers were planning to migrate on-premises workloads to the cloud, while 33.4% stated an intention to migrate from legacy enterprise software tools to cloud-based tools. 

To put it bluntly, today, on-premise data architecture is a dead end. The cloud has been a megatrend for decades, but it continues to shift, evolve and change the way we do business – and the way we work with data. 

Recent years have seen more laws and regulations put in place regarding cloud security. But why view this as an obstacle? The fact there are clear guidelines for security in the cloud means that companies have a framework of legal and privacy best practices to model their migration after. 

Importantly, a big portion of the trend is owing to how business leaders are more aware of the flexibility of cloud migration and how there are different strategies they could adopt. Thinking back to the five Rs of cloud data migration – rehost, refactor, rearchitect, rebuild, and replace – you can choose a faster migration strategy or a slower process that enables more benefits of the cloud. This versatility has worked to the sector’s advantage, with companies of different sizes and priorities no longer seeing as much of a barrier to entry.

Naturally, existing payloads and infrastructure can affect such choices, too. However, cloud migration, hybrid cloud, and multi-cloud architectures will continue to be at the forefront of data tech in 2023 and in the future. 

5. Data Governance and Democratization: Parallel Trends

Questions of data governance bring to the fore the need for better security. And it is the data democratization trend that makes this need more urgent than ever.

In a certain light, one could argue that data democratization was always inevitable in the world of big data. Similar to edge analytics, it ensures that several parties enjoy access in order for teams to work with data horizontally, avoiding gatekeepers and information bottlenecks, boosting productivity, and enabling outcomes.

Of course, this approach also creates enormous challenges – and data governance is called upon to resolve them.

Questions of data governance bring to the fore the need for better security. And it is the data democratization trend that makes this need more urgent than ever.

Once a side effect of new legislation, data governance has become a mainstream necessity. But in 2023, it’s also maturing into opportunities to put in place a framework that makes the most of data throughout its lifecycle and enables data democratization, too.   

Although legislation such as the EU’s GDPR, Canada’s Digital Charter Implementation Act, and India’s PDPB calls for increased investment in the form of data stewards and infrastructure, doing so brings much more than mere compliance with the law. 

What do we mean by this? In practice, data governance solutions may be a substantial investment that can initially disrupt everyday operations, depending on how it is implemented. However, it leads to increased awareness of its exact nature, flow, and potential within an organization – and to better-quality data overall. 

In fact, the benefits of rising to this challenge are immense. In simple terms, better data governance means that decisions within a company are made with more accuracy, they are backed up by data, and new projects and improvement initiatives can be better and more accurately estimated and measured.

More focus on data governance also means that the organization can benefit from data democratization while minimizing risk as much as possible and having in place risk mitigation strategies and risk management plans. 

Looking to the Future: 2024 and Beyond

When considering data technology trends and new developments in the sector, it is as essential to keep a level head as an open mind.

Although all of the above are important in the industry today and in the future, not every solution or strategy suits every organization. It can be tempting to fall into the trap of becoming an early adopter of new technology and solutions, but this is best decided after reflection.

Consider what your organizational targets are and how you measure your growth. It pays to start with your realistic goals and key challenges and work backwards from there, to identify which trends and which technologies can enable it. 

And if you’re not sure how to make the most of your data, the DATAPAO team is always at hand to provide you with expert consultation and information.


  • Globe Newswire: Edge Computing Market Predicted to Cross $101.3 billion by 2027
  • VentureBeat: Report: 80% of global datasphere will be unstructured by 2025 
  • Marketwatch: Geospatial Analytics Market Outlook 2023 and Forecast to 2028 with Top Countries Data  
  • Google Cloud: The digital forecast: 40-plus cloud computing stats and trends to know in 2023
  • Esri Community: 5 Trends in GIS and How to Successfully Navigate Them