Five Data and Analytics Trends: What Lies Ahead in 2023

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Read more about author Angel Viña.

It’s rare, if not impossible, to find a business that does not have a digital presence. The convenience factor of digital is accelerating the pace at which businesses are applying an online presence, including the ones that were mainly brick-and-mortar before COVID. More than just offering products and services in this way, most organizations are using data to generate insights that can affect their revenue, sales, cost, and business decision-making. 

While being data- and insights-driven is a major focus for all CIOs and CDOs, that’s easier said that done. Without proper data management, and a related infrastructure in place, delivering trusted and good-quality data to stakeholders, both internal and external, is challenging. To solve those problems, the data management industry is constantly pushing the envelope with many innovations related to cloud, financial operations, newer data management architectures, automation, machine learning, etc. Here are the top five trends that we believe will have the most impact in 2023, as it related to data and analytics.

Trend #1: As recession looms, companies will look to optimize infrastructure cost

Whether North America is in recession or not, companies are actively cutting costs, and reducing IT infrastructure, which has always been an easy choice for CEOs. While compute and storage costs continue to be reduced through the usage of cloud, it still can lead to huge bills for organizations given their heavy investments in data and analytics infrastructure. Thanks in part to the breadth of choices of storage, compute, and applications, companies often take a rip-and-replace strategy to modernize their data and analytics efforts. That approach is not only costly, but it can often lead to disruption in IT operations. In 2023, more companies will see IT focusing on modern, non-disruptive ways to update their IT infrastructure, whether their data resides entirely in one cloud, multiple clouds, or in a hybrid environment including on-premises.

Trend #2: While multi-cloud gets real, FinOps in cloud becomes necessary

For many companies, strategic data assets are spread across multiple clouds and geographical locations, whether that is because various business units or locations have their preferred cloud service provider (CSP), or because merger and acquisitions have led these assets to reside in different cloud providers’ boundaries. As more data continues to move to the cloud, and different geographies see prominence of certain cloud providers vs. the others, there is accelerated adoption of multi-cloud architecture for multinational corporations. Currently, there is no easy way to manage and integrate data and services across these different CSPs. Failure to address this problem always results in data silos and a fragmented approach to data management, leading to data access and data governance complications. 

Also, contrary to popular belief, cloud costs are increasingly becoming a material expense due to the sheer volume of data and related egress charges, to name a few. For many organizations, cloud investments do not deliver the economic and business benefits as intended. As a result, they are leveraging FinOps to provide a framework for controlling cloud costs and usage, identify cost vs. value, and understand ways to optimally manage it across modern hybrid and multi-cloud environments. In the coming year, expect FinOps to gain momentum as a critical initiative to help companies better manage their hybrid-cloud and multi-cloud spend. 

Trend #3: Accelerated adoption of data fabric and data mesh

Over the past two decades, data management has gone through cycles of centralization vs. decentralization, including databases, data warehouses, cloud data stores, data lakes, etc. While the debate over which approach is best has its own proponents and opponents, the last few years have proven that data is more distributed than centralized for most organizations. While there are numerous options for deploying enterprise data architecture, 2022 saw accelerated adoption of two data architectural approaches – data fabric and data mesh – to better manage and access the distributed data. While there is an inherent difference between the two, data fabric is a composable stack of data management technologies and data mesh is a process orientation for a distributed group of teams to manage enterprise data as they see fit. Both are critical to enterprises that want to manage their data better. Easy access to data and ensuring it’s governed and secure, is important to every data stakeholder – from data scientists all the way to executives. After all, it is critical for dashboarding and reporting, advanced analytics, machine learning, and AI projects. 

Both data fabric and data mesh can play critical roles in enterprise-wide data access, integration, management, and delivery, when constructed properly with the right data infrastructure in place. So in 2023, expect a rapid increase in adoption of both architectural approaches within mid- to large-size enterprises.

Trend #4: Ethical AI becomes paramount as commercial adoption of AI-based decision-making increases

Companies across industries are accelerating the usage of AI for their data-based decision-making – whether it’s about social media platforms suppressing posts, connecting health care professionals with patients, or large wealth management banks granting credits to their end consumers. However, when artificial intelligence decides the end result, currently there is no way to suppress the inherent bias in the algorithm. That is why emerging regulations such as the proposed EU Artificial Intelligence Act, and Canada’s Bill C-27 (which may become the Artificial Intelligence and Data Act if enacted), are starting to put a regulatory framework around the use of AI in commercial organizations. These new regulations classify the risk of AI applications as unacceptable, high, medium, or low risk and prohibit or manage the use of these applications accordingly. 

In 2023, organizations will need to be able to comply with these proposed regulations, including ensuring privacy and data governance, algorithmic transparency, fairness and non-discrimination, accountability, and auditability. With this in mind, organizations have to implement their own frameworks to support ethical AI (e.g., guidelines for trustworthy AI, peer review frameworks, and AI ethics committees). As more and more companies put AI to work, ethical AI is bound to become more important than ever in the coming year.

Trend #5: Augmentation of data quality, data preparation, metadata management, and analytics

While the end result of many data management efforts is to feed advanced analytics and support AI and ML efforts, proper data management itself is pivotal to an organizations’ success. Data is often being called the new oil, because data- and analytics-based insights are constantly propelling business innovation. As organizations accelerate their usage of data, it’s critical for companies to keep a close eye on data governance, data quality, and metadata management. Yet, with the growing amount of volume, variety, and velocity of data continues, these various aspects of data management have become too complex to manage at scale. Consider the amount of time data scientists and data engineers spend finding and preparing the data, before they can start utilizing it. That is why augmented data management has recently been embraced by various data management vendors where, with the application of AI, organizations are able to automate many data management tasks. 

According to some of the top analyst firms, each layer of a data fabric – namely data ingestion, data processing, data orchestration, data governance, etc. – should have AI/ML baked into it, to automate each stage of the data management process. In 2023, augmented data management will find strong market traction, helping data management professionals focus on delivering data-driven insights rather than being held back with routine administrative tasks.

There are many more noteworthy innovations taking place in the data and related infrastructure software market, which includes innovations in DataOps, proliferation of continuous decision process lifecycle, data protection, data utilization, etc. We believe all these innovations will accelerate the pace of digital transformation and digital business in 2023 and beyond. We also believe that the last two to three years have definitely taught us that digital business is where the future lies. 

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