2023 Predictions: What Lies Ahead for Data Management and Analytics
The pandemic may have accelerated digital business transformation but it didn’t create it. How should organizations respond in 2023?
- By Angel Viña
- December 13, 2022
Although COVID-19 accelerated the push for digital business transformation, the consensus is that many businesses had already embraced digital business for good. Even if the brick-and-mortar business comes back full swing, a considerable portion of business transactions will still take place digitally. In fact, digital and data-driven business innovation may likely differentiate the leaders from the rest of the pack.
Data is at the heart of digital business and managing data and related infrastructure with proper strategy and planning will be key for business success. That’s why we see so much innovation taking place in data infrastructure and architecture-related fields. Here are the top three trends that will have the most impact in 2023 on data and analytics.
Trend #1: While multicloud and intercloud get real, FinOps in the cloud becomes necessary
For many multinational companies, strategic data assets are spread across multiple clouds and geographical locations. This can be because various business units or offices have their preferred cloud service provider (CSP) or because mergers and acquisitions have combined assets that reside within different cloud providers' boundaries. 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. Traditionally companies have taken data replication approach to deal with such scenarios.
I believe that in 2023, as multicloud and intercloud usage increase, many organizations will implement technologies such as data virtualization that provides a data discovery and access layer, allowing data users across the organization access the data they need for their work without any replication -- irrespective of the location of the data – from any cloud. This provides quick and easy managed access to data while still providing local control to the data owners; it also helps in complying with local privacy and data protection regulations such as GDPR and CCPA.
Add to this that cloud costs are increasingly becoming a significant expense, in part due to the sheer volume of data being managed and related egress charges. For many organizations, cloud investments do not deliver the economic and business benefits as intended. One of the main reasons behind the lack of cloud ROI is the lack of ownership of cloud usage and associated business value determination, combined with the absence of a central team responsible for cloud cost management working with various teams such as engineering, management, and finance.
According to FinOps Foundation, “FinOps is a public cloud management discipline that enables organizations to get maximum business value from cloud by helping technology, finance, and business teams to collaborate on data-driven spending decisions.” As companies accelerate their adoption of multicloud and intercloud architectures, FinOps helps them provide a framework for controlling cloud costs and use, identifying cost versus value and understanding ways to optimally manage data across modern hybrid and multicloud environments.
In the coming year, expect FinOps to gain momentum as a critical initiative to help companies better manage their intercloud and multicloud expenses.
Trend #2: Adoption of data fabric and data mesh accelerates
Over the past two decades, data management has gone through cycles of preferring centralization to decentralization and back again, including databases, data warehouses, cloud data stores, data lakes, and so on. Though the debate over which approach is best continues, the last few years have been part of the “decentralized” part of the cycle for most organizations.
There are numerous options for deploying enterprise data architecture but 2022 saw accelerated adoption of two data architectural approaches -- the data fabric and the data mesh -- to better manage and access distributed data. Data fabric is a composable stack of data management technologies while the data mesh is more of a process orientation for distributed groups of teams to manage enterprise data as they see fit. Many organizations are taking an either/or approach, choosing one architecture for their needs, but both are critical to enterprises that want to manage their data better. Both data fabric and the data mesh can play critical roles in enterprise-wide data access, integration, management, and delivery when constructed with the right data infrastructure in place. In 2023, expect to see a rapid increase in adoption of both architectural approaches within mid- to large-sized enterprises.
Trend #3: Ethical AI becomes paramount as commercial adoption of AI-based decision-making increases
Companies across industries are accelerating the use of AI for their data-based decision-making. Whether it’s social media platforms filtering posts, platforms connecting healthcare professionals with patients, or large wealth management banks granting credit to their consumers. However, when artificial intelligence makes a decision, 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 and prohibit or manage their use accordingly.
In 2023, organizations will need to be able to comply with these proposed regulations if enacted, including ensuring privacy and data governance, algorithmic transparency, fairness and non-discrimination, accountability, and auditability. With this in mind, organizations must implement their own frameworks to support ethical AI, such as guidelines for trustworthy AI, peer review frameworks, and AI ethics committees.