As data is at the heart of digital, its management and associated infrastructure, as well as the implementation of an appropriate strategy and planning, will be essential to the success of businesses. While the pandemic has accelerated the shift to digital, the consensus is that many businesses that remained primarily brick-and-mortar have made the move in that direction. Even if they reverse course, a considerable part of their transactions will continue to take place electronically. In fact, innovation through digital technology and data will allow leaders to differentiate themselves and stand out from the crowd.
As data is at the heart of digital, its management and associated infrastructure, as well as the implementation of an appropriate strategy and planning, will be essential to the success of businesses. This is why we expect a lot of innovation in the areas related to data management infrastructures and architectures. Here are the five megatrends we see having the most impact in 2024 as they relate to data and its analysis.
Faced With The Specter Of Recession, Companies Will Seek To Optimize Their Infrastructure Costs
Whether France is in recession or not in 2024, companies are actively reducing their costs, as well as their IT infrastructures, which has always been an easy solution for their managers. Processing and storage costs continue to drop due to the use of the cloud. Yet, they can still result in hefty bills for businesses, given their considerable investments in data analytics infrastructure.
Thanks in part to the wide choice of storage, processing, and application solutions, companies often adopt a complete replacement strategy to modernize their infrastructure in this area. Not only is this approach costly, but it can frequently disrupt IT operations. In 2024, more companies will focus on modern, non-disruptive solutions for upgrading their IT infrastructures, whether their data resides entirely in a single cloud, across multiple clouds, or in a hybrid environment, maintaining on-premises facilities.
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With Multi-Cloud, Controlling Cloud Costs Becomes Necessary
For many businesses, data is distributed across multiple clouds and geographic locations. This is due to different preferences in the choice of cloud service provider (CSP) or following mergers and acquisitions between entities depending on distinct CSPs. As data migration to the cloud intensifies and certain CSPs gain traction in some regions over others, the adoption of multi-cloud architecture is accelerating among multinationals.
Currently, there is yet to be a simple solution to manage and integrate data and services across these various CSPs. The persistence of this problem still results in the creation of data silos and fragmentation of data management, leading to complications in access and governance. Additionally, contrary to popular belief, cloud costs are becoming increasingly material due to the sheer volume of data and associated egress fees, to name just a few reasons. For many companies, investments in the cloud do not bring the expected economic returns or business benefits.
This is why they use FinOps methods to control cloud costs and uses, identify the cost/value ratio, and determine how to optimize management between modern hybrid and multi-cloud environments. In the coming year, FinOps can be expected to gain momentum and play a critical role in helping businesses better manage their hybrid cloud and multi-cloud spending.
Acceleration Of Data Fabric And Data Mesh Adoption
Over the last two decades, data management has experienced cycles of centralization and decentralization: databases, data warehouses, cloud data stores, data lakes, etc. While each approach has its supporters and opponents, recent years have proven that data is more distributed than centralized in most companies.
While there are plenty of options for deploying an enterprise data architecture, 2022 saw accelerated adoption of two – data fabric and data mesh – intended to improve management and access to distributed data. The two are different: data fabric is a composable set of data management technologies, and data mesh is a process orientation that allows distributed teams to manage enterprise data as they see fit. Both are essential for businesses wanting to manage their data better. Easy access to data, as well as its governance and security, are critical to every data actor, from data scientists to business leaders.
These are, in fact, essential for the production of dashboards and reports, advanced analytics, Machine Learning (ML), and even artificial intelligence (AI). Both data fabric and data mesh can play a critical role in accessing, integrating, managing, and disseminating data across the enterprise, provided they are implemented with the proper infrastructure. Consequently, in 2024, an apparent acceleration in the adoption of both architectures is expected in medium and large companies.
Ethical AI Becomes Essential As More And More Decisions Rely On Artificial Intelligence
Companies are increasingly using AI for data-driven decision-making, whether it’s social media moderation, healthcare professionals’ relationships with patients, or granting by consumer credit banks. However, when AI conditions the decision, there is currently no way to eliminate the inherent bias of the algorithm. This is why legislation in preparation, such as the The “artificial intelligence” directive proposed by the EU is beginning to regulate the use of AI in commercial companies.
These new regulations classify AI applications based on the risk they present (unacceptable, high, medium, or low) and prohibit or govern their use accordingly. In 2024, companies will need to be able to comply with these regulations, including privacy and data governance, algorithm transparency, fairness, non-discrimination, traceability, and auditability.
With this in mind, they need to put in place their frameworks for ethical AI, for example, in the form of guidelines for reliable AI, peer reviews, or even dedicated ethics committees. As more and more companies implement artificial intelligence, ethical AI is set to gain unprecedented importance next year.
Increased Data Quality And Preparation, Metadata Management, And Analytics
While it is often intended to power advanced analytical tools and AI and ML techniques, proper data management is itself essential to business success. Data is frequently referred to as the new black gold because its analysis constantly drives innovation. As companies scale up their use, they mustn’t maintain their governance and quality, as well as metadata management. Yet, as they continually increase in volume, variety, and speed, these various aspects become too complex to manage on a large scale.
Witness the time that data scientists and data engineers must spend searching for and preparing data before they can even begin to use it. This is why various players in the sector have recently proposed augmented data management allowing companies, through the application of AI, to automate a large number of tasks in this area. According to some of the most eminent analysts, each layer of a data fabric – acquisition, processing, orchestration, governance, etc. data – must integrate AI or ML in order to automate every step of the data management process.
In 2024, augmented data management will significantly gain ground in the market, helping professionals focus on data analysis without being hampered by routine administrative tasks.
While these are five strong trends, other areas of analytics will determine both the survival and success of digital businesses in 2024 and beyond. The last three years have certainly taught us that digital technology is, in reality, not a fallback solution when face-to-face meetings are impossible, but rather a solution for the future.
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