5 Trends in Data Management and Analytics in 2023

As the pandemic has accelerated the transition to digital, the general consensus is that many businesses that have remained primarily physical have taken a step in this direction. Even if they backtrack, a large portion of their transactions will continue to occur electronically. Indeed, innovation through digital and data will enable leaders to differentiate themselves and stand out.

Because data is at the heart of digital, managing it and the associated infrastructure, as well as having the right strategy and planning in place, will be critical to business success. We therefore expect many changes in areas related to data management infrastructures and architectures. Here are the five big trends we see having the biggest impact in 2023 as they relate to data and data analysis.

Faced with the specter of recession, companies will strive to optimize their infrastructure costs

Whether France is in recession or not in 2023, companies are actively reducing their costs, as well as their IT infrastructures, which is always an easy solution for their leaders. Processing and storage costs continue to decrease due to the use of the cloud. However they can still incur heavy charges for businesses due to their large investment in data analytics infrastructure. Due in part to the wide choice of storage, processing and application solutions, companies often use a total replacement strategy to modernize their infrastructure in this area. Not only is this approach costly, but it can often disrupt IT operations. In 2023, more companies will focus on modern, non-disruptive solutions for upgrading their IT infrastructures. This is whether their data resides entirely in a single cloud, in multiple clouds, or in a hybrid environment that maintains on-premise facilities.

With multicloud, controlling cloud costs becomes imperative

For many companies, data is distributed across multiple clouds and geographies. This may be due to different cloud service provider (CSP) selection preferences or as a result of mergers and acquisitions between entities dependent on different CSPs. As data migration to the cloud increases and some CSPs gain traction in some regions over others, adoption of multicloud architecture among multinational organizations is accelerating. Currently, there is no simple solution to manage and integrate data and services between these different CSPs. The persistence of this problem always leads to the creation of data silos and a fragmentation of data management, which leads to complications in their access and management.

Also, contrary to popular belief, cloud costs are increasingly hardware-based due to the volume of data and associated exit charges, to name a few reasons. For many companies, cloud investments are not delivering the expected economic and business benefits. This is why they use FinOps methods to control costs and use of the cloud, to determine the cost/value ratio and to determine how to optimize its management between modern hybrid and multicloud environments . Over the next year, FinOps is expected to grow stronger and play a key role in helping enterprises better manage their hybrid cloud and multicloud spend.

Accelerating Data Fabric and Data Mesh Adoption

Over the past two decades, data management has gone through 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 many options for deploying an enterprise data architecture, 2022 sees accelerated adoption of two – data fabric and data mesh – designed to improve distributed data management and access. The two are different in nature: data fabric is a composable set of data management technologies and data mesh is a process orientation that enables distributed teams to manage enterprise data as they see fit. . Both are important for companies that want to better manage their data. Easy access to data as well as its management and security is important for every data actor, from the data scientist to the business leader. They are, in fact, essential for creating dashboards and reports, advanced analytics, Machine Learning (ML) or artificial intelligence (AI).

Both the data fabric and the data mesh can play a critical role in accessing, integrating, managing and disseminating data across the enterprise when implemented in the right infrastructure. . Therefore, by 2023, a marked acceleration in the adoption of both architectures is expected in medium and large enterprises.

Ethical AI is becoming paramount as more and more decisions rely on artificial intelligence

Businesses are increasingly turning to AI for data-driven decision-making, whether it’s moderating social media, connecting healthcare professionals with patients, or providing 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 the works, such as the “artificial intelligence” directive proposed by the EU, is beginning to regulate the use of AI in commercial enterprises. These new regulations classify AI applications according to the risk they pose (unacceptable, high, medium or low) and prohibit or regulate their use accordingly.

In 2023, companies will need to be able to comply with these regulations, especially in terms of privacy protection and data management, transparency of algorithms, fairness and non-discrimination, traceability and auditability. To this end, they need to put their own frameworks for ethical AI, for example, in the form of guidelines for reliable AI, peer reviews or even dedicated ethics committees. As more companies implement artificial intelligence, ethical AI is set to gain unprecedented popularity 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 for business success. Data is often referred to as the new black gold because its analysis continues to drive innovation. As companies increase their usage, it is very important for them not to forget their management and quality, as well as metadata management. However, as their volume, variety, and speed continue to increase, these various aspects become too complex to manage on a large scale. Witness the time data scientists and data engineers have to spend researching and preparing data before they can even start using it. This is why various players in the sector have recently proposed augmented data management that allows companies, through the application of AI, to automate a large number of tasks in this field.

According to some of the most prominent analysts, each layer of a data fabric – capture, processing, orchestration, management, etc. management – ​​should include AI or ML, to automate each step of the data management process. In 2023, augmented data management will greatly attract the market, helping professionals focus on data analysis without being hindered by routine administrative tasks.

While these are five powerful trends, there are other areas of analytics that will determine the survival and success of digital businesses in 2023 and beyond. The last three years have certainly taught us that digital is not really a fallback solution when face-to-face meetings are impossible, but a solution for the future.