5 Trends in Data Management and Analytics in 2023

Innovation through digital and data will enable leaders to differentiate themselves and stand out.

As the Covid-19 pandemic has accelerated the transition to digital, the general consensus is that many companies that remained primarily physical have taken the step in this direction. Even if they backtrack, a considerable part of their transactions will continue to take place electronically.

With data being 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. That’s why we expect a lot of innovation in areas related to data management infrastructures and architectures. Here are the five big trends we see having the most impact in 2023 as they relate to data and data analysis.

Trend #1: Faced with the specter of recession, companies will seek 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 has always been an easy solution for their leaders. The costs of treatment and storage keep dropping due to cloud usage. Yet they can still incur heavy bills for businesses given their huge investments in data analytics infrastructure. Due in part to the wide choice of storage, processing and application solutions, companies often adopt a full replacement strategy to modernize their infrastructures in this area. Not only is this approach costly, but it can frequently disrupt IT operations. In 2023, more companies will focus on modern, non-disruptive solutions for upgrading their IT infrastructures. This whether their data resides entirely in a single cloud, in multiple clouds, or in a hybrid environment maintaining on-premises facilities.

Trend No. 2: With multicloud, controlling cloud costs becomes necessary

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

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

Trend #3: Accelerating Adoption of Data Fabric and Data Mesh

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, the past few 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 has seen accelerated adoption of two of them – the data fabric and the data mesh – designed to improve the management and access of distributed data. The two are different in nature: the data fabric is a composable set of data management technologies and the data mesh is a process orientation that allows distributed teams to manage enterprise data as they see fit. Both are essential for companies wanting to better manage their data. Easy access to data as well as its governance and security are important for every data actor, from the data scientist to the business leader. These are, in fact, essential for the production of 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 with the right infrastructure. . Therefore, in 2023, a marked acceleration in the adoption of both architectures is to be expected in medium and large enterprises.

Trend #4: Ethical AI becomes 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 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 directive ” artificial intelligence proposed by the EU, begin to frame 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, particularly in terms of privacy protection and data governance, transparency of algorithms, fairness and non-discrimination, traceability and auditability. To this end, they need to put in place 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 prominence next year.

Trend #5: Increasing 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 frequently referred to as the new black gold because its analysis is constantly driving innovation. As companies increase their use, it is crucial for them to not lose sight of their governance and quality, as well as metadata management. Yet, as their volume, variety, and speed continually increase, these various aspects become too complex to manage on a large scale. Witness the time that the data scientists and data engineers must 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 allowing 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 – acquisition, processing, orchestration, governance, etc. management – ​​should incorporate AI or ML, to automate every step of the data management process. In 2023, augmented data management will gain strong market traction, helping professionals focus on data analysis without being hampered by routine administrative tasks.

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

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5 Trends in Data Management and Analytics in 2023

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