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  • The Business Imperative: Refining Your Data Management Strategy for 2024

    Published on December 7, 2023

     By Ananth Chakravarthy, RVP Sales, Denodo India

    As we near the end of 2023, it’s imperative for data management leaders to look in the rear view mirror to assess and, if needed, refine their data management strategy. One thing is clear; if data-centric organizations want to succeed in 2024, certain aspects of data management are going to be mission critical.

    Business stakeholders not only want to be less IT dependent in the coming year, but would like to take an active part in the data and analytics journey for their organizations. They would like to define and create data products and govern the data based on their domain needs. IT, on the other hand, needs to deploy the right infrastructure to enable business users to be more self-sufficient.

    Generative AI (GenAI) will also have a huge impact on data management and result in tools and technologies that are more business friendly. At the same time, a strong data management infrastructure will be key to making GenAI successful because without high quality and trusted data it is bound to be of little or no use.

    And last but not least, while migration to the cloud, cloud data lakes, and/or cloud data warehouses remain big factors behind the success of modern data and analytics, data anti-gravity will become a norm in 2024. In the coming year, it will become increasingly hard for any organization to rely on a single cloud provider, cloud data warehouse or data lake to meet all of their end-to-end data and analytics needs.

    Data will always be distributed, which will be even more evident in 2024. Organizations need to find a way to have a single view of distributed and multi-location data for their enterprise data management; so, look for the following trends to emerge and/or gain traction next year:

    Data Anti-Gravity Will Prevail in a World of Distributed Data

    The notion of data gravity does not exist anymore. Every organization with a modern data strategy needs a data warehouse alongside a data lake, if not multiple ones, to fulfill their business needs. In the last two decades, data warehouses and data lakes became popular to solve enterprise data silo problems, yet what they created were even bigger ones. This is because these data warehouses and data lakes are usually spread across on-premises or in the cloud and are often geographically dispersed; at times spread across the globe. Even though every cloud service provider is trying to solve many of the data and analytics problems by themself, most organizations are running their data and analytics in a multi-cloud environment, cherry picking products and services from various cloud service providers.

    This is why data anti-gravity will be the new norm in 2024 and beyond. There are three forces defining data anti-gravity; technology, geography, and ownership. As fit-for-purpose data stores and applications get geographically dispersed and business users claim increasingly more ownership over data and data products, data anti-gravity will become a standard for modern data and analytics. Other factors contributing to data anti-gravity will be the rising cost of data replication, data sovereignty, local data governance laws and regulations, and speed-to-insights. As data anti-gravity increasingly becomes the preferred approach, data management leaders should invest in technologies that are built on the premise of distributed data management.

    The Rise of Data Products in Enterprises

    As we peer into the future, 2024 stands out as a pivotal year for the ascent of data products in the enterprise landscape. The turning point will be the realization that data products should be treated with the same level of importance as any other product offering. Take, for instance, a Tylenol capsule: its value isn’t just in the capsule itself but in the comprehensive package that earns consumer trust—from the description and intended use to the ingredient list and safety measures. Similarly, data catalogs act as the crucial “packaging” turning raw data into reliable, consumable assets.

    In this data-centric era, it’s not enough to merely package data attractively; the end-user experience is equally vital. Echoing the best practices of e-commerce giants, contemporary data platforms must offer features like personalized recommendations and popular product highlights, while also building confidence through user endorsements and data lineage visibility. Moreover, these platforms should facilitate real-time queries directly from the data catalog and maintain an interactive feedback loop for user inquiries, data requests, and modifications. Just as timely delivery is essential in e-commerce, quick and dependable access to data is becoming indispensable for organizations.

    What makes 2024 the watershed moment for data products is the emergence of unified platforms that encapsulate these facets—from data cataloging to end-user experience, and from rapid data access to robust security and governance measures. These integrated platforms are equipping enterprises with the resources they need to build a thriving data culture and boost user adoption. As such, we anticipate 2024 to be a transformative year in how enterprises manage, utilize, and value their data.

    Data Architectures Emerge to Ease GenAI Adoption and Ensure Success

    Organizations are encountering multiple challenges as they attempt to implement GenAI and Large Language Models, including issues with data quality, governance, ethical compliance, and cost management. Each obstacle has direct or indirect ties to the overarching data management strategy. Whether it’s ensuring the integrity of data fed into AI models, abiding by complex regulatory guidelines, or facilitating smooth integration into existing systems, effective data management is central to surmounting these issues.

    Against this backdrop, a robust data architecture emerges not just as a nice-to-have but as a critical necessity for any GenAI initiative. High-quality, well-governed data form the bedrock upon which these advanced models operate, substantially impacting the reliability and ethical compliance of their outputs. Without a strong data management foundation, even the most advanced GenAI projects are at risk of producing unreliable or non-compliant results.

    Looking ahead to 2024, it’s evident that data management will play an increasingly pivotal role in the successful GenAI adoption. Organizations that proactively invest in a strong data management framework will be better positioned to unlock the full business potential of these advanced technologies. Tackling data-related challenges will set the stage for unprecedented innovation and growth, solidifying data management as the cornerstone of successful GenAI adoption.

    These are not just mere predictions. Today, many enterprises are already investing in architectures such as data mesh with one of its most important pillars being data product creation. Similarly, many organizations have a corporate mandate to maintain data literacy programs so that IT does not become a bottleneck so that business stakeholders can make data- and insight- driven decision making their priority.

    To this end, expect the pace of data product creation across enterprises to accelerate multifold. It’s no surprise that GenAI is on everyone’s mind and it will have a far reaching impact on how we run businesses. But the symbiotic existence of data management and GenAI will have to become a priority so expect that in the coming year. Cloud computing will not only remain prevalent, but will grow for the decades to come. While that is true, a single repository or single cloud provider based data and analytics is just not practical, This is why distributed data management is here to stay as it enables every organization to make informed decisions on how to manage distributed data in the most efficient and cost effective way.

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