3 Best Practices for Maximizing Data Management Efficiency

3 Best Practices for Maximizing Data Management Efficiency

In 2020, global spending on cloud data services reached $312 billion. In 2022, Gartner estimates that this number will rise to a staggering $482 billion. This immense increase proves that the migration to and adoption of cloud platforms is the bona fide standard for contemporary information services and analysis.

With more data accumulating across decentralized cloud platforms, a larger burden is placed on the data teams tasked with managing it. As the cloud continues growing in scale and popularity, organizations need to find sustainable solutions that alleviate this undue stress and keep pace with evolving data trends.

In the new report, Predicts 2022: Data Management Solutions Embrace Automation and Unification, Gartner analysts examine the impacts of accelerating cloud adoption on the data management landscape. Their analysis identified two overarching solutions that will drive a more sustainable data future: automation of processes and unification of data management components. By pursuing automation and unification, Gartner believes that data teams will be able to efficiently match the evolution of data, while simultaneously deriving significant ROI and value from their data and cloud investments.

The Top 3 Best Practices for Maximizing Data Management Efficiency

How does Gartner suggest businesses begin to pursue automation and unification? Here’s an overview of three easily workable proposals:

1. Invest in tools that support diverse and hybrid cloud environments

With the widespread availability of cloud data services, businesses have a range of tools and platforms to consider. They often employ multiple cloud services to guarantee that their data’s value is being maximized. The result is the modern multicloud model, in which several cloud compute and storage platforms are united in a single heterogeneous structure and, ideally, work in unison.

The popularity of multicloud architectures accords them a significant role in the future of data management. In their Predicts 2022 report, Gartner forecasts that “Through 2026, 90% of data management tools and platforms that fail to support multicloud and hybrid capabilities will be set for decommissioning within three years.” As the market trends towards optimizing multicloud models, data tools that cannot support this flexibility will become obsolete and ineffective. Supporting multicloud is becoming less of a perk and more of a necessity.

How does Gartner suggest you approach this shift? Adopting third-party data access control and management solutions will provide the elasticity necessary for maintaining multicloud environments. When these tools provide universal cloud compatibility, access controls can grow and scale with your diverse cloud environments. This helps ensure that your data storage and analysis will not get left in the past, but continue to drive the future.

2. Unify and leverage metadata across platforms

As organizations collect and use more data, metadata provides a way to discover, understand, and leverage their resources efficiently. Metadata provides technical or operational context and information about other data, helping users determine the who, what, when, where, why, and how of their data usage. Simply put, it’s data about data.

Today, metadata is being used as the building block of agile data frameworks. One such framework, known as data fabric, promises to provide frictionless data access and sharing capabilities. To do so, however, this type of architecture needs seamless access to the metadata generated throughout multicloud infrastructures. This, unfortunately, is often easier said than done.

Gartner observes that “Enterprises now report approximately 90% or more of their time is spent preparing data (as high as 94% in complex industries) for data engineering.” What is taking up a majority of this prep time? Locating or inferring missing metadata from across data management systems.

To streamline this process, Gartner suggests that organizations eliminate any data management tools that do not facilitate metadata sharing. Investments should instead go toward automated tools that expedite metadata sharing and analytics. Solutions that can automate functions like dynamic data masking and policy enforcement and auditing allow for real-time capture, analysis, utilization, and maintenance of metadata across a data ecosystem. This drives innovation without requiring time-consuming manual collection.

3. Orchestrate and streamline data access and governance processes

Whether in support of multicloud systems, metadata sharing, or beyond, an overall push towards unifying and streamlining data access and governance is indispensable for creating more efficient modern data strategies.

Unfortunately, many organizations allow their digital business objectives to be impeded by disjointed data access and governance measures. When these measures aren’t unified, efficiency is sacrificed and time-to-value is delayed. Gartner claims that “By 2026, 20% of large enterprises will use a single data and analytics governance platform to unify and automate discrete governance programs.” This indicates a gradual adoption of unified data access platforms.

If data and analytics governance capabilities can be consolidated into a single automated cloud data access control platform that integrates with any data stack, organizations will achieve greater efficiency and productivity. With streamlined data workloads and unified data access directives, Gartner says that paring down tools and relying on those that support multicloud systems will be key for future data success.


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