Maximizing Your Data Value With an Enterprise Data Cloud
Data Is Everywhere, but There’s No Unified Data Control
The data explosion over the last two decades has been a fundamental enabler of our digital lives, and it is now a given that digital data is the lifeblood of modern organizations. But another, more troubling reality exists: Organizations are not only drowning in data, but they are also losing control of it.
According to a recent Enterprise Strategy Group research study of senior IT decision-makers, 63% of the respondents said they regularly encounter visibility-related issues across their data environments.1 Insufficient visibility can lead to ineffective/improper data governance. Additionally, in a world where the success of AI and other innovations is predicated on using data effectively, a lack of control over that data drives up costs, substantially increases risks, and ultimately limits the value that organizations gain.
Conversely, organizations that have control of their data operate efficiently. They move quickly to take advantage of new data-intensive technologies such as AI. They might better mitigate the risks associated with the constantly shifting cyberthreat landscape. And they could achieve compliance with industry and government regulations more effectively.
Although the data control problem has often been acknowledged, in the past it has also been viewed as too complex and costly to solve. Instead, organizations wrapped band-aids around the issue, often exacerbating it in the process. However, for two reasons, inaction or just taking a piecemeal, tactical approach to enterprise data management is no longer viable. First, the costs and risks of those limited approaches are high and will only get worse. Second, the benefits of embracing a modern data approach—such as an enterprise data cloud (EDC) that places control and understanding of data at its center—are increasingly apparent.
The typical approach to infrastructure needs to be challenged and re-examined at its most fundamental layer. The question for many IT decision-makers is where to start. A good first step is to become familiar with the underlying causes of data management challenges, including:
The data explosion over the last two decades has been a fundamental enabler of our digital lives, and it is now a given that digital data is the lifeblood of modern organizations. But another, more troubling reality exists: Organizations are not only drowning in data, but they are also losing control of it.
According to a recent Enterprise Strategy Group research study of senior IT decision-makers, 63% of the respondents said they regularly encounter visibility-related issues across their data environments.1 Insufficient visibility can lead to ineffective/improper data governance. Additionally, in a world where the success of AI and other innovations is predicated on using data effectively, a lack of control over that data drives up costs, substantially increases risks, and ultimately limits the value that organizations gain.
Conversely, organizations that have control of their data operate efficiently. They move quickly to take advantage of new data-intensive technologies such as AI. They might better mitigate the risks associated with the constantly shifting cyberthreat landscape. And they could achieve compliance with industry and government regulations more effectively.
Although the data control problem has often been acknowledged, in the past it has also been viewed as too complex and costly to solve. Instead, organizations wrapped band-aids around the issue, often exacerbating it in the process. However, for two reasons, inaction or just taking a piecemeal, tactical approach to enterprise data management is no longer viable. First, the costs and risks of those limited approaches are high and will only get worse. Second, the benefits of embracing a modern data approach—such as an enterprise data cloud (EDC) that places control and understanding of data at its center—are increasingly apparent.
The typical approach to infrastructure needs to be challenged and re-examined at its most fundamental layer. The question for many IT decision-makers is where to start. A good first step is to become familiar with the underlying causes of data management challenges, including:
- Exploding data volumes. This is not a new phenomenon for IT, and storage innovations have been driving down Flash, cloud, and hard disk drive storage media costs across the board. But data volumes are still becoming ever larger, and that means keeping track of the data is more challenging and costly. As storage costs fall, the cost of creating additional copies of data also continues to fall, ironically further exacerbating the problem. What’s more, the potential of AI means that organizations are incentivized not only to keep creating more data, but also to retain it longer, since historical data can be used to train models. An Enterprise Strategy Group study found wide variance in data volumes/growth rates, although most respondents had at least one pebibyte (PiB) of data under management. Many organizations have substantially more. Similarly, data growth rates vary widely, though more than half of respondents reported growth rates of at least 20% annually, with one in five experiencing growth rates of 41% or more.2
- Data sprawl/fragmentation/entropy. Not only is there more data, but it’s located in more places. IT has penetrated every part of the business; IT models now run off premises as well as on premises, and all locations are generating increasing amounts of data. Compounding this issue is the siloed, fragmented nature of enterprise storage systems. Each system is effective at storing, serving, and protecting data. But relatively little attention has been paid to managing the multitude of often incompatible storage arrays that most organizations now have scattered across their operations.
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