I D C M A R K E T S P O T L I G H T. T a m i n g D a t a M a n a g e m e nt Costs in a " C l o u d y" I T W o rld

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1 I D C M A R K E T S P O T L I G H T T a m i n g D a t a M a n a g e m e nt Costs in a " C l o u d y" I T W o rld June 2015 Adapted from IDC FutureScape: Worldwide Cloud 2015 Predictions by Mary Johnston Turner, Robert P. Mahowald, Melanie Posey, et al., IDC # Sponsored by Delphix This Market Spotlight examines the trend toward deploying data in the cloud and its associated business benefits. After a brief discussion about the rise of cloud services and increased enterprise dependence on data, the document describes the rising costs of data and the ways that organizations can control these costs. It also looks more specifically at data virtualization technology that can help enhance application development and testing agility through such abilities as instant provisioning of development and test data environments and instant restoration of test data to an initial state for repeat testing and discusses the operational cost and risk reduction realized by such technology. In addition, the paper provides some guidance for enterprises looking to lower their overall data costs through data virtualization technology. Introduction The world of enterprise IT is getting cloudier every day. IDC research shows that by the end of 2015, more than 65% of all enterprise organizations will commit to some form of cloud environment, typically encompassing multiple public cloud services, private clouds, and noncloud architectures. For example, the worldwide platform-as-a-service (PaaS) market is expected to grow to over $20 billion in 2018, with a compound annual growth rate (CAGR) nearly five times that of the traditional packaged software delivery market. The rapid growth of PaaS reflects a constantly changing market dynamic. PaaS offerings are typically bundled as full stack solutions and can enable a broad spectrum of customer use cases such as multi-enterprise data and process integration, cloud application development and life-cycle management, and other similar traditional IT functions. When offered with underlying infrastructure, PaaS frequently includes access to capabilities such as workload automation, scheduling, change and configuration management, storage management, and security and network management. There is a proliferation of different types of PaaS offerings, reflecting PaaS' inherent flexibility and the opportunity to deliver integrated platform services that archive business value more rapidly. Automation of business processes, especially those that require collaboration in an industry-specific environment, will further accelerate PaaS adoption. Driving the rise of PaaS in particular are mobility and the continued proliferation of data and associated analytics for business success. PaaS and the Rising Costs of Data PaaS enables enterprises to bring together data from a variety of sources to deliver greater business value. A cloud-enabled platform helps companies keep up with the growth of data without overwhelming their on-premise datacenters. This flow of timely and accurate data is vital to companies' business operations. Thus, ineffective management of data creates high costs. When the impact of data on areas IDC 1941

2 such as business intelligence or application development is considered, it becomes clear that data is playing a more critical role than ever in driving business success. Poor data management negatively affects these activities and exacts significant costs on organizations. These costs take different forms but often are manifested as increased time to market for applications or other products and reduced business productivity. But the business operation costs are more indirect and harder to identify than costs directly related to IT infrastructure or staff time, so companies feel less immediacy to reduce such costs. Nonetheless, as data becomes a central driver of business operations, more companies must figure out where and how the "costs of data" are impacting them and how they can reduce these costs. IDC research shows that organizations need to ensure that their data management strategy incorporates key initiatives such as: Building an efficient, scalable IT infrastructure, which is the key to ensuring low data costs Optimizing IT talent to deliver superior IT services and to free up time for innovation Maximizing end-user productivity with reliable and easy-to-use applications Reducing operational costs and increasing revenue Businesses often find it challenging to manage their total cost of data because data-related costs at one level are sometimes the result of decisions made to reduce costs at another level. For example, the decision to not create a test environment might save money at the IT infrastructure and staff levels, but it could impose significant business productivity or revenue costs by delaying the deployment of a revenue-producing application. Solving the Development/Test Data Provisioning Challenge Because storage technology has become faster, cheaper, more flexible, and easier to manage, organizations have typically handled the proliferation of data by throwing more infrastructure at it. But this does not change one fundamental fact: The chief cause of slow application development, resulting in less business agility, is not storage but complex tasks associated with refreshing and managing test and development copies of data (copy data). The resource deployment, staff time, and recurring tasks required to refresh and manage copy data do not change, regardless of how cheap the storage becomes or how easy it is to use. Allocating storage and copying, moving, and refreshing data are costly and disruptive. Creating multiple copies of data for test purposes is not only time consuming but also error prone and risky from a data security perspective. In a survey of storage professionals, IDC found an average of 65% of external storage systems capacity is used to store nonprimary data, such as snapshots, clones, replicas, archives, or backups. Therefore, better management of copy data can curb the growing percentage of an IT budget allocated to storage. One mechanism to better manage copy data is using data virtualization solutions to create virtual copies for development/test. A data virtualization solution creates an initial copy of the source data and then provisions virtual copies of that data to client systems, tracking any changes, updates, or deletes. The client systems interact with the virtual data as if it were a physical copy so that all applications and tools function normally. Since the underlying physical data blocks can be quickly provisioned as virtual data sets, this solution is perfect for running iterative tests and regression tests for applications. Such technology not only delivers storage efficiency but also enables on-demand deployment of development and test environments. Thus, making an investment in a data virtualization solution can significantly reduce opex and capex across software, compute, and storage infrastructure IDC

3 IDC has found that data virtualization has reduced the problems associated with copy data, including resource allocation, server time, and human error. Rather than allocating multiple physical copies of data for development and test environments, the data virtualization approach enables nearly instant provisioning of data copies with the added benefit that repeat tests do not require restoration of data from a previous state. Virtualizing data and managing it as a service provides a number of other benefits. Clearly, enterprises can reduce storage costs because virtualized copies of data can be stored on existing resources. In addition, data replication is easier and more cost effective. But the greatest value of data virtualization arises from the test and development world. Multiple copies of databases, application binaries, and files can be created for use during test and development quickly and easily without demanding massive storage and administrative resources. Because of virtualization, IT can maintain multiple copies of development and test data with minimal incremental cost. In addition, the time required to create development and testing copies is reduced significantly, enabling development teams to work with data that is more frequently updated, which provides agility and reduces the likelihood of data-related errors. This translates to more rapid application release cycles, which enables the enterprise to make application changes more quickly, ensuring that applications meet the needs of the business. Increased efficiencies also result in more productive IT staff. Organizations do not need to add staff for additional development projects even as application development and data-driven projects are sped up. This translates to overall increases in business productivity. In short, data virtualization technology that can provide data environments on demand without requiring extra storage saves time, reduces risk, and increases flexibility. The time saved in application testing alone translates to faster application cycle times, which ultimately has a positive business impact, including increased revenue. Benefits of Data Virtualization Technology in the Cloud When applications are deployed and run in the cloud, whether public or private, the benefits of data virtualization become even more apparent. The approach to resource allocation and deployment in the cloud promises an on-demand capability so that users can experience self-service provisioning. This is true enough for CPU capacity and for new file-based storage capacity. But when it comes to development and test data for a database application, for example, where the intent is to run tests on existing data to check on a new capability or fix a bug, the process bogs down. The test or development user may allocate the storage, but it generally takes a database administrator (DBA) and other administrators to execute a series of operations to set it up, including formatting the storage, backing up the production database and restoring it to the new test copy, and other associated tasks. This approach also means that each test environment requires as much storage as the original. Most public cloud providers charge for storage by its capacity, so if one sets up six test databases, each a copy of the production database, the storage charge will be the production database charge times six. With data virtualization, the approach is to create an initial copy of the production database, which is then refreshed periodically. If one wants six test databases, one then defines six databases virtually, but no additional storage is consumed. As the test executes, the database changes in each of the six environments are captured by the data virtualization technology in a delta store, and each virtual test environment acts as an individual containerized environment as if the changes in each environment had been applied to the database directly. As a result, instead of being charged for the production database times six, one is charged for the single copy, plus a minimal amount for each of the six delta stores. More important than the cost, however, is the agility gained by using this approach. Once the initial test copy is created, testers and developers can provision new instances for their projects instantly; no DBA or operations involvement is required. To run the test over and over against the same starting point, one 2015 IDC 3

4 simply resets the environment and runs the test again. This is perfect for iteration and regression testing. Often, testing is held up because of the time required to set up the test environment. With data virtualization technology, the dynamism of the cloud is just as available and flexible for data deployment as it is for other aspects of IT work such as infrastructure provisioning. By implementing such a solution, one is, in effect, setting up an internal "data as a service" for application teams. Conclusion and Essential Guidance In the era of mobility and big data, information has become the new coin of the realm. And the growing use of cloud computing and associated offerings has further added to the exploding growth of data. As a result, a substantial roadblock to the agility of the enterprise is the inability to create or evolve applications fast enough to meet the changing requirements of a rapidly evolving business climate. A key aspect of this problem is to be found in the cost, risk, and time involved in building and maintaining development and test environments, which are essential to such an evolution. Data virtualization technology can help enterprises overcome challenges associated with developing and testing data-intensive applications. IT staff can be more productive, and IT organizations can provide solutions faster. Storage requirements are reduced, but most important, time for loads, rollbacks, restores, and other such activities is also reduced. This enables organizations to focus on high-value tasks, such as creating applications that enable the enterprise to gain more value from the data. In addition, enterprises can utilize data virtualization to help overcome their fear of moving data to the cloud. Data virtualization in the cloud also offers additional data security benefits such as enabling efficient data masking capabilities and reducing the number of users who need to interact with the data. For organizations looking to increase the efficiency of IT by better managing their development and test environments, IDC recommends the following: Determine how much time and effort is currently involved in creating development and test environments and in restoring or rolling them back during testing. Calculate how much administrative effort is required to procure and allocate storage for such environments with the realization that most of the storage is likely to contain exactly the same data before and after each test. Determine what type of projects are delayed or deferred because the required amount of data makes them too costly or impractical, preventing the organization from evolving its applications to meet business requirements. Look for solutions that offer the kind of data virtualization capabilities described in this document and can take advantage of existing or planned cloud-based architectures that can help the organization overcome these challenges. Whether the deployment is in a conventional on-premise environment, a private cloud, or a public cloud, the time and effort involved in setting up and managing data copies for development and testing has been a major hindrance to the development, maintenance, and enhancement of applications. This in turn has prevented the ongoing evolution of applications in ways that meet the changing needs of the business. Implementing a data virtualization or "data as a service" technology to manage test data enables a means of making more rapid incremental changes and improvements to applications, which in turn results in a more nimble enterprise. Implementing such an environment in the cloud also increases the flexibility of self-service resource deployment, allowing application developers to leverage the full benefits of cloud-based agility and dynamism to meet the ever-changing needs of the enterprise IDC

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