Automation Takes on the Heavy Lifting of Data Management

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These days, every business is a data business. Every enterprise, regardless of industry or even size, is attempting to manage and draw insights from large volumes of data—now often extending into the petabyte range—surging through and between their systems and applications, both within clouds and on-premise data centers. This all needs to be managed appropriately and securely, as well as backed up and made highly available.

Running data-intensive operations requires more and more resources—in budgets, staff, and infrastructure. In addition, the complexity and requirements of managing data—thanks to increasing technology, greater user demands, more far-reaching data regulations, and highly competitive business landscapes—will only grow more onerous.

The key to managing in this data-intensive era is having the right tools and processes to automate as much as possible. Leading vendors such as Oracle recognize this, focusing on autonomous databases that are supported by internal robots to manage security and availability. A plethora of tools and platforms on the market now apply automated processes and capabilities that shape data environments.

A data automation strategy is key to avoiding the costs and inefficiencies of running data operations in a manual or ad hoc way. Data managers and professionals are needed for higher-level tasks, from consulting with business leaders on data-driven strategies to promoting and nurturing innovation within their enterprises.

Many enterprises are turning to cloud and automation, particularly in the area of backup and recovery, according to a recent survey conducted by Unisphere Research, a division of Information Today, Inc., in partnership with VMware. The survey, which covered 260 members of the Independent Oracle Users Group (“2019 IOUG Data Environment Expansion Survey”) found that, with the increasing demand for real-time analytical capabilities, there is greater pressure on data administrators to deliver responsive, high-performing systems that can scale accordingly.

As a backdrop to this scenario, there is a looming skills shortage that is adding new urgency to efforts to automate a wider breadth of data center operations. “As baby boomers shift out of the daily workforce, the wealth of knowledge they represent regarding mainframes is at risk of being lost,” said Jeff Cherrington, VP of product management, systems at ASG Technologies. “Because Gen Xers joined the workforce as distributed and cloud was taking off, the majority of them focused their careers in those areas. To support this transition, many enterprises expect to use AI and machine learning to capture and transfer knowledge to younger generations, and augment mainframe management by automating time-consuming tasks.”


Some of the most fundamental data management processes are now enabled through automation. Automation—whether via cloud services or through internal systems—is seen as critical to ensuring that complex and sophisticated data systems are delivering to the business. Here, data managers and professionals see the greatest value in automating the backup and recovery processes to ensure their data assets are protected. Automating backup and recovery is most important to the ITI/IOUG survey respondents (74%), followed by business continuity/disaster recovery (62%). Processes such as provisioning (53%) and monitoring (51%) score lower overall as automation priorities.

This is only the start, as increasingly sophisticated aspects of data operations will see automation. “As organizations move more toward cloud and DevOps-style operations, database automation is moving away from regular maintenance operations toward the movement and aggregation of data between disparate database types and locations,” said Dave Brunswick, vice president of solutions at Cleo. “Automating those data flows will be critical to future business growth.”

Additional data management processes that are increasingly being automated include ETL/data movement/data synchronization, security auditing, database maintenance operations—space reclamation, object management—and performance diagnostic collection and analysis, said Robin Schumacher, SVP for DataStax. “Other automated tasks performed less frequently include database provisioning and removal, and software upgrades done on regular cycles,” he noted.

Database release automation helps to break the application release bottleneck created by today’s manual database deployment process, said Robert Reeves, co-founder and CTO of Datical. This, in turn, improves productivity and performance, allowing development and testing teams and DBAs to focus on more important projects and initiatives. “Database release automation also helps eliminate unavoidable incidents of human error, while increasing data security and application performance and reliability,” Reeves added.

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