IT automation central to navigating cloud complexity and data explosion

Organizations have turned to multicloud architectures to deliver better, more secure software faster. But IT teams need to embrace IT automation and new data storage models to truly benefit

Organizations continue to turn to multicloud architecture to deliver better, more secure software faster. But IT teams need to embrace IT automation and new data storage models to benefit from modern clouds.

As they enlist cloud models, organizations now confront increasing complexity and a data explosion. With more cloud-based entities to manage— such as containers, microservices, and other resources –IT pros can easily get overwhelmed by the volume of resources, their various dependencies, and the steady stream of data they generate.

To combat the cloud management inefficiencies that result, IT pros need technologies that enable them to gain insight into the complexity of these cloud architectures and to make sense of the volumes of data they generate.

Indeed, according to Dynatrace data, 61% of IT leaders say observability blind spots in multicloud environments are a greater risk to digital transformation as teams lack an easy way to monitor their infrastructure end to end.

Log management and log analytics have become a particular challenge. Teams find it increasingly difficult to analyze large volumes of disparate and decoupled data quickly, and cost-effectively, to deliver meaningful value.

Instead, as IT pros adopt IT automation and AIOps (or AI for IT operations), IT teams can focus on innovative, high-value tasks that drive better business outcomes.

Data explosion hinders better data insight

Research indicates that IT pros now feel the squeeze of this data explosion and cloud complexity.

According to the recent Dynatrace “2022 Dynatrace CIO Report,” 71% of CIOs say that the explosion of data given cloud-native technology stacks has surpassed human ability to manage. And 59% of CIOs say their teams may become overloaded by the increasing complexity of their technology stack if they don’t identify a more automated approach to IT operations.

Moreover, IT pros say that cloud architecture and data repositories thwart achieving better data insight. Nearly two-thirds (63%) of CIOs say the costs and delays caused by reindexing and rehydration make it challenging to unlock value from the increasing amount of data they capture. And 93% of CIOs say AIOps and automation are increasingly vital to alleviating the shortage of skilled IT, development, and security professional.

Enlisting a data lakehouse, IT automation can manage the next wave of cloud complexity

While IT pros say they are overwhelmed today, they acknowledge that automation and new ways of managing the data explosion are key to solving cloud complexity and data overwhelm.

First, if organizations want to drive greater innovation and efficiency, they need to shift. They should move from technologies that rely on traditional data warehouse and data lake-storage models and embrace a modern data lakehouse-based approach.

Data lakehouse architecture addresses data explosion

That’s why a data lakehouse combines a warehouse and data lake model to incorporate the best of both models—without the tradeoffs. A data lakehouse features the flexibility and cost-efficiency of a data lake with the contextual and high-speed querying capabilities of a data warehouse.

A data lakehouse will enable teams to harness petabytes of data at the speed necessary to turn raw information into actionable answers that drive AISecOps automation (which applies artificial intelligence to organizational security and operations practices and data to improve visibility). In doing so, organizations can free skilled DevOps teams from routine, manual tasks so they can achieve better business outcomes and sustained growth.

Indeed, according to the report, 93% of CIOs say that the application of AIOps (or AI for IT operations) and automation are key to addressing the staffing shortages in IT.

“Teams urgently need a new approach to observability and security data analytics and management,” said Bernd Greifeneder, founder and chief technology officer at Dynatrace. “AI and automation should underpin this approach, and it should be capable of unifying all data and keeping its relationships and dependencies intact. This will enable organizations to maximize the value of their data and people, reducing the time spent on mundane manual tasks and enabling faster, more secure innovation.”