Three Challenges That Impact Successful Data Pipelines

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Three Challenges That Impact Successful Data Pipelines

Big Data projects could account for 25 percent or more of IT budgets this year.

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90% of the world’s data has been created in the last two years alone — and analysts expect that data will grow 61% to 175 zettabytes by 2025 worldwide. Organizations are beginning to understand the value of modern data applications to help gather, process, and manage the increasing data quantities. Sapio Research confirms 74% of businesses expect impactful results from big data, anticipating that it will drive reliable, useful, and profitable business applications.

However, Sapio Research also found that less than one in five business leaders currently rate their data stack as “optimal,” suggesting that current operational strategies are not yet delivering desired results. Most organizations believe in the promise of data — but its inherent operational challenges hold enterprises back from realizing data’s full potential due to a combination of factors. Let’s explore:

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Increasingly Complex Data Pipelines

As digital transformation takes hold, a new generation of applications that depend on a distributed data stack is going into production, such as the Internet of Things (IoT), customer analytics, machine learning, and fraud prevention. These apps require a radically new approach to deal with much higher data volumes and real-time data pipelines.

DevOps-DataOps Collaboration

Big data implementations have become a top priority for businesses of all sizes, yet organizations are experiencing performance problems that frustrate both data analysts and IT operations teams alike. Indeed, DevOps teams struggle in dealing with technical challenges when faced with complex data pipelines and the underlying data stack that supports them — and data analysts traditionally have been excluded from the strategy-making and operations functions of DevOps personnel.

Growing Talent Gap

Organizations looking to leverage insights from their data are lacking the personnel resources and expertise required, and this is an area where companies would do well to focus their attention. Sapio Research confirmed 36% of enterprise businesses cited talent scarcity as a huge pain point. Data applications can be complicated and often require Ph.D.-level expertise to manage and troubleshoot manually.

How Can Organizations Address Big Data Implementation Challenges

Modern applications, both on-premise and in the cloud, run on complex data stacks. Organizations need well-designed, well-tuned data pipelines to ensure data projects deliver increased operational efficiency and anticipated business outcomes. As such, a reliable data pipeline that can process large and unpredictable source data streams and multiple modes of consumption has become an absolute necessity for many data-driven enterprises.

To drive reliable performance and overcome data implementation challenges, organizations should continuously monitor, manage, and improve their data pipelines. For example, by enhancing existing monitoring processes with AI and machine learning capabilities to provide automated recommendations and actions. This can not only fix bottlenecks and errors in complex data pipelines but also significantly help fill the ever-growing talent gap.

Indeed, Sapio Research says over half of all IT decision-makers say big data projects will account for 25 percent or more of their IT budgets this year. By reducing the complexity of data pipelines and optimizing the overall performance of modern data applications, data workflows will be more productive and big data projects will deliver on expectations.

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ai ,apm ,artifical intelligence ,big data ,dataops ,devops ,internet of things ,machine learning ,ml

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