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Cost Optimization

Harnessing Google Cloud BigQuery for Speed and Scale: Data Observability, FinOps, and Beyond

Data is a powerful force that can generate business value with immense potential for businesses and organizations across industries. Leveraging data and analytics has become a critical factor for successful digital transformation that can accelerate revenue […]

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Data is a powerful force that can generate business value with immense potential for businesses and organizations across industries. Leveraging data and analytics has become a critical factor for successful digital transformation that can accelerate revenue growth and AI innovation. Data and AI leaders enable business insights, product and service innovation, and game-changing technology that helps them outperform their peers in terms of operational efficiency, revenue, and customer retention, among other key business metrics. Organizations that fail to harness the power of data are at risk of falling behind their competitors.

Despite all the benefits of data and AI, businesses face common challenges.

Unanticipated cloud data spend

Last year, over $16 billion was wasted in cloud spend. Data management is the largest and fastest-growing category of cloud spending, representing 39% of the typical cloud bill. Gartner noted that in 2022, 98% of the overall database management system (DBMS) market growth came from cloud-based database platforms. Cloud data costs are often the most difficult to predict due to fluctuating workloads. 82% of 157 data management professionals surveyed by Forrester cited difficulty predicting data-related cloud costs. On top of the fluctuations that are inherent with data workloads, a lack of visibility into cloud data spend makes it challenging to manage budgets effectively.

  • Fluctuating workloads: Google Cloud BigQuery data processing and storage costs are driven by the amount of data stored and analyzed. With varying workloads, it becomes challenging to accurately estimate the required data processing and storage costs. This unpredictability can result in budget overruns that affect 60% of infrastructure and operations (I&O) leaders.
  • Unexpected expenses: Streaming data, large amounts of unstructured and semi-structured data, and shared slot pool consumption can quickly drive up cloud data costs. These factors contribute to unforeseen spikes in usage that may catch organizations off guard, leading to unexpected expenses on their cloud bills.
  • Lack of visibility: Without granular visibility into cloud data analytics billing information, businesses have no way to accurately allocate costs down to the job or user level. This makes it difficult for them to track usage patterns and identify areas where budgets will be over- or under-spent, or where performance and cost optimization are needed.

By implementing a FinOps approach, businesses can gain better control over their cloud data spend, optimize their budgets effectively, and avoid unpleasant surprises when it comes time to pay the bill.

Budget and staff constraints limit new data workloads

In 2023, CIOs are expecting an average increase of only 5.1% in their IT budgets, which is lower than the projected global inflation rate of 6.5%. Economic pressures, scarcity and high cost of talent, and ongoing supply challenges are creating urgency to achieve more value in less time.

Limited budget and staffing resources can hinder the implementation of new data workloads. For example, “lack of resources/knowledge to scale” is the leading reason preventing IoT data deployments. Budget and staffing resources constraints pose real risks to launching profitable data and AI projects.

Exponential data volume growth for AI

The rapid growth of disruptive technologies such as generative AI, has led to an exponential increase in cloud computing data volumes. However, managing and analyzing massive amounts of data poses significant challenges for organizations.

Data is foundational for AI and much of it is unstructured, yet IDC found most unstructured data is not leveraged by organizations. A lack of production-ready data pipelines for diverse data sources was the second most cited reason (31%) for AI project failure.

Data pipeline failures slow innovation

Data pipelines are becoming increasingly complex, increasing the Mean Time To Repair (MTTR) breaks and delays. Time is a critical factor that pulls skilled and valuable talent into unproductive firefighting. The more time they spend dealing with pipeline issues or failures, the greater the impact on productivity and new innovation.

Manually testing and running release process checklists are heavy burdens for new and growing data engineering teams. With all of the manual toil, it is no surprise that over 70% of data projects in manufacturing stall at Proof of Concept (PoC) stage and do not see sustainable value realization.

Downtime resulting from pipeline disruptions can have a significant negative impact on the service level agreements (SLAs). It not only affects the efficiency of data processing, but also impacts downstream tasks like analysis and reporting. These slowdowns directly affect the ability of team members and business leaders to make timely decisions based on data insights.

Conclusion

Unravel 4.8.1 for BigQuery provides improved visibility to accelerate performance, boost query efficiency, allocate costs, and accurately predict spend. This launch aligns with the recent BigQuery pricing model change. With Unravel for BigQuery, customers can easily choose the best pricing plan to match their usage. Unravel helps you optimize your workloads and get more value from your cloud data investments.

Unravel for BigQuery product launch webinar on demand