Optimizing Resources: Embrace a Sustainable Lifestyle

Optimizing Resources: Embrace a Sustainable Lifestyle

Introduction:

Cloud spend is on the rise, with end-user spending predicted to grow almost 21 percent this year to exceed $591 billion. However, a recent study found that approximately 28% of cloud spend is going to waste, amounting to almost $168 billion wasted. Managing cloud spend has become the top challenge for many companies, with the dual specters of spend and waste putting pressure on them to become more efficient. Data teams are increasingly turning to the practices of DataOps and FinOps to manage costs and right-size their cloud data resources. By following best practices rooted in DataOps and FinOps principles, companies can accelerate innovation while reducing waste. These practices include gaining deep visibility into cloud consumption, identifying overprovisioned resources, preventing cost anomalies, leveraging AI and machine learning solutions, and creating a cost-conscious culture. Implementing a FinOps practice can help companies reduce waste and get more value from their cloud investments.

Full Article: Optimizing Resources: Embrace a Sustainable Lifestyle

Cloud Spend at All-Time High, But 28% is Going to Waste, According to Flexera

Public cloud services are seeing a significant increase in end-user spending, with Gartner predicting a growth rate of almost 21 percent this year. This translates to a total spend of over $591 billion, surpassing last year’s estimated spend of $490 billion. However, the sobering news is that approximately 28% of this cloud spend is going to waste, according to a recent study by Flexera. Considering Gartner’s estimated dollar spend, this amounts to a staggering $168 billion of wasted resources—the highest amount ever recorded.

The Challenge of Managing Cloud Spend

With such a significant portion of cloud spend going to waste, it’s no surprise that managing cloud spend has become a top challenge for companies. In fact, 82% of survey respondents listed it as their primary cloud challenge. While the good news is that the percentage of waste compared to overall spend has decreased from 32 percent last year, companies still tend to underestimate the amount of waste generated.

Increasing Pressure on Efficiency

Coupled with economic headwinds, the high cloud spend and waste are putting increased pressure on companies to become more efficient. CFOs and CIOs are holding CIOs accountable for their data teams’ cloud spend, making it crucial for these teams to get a handle on cloud spend as soon as possible. This is where DataOps and FinOps (DataFinOps) practices come into play.

Best Practices for Rightsizing Cloud Data Resources

Data teams can use DataOps and FinOps practices to both accelerate innovation and reduce cloud waste. The following best practices, rooted in these principles, can help companies achieve these goals:

1. Know what you’re using and spending: Getting deep visibility into job, cluster, pipeline, and user spending, as well as resource utilization and efficiency metrics, is essential to avoid budget overages. Continuous consumption reporting provides real-time feedback for more efficient cloud spend.

2. Identify overprovisioned cloud resources: Redeploying underutilized cloud investments allows teams to deliver more business value without increasing spend. A FinOps practice brings increased collaboration and visibility into cloud resources, usage, and costs, leading to more informed decisions about cloud allocation.

3. Catch cost anomalies early: Implement code reviews, automation, and cost-monitoring best practices to prevent and detect cloud data cost anomalies before they spiral out of control. Orchestrate workloads for faster cluster startup times, take advantage of off-peak cloud computing rates, and only move optimized, high-value projects to the cloud during a digital transformation.

4. Leverage AI and machine learning: Though not self-healing, improved AI and machine learning solutions can provide recommendations to improve cloud data spend efficiency. Implementing guardrails based on these technologies can alert teams to potential overspend and provide actionable steps to optimize jobs and prevent budget overruns.

5. Foster a cost-conscious culture: Implement a FinOps practice to reduce waste and get more value from cloud investments. This requires company-wide buy-in and ownership of cloud usage and spend, creating a culture that facilitates cost savings.

Realizing Returns on Cloud Spend

As companies look for ways to weather economic challenges, data leaders should focus on redeploying overprovisioned and underutilized cloud spend. A collaborative FinOps approach can help data teams achieve returns on their data stack investments while fostering innovation. To avoid inadvertently tossing away your cloud budget, it’s crucial to implement these best practices and adopt a cost-conscious culture.

About the Author:

Clinton Ford, a DataOps champion for Unravel Data, is passionate about unifying data science, engineering, and business. With experience in launching data and AI cloud services at global companies like Microsoft, AWS, and Databricks, he holds a B.S. in Computer Science and an MBA. He can be reached at [email protected]

Summary: Optimizing Resources: Embrace a Sustainable Lifestyle

Cloud spend is reaching record levels, with end-user spending on public cloud services predicted to grow 21% this year. However, a significant portion of cloud spend is going to waste, estimated at around 28%. This equates to approximately $168 billion wasted, highlighting the importance of managing cloud spend. While the percentage of waste compared to overall spend has decreased, many companies still underestimate their waste. As economic pressures mount, companies must become more efficient, and CIOs are being held accountable for cloud spend. Implementing best practices rooted in DataOps and FinOps, such as understanding usage and spending, identifying overprovisioned resources, and implementing cost-monitoring automation, can help reduce waste and manage cloud costs effectively. Improved AI and machine learning solutions can also provide recommendations for optimizing cloud data spend. A culture shift towards a cost-conscious mindset and implementing a FinOps practice can further drive cost savings and efficiency. As economic uncertainties persist, data leaders are advised to redeploy overprovisioned and underutilized cloud spend and utilize a collaborative FinOps approach to realize returns on their data investments and spur innovation.

Frequently Asked Questions:

1. What is data science and what role does it play in today’s world?
Data science is a multidisciplinary field that involves using scientific methods, processes, and algorithms to extract valuable knowledge and insights from data. Its primary aim is to uncover patterns, make predictions, and drive informed decision-making. In today’s data-driven world, data science plays a pivotal role in various industries, such as finance, healthcare, marketing, and technology, by helping businesses analyze large volumes of data and derive actionable insights to optimize operations and improve overall performance.

2. What are the key skills and qualifications required to become a successful data scientist?
To excel as a data scientist, one needs a strong foundation in mathematics, statistics, and programming. Proficiency in programming languages such as Python or R is highly preferred. Additionally, expertise in machine learning, data visualization, and big data tools like Hadoop and Spark is beneficial. Apart from technical skills, excellent analytical thinking, problem-solving abilities, and effective communication skills are essential to effectively interpret and communicate data-driven findings to non-technical stakeholders.

3. How does data science contribute to business growth and innovation?
Data science enables businesses to gain a comprehensive understanding of their customers, operations, and industry trends. By leveraging data science techniques, organizations can identify patterns and trends, make accurate predictions, and drive evidence-based decision-making. This, in turn, helps businesses optimize various aspects of their operations, enhance customer satisfaction, develop personalized marketing strategies, improve efficiencies, and uncover new opportunities for growth and innovation.

4. What are the challenges associated with implementing data science in organizations?
Implementing data science in organizations may present certain challenges. Firstly, acquiring and managing large volumes of data can be complex and requires robust data infrastructure and storage solutions. Secondly, ensuring data quality and accuracy is crucial, as inaccurate or incomplete data can lead to flawed analysis and incorrect insights. Additionally, organizations should address issues related to data privacy, security, and ethical considerations to maintain customer trust and compliance with regulatory requirements.

5. What are some real-world examples of data science applications?
Data science has a wide range of applications across various industries. In healthcare, it can be used for disease prediction, personalized medicine, and optimizing patient care. In finance, data science is employed for fraud detection, risk assessment, and algorithmic trading. Retailers utilize data science to analyze customer behavior, enhance inventory management, and forecast demand. Other examples include recommendation systems in online platforms, sentiment analysis in social media, and predictive maintenance in manufacturing.