The value of your data in today's data-driven world is only as good as the end services, like Artificial Intelligence (AI), that can effectively utilize it. While collecting data may seem like a straightforward process, true value can only be unlocked after it has been prepared to extract its insights. During this data preparation, or modern data operations process, organizations may discover unforeseen efficiencies they hadn't anticipated, in addition to getting their data ready for end services like AI.
At StandardData, we specialize in guiding organizations through this transformative journey, particularly those grappling with substantial data volumes yet unsure where to begin. Recently, we collaborated with a leading automotive supplier, possessing a large amount of data sourced from IoT vehicle sensors. Faced with missed opportunities stemming from outdated data pipelines, our client sought our expertise to revamp their infrastructure in preparation for predictive machine learning implementations.
Through our partnership, StandardData not only achieved exceptional results in data preparation but also unlocked immediate, unforeseen benefits for our client, exceeding their initial expectations. By streamlining data storage and enhancing query efficiency, we slashed storage size by 50% and query time by a staggering 99%, concurrently trimming technology costs by 70%. Moreover, our data analysis unearthed hidden insights, revealing safety faults that prompted a proactive product recall, safeguarding consumers and bolstering the client's reputation.
This success story underscores the potential of modern data operations in preparation for AI, illustrating how strategic integration can not only optimize efficiency but also unearth critical insights, ultimately driving innovation and enhancing product safety.
In this blog, we will dive deeper into the following topics based on this project:
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The importance of modern data operations for organizations and how to get started.
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How cloud-based solutions improve efficiency of modern data operations.
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The benefits of end services, such as AI and machine learning, for organizations.
The Importance of Modern Data Operations and How to Get Started
Simply put, modern data operations refers to the process of preparing and delivering an organization's data. It equips businesses to navigate the complex terrain of preparing data for end services such as machine learning, data analysis, artificial intelligence (AI), and modern applications and databases. Before data can be integrated into end services, it requires meticulous preparation. Our team at StandardData focuses on designing and optimizing data specifically tailored for these models. As a result, modern data operations offers critical benefits:
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Streamlining data management processes: By prioritizing clean modern data operations, organizations can significantly minimize the time and resources required to manage their data efficiently.
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Improving data quality: Through rigorous examination, errors and inconsistencies within the data can be identified and rectified, ensuring high-quality, reliable data for analysis.
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Increasing data visibility: Modern data operations not only enhance the visibility of existing data but also unveils hidden insights, thereby enriching the organization's ability to derive meaningful insights from its data assets.
Partnering with expert companies such as StandardData provides organizations a seamless path toward faster and easier access to data-driven insights. At StandardData, we pride ourselves on leveraging agile solutions to transcend traditional, lengthy roadmaps and focus instead on delivering swift, intelligent strategies tailored to our clients' needs.
Our approach is founded on the initial investigation phase which allows us to swiftly identify the core characteristics of our clients' data ecosystems. In this case, when we discovered that our client predominantly handled time-series data on Microsoft Azure, we immediately recognized the opportunity to optimize their system. Based on their goals to reduce storage costs and query times, our recommendation to migrate to Apache Parquet file format and utilize Azure's Databricks service was a game-changer. The transformation we facilitated for our client—from using conventional CSV file storage to a more sophisticated, cost-effective processing system—was completed with precision within a mere two weeks, from inception to completion.
How Cloud-Based Solutions Improve Efficiency of Modern Data Operations
Cloud computing has revolutionized the way organizations manage and store their data. By leveraging cloud-based technologies versus utilizing on-premise servers, organizations can improve efficiency and cut costs in several ways, including:
1. Reducing infrastructure costs: Eliminate the need to invest in expensive hardware and software infrastructure, reducing infrastructure costs. Not only can expensive hardware and software overhead be reduced, but the serverless capabilities of cloud vendors, such as AWS or Microsoft Azure, can reduce computation costs of infrastructure dramatically. Only pay for what you use!
2. Improving scalability: Scale data management and analysis capabilities up or down as needed, providing greater flexibility and agility.
3. Streamlining technology: Cloud providers can deliver comprehensive big data solutions, enabling organizations to easily ingest, store, process, analyze, and visualize large amounts of data in one place.
Since our client was already streaming their Internet of Things (IoT) data to blob storage in Microsoft Azure, it was a great start to building a modernized data pipeline. From there, we migrated the data into Apache Parquet, which is the common data lake file format for cost-effective storage and processing. After converting the data, we utilized Azure’s Databricks service to extract meaningful insights. Specifically, we identified a small percentage of rows—within the vast dataset of 30 trillion—which exhibited certain sensor faults.
By harnessing the capabilities of big data solutions within their cloud provider, Microsoft Azure, we streamlined processes that previously took hours to mere minutes. This not only significantly reduced query times but also empowered the organization with newfound visibility into deep-seated data anomalies. As a result, we were able to pinpoint four safety faults through Vehicle Identification Numbers (VINs) and promptly initiate a recall, ensuring the safety of consumers.
The Benefits of End Services (AI/Machine Learning) for Organizations
Once an organization’s data has undergone the preparation and optimization process, similar to the successful endeavor we undertook for our client, it becomes ripe for end services, such as AI or machine learning algorithms to leverage. These algorithms utilize crafted models of your data to decode and transcribe it, unveiling invaluable insights that drive actionable decisions. We've seen AI and machine learning models improve organizations' data and cut costs by:
- Improving data analysis: Analyze large volumes of data quickly and accurately, enabling organizations to identify trends and insights that would be difficult or impossible to detect using traditional data analysis methods.
- Enhancing predictive analytics: Develop predictive models to accurately predict future trends and events, enabling organizations' early detection and prevention of expensive circumstances before they unfold.
- Reducing costs: By automating data processing tasks and improving data analysis, machine learning can help organizations reduce the time and resources required to manage and analyze data, resulting in cost savings.
Specifically, the adoption of modernized data formats such as Parquet, coupled with the robust multiprocessing capabilities of platforms like Databricks, lays a robust foundation for organizations to fully harness the potential of AI-driven services. At the heart of our approach lies a steadfast commitment to data, positioning our clients to seamlessly integrate their data with any AI service of their choosing, be it AWS Textract, ChatGPT, or beyond. While the landscape of AI services and models continues to evolve, one constant remains: Data serves as a bedrock of unparalleled importance, forming the essential foundation for transformative insights and decisions.
