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As AI adoption grows, the need for seamless integration between deep learning frameworks like PyTorch and Big Data ecosystems becomes increasingly important. While PyTorch excels in model development and training, its success often depends on how effectively it can handle large-scale data pipelines. These pipelines are typically powered by distributed systems like Hadoop Distributed File System (HDFS) or Apache Spark, requiring efficient data integration and processing practices. This guide dives into the challenges and strategies for integrating PyTorch with Big Data ecosystems. Let's discuss the very real roadblocks working with big data can impose and technologies we have to derive value from them. <h3>Challenges of Scaling PyTorch with Big Data</h3>While PyTorch is flexible and efficient for AI tasks, integrating it with distributed systems presents unique challenges: Data Access: Loading massive datasets directly from distributed file systems or cloud storage into PyTorch can be inefficient without the right tools.Batch Processing: Efficiently handling large datasets requires strategies to prevent memory bottlenecks while maintaining high throughput.Distributed Training: Scaling model training across multiple GPUs or cluster nodes adds further complexity.These challenges call for specialized integration tools and techniques to ensure smooth workflows.<h3>Loading Data from Distributed Systems</h3>Big Data ecosystems often rely on formats like Parquet or Avro for structured data storage, which aren’t natively compatible with PyTorch. Libraries like PyArrow and Petastorm act as bridges, enabling data conversion and loading. For instance, PyArrow can read Parquet data directly from distributed file systems like HDFS and convert it into a format that PyTorch’s data structures can process.When designing a data pipeline, it’s best practice to preprocess data as close to its storage location as possible. For example, reading Parquet files directly into memory, transforming them into tensors, and leveraging PyTorch’s Dataset and DataLoader APIs allow for efficient batching and shuffling during training.<h3>Preprocessing at Scale with Apache Spark</h3>Apache Spark is a powerful tool for distributed data preprocessing, and it integrates well with PyTorch through libraries like Petastorm. Spark’s distributed computing capabilities enable large-scale data transformations, such as cleaning, feature engineering, and aggregations. By preprocessing data in Spark and storing it in a format optimized for PyTorch (e.g., Petastorm’s parquet-based datasets), organizations can streamline their workflows. This integration eliminates the need to manually split datasets or deal with memory constraints, as Spark handles data partitioning and parallelism while Petastorm provides seamless connectivity to PyTorch.<h3>Distributed Training in PyTorch</h3>Once the data pipeline is set, scaling PyTorch for distributed training is the next step. PyTorch Distributed Data Parallel (DDP) enables training across multiple GPUs or cluster nodes. By dividing data across processes and synchronizing gradient updates, DDP ensures efficient utilization of computational resources.For optimal performance, it’s critical to use distributed samplers in PyTorch’s DataLoader, ensuring that each process receives a unique subset of data. This prevents data duplication and maximizes throughput during training. Tools like PyTorch Lightning can further simplify distributed training by automating repetitive boilerplate code, allowing developers to focus on model design and tuning.<h3>Best Practices for Integrating PyTorch with Big Data</h3>1. Minimize Data Movement: Instead of copying data between storage systems, use tools like PyArrow or Petastorm to preprocess data directly where it resides.2. Leverage Distributed Preprocessing: Use Spark to handle large-scale data cleaning and transformation tasks, reducing the workload on PyTorch.3. Optimize Data Loading: Use PyTorch’s Dataset and DataLoader APIs to manage batching, shuffling, and preprocessing efficiently.4. Distributed Training: Scale training workflows using PyTorch’s DDP or PyTorch Lightning, ensuring that compute resources are fully utilized.5. Experiment Tracking: Implement tools like MLflow or TensorBoard to monitor performance metrics, especially in distributed environments.Integrating PyTorch with Big Data ecosystems enables organizations to scale their AI workflows to handle massive datasets efficiently. By leveraging tools like Spark for distributed preprocessing and PyTorch Distributed for multi-GPU training, teams can build robust pipelines that maximize both scalability and performance. These integrations ensure that AI models can meet the demands of modern data environments, bridging the gap between data engineering and deep learning.

Integrating PyTorch with Big Data Ecosystems

In technical organizations, productivity is often seen as the holy grail of metrics. Leaders want to know: are we doing enough, fast enough? Unfortunately, measuring productivity in a meaningful way is nearly impossible. Metrics like lines of code written, number of commits, or tickets closed are superficial and often misleading, failing to account for the nuances of technical work. This is particularly true in the context of a data strategy, where the focus should be on long-term value, quality, and organizational efficiency rather than short-term outputs.As we continue to strive for greater productivity, it’s crucial to measure the right things. Instead of attempting to quantify productivity directly, organizations should focus on three key indicators:<ol><li>Decreases in Defects</li><li>Amount of Reuse Across the Organization</li><li>Time to Resolve Technical Debt</li></ol>These measures collectively point to a more productive, efficient, and sustainable technical organization. Let’s explore each of them in detail.<h3>Decreases in Defects</h3>One of the clearest signals of improved productivity is a reduction in defects. Defects—whether in code, data pipelines, or dashboards—represent inefficiencies. They create rework, delays, and frustration, all of which diminish an organization’s ability to deliver value.When defects decrease, it suggests that systems and processes are improving. This often correlates with better testing, more thorough code reviews, and a deeper commitment to data quality. Fewer defects mean that the team can focus on new initiatives instead of firefighting, enabling faster delivery and greater innovation. To measure this effectively, track:<ul><li>The number of defects detected per release or sprint.</li><li>The severity of those defects (e.g., minor issues vs. critical failures).</li><li>Trends over time, ensuring a steady decline in defect counts.</li></ul><h3>Amount of Reuse Across the Organization</h3>Efficiency in a technical organization is closely tied to the ability to reuse existing work. Whether it’s reusing components, data models, or automation scripts, a culture of reuse eliminates redundancies, accelerates development, and standardizes processes.For a successful data strategy, reuse can manifest in many ways:<ul><li>Standardized data pipelines or ETL workflows used across multiple projects.</li><li>Reusable analytics models or pre-trained machine learning algorithms.</li><li>Shared documentation, templates, and frameworks for consistent practices.</li></ul>By measuring reuse, organizations gain insight into their ability to leverage past work and reduce unnecessary duplication. This metric also fosters collaboration and the creation of shared resources, breaking down silos and encouraging a more cohesive technical culture. To assess reuse, track:<ul><li>The number of projects utilizing shared assets or components.</li><li>The frequency with which reusable frameworks or pipelines are adopted.</li><li>Feedback from teams on the effectiveness and accessibility of reusable tools</li></ul><h3>Time to Resolve Technical Debt</h3>Technical debt is an unavoidable reality in any organization, but how quickly it is resolved can speak volumes about productivity and efficiency. If resolving technical debt consistently takes years or is perpetually delayed, it indicates a systemic problem. Long-term delays in addressing debt often result in compounded inefficiencies, greater risks, and slower development cycles.A key aspect of a successful data strategy—and any technical strategy—is maintaining a balance between new initiatives and the regular resolution of technical debt. Shorter resolution times demonstrate that an organization values long-term sustainability over short-term gains. To measure this effectively:<ul><li>Track the time it takes to resolve planned technical debt from the moment it’s identified.</li><li>Monitor the backlog of unresolved technical debt, ensuring it doesn’t grow unmanageable.</li><li>Ensure there is a clear plan and cadence for addressing debt in every sprint or project cycle.</li></ul><h3>A Holistic Approach to Measuring Success</h3>By focusing on these three areas—defects, reuse, and technical debt resolution—organizations can move beyond the flawed notion of measuring productivity directly. Instead, they can measure outcomes that correlate with a healthier, more effective technical ecosystem. Fewer defects signal that systems and processes are improving, allowing teams to focus on creating value rather than fixing problems. Increased reuse demonstrates that the organization is working smarter, not harder, by eliminating redundancies and promoting collaboration.Consistent resolution of technical debt ensures that the organization remains agile and avoids being bogged down by legacy issues. These metrics don’t just apply to data strategy—they are equally relevant for any technical organization striving for long-term success.&nbsp;By targeting these areas, leaders can create an environment where teams are empowered to work efficiently, deliver quality results, and sustain progress over time. When you stop chasing the impossible goal of measuring productivity and instead focus on these meaningful indicators, you’ll find that true productivity emerges naturally—through fewer bottlenecks, greater innovation, and a stronger foundation for growth.

Rethinking Productivity: Better Metrics for a Successful Data Strategy

As the year comes to an end we wanted to reflect on this year and what it has meant to the data industry. The Domo "Data Never Sleeps" report, an annual deep dive into the exponential growth of data, presents some striking statistics that illuminate the current landscape of our data-driven world. As organizations generate and store more data than ever before, the report underscores a critical reality: data is being created at an overwhelming rate. In fact, Domo's 2024 report reveals that every minute, over 16,000 new videos are uploaded on TikTok, more than 3,472,222 million YouTube videos are watched, and over 1.04 messages are sent on slack. These numbers are not just eye-popping—they are a signal of the tremendous scale and speed at which we are producing data, presenting both opportunities and challenges for the data industry.<figure id="figure-76qtr18pzvm51j0tbx"><img id="76qtr18pzvm51j0tbx" src="https://cdn.durable.co/blocks/30UA8UIGNH5zMHSPLZ9cjkz3Bwxag5NTjj125kVuONnv6hM1HBD6qgQSeZBlhyyO.png" alt="" title="" class="object-cover bg-gray-100" style="aspect-ratio: 1/1; object-position: 50% 50%;" data-corners="default" data-aspect="1:1" data-caption="" data-positionx="50%" data-positiony="50%" data-fullwidth="false" data-mce-src="https://cdn.durable.co/blocks/30UA8UIGNH5zMHSPLZ9cjkz3Bwxag5NTjj125kVuONnv6hM1HBD6qgQSeZBlhyyO.png" data-mce-style="aspect-ratio: 1/1; object-position: 50% 50%;"></figure>For organizations navigating this data deluge, Domo’s report offers valuable insights into both the areas of success and the warning signs they should heed. Let’s explore two key areas of success, as well as a critical warning that the report raises for businesses and data professionals alike.<h3>Successful Data Accessibility and Real-Time Analytics</h3>One of the most significant successes highlighted by Domo is the ability to access and analyze data in real time. With the growing adoption of cloud-based platforms, businesses are now able to process vast amounts of data almost instantaneously. This trend is empowering organizations to make data-driven decisions at speeds that were previously unimaginable. The ability to integrate data across various systems, tools, and departments allows businesses to get a holistic view of their operations in real time.For data engineers, data scientists, and business leaders, this means that the ability to quickly process and visualize data is no longer a luxury—it’s a necessity. Real-time analytics provides a competitive edge, enabling businesses to respond to changes in customer behavior, track operational metrics, and fine-tune marketing campaigns instantly. Cloud platforms like Domo and others are paving the way for this transformation, helping companies extract actionable insights from data and drive business growth.<h3>We have achieved Data Democratization</h3>Another success that the Data Never Sleeps report touches upon is the democratization of data. Traditionally, accessing and analyzing data required specialized knowledge and complex tools, which often left non-technical staff at a disadvantage. However, the emergence of self-service BI tools and no-code platforms has empowered business users to access and interact with data without relying on technical teams.This democratization of data allows individuals across an organization—from marketing teams to sales managers—to make more informed decisions based on data, improving efficiency and collaboration. There are now many Business Intelligence and Analytics tooling that provides an intuitive interface that allows users to drag and drop data visualizations, making it easy for anyone, regardless of technical background, to gain insights from data. Now, business users are not only consumers of data—they are becoming data-driven decision-makers, accelerating innovation and growth within organizations.Also, there are various tools to connect data from different sources - for many years we had data lakes and have successfully migrated to data fabrics - where data exists in many locations and can easily be pulled together 'as if' data was physically stored together. Now data fabric is the norm, and we can focus our efforts into optimizing the ETL and post processing of the data needed for analytics and AI development.<h3>A Warning: The Risk of Data Overload</h3>Despite these advancements, there’s a significant warning raised by the report: data overload. With the explosion of data comes a growing challenge: how to properly manage and use it. While it’s easier than ever to collect and store data, many organizations are struggling to extract actionable insights from this ever-growing pool of information. Without proper data governance, analytics frameworks, and talent to interpret the data, companies can easily fall victim to paralysis by analysis.The rapid pace of data growth and the increased volume of digital content mean that businesses need to be cautious of data fatigue. Having access to vast amounts of data is only valuable if you have the tools, processes, and strategies in place to make sense of it all. Too much data—without effective data quality and data management practices—can lead to inefficient decision-making and even lost opportunities. This is especially true when data from different sources is inconsistent or unreliable.Moreover, organizations may find themselves overwhelmed by the sheer scale of data they are collecting, leading to wasted resources or missed insights. With data being generated at such a high velocity, it becomes essential to apply strong data governance and analytics practices to ensure that the right data is being collected, cleaned, and used effectively.<h3>Conclusion: Embracing Data, But with Caution</h3>Domo’s Data Never Sleeps report offers a snapshot of how data is reshaping industries. While the advancements in real-time analytics and data democratization are undeniably positive, the warning about data overload is a reminder that success in the data-driven age doesn’t just come from collecting more data—it comes from knowing how to manage, interpret, and act on it effectively.For the data industry, this means a growing need for data engineers, data scientists, and business leaders to not only embrace these tools and technologies but also focus on the key challenges of data quality, governance, and strategy. By doing so, organizations can avoid the pitfalls of data overload while continuing to harness the power of data to drive business outcomes.

Data Never Sleeps

In cloud environments, the deletion of services and data does not always mean that data is permanently gone. Even when you decommission cloud resources, your data may still exist on the cloud provider’s servers until it is overwritten with new data. This lingering data—often referred to as residual data—can present a serious security risk, as it may remain accessible until fully wiped, leading to potential exposure.Residual data protection has become a crucial concern for organizations storing sensitive information in the cloud, as improper management of deleted data can lead to unintended data breaches. In this guide, we’ll explore practical methods for securely managing residual data, including crypto shredding with envelope encryption. These techniques ensure that data is securely rendered inaccessible before decommissioning cloud services. We’ll also discuss the importance of following established standards like NIST SP 800-88 for data wiping and offer insights into managing residual data effectively in the cloud.<h3>Understanding Residual Data in the Cloud</h3>Residual data refers to data that remains on a server or storage device after the files or services associated with it have been deleted. This is particularly concerning in the cloud, as cloud providers typically use shared infrastructure, and physical storage is managed by the provider. While deleting a file or a service within a cloud environment may make it invisible or inaccessible to users, the underlying data might still be lingering on physical disks, potentially accessible until it is overwritten with new data.This challenge is compounded by the fact that cloud providers often employ thin provisioning and data deduplication, which means that the actual physical space used by deleted data may not be immediately reclaimed. Until the data is overwritten by other processes, it can remain in a “residual” state. The result is a window of time during which sensitive data is exposed to potential risk, even though the user believes it has been deleted.<h3>Crypto Shredding: A Practical Solution to Residual Data Protection</h3>One of the most effective techniques for ensuring that residual data is securely destroyed is Crypto Shredding. Crypto shredding involves encrypting the data in such a way that even if residual data is exposed, it cannot be read without the proper encryption keys. A common method used to implement crypto shredding is envelope encryption, which uses a two-layer encryption model:1. Data Encryption: The data itself is encrypted using a strong encryption algorithm, such as AES-256, ensuring that the data is unreadable without the decryption key.2. Key Management: The encryption key used to encrypt the data is securely managed and stored separately from the encrypted data itself. The key might be stored in a secure key management system (KMS) or hardware security module (HSM) provided by the cloud provider.When a user wishes to delete the data permanently, they simply need to destroy the encryption key. Without the key, the encrypted data becomes unreadable and effectively destroyed, even if it physically remains on the cloud provider’s disk. This process is referred to as “crypto shredding” because the data is made irrecoverable, even if it hasn’t been overwritten.By using envelope encryption and securely managing the encryption keys, organizations can implement a strong residual data protection strategy that ensures data is easily and securely deleted before decommissioning cloud services.<h3>Standards and Best Practices</h3>Proper management of residual data is critical for organizations that store sensitive data in the cloud. Several standards and guidelines have been established to help ensure data is properly wiped or made inaccessible before decommissioning cloud services.NIST SP 800-88 Guidelines for Media Sanitization - The National Institute of Standards and Technology (NIST) provides clear guidelines on how to handle residual data in their NIST Special Publication 800-88 (SP 800-88), “Guidelines for Media Sanitization.” While the document primarily focuses on physical media, the principles can be applied to virtual and cloud environments.NIST recommends several methods for data sanitization, including purging and destroying data, which typically involves overwriting the data or physically destroying the storage media. In the cloud context, however, crypto shredding is a more practical and effective approach since physical destruction is not possible in a shared cloud environment. NIST also recommends that organizations ensure the media is sanitized to a level that prevents the recovery of any residual data before decommissioning services.<h3>Managing Residual Data in Cloud Services</h3>In addition to cryptographic techniques, organizations should consider the following best practices to manage residual data when using cloud services:1. Use Cloud Provider’s Built-In Encryption: Many cloud providers offer built-in encryption services that allow for seamless data encryption and key management. Ensure that encryption is enabled at rest and in transit, and utilize the cloud provider’s key management services (KMS) for secure key rotation and deletion.2. Additionally, Leverage Strong Key Management Practices: Strong key management is essential to ensure that the encryption keys are properly controlled and stored. Implement policies that require keys to be deleted when data is decommissioned to ensure the data cannot be decrypted after deletion.3. Monitor and Audit Data Usage: Regularly monitor the use of cloud services to identify and clean up orphaned data. Many cloud providers offer tools to track data lifecycle management, which can help automate the process of securely deleting unused data.4. Ensure Compliance with Data Privacy Regulations: Compliance with regulations like GDPR, CCPA, or HIPAA requires organizations to ensure that data is not retained longer than necessary. Using encryption and key management to securely delete data is an effective way to meet these regulatory requirements.Several academic studies have examined the importance of managing residual data, especially in cloud environments. In particular, research highlights the risks associated with unintentional data exposure after service termination. For example, a <a data-mce-href="https://commons.erau.edu/jdfsl/vol11/iss1/1/" href="https://commons.erau.edu/jdfsl/vol11/iss1/1/">study</a> published discusses how cloud providers' data retention practices can leave organizations vulnerable to data breaches if proper residual data management techniques, such as crypto shredding, are not employed.Another important study in <a data-mce-href="https://ieeexplore.ieee.org/document/7072526" href="https://ieeexplore.ieee.org/document/7072526">IEEE Transactions on Cloud Computing</a> addresses the implications of residual data storage in shared cloud environments, providing a comprehensive analysis of the challenges and the need for cryptographic techniques to mitigate risks. These studies underscore the importance of implementing strict data deletion protocols and encryption strategies to protect sensitive information.To conclude, residual data protection is an often-overlooked aspect of cloud security, yet it is critical to ensuring that your sensitive data remains secure, even after it has been deleted from cloud services. Implementing techniques like crypto shredding through envelope encryption can help protect data by ensuring that it is rendered inaccessible when decommissioning cloud services. By following best practices, leveraging cloud provider tools, and adhering to standards like NIST SP 800-88, organizations can mitigate the risk of residual data exposure and protect against potential data breaches. As cloud adoption continues to grow, addressing residual data protection proactively is essent

A Technical Guide to Residual Data Protection

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As data science continues to become a more common role in organizations, the need for a strong partnership with data engineering has never been more critical to ensure scalable, accurate, and actionable insights. In today’s organizations, the distinction between data science and data engineering has become clearer than ever. While data scientists are often in the spotlight for their role in analyzing data and building predictive models, data engineers provide the essential infrastructure and processes that allow these models to function effectively. As someone who has been in the role of a data scientist and data engineer, let's discuss why both roles play &nbsp;their own part and how an organization should be structured to allow for partnership between them. The collaboration between these two roles is the backbone of any successful data initiative. In many cases, the tension between these two functions stems from differences in their skill sets, workflows, and objectives. Data engineers focus on building the systems that handle, process, and store data, while data scientists are concerned with extracting insights and building models based on that data. Though their goals may differ, the end result—a robust, actionable data strategy—is highly dependent on the smooth integration of both roles. Without the right groundwork laid by data engineers, data scientists can quickly hit roadblocks when attempting to turn data into insights.<h3>The Role of Data Engineering: Laying the Groundwork for Success</h3>Data engineering is often described as the “plumbing” behind data science. It involves the design, construction, and management of systems that allow data to be collected, stored, processed, and accessed for analysis. Data engineers are responsible for building the data pipelines that bring raw data into usable formats, ensuring the data is clean, transformed, and readily available for data scientists to work with. Before data scientists can start applying machine learning algorithms or generating meaningful reports, data engineering ensures that the data is in the right place, in the right format, and of the right quality. Data engineers don’t just prepare the data; they also build the foundation for its use, which is why they are indispensable to the success of data science. Here are several key areas where data engineering plays a crucial role:1. Data Pipeline Construction - The backbone of modern data workflows is the data pipeline. Data engineers are tasked with designing and building pipelines that ensure data is automatically ingested, processed, and made available for analysis. These pipelines connect a variety of data sources—from internal databases and APIs to external datasets—and ensure that the data is processed consistently, efficiently, and without error. For data scientists, this means having seamless access to clean, updated data with minimal delays.2. Data Transformation and Cleaning - Raw data often arrives in a messy, inconsistent, and unstructured state. Data engineers are responsible for transforming and cleaning this data to make it usable for analysis. This process might involve removing duplicates, handling missing data, applying data type conversions, and ensuring the data is formatted correctly. By automating these tasks, data engineers save data scientists significant time and effort, allowing them to focus on building models and extracting insights instead of spending hours cleaning data.3. Data Integration and Access - Data often exists in silos across different platforms and sources. One of the major responsibilities of data engineers is to integrate these disparate sources into a unified data lake or data warehouse, ensuring data is easily accessible for data scientists. Data engineers create systems that allow data scientists to query, analyze, and access the data in real time, without having to worry about how it’s stored or whether it’s up to date.4. Data Scalability and Performance Optimization - As organizations grow, the volume of data grows exponentially. A major responsibility of data engineers is ensuring that the systems they build can handle these growing amounts of data without sacrificing performance. This includes optimizing data storage and query performance to ensure that data scientists can work with large datasets efficiently. Scalable systems also enable more complex analysis, allowing data scientists to experiment with larger datasets or more intricate models without encountering performance bottlenecks.<h3>Specialization is Key</h3>While the roles of data scientists and data engineers are distinct, each is critical to the success of a data-driven organization. Data science is primarily concerned with extracting value from data, while data engineering focuses on the systems and infrastructure required to collect and process that data. These two roles should not be viewed as interchangeable, but complementary.Data engineers possess deep knowledge of databases, ETL processes, and cloud infrastructure, while data scientists have expertise in statistics, machine learning, and data modeling. By recognizing these areas of specialization, organizations can ensure that both functions are supported by individuals who are true experts in their respective fields. When both teams can focus on what they do best, the result is a more efficient and effective data strategy.Attempting to combine these two roles into one person or one team often leads to bottlenecks, inefficiencies, and lower-quality outputs. For example, while it may be tempting to ask data scientists to manage their own data pipelines, this detracts from their ability to focus on modeling and insights. Similarly, data engineers need time and expertise to focus on building reliable, scalable data systems, which may not always align with the goals of data scientists. By establishing specialized teams—one focused on data engineering and the other on data science—organizations can ensure that both functions thrive, producing the highest quality results. Leaders should champion the idea that both data engineers and data scientists are equally important to the success of data-driven projects, and that neither role can exist in a vacuum.<h3>How Technical Leaders Can Foster Collaboration</h3>To truly unlock the potential of data engineering and data science, technical leaders must ensure that these two teams collaborate effectively. Here are several strategies to bridge the gap between the two disciplines:1. Create Clear Communication Channels - Regular communication between data engineers and data scientists is essential. Leaders should facilitate regular check-ins, collaboration sessions, and joint problem-solving activities to ensure that both teams are aligned. Understanding each other’s constraints and workflows helps prevent miscommunication and ensures that both teams are working toward the same goals.2. Align on Data Requirements and Goals - Data engineers and data scientists must align on the data needs and project goals from the outset. This includes discussing what data sources need to be integrated, what kind of preprocessing or transformation will be required, and what data quality standards are expected. By ensuring everyone is on the same page from the beginning, teams can avoid costly rework or misaligned expectations later on.3. Foster Joint Ownership of Projects - Rather than creating a silo between the two teams, encourage joint ownership of data projects. Both data engineers and data scientists should have a stake in the success of the project, with responsibilities that are shared but distinct. This can be achieved by having engineers support data scientists in accessing data and ensuring its quality while also allowing scientists to provide feedback on how the data can be structured or improved for modeling purposes.4. Invest in Tools and Automation - Using the right tools can simplify the collaboration process. Invest in platforms that facilitate easy sharing of data, version control of models, and integration of data systems. Automation of repetitive tasks, such as data cleaning and transformation, also frees up both teams to focus on higher-value work.<h3>A Partnership for Success</h3>Bridging the gap between data engineering and data science is not just about improving the workflow between teams; it’s about setting the foundation for data-driven success. Data engineers lay the groundwork for data scientists to build accurate models, generate insights, and ultimately drive business value. By embracing specialization and fostering collaboration, technical leaders can ensure that both teams are empowered to do what they do best, leading to better results, more efficient processes, and more impactful data-driven decisions. In an era where data is king, the true potential of data science can only be realized when it is supported by a strong, well-built data infrastructure.

Bridging the Gap between Data Science and Data Engineering

Data science and big data technology. Data scientist computing, analysing and visualizing complex data set on computer. Data mining, artificial intelligence, machine learning, business analytics.

&nbsp;What needs to change in the world of data management to support AI?&nbsp;Data quality has always been crucial to any data-driven initiative, but in the age of AI and ML, ensuring the quality of data has become more important than ever. AI systems are only as effective as the data they are trained on, and the quality of this data directly impacts the performance and reliability of these models. One of the most widely recognized frameworks for ensuring data quality is the CARE method, which stands for Completeness, Accuracy, Relevance, and Enough to Work With. This methodology, which has traditionally been applied in the context of business intelligence, has evolved to address the unique challenges posed by AI, helping organizations ensure that the data they feed into AI systems is of the highest quality.<h3>What is the CARE Method?</h3>The CARE framework focuses on four key components: Completeness: The data should have all the necessary attributes and records, with no missing or incomplete data entries.Accuracy: The data must accurately represent the real-world values it is meant to model, without errors or misinterpretations.Relevance: The data should be pertinent to the business or operational needs, ensuring it is aligned with the goals of the AI model.Enough to Work With: This concept, while often interpreted as a measure of volume or scale, essentially means having sufficient quality and quantity of data to allow AI systems to learn meaningful patterns. Without enough usable data, AI models may struggle to generalize or produce reliable results.While these principles have always been important, the advent of AI has added layers of complexity, making each element of the CARE framework even more vital. The demand for high-quality data in AI applications has evolved, and ensuring data meets the CARE standards is now a key step in delivering effective and trustworthy AI systems.<h3>The Evolving Role of the CARE Method in AI</h3>Completeness: In traditional data management, completeness meant ensuring datasets had no missing values or incomplete records. In the world of AI, completeness goes beyond merely filling in gaps—it’s about ensuring that AI training datasets are diverse and inclusive enough to help the model learn without introducing bias or inaccuracies. In AI applications, incomplete or biased data can lead to poor model performance or even catastrophic outcomes. For example, in healthcare AI, missing or incomplete patient data can result in inaccurate diagnoses or treatment recommendations. AI systems today emphasize:<ul style="list-style-type: square;" data-mce-style="list-style-type: square;"><li>Data augmentation and synthetic data generation techniques to fill gaps in training sets. See my whole <a data-mce-href="https://data-sphere-chronicle.com/blog/two-sides-of-synthetic-data" href="https://data-sphere-chronicle.com/blog/two-sides-of-synthetic-data">analysis of synthetic data here</a>. </li><li>Data imputation techniques, using algorithms to predict missing values based on existing data.</li><li>Ensuring balanced datasets to avoid skewed predictions, especially for underrepresented categories.</li></ul>The importance of completeness in AI has shifted from filling in missing values to ensuring that the entire dataset captures the diversity needed for accurate model learning.Accuracy: Accuracy in traditional data management was about ensuring that data reflected reality. For AI, accuracy is even more critical—small inaccuracies can snowball into large errors when AI models are deployed in real-world applications. A slight mistake in training data can cause the model to generate incorrect predictions that have serious consequences, especially in sectors like finance or healthcare. To ensure accuracy in AI, companies have to:<ul style="list-style-type: square;" data-mce-style="list-style-type: square;"><li>Implement real-time data validation to detect errors before data enters the system.</li><li>Employ cross-validation techniques during model training to check that the model is learning the right patterns from the data.</li><li>Use error detection tools that continuously monitor data and model performance after deployment.</li></ul>Accuracy ensures that AI doesn’t just produce predictions—it produces reliable, actionable insights. The demand for precision has never been higher.Relevance: For traditional data use cases, relevance meant ensuring that the data served the business objectives. For AI, relevance goes further. AI models often work with large volumes of data, and relevance refers to ensuring that only the most useful data is being processed by the model. Irrelevant or extraneous data can introduce noise, reducing model performance. For AI, relevance includes:<ul style="list-style-type: square;" data-mce-style="list-style-type: square;"><li>Feature engineering, where only the most important variables (features) are selected for model training.</li><li>Data curation to ensure that the data reflects current trends, behaviors, or external factors that are critical for prediction.</li></ul>To avoid the pitfalls of irrelevant data, AI models demand that data be curated to ensure it aligns with the task or problem the model is solving. The relevance of data is directly tied to the model’s ability to produce high-quality outputs.Enough to Work With: Traditionally, the Enough to Work With component referred to ensuring that there was a sufficient amount of data to make decisions. For AI, this principle focuses on having enough high-quality, diverse data to train the model adequately. This means not just having large volumes of data, but ensuring that the data is representative of the problem the model will solve. For AI models to be effective, they need to learn from vast amounts of data, but that data must also be varied and comprehensive enough to reflect the nuances of real-world scenarios. Without enough data:<ul style="list-style-type: square;" data-mce-style="list-style-type: square;"><li>AI models risk overfitting, where they learn the noise in the data rather than the signal.</li><li>The model may fail to generalize well to new, unseen data, reducing its effectiveness in real-world applications.</li></ul>Ensuring there is enough diverse, high-quality data has become one of the most challenging aspects of AI. AI projects now focus on acquiring diverse datasets that can capture the full spectrum of conditions under which the model will operate.<h3>Why the CARE Method is More Important for AI Than Ever</h3>With the growing dependence on AI to make decisions, the importance of high-quality data cannot be overstated. AI systems rely heavily on the data they are trained on, and poor-quality data can lead to biased, inaccurate, or unethical outcomes. The CARE methodology provides a clear framework for addressing these challenges and ensuring data is suitable for AI.However, as AI has evolved, so too has the complexity of the data it requires. Ensuring completeness, accuracy, relevance, and enough to work with is not only important for model performance, but it’s also essential for the ethical and fair deployment of AI systems. As organizations continue to integrate AI into their operations, maintaining high data quality through the CARE method is key to mitigating risks and maximizing the benefits of AI. By revisiting and refining the CARE framework to address the growing needs of AI, businesses can ensure that their AI systems are not only powerful but also responsible and reliable. The future of AI depends on carefully curated, accurate, relevant, and comprehensive data—and the CARE method is more critical than ever in the future.

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