The Definitive Guide to Dell Data Engineering Exam Success

In the rapidly evolving landscape of data and Artificial Intelligence, mastering the art and science of data engineering is no longer an option but a necessity for career progression. As organizations increasingly rely on robust data pipelines and sophisticated analytics to drive insights, the demand for certified data engineering professionals is soaring. Dell Technologies, a leader in enterprise solutions, offers a prestigious certification that validates expertise in this critical domain: the Dell Technologies Certified Data Engineering Optimize certification.

This comprehensive guide is meticulously designed to provide you with the definitive roadmap to Dell Data Engineering exam success. Whether you're a seasoned data professional looking to validate your skills or an aspiring engineer aiming to carve out a niche in the data-driven world, this article will illuminate every step of your journey towards earning this highly valued credential.

Understanding the Dell Technologies Data Engineering Optimize D-DS-OP-23 Exam

The Dell Technologies Data Engineering Optimize exam (D-DS-OP-23) is a rigorous assessment designed to validate a candidate's proficiency in optimizing data engineering processes and infrastructure. It focuses on the practical application of big data technologies, data pipeline construction, and ensuring data governance and security within complex data environments. Earning this certification signifies your capability to design, implement, and manage high-performance data solutions that drive business value.

What is the Dell Technologies D-DS-OP-23 Exam About?

This certification specifically targets the skills required to operate effectively within Dell's ecosystem, covering topics from traditional data warehousing to modern streaming data processing and advanced data governance. It demonstrates an individual's readiness to tackle real-world data challenges, making them an invaluable asset in any data-centric organization. The exam ensures candidates can navigate the complexities of data integration, processing, and optimization, crucial for leveraging data in today's AI-driven world.

Dell Technologies Certified Data Engineering Optimize: Exam Details

To embark on your certification journey, it's essential to be familiar with the core details of the Dell Technologies Data Engineering Optimize exam:

• Exam Name: Dell Technologies Certified Data Engineering Optimize
• Exam Code: D-DS-OP-23
• Exam Price: $230 (USD)
• Duration: 90 minutes
• Number of Questions: 60
• Passing Score: 63%

Dell Technologies Certified Data Engineering Optimize Benefits

Achieving the Dell Technologies Certified Data Engineering Optimize certification offers a myriad of professional advantages. It's more than just a piece of paper; it's a testament to your specialized skills and commitment to excellence in the Artificial Intelligence data engineering certification Dell sphere. The benefits include:

• Enhanced Career Opportunities: Certified professionals are often prioritized for advanced roles and leadership positions in data engineering.
• Industry Recognition: Dell Technologies is a globally recognized vendor, and its certifications carry significant weight in the tech industry, boosting your professional credibility.
• Higher Earning Potential: Specialized certifications frequently correlate with increased salaries and better compensation packages. According to the U.S. Bureau of Labor Statistics, occupations in computer and information technology are projected to grow much faster than the average for all occupations, indicating a strong demand for skilled professionals in this field. You can explore more about career prospects in computer and information technology. Learn more about the outlook for computer and information technology occupations.
• Validation of Expertise: It objectively validates your skills in core data engineering concepts, from ETL processes to big data security and pipeline construction.
• Competitive Edge: In a competitive job market, this certification sets you apart, demonstrating your expertise and dedication to continuous learning.

Dell Technologies Data Engineering Optimize D-DS-OP-23 Exam Syllabus: A Deep Dive

Understanding the D-DS-OP-23 exam topics and objectives is paramount for effective preparation. The exam covers six key areas, each contributing a specific percentage to the overall score. A detailed breakdown of the Dell Technologies Data Engineering Optimize D-DS-OP-23 exam syllabus is crucial for structuring your study plan.

For an in-depth understanding of the exam's structure and content, candidates should always refer to the official documentation. You can review the complete Dell Technologies Data Engineering Optimize exam syllabus at this dedicated page. Furthermore, a detailed official exam blueprint and objectives document is available directly from Dell Technologies, providing granular insights into each topic area.

The Role of the Data Engineer (5%)

This section introduces the foundational responsibilities and importance of the data engineer within an organization. It covers the understanding of data engineering principles, its role in the data lifecycle, and how it supports data scientists and business analysts. This foundational knowledge is key to appreciating the broader context of data operations.

Data Warehousing with SQL and NoSQL (17%)

Candidates need to demonstrate a strong understanding of both traditional SQL-based data warehousing and modern NoSQL database solutions. This includes designing schemas, optimizing queries, managing data consistency, and selecting appropriate storage solutions for different types of data. Proficiency in handling large datasets efficiently is a core requirement.

Extract-Transform-Load (ETL) Offload with Hadoop and Spark (18%)

This domain focuses on the critical processes of data extraction, transformation, and loading, particularly utilizing big data frameworks like Hadoop and Spark for offloading these tasks. It covers designing and implementing ETL pipelines, understanding distributed processing concepts, and optimizing performance for large-scale data ingestion and preparation. Expertise in these areas is vital for any aspiring data engineer.

Data Governance, Security and Privacy for Big Data (20%)

With the increasing volume and sensitivity of data, robust data governance, security, and privacy measures are non-negotiable. This section assesses your knowledge of implementing security protocols, ensuring data compliance (e.g., GDPR, CCPA), managing access controls, and understanding the ethical implications of handling big data. It's the largest weighted section, underscoring its importance.

Processing Streaming and IoT Data (20%)

The rise of real-time applications and the Internet of Things (IoT) has made streaming data processing a critical skill. This segment evaluates your ability to design and implement solutions for ingesting, processing, and analyzing data streams in real time. Understanding technologies and patterns for handling continuous data flows is essential for modern data architectures.

Building Data Pipelines with Python (20%)

Python has become the lingua franca for data engineering, and this section tests your practical skills in using Python to construct robust and automated data pipelines. This includes scripting for data manipulation, integration with various data sources and sinks, and developing modular and scalable data processing workflows. Practical coding skills in Python are fundamental here.

How to Prepare for Dell Technologies Data Engineering Optimize Exam: Your Blueprint for Success

Effective preparation is the cornerstone of Dell D-DS-OP-23 certification study guide success. A structured approach, combined with the right learning resources, will significantly enhance your chances of passing the Dell Data Engineering exam on your first attempt.

Official Training and Learning Resources for Dell Technologies Data Engineering Optimize

Dell Technologies provides excellent resources to help candidates prepare. The official training is your best bet for comprehensive coverage of all exam topics. Specifically, Dell offers a detailed PDF outlining recommended training for the D-DS-OP-23 exam. You can access the official Data Scientist and Data Engineering Optimize Training guide for a structured learning path. Additionally, for an overview of the certification itself and further resources, visit the Official Page for Dell Technologies Data Engineering Optimize certification.

Crafting Your Dell D-DS-OP-23 Certification Study Guide

Beyond official training, creating a personalized study guide is invaluable. Break down the syllabus topics into manageable chunks. Allocate study time based on the percentage weight of each section, giving more attention to Data Governance, Security, Privacy, Processing Streaming/IoT Data, and Building Data Pipelines with Python. Utilize flashcards, summary notes, and conceptual diagrams to reinforce understanding.

Consistency is key. Regular review sessions help in retaining complex information. For more specific insights on improving your study habits, consider exploring top study tips to earn Dell certifications.

Dell Data Engineering Optimize Practice Questions

Practicing with mock exams and Dell Data Engineering Optimize practice questions is indispensable. This helps you familiarize yourself with the exam format, question types, and time constraints. Focus on understanding the rationale behind both correct and incorrect answers. Practice tests not only gauge your knowledge but also help identify areas where you need further study. Several online platforms offer practice questions tailored to the D-DS-OP-23 exam objectives.

Time Management and Exam Day Strategies

The Dell Technologies Data Engineering Optimize exam duration of 90 minutes for 60 questions means you have approximately 1.5 minutes per question. Pacing yourself is crucial. During the exam, read each question carefully, eliminate obviously wrong answers, and if unsure, make an educated guess and flag the question for review if time permits. Ensure you are well-rested and approach the exam with a clear mind.

Dell Technologies Data Engineering Optimize Passing Score Insights

A passing score of 63% means you need to correctly answer at least 38 out of 60 questions. This provides a clear target for your preparation. While aiming for a perfect score is admirable, focusing on consistently clearing the 63% threshold in your practice tests should be your immediate goal. Understand that a strong grasp of the higher-weighted sections can significantly impact your overall score.

Certification Pathways and Career Advancement with Dell

The Dell Technologies Certified Data Engineering Optimize certification is not just an endpoint; it's a stepping stone in a rewarding career path within Artificial Intelligence and data. It signifies that you possess the advanced skills required to contribute meaningfully to data-driven initiatives.

Dell Technologies Data Engineering Optimize Certification Requirements

While there are no strict prerequisites for taking the D-DS-OP-23 exam, candidates are generally expected to have a foundational understanding of data concepts, programming skills (especially Python), and exposure to big data technologies. Practical experience in data engineering roles is highly recommended as the exam is designed to test real-world application of knowledge.

Dell Technologies Data Engineering Optimize Career Path

Earning this certification opens doors to various specialized roles. Professionals can pursue positions such as Senior Data Engineer, Big Data Engineer, Cloud Data Engineer, Data Architect, or even contribute to Machine Learning Operations (MLOps) as a foundational engineer. Your expertise in data pipelines and big data ecosystems, validated by Dell, makes you an attractive candidate for companies driving innovation with data.

Artificial Intelligence Data Engineering Certification Dell Value

In the realm of Artificial Intelligence, the quality and accessibility of data are paramount. Data engineers certified by Dell play a crucial role in preparing, managing, and optimizing the data infrastructure that powers AI and machine learning models. This certification directly contributes to building robust AI solutions, making you a vital component in the AI lifecycle. It bridges the gap between raw data and actionable AI insights.

The Logistics: Cost, Registration, and Validity

Understanding the administrative aspects of the Dell Data Engineering exam is straightforward, ensuring a smooth journey from preparation to certification.

Dell Technologies Data Engineering Optimize Exam Cost

The Dell Technologies Data Engineering Optimize exam cost is $230 USD. This fee is standard globally, though local taxes may apply in some regions. It's advisable to factor this cost into your overall certification budget. Keep an eye out for any Dell promotional offers or discounts that might be available through their learning programs.

Dell Technologies D-DS-OP-23 Exam Registration Process

Registering for the exam is a simple process. Candidates can schedule their exam through Pearson VUE, Dell's official testing partner. The Pearson VUE platform allows you to find testing centers near you or opt for online proctored exams, offering flexibility. Visit Pearson VUE to schedule your Dell Technologies exam and select the D-DS-OP-23 code.

Dell D-DS-OP-23 Exam Validity Period

Dell Technologies certifications typically have a validity period to ensure that certified professionals maintain up-to-date knowledge in a rapidly changing technological landscape. While specific validity periods can vary, it is generally recommended to check the official Dell certification policy for the most current information regarding the Dell D-DS-OP-23 exam validity period and recertification requirements.

Conclusion

The Dell Technologies Certified Data Engineering Optimize certification (D-DS-OP-23) is an invaluable asset for any professional aiming to excel in the dynamic field of data engineering. It validates your expertise in critical areas, from building scalable data pipelines to ensuring data governance and security in big data environments. By following this definitive guide, leveraging official training, practicing diligently with Dell Data Engineering Optimize practice questions, and understanding the exam structure, you are well on your way to achieving certification success.

Embrace this journey to not only enhance your technical skills but also to significantly boost your career prospects in the ever-expanding world of Artificial Intelligence and data. Your commitment to mastering these skills will open new doors and establish you as a trusted expert. For a comprehensive strategy to prepare for complex IT certifications, consider reviewing the 5-step blueprint for certification success.

Frequently Asked Questions (FAQs)

1. What is the primary focus of the Dell Technologies Data Engineering Optimize (D-DS-OP-23) exam?

The primary focus of the D-DS-OP-23 exam is on validating a candidate's ability to design, implement, and manage robust data engineering solutions, covering topics like ETL with Hadoop/Spark, data warehousing (SQL/NoSQL), streaming data processing, and crucial aspects of data governance and security for big data environments, often with a strong emphasis on Python for pipeline construction.

2. How does the Dell Technologies Certified Data Engineering Optimize certification benefit my career?

This certification offers significant career benefits, including enhanced job opportunities in high-demand roles, increased earning potential, industry recognition from a leading technology vendor, and a strong competitive edge. It validates specialized skills vital for modern data and Artificial Intelligence initiatives.

3. Are there any prerequisites for taking the Dell Data Engineering exam?

While there are no strict formal prerequisites specified by Dell, it is highly recommended that candidates have a foundational understanding of data concepts, practical programming experience (especially with Python), and familiarity with big data technologies to succeed in the Dell Data Engineering exam.

4. What kind of learning resources are available for the D-DS-OP-23 exam?

Dell Technologies provides official recommended training documents and courses. Additionally, candidates should leverage a Dell D-DS-OP-23 certification study guide, practice questions, hands-on lab experience, and community forums. The official Dell learning portal is the best starting point for comprehensive resources.

5. What is the Dell Technologies Data Engineering Optimize exam cost and how do I register?

The Dell Technologies Data Engineering Optimize exam cost is $230 USD. You can register for the D-DS-OP-23 exam through Pearson VUE, Dell's authorized testing partner. Their platform allows you to schedule your exam at a local testing center or via online proctoring.

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POWERSTORE Lifecycle Extension with ProSupport Simplifies Storage Infrastructure Ownership

Are you tired of the headaches that come with managing your storage infrastructure? Well, there’s no need to take two aspirins and call your doctor in the morning. Get relief with Dell’s new Lifecycle Extension with our ProSupport Infrastructure suite. It’s a game-changer.

The Head-Pounding Challenge of Traditional Storage Lifecycle Management

If you’ve dealt with traditional storage lifecycle management, you know it’s no walk in the park. Upgrades can be complex, capacity seems just a bit too limited and the risk of disrupting your business operations is a constant worry. The goal is to keep your storage infrastructure not just running but running efficiently and effectively. You need expert resources, end-to-end planning and deep knowledge to make sure your risks don’t turn into disasters.

Relief You Can Count On: Lifecycle Extension with ProSupport or ProSupport Plus

What if I told you that you could have a hassle-free storage infrastructure experience? Imagine minimal downtime, continuous peak performance from your PowerStore investments and expert advice to keep up with the latest technologies without disrupting your business. That’s exactly what Lifecycle Extension with ProSupport or ProSupport Plus delivers.

Expert Assistance at Your Fingertips

With the dynamic nature of business needs, finding the right resources to keep up can be tough. But with Lifecycle Extension, you get more than just the operational support of ProSupport or ProSupport Plus. You’re looking at additional benefits that streamline the modernization of your storage infrastructure and maintain its peak efficiency.

Appointed technical advisors will work with you to develop a strategy for upgrading your PowerStore infrastructure technology. They will consider your specific workload dependencies, business needs, budget constraints and technical skills.

Key benefits of Lifecycle Extension include:

• Minimized downtime. Access to live support experts ensures your storage infrastructure is always up and running.
• Adoption of new workloads. Seamlessly upgrade to new capabilities with storage node controller upgrades included.
• Investment confidence. A technical advisor will guide you through complex storage technology transitions.

Tailored ProSupport Tiers

Lifecycle Extension is available with either ProSupport or ProSupport Plus, each offering distinct benefits:

• ProSupport offers 24×7 remote technical support for both hardware and software, next business day or four-hour mission-critical onsite hardware support and more.
• ProSupport Plus includes all ProSupport features plus extras like proactive storage health monitoring, predictive hardware failure detection and specialized support.

Choosing between ProSupport and ProSupport Plus depends on your organization’s needs. Both offer great foundational features like 24×7 remote support, hardware and software coverage and flexible technology upgrades. ProSupport Plus, however, takes it a step further with predictive analytics, specialized expert access and proactive assessments.

Why Lifecycle Extension is a Smart Move

Whether you’re a small business or a large enterprise, adopting new technologies confidently is crucial. Lifecycle Extension with ProSupport or ProSupport Plus offers a solution to the often-daunting task of refreshing your storage technology stack. Let our experts handle the disruptive elements while you focus on keeping your operations smooth and efficient, with the following benefits:

• Hassle-free tech refreshes. Forget the complexities of updating your storage infrastructure.
• World-class support. Keep your storage investments efficient with top-notch technical support.
• Guaranteed satisfaction. Peace of mind with a three-year satisfaction guarantee.
• Exclusive member promotions. Discounts, free trials and more.

Start Your Modernization Journey

Lifecycle Extension, coupled with ProSupport or ProSupport Plus, is more than just a service; it’s a partnership that ensures your storage infrastructure is not just maintained but thrives. So why wait? Begin your storage modernization journey today and take the first step towards a more efficient, hassle-free storage management experience. Your business (and your sanity) will thank you!

Source: dell.com

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Scaling Data and Analytics Productization: Key Strategies for Success

In the ever-evolving landscape of data-driven decision making, successfully scaling data and analytics productization demands a nuanced approach. While productization can be one of the most challenging stages, we will dive into the key strategies that serve as the cornerstones of this transformative journey.

Robust Infrastructure Focused on Scalability and Flexibility

Building a scalable and flexible infrastructure is more than selecting the right technology; it’s about crafting a resilient foundation capable of accommodating the growing complexities of data. On-premises hardware, carefully selected and optimized, forms the backbone of this infrastructure. It involves considering factors such as storage capacity, processing power and network bandwidth to ensure seamless data handling.

A strong starting point should include:

• Scalable storage solutions. Invest in storage systems that can scale horizontally or vertically to accommodate increasing data volumes without compromising performance.
• High-performance processing. Optimize processing capabilities to handle complex analytical workloads efficiently.

The emphasis is on creating an environment that can effortlessly adapt to the expanding demands of data processing and analysis. This will only increase in importance as AI begins to power more facets of every organization in the future.

Modularity, Reusability and Accessibility

This design philosophy is pivotal to enabling agility and collaboration within the organization.  On-premises and multicloud environments benefit significantly from a design that allows for the modular integration of new features and functionalities with ease. This not only facilitates scalability, but it also empowers different teams to contribute specialized components, creating a cohesive and adaptable data ecosystem.

A strong starting point should include:

• API-driven architectures. Adopt an architecture that relies on well-defined APIs, promoting interoperability between different components and systems.
• Containerization. Explore containerization technologies like Docker and Kubernetes to encapsulate and deploy modular components independently, fostering flexibility and scalability.

Reusability and accessibility complete this “philosophy of flexibility” by expanding data accessibility across the organization using technologies such as Data Virtualization and Open Data Formats.

With Data Virtualization and Open Data formats, multiple teams have an expansive reach across data silos, with open data formats (e.g., Iceberg, Parquet, etc.) allowing teams to easily share and reuse data, compounding the value of previous efforts on these data resources.

Embrace Automation and Continuous Streaming Data

Automation is the catalyst that propels scalability by reducing manual intervention, enhancing efficiency and minimizing errors. In the context of on-premises environments, this involves implementing automation tools tailored to local infrastructure. From data ingestion to analytics and reporting, automation ensures routine tasks are executed seamlessly, freeing up human resources for more strategic endeavors.

A strong starting point should include:

• Workflow orchestration. Implement tools for orchestrating end-to-end data workflows, ensuring seamless transitions between different stages of the data processing pipeline.
• Monitoring and alerts. Set up automated observability and monitoring systems to track system performance and data pipelines with alerts for potential issues that require attention.

The power of automation is the removal of manual processes in place of continuous operations.  Expanding this concept to your data ingestion and consumption is the logical next step. Moving to true Streaming Data pipelines can dramatically improve the latency and value provided for real-time, continuous business results. The batch or ad-hoc approach to data ingestion limits the possible use cases and value the organization can achieve. Moving to continuous streaming data ingestion is a logical next step and allows developers and data scientists to solve business problems in new and more streamlined ways.

Unlock the Full Potential of Your Data

In the dynamic landscape of data and analytics, the success of scaling productization lies in the three-way intricate balance of a robust infrastructure, modular/reusable/accessible design, with an efficient automation approach. Organizations that meticulously implement these strategies are well-positioned to navigate the challenges of scaling data and analytics productization, unlocking the full potential of their data assets across on-premises and multicloud environments.

Source: dell.com

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Plan a Seamless Transition to Microsoft Fabric

“Data” has undoubtedly been a buzzword. Microsoft recently concluded the preview for Fabric, a comprehensive solution that promises to unify data analytics tools within the Microsoft ecosystem. With Fabric, data scientists, data engineers and data analysts can work together in a common environment from the same data. This means less focus on infrastructure and more focus on data science.

Transitioning to Microsoft Fabric is a strategic move that requires careful planning and execution. Given that Fabric is an expansion of existing Power BI capabilities—by its joining with other Azure AI tools—we should consider the advantages and disadvantages that come with adding such tools to your existing solutions. It can also be helpful to conduct a cost-benefit analysis with the single storage location for the entire toolset and with the reduction in infrastructure decisions.

As a trusted Microsoft partner, Dell Technologies is uniquely positioned to help your organization harness the full potential of Fabric through a comprehensive analysis of your environment to propose strategies based on your unique needs.

Considerations for Adding Synapse Tools to Your Power BI Solution

If Power BI is your only analytics tool and is meeting all your requirements for data transformation and insight generation, there may be no need to make a change. New technologies require training time and expenses not only for the users of the technologies, but also to determine how those tools fit into your solution architecture. If, however, you have considered how machine learning might provide better predictive or prescriptive analytics or if your data transformations need the big data power Spark provides, it could be time to consider Fabric. Here are some advantages and disadvantages related to a move to the Fabric toolset.

Data transformation options. Power BI alone limits your team to a single transformation tool: Power Query. Fabric expands that to a selection of tools to better fit different scenarios such as Spark, fast copy and data warehouse store procedures and views.

Versioning confusion. Power BI has never supported version control systems such as Git. Power BI does have a deployment pipeline, which continues to work for Fabric-based Power BI objects. Synapse Studio supports using Git repositories to version control the objects in its workspace. This is a loose end that Microsoft has not yet tied—a common versioning and deployment system for all the components of Fabric. If your team is segmented by roles, each team member can focus on their versioning. If, however, you have a smaller team with multiple hats being worn by each team member, there will be some learning to keep versioning straight.

The Pros and Cons of Common Storage

A common storage location and format for all tools, named “OneLake” within Fabric, gives data scientists, data engineers and analysts the opportunity to all work on and with the same data. The advantages include reduced data duplication and a common location for data set endorsement through certification or promotion. The pitfalls include the need to carefully plan security and architectures that support dev/test/prod environments for your various roles.

Reduced Infrastructure Decisions: Not Always the Best Choice

Much of the documentation around Fabric labels it or parts of it as a SaaS environment. Many of the Azure Synapse configurations are simplified or handled behind the scenes for users of Fabric. Previously you would have to decide different options for the various tools under the Azure Synapse umbrella (Data Warehouse Units, Spark Pools, etc.). Fabric, instead, asks customers only to select the compute and storage that support all tools in the Fabric capacity. You still must monitor your compute and storage usage and adjust your chosen environment based on actual usage, but the decisions are reduced and thus simplified.

For some scenarios, separate billing and compute may be preferred over the combined compute of Fabric. For example, if your solution heavily depends on Data Warehouse and has no Spark pools, the separate compute selections could be more performant for comparable or less cost.

For the foreseeable future, these separate billing and configuration options are still available, so teams can decide if they want to fine tune different infrastructure elements or if they want the combined toolset that Fabric provides.

Finding Guidance from Dell Services

Having been a close partner of Microsoft for over 35 years, Dell has deep design, implementation and troubleshooting knowledge and skills across Microsoft’s product line. Dell Technologies consulting experts can guide you to the right solution that meets your specific needs.

Dell can help your organization make the most of its move to Fabric from a variety of angles including training, advisory workshops, staff augmentation, health checks and complete solution design and development.

Source: dell.com

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Jumpstart Your AI Journey with NVIDIA and Dell Technologies

Artificial intelligence has become an increasingly fundamental tool for businesses looking to gain a competitive advantage. However, building an AI infrastructure from scratch can be a daunting task, especially for those who are just starting out. That’s where NVIDIA and Dell Technologies come in, offering an AI starter kit that can help data science teams get started quickly and easily. Combining Dell’s expertise in hardware and NVIDIA’s expertise in software, the solution delivers on performance and flexibility.

The NVIDIA and Dell AI Starter Kit is a bundle of hardware and software that provides a high-performance computing environment for training and running AI models. It includes a Dell Precision workstation with NVIDIA GPUs, Dell PowerScale storage and NVIDIA’s AI software stack. This combination of hardware and software offers an efficient and effective solution for data science teams to build their AI infrastructure.

Giving Data Science Teams a Head Start

The NVIDIA and Dell AI Starter Kit provides a high-performance computing environment that significantly reduces the time it takes to get results. This can help data science teams to quickly iterate and refine their models, leading to better outcomes. Other benefits include:

◉ Simplified deployment. The NVIDIA and Dell AI Starter Kit is designed to be easy to deploy, reducing the time and effort required to set up an AI infrastructure and enabling data science teams to focus on building and refining models.

◉ Scalability. The NVIDIA and Dell AI Starter Kit is designed to be scalable, so it can grow with your business while your AI needs expand. This can help to future-proof your investment and avoid the need for costly upgrades in the future.

◉ High performance. The NVIDIA and Dell AI Starter Kit includes NVIDIA GPUs, which are specifically designed for high-performance computing. This can help data science teams train and run AI models faster and more efficiently.

◉ Flexibility. The NVIDIA and Dell AI Starter Kit is designed to be flexible, meaning you can tailor it to your specific needs. This can help you to build an AI infrastructure that is optimized for your business and your specific use cases.

Integrations Take Your AI to the Next Level

Still feeling overwhelmed with the implementation piece? In addition to the benefits provided by the NVIDIA and Dell AI Starter Kit, customers can also leverage integration partners to help with the hardware and software integration. Integration partners can provide expertise and guidance on how to optimize the kit for specific use cases and business needs. This can help ensure the AI infrastructure is fully integrated with existing systems and workflows, and that it meets the unique requirements of the business. 

Furthermore, integration partners can help with deploying and managing the AI infrastructure, including monitoring performance and making necessary adjustments to ensure optimal performance. This can help businesses focus on their core competencies and leave the AI infrastructure management to the experts who can maximize the value of the NVIDIA and Dell AI Starter Kit, allowing businesses to achieve their AI goals more quickly and efficiently. 

If you’re looking to get started with AI but don’t know where to start – let us help! The NVIDIA and Dell AI Starter Kit provides a solid foundation for your AI infrastructure. With faster time to results, simplified deployment, scalability, high performance and flexibility, you can be sure you’re making the right investment for your business. 

Source: dell.com

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Insights Unlocked: Introduce AI to Your Data

Data is the Fuel for Use Cases Like Generative AI

I’m calling it: Artificial intelligence is the hottest topic of 2023. With the release of ChatGPT and Google Bard, there is plenty to be excited about. AI promises to drive business growth around the world, change the way we work and unlock new use cases we aren’t even thinking about yet. But behind all the model training, all the querying and inferencing is data. Data is fueling the AI rocket ship that will lead to better and more informed decisions, generate new content, improve operational efficiencies and ultimately drive greater predictability into business models. Pretty exciting, right?

But in the middle of all the AI hype are the data scientists and IT teams trying to make sense of it all. The truth is – uncovering insights with data can be hard. According to an Enterprise Strategy Group (ESG) survey, 47% of all data within organizations is dark on average (i.e., data that is stored but not ready for analysis), with most of that data being unstructured. Over 90% of the global datasphere is made up of unstructured data that doesn’t present itself in nice and neat columns for analysis. So, how can data scientists and IT partner together to unlock these insights that are hidden in images, videos and sensor data?

Dell Unstructured Data Solutions: AI Innovations, Delivered

With the Dell Unstructured Data Solutions portfolio – which includes powerful and highly secure file and object storage platforms such as PowerScale, ECS and ObjectScale – our main goal is to help you leverage every drop of valuable data to drive your demanding workloads. In this blog, we’ll take you on a journey to build the optimal IT environment.  Modernize and create a next-generation storage foundation with PowerScale OneFS and ECS or ObjectScale, secure your data with Dell Cyber Protection for Unstructured Data and act on your data with a full suite of data management tools and partner ecosystem.

Modernize Your Infrastructure to Create a Next Gen Storage Foundation for AI Workloads

While it’s tempting to skip right to the inferencing, driving effective training and modeling for AI use cases requires a solid storage foundation. You may not know what questions to ask AI today, but the data you store will drive predictions and greater insight in the future. What’s more, your datasets are bound to grow, and your data collection and model training cycles are going to demand higher performance. The good news is we designed PowerScale OneFS, ECS and ObjectScale for the most intensive workloads such as generative AI. With a range of available models, you have the flexibility to right-size your environment for your specific needs. Let’s also talk about scalability. With our portfolio of file and object storage platforms, your datasets can expand as fast as they need to. For example, increase AI model accuracy for higher resolution data sets with up to 186PB of raw storage capacity in a single cluster on PowerScale. Adding capacity is non-disruptive, meaning your data scientists and LOB teams can continue learning and innovating with your data without skipping a beat.

“PowerScale enables us to access data and analytics in real time — without the latency caused by sending it from the edge to the core or the cloud.”

– Keith Bradley VP of Information, Technology and Security – Nature Fresh Farms

Additionally, data processing for use cases like generative AI require a combination of training the model, fine-tuning it and generating predictions based on input data. The reality is all three of these steps may occur in different locations. Depending on latency requirements, deployment time, security and a host of other considerations, you may need a combination of on-premises, edge and cloud deployments to truly meet your AI objectives. With our Unstructured Data Solutions, we believe you should be able to run workloads like generative AI wherever you’d like. That’s why our portfolio is multicloud-enabled, giving you a single point of data access from core to edge to cloud. For example, with APEX File Storage for AWS, you can easily move data from on-premises to the cloud using advanced native replication without having to refactor your storage architecture. And once in the cloud, you can use all enterprise-grade PowerScale OneFS features. This is critical for driving operational efficiencies, as there is no need to retrain your IT teams because everything is familiar and predictable.

Secure Your Data with Intelligent, Real-time Detection Tools and Fast Recovery Time

Unfortunately, security threats have shown no sign of slowing down. The cost of cybercrime is expected to reach $10.5 trillion by 2025, and a whopping 76% of organizations have experienced an attempted ransomware attack. Attackers are specifically targeting unstructured data because of its value (i.e., sensitive data that can be sold) and sheer data volume. Because of this, multilayered security is a requirement when setting up any environment for AI training and modeling. Dell Cyber Protection for Unstructured Data helps stop cyber-attackers in their tracks with a multilayered approach.

Our commitment to security begins right at the start, as we conduct a comprehensive assessment of your current cybersecurity state through the lens of Zero Trust with Dell security experts. Next, we establish an air-gapped cyber vault that creates an impenetrable barrier between your mission-critical data and general storage. But that’s not all – we seamlessly integrate AI and machine learning software, empowering you to detect and analyze any suspicious activity in real-time. If a threat is detected, our proactive measures kick in. Track and identify all accessed or modified files and quickly lock out accounts and shut down data replication pipelines to mitigate your risk. In the unfortunate event of an attack, our fast recovery time ensures that operations can be restored – recovering petabytes of data in hours versus weeks. With our end-to-end cybersecurity solution, you can rest easy knowing that you have the right level of visibility and security to safeguard your AI environment in real time. 

Act on and Analyze Your Data Wherever It Lives

Now that you’ve established a modern, secure foundation for your data, the excitement continues. Our team of in-house Dell AI experts are dedicated to supporting you throughout your entire AI journey – from model training to fine-tuning to inferencing – so you can act on your insights faster. Our in-house data scientists will meet with you to identify your most pressing business problems and recommend ways to run your workloads, like generative AI, where it makes the most sense. Dell also has a broad ecosystem of data management partners – such as Starburst, Snowflake and Databricks – that is tightly integrated to help simplify data discovery and unlock valuable insights. Establish a modern data lakehouse to streamline data access while utilizing the most robust data querying and engineering tools on the market for your AI projects.

“With all-flash PowerScale at the edge we’re able to run analytics and deliver insights faster, including both training and inference.”

– Arnaud Langer, Global Edge and IoT Senior Product Director – Atos

Choose the Provider You Know and Trust to Power Your AI Journey

Dell is here to partner with you to successfully make use of your data and innovate with AI. With market-validated technology and over 16,000 customer installations spanning the globe, you can rest easy knowing you’re choosing a provider that some of the largest companies in the world trust. Additionally, we have the most flexible, efficient and secure storage on the market and have been named a leader in Gartner’s Magic Quadrant for seven consecutive years. In the report, Gartner states that “Dell Technologies has the broadest portfolio of products and builds on insights gathered from the largest installed base in the market to address the challenges of unstructured data.”

Our in-house AI experts and data scientists are ready to ideate and develop a comprehensive AI strategy for your business. Get started today by taking advantage of our Dell AI Starter Kit, which is based on PowerScale, Dell PowerEdge servers and NVIDIA.

Source: dell.com

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Optimizing for Analytics, Machine Learning Experimentation

As artificial intelligence (AI) and machine learning (ML) are becoming part of our daily lives, we hear about the importance of data and data science almost daily, as we have for over a decade now. Data is often called “fuel”— for economic growth, innovation and even AI itself. And while data may be the fuel, it still takes science and engineering to build the rocket ship.

One of the keys to that science and engineering is optimization. Specifically, optimizing processes and methods for analytics and ML: experimentation and modeling. So, while data powers your rocket ship, optimizing analytics ensures its design and performance are state-of-the-art. And while working with machine learning isn’t rocket science (anymore), it still requires a bit of paradigm shift – one that realizes the importance of data and a data-driven enterprise.

Plumber, Engineer, or Scientist?

In both data science and plumbing, functional pipelines are essential to optimal performance and results. Both disciplines also often require a person to roll up their sleeves. For many data scientists, most of their time is still spent accessing, integrating and wrangling data to clean it and transform it for their day-to-day needs. What if there was a way to automate this “plumbing?” Data engineers should be up for that task!

By leveraging modern data infrastructure to speed up the exploration and data preparation phases, we can enable quick access to data across multiple systems as needed to explore and experiment. Data engineers can do their work focusing on the infrastructure and data pipelines and keep all consumers – including the data scientists – happy and productive.

Doing it All Over Again

Furthermore, in science, to get to the right answer, a team needs to run various explorations and experiments, including multiple iterations. These experiments exist to find relevant “features” –essentially the relevant data fields or their transformed values directly impacting the quality of the ML model – and to select the right methods, fine-tuning all parameters and hyperparameters required. Speed of experimentation is also critical. (No wonder hardware accelerators are so popular.)

To be even more effective, teams should track their experiments to make the results and models reproducible. Parameters and result metrics are key, to be captured and made available for comparison to understand how the team got to a specific model or conclusion.

Finally, these experiments and the follow-up model training might require managing various artifacts – sometimes very large data sets. To work seamlessly between data and code, these teams could utilize data versioning to capture a point-in-time version of those large data sets and make them easily available. 

Pushing the Limits

As more and more successful AI/ML use cases (or rocket ships) are built, we will keep pushing the frontiers and applicability of machine learning use cases. Whether to target huge or low-latency models, deploying in true edge locations, using large language models or Generative AI, the real starting point of innovation is to realize the importance of data and work toward a data-driven enterprise. This will help analysts and data scientists focus on what they are good at: effectively iterating to resolve complex data science problems and helping drive those to production. When done correctly, this type of optimization creates real business value and can take your productivity and use cases (rocket ship) to new heights.

Let Dell Data Management Lead the Way

Learn more about the data management journey with our interactive infographic here. And stay on the lookout for our blog on step four of the data management journey, coming next month. You can also learn more about Dell Data Management solutions on our Enterprise Data Management page.

Source: dell.com

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Data Data Everywhere, Not a Byte to Use

The global data footprint is growing at an exponential rate. It’s expected to be a few hundred zettabytes (that’s 1,000,000,000,000,000,000,000 bytes and counting) in the next few years. Meanwhile, a typical enterprise today manages petabytes of data – spread across on-prem, cloud, edge and multiple systems ranging from relational databases, warehouses, data marts and data lakes.

Recognizing that data access is often the first (and most cumbersome) aspect of activating data, Dell recently announced a partnership with Starburst Data, whose industry-leading platform offers answers to many problems organizations are facing with their data.

The Data-driven Enterprise

There is no dearth of tools and platforms that can help you manage your data. Yet, the rate at which we create data far exceeds the rate at which we convert it to insights. So, what has changed, and how can you better prepare your enterprise to be truly data driven?

A modern enterprise data architecture needs to embrace some key tenets:

◉ Data will remain distributed across multiple data centers, public clouds and edge – often resulting in data silos. You need solutions that can help bridge these data silos.

◉ Data movements are expensive and error prone. It is more efficient to move the consumption tools close to data, rather than moving data near the tools.

◉ We must balance the flexibility and ease of democratizing data into the hands of data citizens against the increasing need for security and governance.

◉ An open data architecture, composed of open data formats, decoupled compute and storage, ensures there is no vendor lock-in and provides interoperability with the ever-changing ecosystem of tools and platforms.

The Journey of a Data Use Case

A data use case goes through three broad phases: exploration, engineering and operationalization. The challenges around data access vary for each of these phases. Here’s an example.

◉ Exploration. James is a data analyst who wants to create a live report that shows revenue trends by product, region and week. As James starts his use case, he needs quick access to data across multiple systems to explore and experiment. This involves reaching out to the admins of each of the data sources to get individual access, which is often time-consuming. Once he receives access, there is no easy way to discover data relevant to his use case.

◉ Engineering. Once James knows exactly what data to use and the processing required, Sally from the data engineering team creates data pipelines to ingest this data into a data lake/ lakehouse, apply transformations and tune the workflow for high performance. The report is ready to be published.

◉ Operationalization. Once the report is published, Lee from the operations team ensures the data pipelines work reliably to refresh the data regularly, thus providing the latest insights to the business.

As you can see, activating a single use case often involves multiple user personas, environments, tools and outcomes. A modern data stack should embrace these interplays of people, process and technology and enable the most efficient path to access data and convert it into insights.

The Dell Technologies and Starburst Partnership

Together, Dell and Starburst bring you a Multicloud Data Analytics solution with the following key capabilities:

◉ Built on the highly popular open-source federated query engine Trino, Starburst simplifies data discovery and accelerates data access by enabling in-situ consumption of data – no matter where it resides on-prem, cloud or edge.

◉ With more than 50 data source connectors supported, you can instantly connect to your operational systems, data warehouse, data lakes and run lightning-fast queries to get instant insights.

◉ When required, data from remote sources can be persisted into a data lake/lakehouse to overcome network/system latency and deliver production-grade SLAs. Dell Elastic Cloud Storage (ECS) provides best of breed object storage solutions for a modern-day data lake/ lakehouse.

◉ It provides a single point of access to data with unified security and governance to ensure the right authorization and access control.

◉ You can create and publish data products, which can be organized by domains – thus supporting a data mesh architecture.

◉ Power and scale enterprise-wide analytics and AIML experiences with industry-best Dell PowerEdge servers.

Source: dell.com

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Achieve Consolidated Data Insights Faster with Dell and Teradata

Harnessing the full power of your data assets can accelerate your business in profound ways by enabling deeper insights for enhanced decision making. But doing so can be complex and challenging.  Whether it’s data repatriation from public cloud deployments, data consolidation across multiple disparate assets, or exponential growth in data volume, enterprises struggle to contain costs and manage inefficiencies.

At Dell Technologies, we are bringing together an ecosystem of leading technologies in the data space that are helping transform the approach to data and analytics to work seamlessly alongside our infrastructure offerings. Coupled with Dell’s extensive network of value-added professional services and support, pre-validated solutions, and reference architectures, along with the expert knowledge of these partners, our joint offerings provide you with better control over how you extract insights from your data with the safety of best in breed governance and security from your trusted partner for essential infrastructure.

With this vision of delivering transformative outcomes for our customers, we are collaborating with Teradata, an industry leader in enterprise data management and analytics, and expanding it to unveil a new and exciting joint solution. Through this joint solution, we have worked together to deliver a seamless experience for Teradata Vantage Solutions powered by Dell Technologies.

“These solutions offer customers access to Teradata’s best-of-breed data and analytics software with Dell’s industry-leading enterprise infrastructure, enhancing agility, speed and performance,” said Hillary Ashton, Chief Product Officer at Teradata. “The integrated technology provides the control and security of on-prem to meet customers’ need for holistic, data-driven insights.”

Teradata Vantage Solutions Powered by Dell Technologies

Teradata Vantage is a modern data platform that unifies data lakes, data warehouses, analytics and new data sources and types. This means organizations ca access all their data from an integrated environment, reducing complexity while maintaining flexibility to support a wide range of deployment models based on their needs. These deployment options include multicloud, hybrid or on-premises for enhanced security and control.

“Vantage helps organizations use analytics to deliver proven value and superior business outcomes,” stated Ashton. “Vantage offers high impact results with cost-based query optimization, massive multiway joins, support for real-time data streaming, management of complex analytic pipelines directly within the database and more.”

Together, Teradata and Dell have developed three fully integrated and optimized solutions that bring together the market-leading Vantage data management and enterprise analytics software from Teradata and the award-winning compute, storage and networking infrastructure from Dell, with VMware providing the private cloud layer. These solutions, shown in Figure 1, address a wide range of data analytic use cases, from a quick start package to enterprise-scale solutions for analytic archives and enterprise data warehouses.

Figure 1: Teradata Vantage Solutions Powered by Dell Technologies

Teradata Vantage Quick Start is optimized for ease and speed of deployment, with fast and efficient processing to power development. Quick Start is designed to enable customers to start small and scale up to larger deployments, all within the consistent Vantage software environment. The Dell PowerEdge R740xd Server, which is designed to address application performance and acceleration, powers Quick Start.

Teradata Vantage Analytic Archive offers an active archive for querying and reporting use cases for historical data. Analytic Archive allows companies to store a large amount of data cost-effectively while still being able to use it for analytics. Analytic Archive is powered by the Dell PowerEdge R640 server together with Dell PowerSwitch and Dell Unity XT Storage that provides flexible hybrid flash and disk storage for high capacity and cost-effective archival applications.

Teradata Vantage Enterprise Data Warehouse is a purpose-built, highly available data and analytics solution for enterprise production-scale deployment with optimized performance and reliability. It unifies and consolidates silos of enterprise data from multiple sources, facilitating broad access in the organization while enabling advanced analytics for complex and mixed workloads with prioritization capabilities. Vantage Enterprise Data Warehouse is powered by the Dell PowerEdge R640 server together with Dell PowerSwitch, Dell PowerVault and ECS Object storage featuring capacity, performance and operational simplicity.

Driving Value for Your Business

Drive value for your organization with a broad range of outcomes, including product innovation, enhanced customer experience, financial transformation, operational excellence, asset optimization, risk mitigation and more. Whether you want to expand your existing capabilities or get started with your first project, Teradata Vantage Solutions powered by Dell Technologies address a variety of data analytic use cases with rapid time to value. We are excited for this new stage in our collaboration with Teradata and look forward to helping our joint customers achieve their business goals by harnessing the power of their data.

Source: dell.com

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Understand the Potential of your Data with Data Discovery

Imagine trying to find five needles in a haystack. Now, imagine trying to find those same five needles using a metal detector – one equipped with multi-scan and multi-frequency capabilities designed for identifying and locating the items for which you’re looking. How long would each task take? How easy (or difficult) would they be?

Data discovery is defined as a process of identifying and exploring large volumes of data to understand its potential value for analysis. It is a crucial process that involves identifying and locating relevant data assets within an organization. With the exponential growth of data in the enterprise, data discovery has become an essential component of data management that helps with making business decisions.

Key Challenges

Today, data consumers spend far too much time seeking, accessing, preparing and manually cataloging data. This delays the uncovering of analytics insights necessary to drive quantifiable business value. Many organizations are facing several of the same issues.

First, there’s the data silos problem, which is when data is stored in various systems within an organization, making it difficult to discover all relevant data assets. Next, there’s a lack of standardization. Since data is often stored in different formats, it’s difficult to standardize and integrate from multiple data systems. There’s also the reality that the volume of data generated by organizations is increasing rapidly, making it challenging to identify and manage data assets. Organizations also struggle with data quality, when the quality of data varies across the data systems and makes it difficult to ensure that data’s accuracy and reliability. Finally, there’s the data access problem. As data reside in different systems, it’s a challenging task for admins to make sure the right users have access to critical data assets.

So, how do we address data silos, lack of standardization, data volume, data quality and access challenges?

The Importance of Data Discovery

Enterprises can organize and standardize large volumes of data by enabling data discovery across different data systems. First, data discovery enables organizations to identify and locate valuable data assets that were previously unknown or underutilized. Data discovery also helps identify data quality issues such as duplicates and inconsistencies and takes required actions to improve the quality of data.

Organizations can use data discovery to find and analyze data quickly and make informed business decisions faster. Data discovery also helps users identify data sources that can be integrated to provide a more comprehensive view of their data, which also enables them to implement access control over data assets. Best of all, data discovery establishes a clear line of sight from data to value creation, which is the ultimate goal of data management. 

Best Approach

The best approach to kicking off your data discovery process involves a few steps.

1. Define Objectives: Before embarking on a data discovery process, it is essential to define the business objectives an organization needs to achieve. Define the questions that need answers, the problems that need to be solved or the opportunities that need to be pursued.

2. Recognize the Data Estate: Understand the data landscape across the enterprise, including data formats, data systems, quality, users, volume and usage.

3. Utilize Tools: Data discovery is a time-consuming process if done manually. (See: haystack example.) Consider using tools that can help identify data sources, structures, patterns, data lineage and relationships effectively.

4. Collaborate: Data discovery cannot be successful with a siloed team working on it. Effective data discovery requires collaboration with different teams across the enterprise.

5. Explore Centralized Data: Data discovery should be considered as a unified repository that helps streamline the discovery process to search and locate relevant data assets by different teams including IT, analysts and business users.

Let Dell Data Management Lead the Way

Data discovery is an essential process that can help organizations unlock the potential of their data assets by creating a clear line of sight from data to value creation. Enterprises can leverage data discovery to improve quality of their data and support data consumption efficiently. It enhances the quality of results and enables decision-making as an effective process for business users.

Source: dell.com

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Your Data Management Journey: Keep the End in Mind

“Begin with an end in mind” —from “The 7 Habits of Highly Effective People” by Stephen Covey

In our previous blog, we introduced the concept of The Dell Data Management Journey Map – a map that guides organizations in their ability to leverage their data and advanced analytics to accelerate their journey from business need to business outcome.

The business importance of data management has grown dramatically over the past couple of years as leading artificial intelligence (AI) pundits have advocated for the transition from algorithm-centric AI to data-centric AI. Dr. Andrew Ng, Stanford professor and the godfather of modern AI, has been championing the critical transition of spending less time tweaking the AI algorithms, and instead investing more time improving the data quality, latency, granularity and robustness that power the AI models.

“If 80 percent of our work is data preparation, then ensuring data quality is the important work of a machine learning team.” – Andrew Ng

Professor Ng’s message is quite clear. While tweaking AI math algorithms will lead to incremental improvements in the effectiveness of AI models, we can achieve much larger improvements through quality and completeness improvements in the data that feed the AI algorithms. And as an added benefit, improvements in the data sets also improve the predictive performance of any use cases that use that same data set – the basis for the Schmarzo Data Economic Multiplier Effect.

Yeah, us old data dogs know this observation as GIGO, or “garbage in, garbage out.”

Starting Your Data Management Journey

In his seminal book “The 7 Habits of Highly Effective People,” Stephen Covey famously said, “Begin with an end in mind.” And your data management journey starts with the same advice: Identify, understand and collaborate around a targeted, well-articulated business need, challenge or initiative. This is step one.

So, to ensure a successful data management journey, we must first understand user intent. That is, what is the user trying to accomplish? How will he or she measure success? To thoroughly determine and understand user intent, we must address the following questions as the first step in our data management journey.

Figure 1: Source: “The Art of Thinking Like a Data Scientist” by Bill Schmarzo.

Which business initiative are we trying to address? What are the KPIs and metrics against which we will measure progress and success? What are the desired or ideal outcomes?

And while addressing these questions is a great start, you also need to engage key internal and external stakeholders – those who impact or are impacted by the initiatives – making sure to understand their perspectives as the first step in your data management journey.

Figure 2: Source: “The Art of Thinking Like a Data Scientist” by Bill Schmarzo.

What are these stakeholders’ desired outcomes? What KPIs and metrics will they use to measure success? What are the potential ramifications and risks from the failure of the initiative?

Gaining a thorough and holistic perspective on those questions requires collaboration across internal and external stakeholders. Not only does this help to ensure the data management efforts and resulting AI outcomes are properly aligned and focused, but it also creates buy-in from these key stakeholders who will be responsible for applying the data and AI results to their daily jobs.

Also, close collaboration with the subject matter experts drives better outcomes. If data science is about identifying those variables and metrics that “might” be better predictors of performance (otherwise known as ML features), then engaging and empowering the subject matter experts is critical to identifying those variables, metrics and supporting data sources that “might” lead to more relevant and accurate analytic results.

“If you don’t have enough ‘might’ moments, you’ll never have any ‘breakthrough’ moments.”

If AI has the business and economic potential to transform every industry and every aspect of society, and quality of data directly drives the effectiveness of the AI models, then it’s logical to see the transformation of data management from an IT practice into a business discipline –focused on leveraging data and analytics to deliver more relevant, more meaningful and more material business and operational outcomes.

Let Dell Data Management Lead the Way

Learn more about the Data Management Journey with our interactive infographic here. And stay on the lookout for our blog on step two of the data management journey, coming next month. You can also learn more about Dell Data Management solutions on our Enterprise Data Management page.

Source: dell.com

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Data Everywhere and Not an Insight to be Found

In today’s digital age, we are existing among a storm of data, treading water to stay afloat. Our devices are watching and listening to us constantly. With every scroll, every social share, every conversation, companies are constantly collecting data. But where does this storm surge of data go, and how is it used? With the rate of data continuously climbing, up to a rate of 35% annually, companies are faced with more data than they often know what to do with. They want the value and crave the benefits, but achieving those can be overwhelming at best. As we transition from the era of digital transformation to the era of digital business, organizations recognize they must move quickly to remain competitive. Yet, it takes many organizations months to act on data insights, a lag that can leave a company far behind.

We must process data and make it available in a timely, relevant and actionable way to enable faster, better business decisions. Digital resiliency prioritizes data-centric processes – improving data usage and enhancing efficiency of the data lifecycle. In fact, an estimated two thirds of organizations rank intelligent data management among their top five management priorities.¹ Companies know those who act on insights faster will gain a competitive edge.

Prevail Through the Hurricane that is Data

As data floods in from every angle, pelting down on top of companies that are likely already overtaxed, how will you modernize to meet data consumption needs? If you don’t feel like you have the technical skills in-house or your teams are already overextended and can’t dedicate time to these objectives, Dell Technologies Services can provide the lifeboat you desperately need. With guidance and assistance from our experts, you will gain value from data faster. We are the storm chasers who will evaluate what you need to do to stay ahead of the storm, accelerating your journey to achieving the powerful data insights your company needs to thrive. Leverage the power of our experts, backed by Dell Technologies’ extensive global network, to help you realize:

◉ Increased server performance and flexibility to give you a single view of data on-premises and in the cloud through modernized workloads

◉ A unified data analytics platform that scales to meet your business needs

◉ Enhanced data governance and visibility, plus reduced cost and complexity

Using modern architecture, proven solutions and advanced techniques, we address needs across the lifecycle, helping you develop a strategy, implement new technologies, improve adoption and scale to the needs of your organization. We help you break through the storm of data, eliminating data silos that create extra cost and security risks and enhancing data visibility, control and access to enable you to make faster, better data-driven decisions.

Learn From Those Who Weathered the Storm

Consider a recent engagement with one of Dell’s customers as an example. Working with a leading life insurance provider, we focused on optimizing their server environment. This company supports a custom application for analyzing and managing insurance portfolio investment options, which requires processing large volumes of data daily and storing billions of rows of data in the data warehouse. By reducing data warehouse load times and maximizing utilization of data storage assets, we helped the business achieve lowered projected storage cost by about $1 million per year and increased data throughput by over 100%.

Dell Technologies Services will set you on the path to unlocking data value quickly and efficiently, enabling you to create a more intelligent, competitive business. Interested in learning more about how your business can better leverage and gain advantage from data?

Source: dell.com

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