Why Your DBA Career Needs Db2 z/OS Fundamentals

In the dynamic world of data management, staying ahead means continuously enhancing your skill set and validating your expertise. For database administrators (DBAs) working with enterprise-level systems, particularly those built on IBM's robust mainframe technology, mastering IBM Db2 z/OS DBA fundamentals is not just an advantage—it's a career imperative. This certification pathway opens doors to specialized roles, increased responsibilities, and significant professional growth within organizations relying on high-performance, secure, and scalable data solutions.

IBM Db2 for z/OS is the backbone for countless critical applications across various industries, from finance and healthcare to government and manufacturing. As such, the demand for skilled DBAs who understand its intricacies remains consistently high. Pursuing the IBM Associate Certified DBA - Db2 12 for z/OS Fundamentals certification (Exam C1000-122) demonstrates your foundational knowledge and commitment to excellence in this specialized field.

The Indispensable Role of a Db2 z/OS DBA

A Database Administrator for Db2 z/OS is a pivotal figure in any organization that leverages IBM mainframes. These professionals are entrusted with the design, implementation, maintenance, and performance of mission-critical databases. Their expertise ensures data integrity, availability, and security, directly impacting business continuity and operational efficiency.

Key Responsibilities and Skills for Db2 z/OS DBAs

The role of a Db2 z/OS DBA is multifaceted, demanding a blend of technical prowess, problem-solving abilities, and an understanding of business needs. Key responsibilities typically include:

• Database installation and configuration.
• Designing and implementing database schemas and objects.
• Monitoring database performance and health.
• Implementing and maintaining security protocols.
• Performing backup and recovery operations.
• Troubleshooting database issues.
• Optimizing SQL queries and database structures.
• Planning for capacity and future growth.

To excel in these areas, a Db2 z/OS DBA needs a strong grasp of operating system concepts (z/OS), SQL, database theory, and specific Db2 features. The IBM Associate Certified DBA Db2 z/OS career path typically starts with fundamental knowledge and progresses to advanced administration and specialization in areas like performance tuning, security, or data warehousing.

Why Pursue the IBM Db2 12 for z/OS DBA Fundamentals Certification?

Obtaining the IBM Associate Certified DBA - Db2 12 for z/OS Fundamentals certification (Exam C1000-122) is more than just adding a badge to your resume; it's a strategic investment in your professional future. This certification validates your foundational skills in administering Db2 12 for z/OS databases, making you a more attractive candidate for employers and positioning you for career advancement.

Benefits of IBM C1000-122 Certification

The IBM C1000-122 certification benefits are numerous and impactful:

• Credibility and Recognition: It serves as a globally recognized standard of your foundational expertise in Db2 12 for z/OS.
• Enhanced Career Opportunities: Certified professionals are often preferred for challenging roles and have better prospects for promotions.
• Increased Earning Potential: Specialized skills and certifications typically command higher salaries.
• Validation of Skills: The exam objectively assesses your knowledge against industry benchmarks.
• Confidence: Successfully passing a rigorous IBM exam boosts your professional confidence and competence.
• Foundational Knowledge: It solidifies your understanding of core concepts, paving the way for advanced certifications.

Understanding requirements for IBM Associate Certified DBA - Db2 12 for z/OS involves a solid grasp of database concepts and practical experience, though the fundamentals exam is designed for entry-level professionals or those transitioning into Db2 z/OS roles.

Deep Dive into the C1000-122 Exam: IBM Db2 12 for z/OS DBA Fundamentals

The IBM Db2 12 for z/OS DBA Fundamentals exam, designated as C1000-122, is designed to assess a candidate's fundamental knowledge and skills required for basic database administration tasks on Db2 12 for z/OS. This is an essential step for anyone looking to establish a strong career in enterprise data management.

Exam Details and Structure

To effectively prepare, it's crucial to understand the logistics of the exam:

• Exam Name: IBM Associate Certified DBA - Db2 12 for z/OS Fundamentals
• Exam Code: C1000-122
• Exam Price: $200 (USD)
• Duration: 90 minutes
• Number of Questions: 63 multiple-choice questions
• Passing Score: 70%

These details highlight the need for efficient time management and a thorough understanding of the syllabus topics to achieve the passing score. For more detailed information, you can always refer to the official IBM certification page.

C1000-122 Exam Syllabus Topics: A Comprehensive Overview

The C1000-122 exam syllabus topics are carefully structured to cover the essential aspects of Db2 12 for z/OS database administration. Each section carries a specific weight, indicating its importance in the exam. Familiarity with the IBM Db2 12 for z/OS fundamentals course content will provide a solid base.

For a detailed breakdown and to explore the comprehensive Db2 12 for z/OS exam syllabus, a full review is highly recommended. Below is an outline of the key areas:

Planning - 11%

This section focuses on the initial stages of database management, emphasizing the foundational decisions that impact a Db2 system's lifecycle. It includes understanding the components of Db2 z/OS, the purpose of key system parameters, and how to plan for installation and upgrades. Aspiring DBAs need to grasp how to establish the environment for Db2, considering factors like storage, memory, and connectivity. This involves making informed choices about buffer pools, log management, and system catalogs, which are crucial for optimal performance and stability. Effective planning prevents costly issues down the line and ensures the database aligns with business requirements.

Working with SQL and XML - 21%

As the largest weighted section, this highlights the critical importance of data manipulation and retrieval. Candidates must demonstrate proficiency in SQL (Structured Query Language) for creating, modifying, and querying data. This includes understanding DDL (Data Definition Language) for defining database objects, DML (Data Manipulation Language) for managing data, and DCL (Data Control Language) for permissions. Furthermore, knowledge of working with XML data within Db2 z/OS, including storing, querying, and managing XML schemas, is essential. This covers techniques for integrating XML data effectively, utilizing XML functions, and ensuring performance when handling semi-structured data. This core area underpins almost all daily DBA tasks and is crucial for interacting with application developers.

Security - 8%

Database security is paramount in any enterprise environment. This section covers the fundamental concepts of securing a Db2 z/OS system. It involves understanding how to manage user authentication and authorization, assign privileges, and implement roles. Knowledge of various Db2 security mechanisms, including RACF (Resource Access Control Facility) integration and native Db2 security, is vital. DBAs must be able to protect sensitive data, control access to database objects, and ensure compliance with organizational security policies. This also includes understanding audit trails and monitoring access to identify and prevent potential breaches, ensuring data confidentiality and integrity.

Operations - 16%

This area focuses on the day-to-day operational tasks required to keep a Db2 z/OS system running smoothly. It includes managing utilities for maintenance, such as REORG, RUNSTATS, and COPY, which are crucial for performance, data integrity, and recovery. Candidates should understand how to perform backup and recovery operations, including point-in-time recovery, and how to use the Db2 log. Monitoring the database system using tools and commands, managing stopped or started databases, and handling errors and alerts are also key components. This section tests practical knowledge of keeping the Db2 environment operational, responsive, and recoverable in case of failures.

Data Concurrency - 11%

Understanding how Db2 z/OS manages concurrent access to data is essential for maintaining data integrity and application performance. This section covers locking mechanisms, transaction isolation levels, and how to prevent deadlocks and contention. DBAs need to know how to identify and resolve concurrency issues that can degrade performance or lead to incorrect data. This includes understanding the impact of different isolation levels on application behavior and how to tune them for optimal balance between concurrency and data consistency. Mastery of these concepts ensures that multiple users and applications can access and modify data simultaneously without compromising its accuracy or the system's responsiveness.

Application Design - 21%

Equally weighted with SQL and XML, this section emphasizes the DBA's role in supporting application development. It focuses on how database design choices impact application performance and efficiency. This includes understanding data modeling principles, denormalization strategies, and the appropriate use of indexes. Candidates should know how to advise developers on writing efficient SQL, utilizing stored procedures and user-defined functions, and managing application connections. This also covers understanding distributed data access (DRDA) and how it affects application design in a distributed environment. A DBA's input during application design phases can significantly improve the overall system's performance and maintainability.

Working with Database Objects - 12%

This section covers the practical aspects of creating, altering, and managing the various objects within a Db2 z/OS database. This includes tablespaces, table definitions, indexes, views, sequences, and aliases. DBAs must understand the attributes and characteristics of each object, how to create them efficiently, and best practices for their management. This also encompasses understanding data types, constraint definitions (primary keys, foreign keys, check constraints), and how they enforce data integrity. Proficiency in this area is fundamental for building and maintaining the structural components of a Db2 database.

Effective Preparation Strategies for IBM Db2 12 for z/OS DBA Fundamentals

To pass the C1000-122 exam, a structured and comprehensive preparation approach is key. This includes utilizing official resources, practical experience, and consistent study.

Study Guide and Resources

Start with an official IBM Db2 12 for z/OS DBA Fundamentals study guide. IBM often provides detailed exam objectives that serve as an excellent roadmap for your study. Key resources include:

• IBM Documentation: The official Db2 12 for z/OS documentation is an invaluable, authoritative source for in-depth information on all topics.
• IBM Training Courses: Consider enrolling in official IBM training. The Db2 12 for z/OS Basic Database Administration (CV844G) course is specifically designed to provide the foundational knowledge required for this certification. This type of structured learning can significantly enhance your understanding.
• Practice Exams: Utilize IBM Db2 12 for z/OS DBA certification practice exam questions. These help you familiarize yourself with the exam format and identify areas where you need more study. Many platforms offer IBM Db2 12 for z/OS DBA exam questions that simulate the actual test.
• Online Communities and Forums: Engage with other Db2 professionals. These platforms can offer insights, tips, and solutions to complex problems.

Tips on How to Pass the IBM Db2 12 for z/OS DBA Fundamentals Exam

Beyond studying the material, strategic exam-taking skills can make a difference:

1. Understand the Exam Objectives: Go through the IBM Db2 12 for z/OS exam objectives thoroughly. Each objective represents a topic that could appear on the exam.
2. Prioritize High-Weighted Sections: Focus more heavily on "Working with SQL and XML" and "Application Design" as they constitute the largest portions of the exam.
3. Hands-on Practice: If possible, gain practical experience with Db2 z/OS. Nothing beats real-world application of concepts. Even simulated environments can be beneficial.
4. Time Management: During practice exams, work on managing your time effectively. 90 minutes for 63 questions means less than 1.5 minutes per question.
5. Review Weak Areas: Use practice exam results to pinpoint your weaknesses and dedicate extra study time to those specific topics.
6. Rest Before the Exam: Ensure you are well-rested on exam day to maintain focus and concentration.

For individuals seeking comprehensive preparation, an IBM C1000-122 exam preparation course can provide structured learning and expert guidance. This kind of specialized training often includes practical exercises and insights that are hard to gain from self-study alone. Staying updated with IBM's latest insights for business leaders can also provide a broader context for your technical expertise.

Career Growth and the Future of Db2 z/OS

The mainframe continues to be a cornerstone of enterprise computing, and Db2 z/OS remains a critical data platform. This ensures a stable and rewarding career path for skilled DBAs. Organizations are increasingly looking for professionals who can manage these robust systems efficiently and securely.

Advancing Your IBM Associate Certified DBA - Db2 z/OS Career Path

Once you achieve the Associate Certified DBA - Db2 12 for z/OS Fundamentals certification, consider pursuing advanced IBM certifications in Db2 for z/OS, such as those focusing on advanced administration, performance tuning, or application development. Continuous learning is crucial in the technology sector.

The best resources for IBM Db2 z/OS DBA certification extend beyond individual courses to include regular engagement with IBM's technological advancements and industry trends. The role of a DBA is evolving, incorporating elements of cloud integration, data analytics, and AI. Understanding these broader trends can help you future-proof your career.

For those interested in understanding the broader landscape of computer and information technology professions, including salary expectations and job outlook, the U.S. Bureau of Labor Statistics provides valuable insights.

FAQs About IBM Db2 12 for z/OS DBA Fundamentals (C1000-122)

1. What is the IBM Db2 12 for z/OS DBA Fundamentals certification?

The IBM Db2 12 for z/OS DBA Fundamentals certification validates an individual's foundational knowledge and skills in administering Db2 12 for z/OS databases. It demonstrates proficiency in basic database administration tasks, including planning, SQL, security, operations, data concurrency, application design, and working with database objects.

2. Who should take the C1000-122 exam?

This exam is ideal for entry-level DBAs, database specialists, system programmers, application developers, and anyone seeking to establish or validate their fundamental understanding of Db2 12 for z/OS database administration. It's also suitable for professionals transitioning into a Db2 z/OS environment.

3. What is the cost of the IBM Db2 12 for z/OS DBA Fundamentals exam?

The IBM Db2 12 for z/OS DBA Fundamentals exam cost is typically $200 USD. Prices may vary by region or testing center, so it's always best to check the official Pearson VUE website for the most current information when you are ready to schedule your C1000-122 exam.

4. How much time should I allocate for studying for the C1000-122 exam?

The amount of study time required varies based on your existing experience with databases and Db2. For someone with some database background but new to Db2 z/OS, several weeks to a few months of dedicated study, including coursework and practical exercises, is generally recommended to cover all the syllabus topics comprehensively.

5. Does this certification require practical experience?

While the C1000-122 exam focuses on fundamental knowledge, having some practical experience with Db2 or similar database systems can significantly aid in understanding the concepts and scenarios presented in the exam. Although it's an associate-level certification, hands-on exposure is always beneficial for a deeper grasp of the material.

Conclusion

Mastering IBM Db2 z/OS DBA fundamentals is a strategic move for any DBA looking to solidify their career in enterprise data management. The IBM Associate Certified DBA - Db2 12 for z/OS Fundamentals certification (C1000-122) provides a robust foundation, validating your expertise and opening doors to advanced opportunities. By understanding the exam structure, delving into the syllabus topics, and employing effective study strategies, you can confidently pursue this valuable credential.

The demand for skilled Db2 z/OS DBAs remains strong, reflecting the continued reliance on IBM mainframes for mission-critical operations. Investing in this certification not only enhances your technical capabilities but also demonstrates your commitment to professional excellence in a specialized and high-value field. Start your journey today to become an indispensable asset in the world of Db2 z/OS administration. Exploring IBM's impact across various industries can also highlight the diverse applications of your expertise.

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Enhancing data security and compliance in the XaaS Era

Recent research from IDC found that 85% of CEOs who were surveyed cited digital capabilities as strategic differentiators that are crucial to accelerating revenue growth. However, IT decision makers remain concerned about the risks associated with their digital infrastructure and the impact they might have on business outcomes, with data breaches and security concerns being the biggest threats.

With the rapid growth of XaaS consumption models and the integration of AI and data at the forefront of every business plan, we believe that protecting data security is pivotal to success. It can also help clients simplify their data compliance requirements as organizations to fuel their AI and data-intensive workloads.

Automation for efficiency and security 

Data is central to all AI applications. The ability to access and process the necessary data yields optimal results from AI models. IBM® remains committed to working diligently with partners and clients to introduce a set of automation blueprints called deployable architectures. 

These blueprints are designed to streamline the deployment process for customers. We aim to allow organizations to effortlessly select and deploy their cloud workloads in a way that is tailor-made to align with preset, reviewable security requirements and to help to enable a seamless integration of AI and XaaS. This commitment to the fusion of AI and XaaS is further exemplified by our recent accomplishment this past year. This platform is designed to enable enterprises to effectively train, validate, fine-tune and deploy AI models while scaling workloads and building responsible data and AI workflows. 

Protecting data in multicloud environments 

Business leaders need to take note of the importance of hybrid cloud support, while acknowledging the reality that modern enterprises often require a mix of cloud and on-premises environments to support their data storage and applications. The fact is that different workloads have different needs to operate efficiently.

This means that you cannot have all your workloads in one place, whether it’s on premises, in public or private cloud or at the edge. One example is our work with CrushBank. The institution uses watsonx to streamline desk operations with AI by arming its IT staff with improved information. This has led to improved productivity and , which ultimately enhances the customer experience. A custom hybrid cloud strategy manages security, data latency and performance, so your people can get out of the business of IT and into their business. 

This all begins with building a hybrid cloud XaaS environment by increasing your data protection capabilities to support the privacy and security of application data, without the need to modify the application itself. At IBM, security and compliance is at the heart of everything we do.

We recently expanded the IBM Cloud Security and Compliance Center, a suite of modernized cloud security and compliance solutions designed to help enterprises mitigate risk and protect data across their hybrid, multicloud environments and workloads. In this XaaS era, where data is the lifeblood of digital transformation, investing in robust data protection is paramount for success. 

XaaS calls for strong data security

IBM continues to demonstrate its dedication to meeting the highest standards of security in an increasingly interconnected and data-dependent world. We can help support mission-critical workloads because our software, infrastructure and services offerings are designed to support our clients as they address their evolving security and data compliance requirements. Amidst the rise of XaaS and AI, prioritizing data security can help you protect your customers’ sensitive information. 

Source: ibm.com

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VeloxCon 2024: Innovation in data management

VeloxCon 2024, the premier developer conference that is dedicated to the Velox open-source project, brought together industry leaders, engineers, and enthusiasts to explore the latest advancements and collaborative efforts shaping the future of data management. Hosted by IBM® in partnership with Meta, VeloxCon showcased the latest innovation in Velox including project roadmap, Prestissimo (Presto-on-Velox), Gluten (Spark-on-Velox), hardware acceleration, and much more.

An overview of Velox

Velox is a unified execution engine that is built and open-sourced by Meta, aimed at accelerating data management systems and streamlining their development. One of the biggest benefits of Velox is that it consolidates and unifies data management systems so you don’t need to keep rewriting the engine. Today Velox is in various stages of integration with several data systems including Presto (Prestissimo), Spark (Gluten), PyTorch (TorchArrow), and Apache Arrow.

Velox at IBM

Presto is the engine for watsonx.data, IBM’s open data lakehouse platform. Over the last year, we’ve been working hard on advancing Velox for Presto – Prestissimo – at IBM. Presto Java workers are being replaced by a C++ process based on Velox. We now have several committers to the Prestissimo project and continue to partner closely with Meta as we work on building Presto 2.0.

Some of the key benefits of Prestissimo include:

• Hugh performance boost: query processing can be done with much smaller clusters
• No performance cliffs: no Java processes, JVM, or garbage collections, as memory arbitration improves efficiency
• Easier to build and operate at scale: Velox gives you reusable and extensible primitives across data engines (like Spark)

This year, we plan to do even more with Prestissimo including:

• The Iceberg reader
• Production readiness (metrics collection with Prometheus)
• New Velox system implementation
• TPC-DS benchmark runs

VeloxCon 2024

We worked closely with Meta to organize VeloxCon 2024, and it was a fantastic community event. We heard speakers from Meta, IBM, Pinterest, Intel, Microsoft, and others share what they’re working on and their vision for Velox over two dynamic days.

Day 1 highlights

The conference kicked off with sessions from Meta including Amit Purohit reaffirming Meta’s commitment to open source and community collaboration. Pedro Pedreira, alongside Manos Karpathiotakis and Deblina Gupta, delved into the concept of composability in data management, showcasing Velox’s versatility and its alignment with Arrow.

Amit Dutta of Meta explored Prestissimo’s batch efficiency at Meta, shedding light on the advancements made in optimizing data processing workflows. Remus Lazar, VP Data & AI Software at IBM presented Velox’s journey within IBM and vision for its future. Aditi Pandit of IBM followed with insights into Prestissimo’s integration at IBM, highlighting feature enhancements and future plans.

The afternoon sessions were equally insightful, with Jimmy Lu of Meta unveiling the latest optimizations and features in Velox. While Binwei Yang of Intel discussed the integration of Velox with the Apache Gluten project, emphasizing its global impact. Engineers from Pinterest and Microsoft shared their experiences of unlocking data query performance by using Velox and Gluten, showcasing tangible performance gains.

The day concluded with sessions from Meta on Velox’s memory management by Xiaoxuan Meng and a glimpse into the new simple aggregation function interface that was presented by Wei He.

Day 2 highlights

The second day began with a keynote from Orri Erling, co-creator of Velox. He shared insights into Velox Wave and Accelerators, showcasing its potential for acceleration. Krishna Maheshwari from NeuroBlade highlighted their collaboration with the Velox community, introducing NeuroBlade’s SPU (SQL Processing Unit) and its transformative impact on Velox’s computational speed and efficiency.

Sergei Lewis from Rivos explored the potential of offloading work to accelerators to enhance Velox’s pipeline performance. William Malpica and Amin Aramoon from Voltron Data introduced Theseus, a composable, scalable, distributed data analytics engine, using Velox as a CPU backend.

Yoav Helfman from Meta unveiled Nimble, a cutting-edge columnar file format that is designed to enhance data storage and retrieval. Pedro Pedreira and Sridhar Anumandla from Meta elaborated on Velox’s new technical governance model, emphasizing its importance in guiding the project’s development sustainability.

The day also featured sessions on Velox’s I/O optimizations by Deepak Majeti from IBM, strategies for safeguarding against Out-Of-Memory (OOM) kills by Vikram Joshi from ComputeAI, and a hands-on demo on debugging Velox applications by Deepak Majeti.

What’s next with Velox

VeloxCon 2024 was a testament to the vibrant ecosystem surrounding the Velox project, showcasing groundbreaking innovations and fostering collaboration among industry leaders and developers alike. The conference provided attendees with valuable insights, practical knowledge, and networking opportunities, solidifying Velox’s position as a leading open source project in the data management ecosystem.

Source: ibm.com

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Take advantage of AI and use it to make your business better

Artificial intelligence (AI) adoption is here. Organizations are no longer asking whether to add AI capabilities, but how they plan to use this quickly emerging technology. In fact, the use of artificial intelligence in business is developing beyond small, use-case specific applications into a paradigm that places AI at the strategic core of business operations. By offering deeper insights and eliminating repetitive tasks, workers will have more time to fulfill uniquely human roles, such as collaborating on projects, developing innovative solutions and creating better experiences.

This advancement does not come without its challenges. While 42% of companies say they are exploring AI technology, the failure rate is high; on average, 54% of AI projects make it from pilot to production. To overcome these challenges will require a shift in many of the processes and models that businesses use today: changes in IT architecture, data management and culture. Here are some of the ways organizations today are making that shift and reaping the benefits of AI in a practical and ethical way.

How companies use artificial intelligence in business

Artificial intelligence in business leverages data from across the company as well as outside sources to gain insights and develop new business processes through the development of AI models. These models aim to reduce rote work and complicated, time-consuming tasks, as well as help companies make strategic changes to the way they do business for greater efficiency, improved decision-making and better business outcomes.

A common phrase you’ll hear around AI is that artificial intelligence is only as good as the data foundation that shapes it. Therefore, a well-built AI for business program must also have a good data governance framework. It ensures the data and AI models are not only accurate, providing a higher-quality outcome, but that the data is being used in a safe and ethical way.

Why we’re all talking about AI for business

It’s hard to avoid conversations about artificial intelligence in business today. Healthcare, retail, financial services, manufacturing—whatever the industry, business leaders want to know how using data can give them a competitive advantage and help address the post-COVID challenges they face each day.

Much of the conversation has been focused on generative AI capabilities and for good reason. But while this groundbreaking AI technology has been the focus of media attention, it only tells part of the story. Diving deeper, the potential of AI systems is also challenging us to go beyond these tools and think bigger: How will the application of AI and machine learning models advance big-picture, strategic business goals?

Artificial intelligence in business is already driving organizational changes in how companies approach data analytics and cybersecurity threat detection. AI is being implemented in key workflows like talent acquisition and retention, customer service, and application modernization, especially paired with other technologies like virtual agents or chatbots.

Recent AI developments are also helping businesses automate and optimize HR recruiting and professional development, DevOps and cloud management, and biotech research and manufacturing. As these organizational changes develop, businesses will begin to switch from using AI to assist in existing business processes to one where AI is driving new process automation, reducing human error, and providing deeper insights. It’s an approach known as AI first or AI+.

Building blocks of AI first

What does building a process with an AI first approach look like? Like all systemic change, it is a step-by-step process—a ladder to AI—that lets companies create a clear business strategy and build out AI capabilities in a thoughtful, fully integrated way with three clear steps.  

Configuring data storage specifically for AI

The first step toward AI first is modernizing your data in a hybrid multicloud environment. AI capabilities require a highly elastic infrastructure to bring together various capabilities and workflows in a team platform. A hybrid multicloud environment offers this, giving you choice and flexibility across your enterprise.

Building and training foundation models

Creating foundations models starts with clean data. This includes building a process to integrate, cleanse, and catalog the full lifecycle of your AI data. Doing so allows your organization the ability to scale with trust and transparency.

Adopting a governance framework to ensure safe, ethical use

Proper data governance helps organizations build trust and transparency, strengthening bias detection and decision making When data is accessible, trustworthy and accurate, it also enables companies to better implement AI throughout the organization. 

What are foundation models and how are they changing the game for AI?

Foundation models are AI models trained with machine learning algorithms on a broad set of unlabeled data that can be used for different tasks with minimal fine-tuning. The model can apply information it’s learned about one situation to another using self-supervised learning and transfer learning. For example, ChatGPT is built upon the GPT-3.5 and GPT-4 foundation models created by OpenAI.

Well-built foundation models offer significant benefits; the use of AI can save businesses countless hours building their own models. These time-saving advantages are what’s attracting many businesses to wider adoption. IBM expects that in two years, foundation models will power about a third of AI within enterprise environments.

From a cost perspective, foundation models require significant upfront investment; however, they allow companies to save on the initial cost of model building since they are easily scaled to other uses, delivering higher ROI and faster speed to market for AI investments.

To that end, IBM is building a set of domain-specific foundation models that go beyond natural language learning models and are trained on multiple types of business data, including code, time-series data, tabular data, geospatial data, semi-structured data, and mixed-modality data such as text combined with images. The first of which, Slate, was recently released.

AI starts with data

To launch a truly effective AI program for your business, you must have clean quality datasets and an adequate data architecture for storing and accessing it. The digital transformation of your organization must be mature enough to ensure data is collected at the needed touchpoints across the organization and the data must be accessible to whoever is doing the data analysis.

Building an effective hybrid multicloud model is essential for AI to manage the massive amounts of data that must be stored, processed and analyzed. Modern data architectures often employ a data fabric architectural approach, which simplifies data access and makes self-service data consumption easier. Adopting a data fabric architecture also creates an AI-ready composable architecture that offers consistent capabilities across hybrid cloud environments.

Governance and knowing where your data come from

The importance of accuracy and the ethical use of data makes data governance an important piece in any organization’s AI strategy. This includes adopting governance tools and incorporating governance into workflows to maintain consistent standards. A data management platform also enables organizations to properly document the data used to build or fine-tune models, providing users insight into what data was used to shape outputs and regulatory oversight teams the information they need to ensure safety and privacy.

Key considerations when building an AI strategy

Companies that adopt AI first to effectively and ethically use AI to drive revenue and improve operations will have the competitive advantage over those companies that fail to fully integrate AI into their processes. As you build your AI first strategy, here are some critical considerations:

How will AI deliver business value?

The first step when integrating AI into your organization is to identify the ways various AI platforms and types of AI align with key goals. Companies should not only discuss how AI will be implemented to achieve these goals, but also the desired outcomes.

For example, data opens opportunities for more personalized customer experiences and, in turn, a competitive edge. Companies can create automated customer service workflows with customized AI models built on customer data. More authentic chatbot interactions, product recommendations, personalized content and other AI functionality have the potential to give customers more of what they want. In addition, deeper insights on market and consumer trends can help teams develop new products.

For a better customer experience—and operational efficiency—focus on how AI can optimize critical workflows and systems, such as customer service, supply chain management and cybersecurity.

How will you empower teams to make use of your data?

One of the key elements in data democratization is the concept of data as a product. Your company data is spread across on-premises data centers, mainframes, private clouds, public clouds and edge infrastructure. To successfully scale your AI efforts, you will need to successfully use your data “product.”

A hybrid cloud architecture enables you to use data from disparate sources seamlessly and scale effectively throughout the business. Once you have a grasp on all your data and where it resides, decide which data is the most critical and which offers the strongest competitive advantage.

How will you ensure AI is trustworthy?

With the rapid acceleration of AI technology, many have begun to ask questions about ethics, privacy and bias. To ensure AI solutions are accurate, fair, transparent and protect customer privacy, companies must have well-structured data management and AI lifecycle systems in place.

Regulations to protect consumers are ever expanding; In July 2023, the EU Commission proposed new standards of GDPR enforcement and a data policy that would go into effect in September. Without proper governance and transparency, companies risk reputational damage, economic loss and regulatory violations.

Examples of AI being used in the workplace

Whether using AI technology to power chatbots or write code, there are countless ways deep learning, generative AI, natural language processing and other AI tools are being deployed to optimize business operations and customer experience. Here are some examples of business applications of artificial intelligence:

Coding and application modernization

Companies are using AI for application modernization and enterprise IT operations, putting AI to work automating coding, deploying and scaling. For example, Project Wisdom lets developers using Red Hat Ansible input a coding command as a straightforward English sentence through a natural-language interface and get automatically generated code. The project is the result of an IBM initiative called AI for Code and the release of IBM Project CodeNet, the largest dataset of its kind aimed at teaching AI to code.

Customer service

AI is effective for creating personalized experiences at scale through chatbots, digital assistants and other customer interfaces. McDonald’s, the world’s largest restaurant company, is building customer care solutions with IBM Watson AI technology and natural language processing (NLP) to accelerate the development of its automated order taking (AOT) technology. Not only will this help scale the AOT tech across markets, but it will also help tackle integrations including additional languages, dialects and menu variations.

Optimizing HR operations

When IBM implemented IBM watsonx Orchestrate as part of a pilot program for IBM Consulting in North America, the company saved 12,000 hours in one quarter on manual promotion assessment tasks, reducing a process that once took 10 weeks down to five. The pilot also made it easier to gain important HR insights. Using its digital worker tool, HiRo, IBM’s HR team now has a clearer view of each employee up for promotion and can more quickly assess whether key benchmarks have been met.

The future of AI in business

AI in business holds the potential to improve a wide range of business processes and domains, especially when the organization takes an AI first approach.

In the next five years, we will likely see businesses scale AI programs more quickly by looking to areas where AI has begun to make recent advancements, such as digital labor, IT automation, security, sustainability and application modernization.

Ultimately, success with new technologies in AI will rely on the quality of data, data management architecture, emerging foundation models and good governance. With these elements—and with business-driven, practical objectives—businesses can make the most out of AI opportunities.

Source: ibm.com

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How to modernize data lakes with a data lakehouse architecture

Data Lakes have been around for well over a decade now, supporting the analytic operations of some of the largest world corporations. Some argue though that the vast majority of these deployments have now become data “swamps”. Regardless of which side of this controversy you sit in, reality is that there is still a lot of data held in these systems. Such data volumes are not easy to move, migrate or modernize.

The challenges of a monolithic data lake architecture

Data lakes are, at a high level, single repositories of data at scale. Data may be stored in its raw original form or optimized into a different format suitable for consumption by specialized engines.

In the case of Hadoop, one of the more popular data lakes, the promise of implementing such a repository using open-source software and having it all run on commodity hardware meant you could store a lot of data on these systems at a very low cost. Data could be persisted in open data formats, democratizing its consumption, as well as replicated automatically which helped you sustain high availability. The default processing framework offered the ability to recover from failures mid-flight. This was, without a question, a significant departure from traditional analytic environments, which often meant vendor-lock in and the inability to work with data at scale.

Another unexpected challenge was the introduction of Spark as a processing framework for big data. It gained rapid popularity given its support for data transformations, streaming and SQL. But it never co-existed amicably within existing data lake environments. As a result, it often led to additional dedicated compute clusters just to be able to run Spark.

Fast forward almost 15 years and reality has clearly set in on the trade-offs and compromises this technology entailed. Their fast adoption meant that customers soon lost track of what ended up in the data lake. And, just as challenging, they could not tell where the data came from, how it had been ingested nor how it had been transformed in the process. Data governance remains an unexplored frontier for this technology. Software may be open, but someone needs to learn how to use it, maintain it and support it. Relying on community support does not always yield the required turn-around times demanded by business operations. High availability via replication meant more data copies on more disks, more storage costs and more frequent failures. A highly available distributed processing framework meant giving up on performance in favor of resiliency (we are talking orders of magnitude performance degradation for interactive analytics and BI).

Why modernize your data lake?

Data lakes have proven successful where companies have been able to narrow the focus on specific usage scenarios. But what has been clear is that there is an urgent need to modernize these deployments and protect the investment in infrastructure, skills and data held in those systems.

In a search for answers, the industry looked at existing data platform technologies and their strengths. It became clear that an effective approach was to bring together the key features of traditional (legacy, if you will) warehouses or data marts with what worked best from data lakes. Several items quickly raised to the top as table stakes:

• Resilient and scalable storage that could satisfy the demand of an ever-increasing data scale.
• Open data formats that kept the data accessible by all but optimized for high performance and with a well-defined structure.
• Open (sharable) metadata that enables multiple consumption engines or frameworks.
• Ability to update data (ACID properties) and support transactional concurrency.
• Comprehensive data security and data governance (i.e. lineage, full-featured data access policy definition and enforcement including geo-dispersed)

The above has led to the advent of the data lakehouse. A data lakehouse is a data platform which merges the best aspects of data warehouses and data lakes into a unified and cohesive data management solution.

Benefits of modernizing data lakes to watsonx.data

IBM’s answer to the current analytics crossroad is watsonx.data. This is a new open data store for managing data at scale that allows companies to surround, augment and modernize their existing data lakes and data warehouses without the need to migrate. Its hybrid nature means you can run it on customer-managed infrastructure (on-premises and/or IaaS) and Cloud. It builds on a lakehouse architecture and embeds a single set of solutions (and common software stack) for all form factors.

Contrasting with competing offerings in the market, IBM’s approach builds on an open-source stack and architecture. These are not new components but well-established ones in the industry. IBM has taken care of their interoperability, co-existence and metadata exchange. Users can get started quickly—therefore dramatically reducing the cost of entry and adoption—with high level architecture and foundational concepts are familiar and intuitive:

• Open data (and table formats) over Object Store
• Data access through S3
• Presto and Spark for compute consumption (SQL, data science, transformations, and streaming)
• Open metadata sharing (via Hive and compatible constructs).

Watsonx.data offers companies a means of protecting their decades-long investment on data lakes and warehousing. It allows them to immediately expand and gradually modernize their installations focusing each component on the usage scenarios most important to them.

A key differentiator is the multi-engine strategy that allows users to leverage the right technology for the right job at the right time all via a unified data platform. Watsonx.data enables customers to implement fully dynamic tiered storage (and associated compute). This can lead, over time, to very significant data management and processing cost savings.

And if, ultimately, your objective is to modernize your existing data lakes deployments with a modern data lakehouse, watsonx.data facilitates the task by minimizing data migration and application migration via choice of compute.

What can you do next?

Over the past few years data lakes have played an important role in most enterprises’ data management strategy. If your goal is to evolve and modernize your data management strategy towards a truly hybrid analytics cloud architecture, then IBM’s new data store built on a data lakehouse architecture, watsonx.data, deserves your consideration.

Source: ibm.com

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IBM to help businesses scale AI workloads, for all data, anywhere

IBM today announced the coming launch of IBM watsonx.data, a data store built on an open lakehouse architecture, to help enterprises easily unify and govern their structured and unstructured data, wherever it resides, for high-performance AI and analytics. The solution is currently in a closed beta phase and is expected to be generally available in July 2023.

What is watsonx.data?

Watsonx.data will be core to IBM’s coming AI and Data platform, IBM watsonx, announced today at IBM Think. With watsonx, IBM will launch a centralized AI development studio that gives businesses access to proprietary IBM and open-source foundation models, watsonx.data to gather and clean their data, and a toolkit for governance of AI.

Watsonx.data will allow users to access their data through a single point of entry and run multiple fit-for-purpose query engines across IT environments. Through workload optimization an organization can reduce data warehouse costs by up to 50 percent by augmenting with this solution. It also offers built-in governance, automation and integrations with an organization’s existing databases and tools to simplify setup and user experience.

Supporting the data management life cycle

According to IDC’s Global StorageSphere, enterprise data stored in data centers will grow at a compound annual growth rate of 30% between 2021-2026. With increased data volumes comes increased data silos, operational costs, and regulatory pressures, which can lead to greater scrutiny and demand for improved business outcomes from data, analytics and AI investments.

This proliferation of data spans every industry, and organizations have an opportunity to turn it into actionable insights that can inform revenue strategies and enhance operational efficiencies.

“The media and entertainment industry has undergone a significant digital transformation, with viewers consuming content across different devices and platforms,” said Vitaly Tsivin, EVP Business Intelligence at AMC Networks. “Watsonx.data could allow us to easily access and analyze our expansive, distributed data to help extract actionable insights and maximize our resource utilization to deliver superior user experiences for viewers of AMC Networks’ curated, high-quality content.”

Notably, watsonx.data runs both on-premises and across multicloud environments. The solution will help businesses harness their increasingly siloed data and apply advanced AI and analytics to derive actionable insights, all while supporting robust data governance and observability throughout the data management life cycle.

Strong partnerships for even stronger solutions

Watsonx.data is engineered to use Intel’s built-in accelerators on Intel’s new 4th Gen Xeon Scalable Processors and open-source query engines such as Presto, the Velox acceleration library and Spark, to deliver rapid and reliable data processing for high performance SQL querying, reporting, business intelligence, and machine learning.

“We recognize the importance of watsonx.data and the development of the open-source components that it’s built upon,” said Das Kamhout, VP and Senior Principal Engineer of the Cloud and Enterprise Solutions Group at Intel. “We look forward to partnering with IBM to optimize the watsonx.data stack, achieving breakthrough performance through our joint technological contributions to the Presto open-source community.”

IBM and Intel have a long history of collaboration on data and AI products, including the optimization of IBM Db2 on Intel Xeon platforms, AI acceleration with IBM Watson NLP Library for Embed with OneAPI, and now watsonx.data.

Watsonx.data will allow users to modernize their data repositories with data warehouse-like capabilities, while benefiting from low-cost object storage and open data and table formats like Iceberg, to help them make data-driven decisions.

“Open data lakehouse architectures powered by the Apache Iceberg table format give organizations the flexibility to use fit-for-purpose analytical solutions to future-proof their data platforms for all workloads,” said Paul Codding, EVP of Product Management of Cloudera. “IBM and Cloudera customers will benefit from a truly open and interoperable hybrid data platform that fuels and accelerates the adoption of AI across an ever-increasing range of use cases and business processes.”

IBM and Cloudera have a long-standing strategic partnership that includes certified product integrations and joint sales and support models.

Wasonx.data will be available on premises and across multiple cloud providers, including IBM Cloud and Amazon Web Services (AWS). This builds on last year’s announcement of IBM expanding their relationship with AWS to offer IBM software as a service on AWS. The solution will also be available in AWS Marketplace.

“Organizations are increasingly adopting data lakehouse solutions to support their growing data needs, especially as we see an industry-wide shift toward AI solutions,” said Soo Lee, Director Worldwide Strategic Alliances at AWS. “Making watsonx.data available as a service in AWS Marketplace further supports our customers’ increasing needs around hybrid cloud – giving them greater flexibility to run their business processes wherever they are, while providing choice of a wide range of AWS services and IBM cloud native software attuned to their unique requirements.”

The coming launch of watsonx.data will extend IBM’s market leadership in data and AI, most recently demonstrated by its evaluation as a leader in The Forrester Wave: Data Management for Analytics, by integrating with existing IBM solutions like StepZen, Databand.ai, IBM Watson Knowledge Catalog, IBM zSystems, IBM Watson Studio, and IBM Cognos Analytics with Watson. These integrations can enable watsonx.data users to implement various industry-leading data catalog, lineage, governance, and observability solutions across their data ecosystems.

Beyond launch, watsonx.data is expected to undergo continuous development, incorporating the latest performance enhancements to the Presto open-source query engine via Velox and through IBM’s recent acquisition of Ahana, the only SaaS for Presto and a strong contributor to the Presto open-source community. Further development of watsonx.data will also incorporate IBM’s Storage Fusion technology to enhance data caching across remote sources as well as semantic automation capabilities built on IBM Research’s foundation models to automate data discovery, exploration, and enrichment through conversational user experiences.

Source: ibm.com

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Why companies need to accelerate data warehousing solution modernization

Unexpected situations like the COVID-19 pandemic and the ongoing macroeconomic atmosphere are wake-up calls for companies worldwide to exponentially accelerate digital transformation. During the pandemic, when lockdowns and social-distancing restrictions transformed business operations, it quickly became apparent that digital innovation was vital to the survival of any organization.

The dependence on remote internet access for business, personal, and educational use elevated the data demand and boosted global data consumption. Additionally, the increase in online transactions and web traffic generated mountains of data. Enter the modernization of data warehousing solutions.

Companies realized that their legacy or enterprise data warehousing solutions could not manage the huge workload. Innovative organizations sought modern solutions to manage larger data capacities and attain secure storage solutions, helping them meet consumer demands. One of these advances included the accelerated adoption of modernized data warehousing technologies. Business success and the ability to remain competitive depended on it.

Why data warehousing is critical to a company’s success

Data warehousing is the secure electronic information storage by a company or organization. It creates a trove of historical data that can be retrieved, analyzed, and reported to provide insight or predictive analysis into an organization’s performance and operations.

Data warehousing solutions drive business efficiency, build future analysis and predictions, enhance productivity, and improve business success. These solutions categorize and convert data into readable dashboards that anyone in a company can analyze. Data is reported from one central repository, enabling management to draw more meaningful business insights and make faster, better decisions.

By running reports on historical data, a data warehouse can clarify what systems and processes are working and what methods need improvement. Data warehouse is the base architecture for artificial intelligence and machine learning (AI/ML) solutions as well.

Benefits of new data warehousing technology

Everything is data, regardless of whether it’s structured, semi-structured, or unstructured. Most of the enterprise or legacy data warehousing will support only structured data through relational database management system (RDBMS) databases. Companies require additional resources and people to process enterprise data. It is nearly impossible to achieve business efficiency and agility with legacy tools that create inefficiency and elevate costs.

Managing, storing, and processing data is critical to business efficiency and success. Modern data warehousing technology can handle all data forms. Significant developments in big data, cloud computing, and advanced analytics created the demand for the modern data warehouse.

Today’s data warehouses are different from antiquated single-stack warehouses. Instead of focusing primarily on data processing, as legacy or enterprise data warehouses did, the modern version is designed to store tremendous amounts of data from multiple sources in various formats and produce analysis to drive business decisions.

Data warehousing solutions

A superior solution for companies is the integration of existing on-premises data warehousing with data lakehouse solutions using data fabric and data mesh technology. Doing so creates a modern data warehousing solution for the long term.

A data lakehouse contains an organization’s data in a unstructured, structured, semi-structured form, which can be stored indefinitely for immediate or future use. This data is used by data scientists and engineers who study data to gain business insights. Data lake or data lakehouse storage costs are less expensive than a enterprise data warehouse. Further, data lakes and data lakehouse are less time-consuming to manage, which reduces operational costs. IBM has a next-generation data lakehouse solution to achieve these business situations.

Data fabric is the next-generation data analytics platform that solves advanced data security challenges through decentralized ownership. Typically, organizations have multiple data sources from different business lines that must be integrated for analytics. A data fabric architecture effectively unites disparate data sources and links them through centrally managed data sharing and governance guidelines.

Many enterprises seek a flexible, hybrid, and multi-cloud solution based on cloud providers. The data mesh solution pushes down the structured query language (SQL) queries to the related RDBMS or data lakehouse by managing the data catalog, giving users virtualized tables and data. In data mesh principles, it never stores business data locally, which is an advantage for a business. A successful data mesh solution will reduce a company’s capital and operational expenses.

IBM Cloud Pak for Data is an excellent example of a data fabric and data mesh solution for analytics. Cloud technology has emerged as the preferred platform for artificial intelligence (AI) capabilities, intelligent edge services, and advanced wireless connectivity and etc. Many companies will leverage a hybrid, multi-cloud strategy to improve business performance and success and thrive in the business world. 

Best practices for adopting data warehousing technology

Data warehouse modernization includes extending the infrastructure without compromising security. This allows companies to reap the advantages of new technologies, inducing speed and agility in data processes, meeting changing business requirements, and staying relevant in this age of big data. The growing variety and volume of current data make it essential for businesses to modernize their data warehouses to remain competitive in today’s market. Businesses need valuable insights and reports in real-time and enterprise or legacy data warehouses cannot keep pace with modern data demands.

Data warehouses are at an exciting point of evolution. With the global data warehousing market size estimated to grow at a compound grow over 250% in next 5 years, companies will rely on new data warehouse solutions and tools that make them easier to use than ever before.

Cutting-edge technology to keep up with constant changes

AI and other breakthrough technologies will propel organizations into the next decade. Data consumption and load will continue to grow and provoke companies to discover new ways to implement state-of-the-art data warehousing solutions. The prevalence of digital technologies and connected devices will help organizations remain afloat, an unimaginable feat 20 years ago.

Essential lessons arise from an organization’s efforts to optimize its enterprise or legacy data warehousing technology. One vital lesson is the importance of making specific changes to modernize technology, processes, and organizational operations to evolve. As the rate of change will only continue to increase, this knowledge—and the capability to accelerate modernization—will be critical going forward.

No matter where you are at data warehouse modernization today, IBM experts are here to help modernize the right approach to fit your needs. It’s time to get started with your data warehouse modernization journey.

Source: ibm.com

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Challenges of today’s monolithic data architecture

The real-world challenges organizations are facing with big data today are multi-faceted. Every IT organization knows there is more raw data than ever before; data sources are in multiple locations, in varying forms and of questionable data quality. To add complexity, the business users of, and use cases for, data have become even more varied. The new data being used for decision support and business intelligence might also be used for developing machine learning models. In addition, semi-structured data, such as JSON and IoT log files, might need to be mixed with transactional data to get a complete picture of a customer buying experience, while emails and social media content might need to be interpreted to understand customer sentiment (i.e., emotion) to enrich operational decisions, machine learning (ML) models or decision-support applications. Choosing the right technology for your organization can help solve many of these challenges. 

Addressing these data integration issues might appear to be relatively easy: land all enterprise data in a centralized data lake and process it from start to finish. But that is a bit too simplistic because simultaneously, real-time data needs to be processed for decision-support access and often the curated data inputs reside in a data warehouse repository. And keeping data copies synchronized between the physical platforms supporting Hadoop based data lakes and data warehouses and data marts can be challenging.

Warehouses are known for the high-performance processing of terabytes of structured data for business intelligence but can quickly become expensive for new, evolving workloads. And when it comes to price-performance, the reality is that organizations are running data engineering data pipelines and data science machine learning model building workflows in data warehouses that are not necessarily optimized for scalability or to run these challenging workloads – impacting pipeline performance and driving up costs. It is this complex set of different data relationship dependencies requiring continuous data movement of interdependent data sets across platforms that makes these data challenges so complex to solve.

Rethinking data analytics architecture

Software architects at vendors understand these challenges, and several companies have tried to address the challenges in their own way. New workload requirements led to new functionality in software platforms that were not specifically optimized for these workloads, reducing their efficiency and worsening data silos within many organizations. Additionally, each platform must have overlapping copies of data, implying issues with data management (data governance, privacy, and security) and higher costs for data storage.

For these reasons, the challenges of the traditional data warehouse and data lake architecture have led businesses to operate complex architectures, with data siloed and copied across data warehouses, data marts, data lakes, and other relational databases throughout the organization. Given the prohibitive costs of high-performance on-premises and cloud data warehouses, and performance challenges within legacy data lakes, neither of these repositories satisfy the need for analytical flexibility and price-performance.

Instead of having each new technology solve the same problem, what is needed is a fresh, new architectural style.

Fortunately, the IT landscape is changing due to a mix of cloud computing platforms, open source, and traditional software vendors. Cloud vendors, leading with object storage, have helped to drive down the cost of disk storage. But data stored in object storage cannot readily be updated and object storage does not offer the type of query performance to which business users have become accustomed. Open-source technology such as Apache Iceberg combined with open-source engines such as Presto and Apache Spark are providing the advantage of object storage along with the business capabilities of better SQL performance and the ability to update large structured and semi-structured data in place. But there is still a gap to be filled that allows all these technologies to work together as a coordinated, integrated platform.

To truly solve these challenges, query, and reporting, provided by engines such as Presto, needs to work along with the Spark infrastructure framework to support advanced analytics and complex data transformations. And Presto and Spark need to readily work with existing and modern data warehouse infrastructures.

The industry is waiting for a breakthrough approach that allows organizations to optimize their analytics ecosystem by selecting the right engine for the right workload at the right cost — without having to copy data to multiple platforms and while taking advantage of integrated metadata. Whichever vendor gets there first will allow organizations to reduce cost and complexity and drive the greatest return on investment from their analytics workloads while also helping to deliver better governance and data security.

Source: ibm.com

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Data architecture strategy for data quality

Poor data quality is one of the top barriers faced by organizations aspiring to be more data-driven. Ill-timed business decisions and misinformed business processes, missed revenue opportunities, failed business initiatives and complex data systems can all stem from data quality issues. Just one of these problems can prove costly to an organization. Having to deal with all of them can be devastating.

Several factors determine the quality of your enterprise data like accuracy, completeness, consistency, to name a few. But there’s another factor of data quality that doesn’t get the recognition it deserves: your data architecture.

How the right data architecture improves data quality

The right data architecture can help your organization improve data quality because it provides the framework that determines how data is collected, transported, stored, secured, used and shared for business intelligence and data science use cases.

The first generation of data architectures represented by enterprise data warehouse and business intelligence platforms were characterized by thousands of ETL jobs, tables, and reports that only a small group of specialized data engineers understood, resulting in an under-realized positive impact on the business. Next generation of big data platforms and long running batch jobs operated by a central team of data engineers have often led to data lake swamps.

Both approaches were typically monolithic and centralized architectures organized around mechanical functions of data ingestion, processing, cleansing, aggregation, and serving. This created number of organizational and technological bottlenecks prohibiting data integration and scale along several dimensions: constant change of data landscape, proliferation of data sources and data consumers, diversity of transformation and data processing that use cases require, and speed of response to change.

What does a modern data architecture do for your business?

A modern data architecture like Data Mesh and Data Fabric aims to easily connect new data sources and accelerate development of use case specific data pipelines across on-premises, hybrid and multicloud environments. Combined with effective data lifecycle management, which evolves into data as product management, a modern data architecture can enable your organization to:

 

◉ Allow data stewards to ensure data compliance, protection and security

◉ Enhance trust in data by getting visibility into where data came from, how it has changed, and who is using it

◉ Monitor and identify data quality issues closer to the source to mitigate the potential impact on downstream processes or workloads

◉ Efficiently adopt data platforms and new technologies for effective data management

◉ Apply metadata to contextualize existing and new data to make it searchable and discoverable

◉ Perform data profiling (the process of examining, analyzing and creating summaries of datasets)

◉ Reduce data duplication and fragmentation

Because your data architecture dictates how your data assets and data management resources are structured, it plays a critical role in how effective your organization is at performing these tasks. Meaning, data architecture is a foundational element of your business strategy for higher data quality. Critical capabilities of modern high-quality data quality management solutions require an organization to:

◉ Enforce data governance across an organization by augmenting manual data quality processes with metadata and AI-related technologies

◉ Perform data quality monitoring based on pre-configured rules

◉ Build data modeling lineage to perform root cause analysis of data quality issues

◉ Make a dataset’s value immediately understandable

◉ Practice proper data hygiene across interfaces

How to build a data architecture that improves data quality

A data strategy can help data architects create and implement a data architecture that improves data quality. Steps for developing an effective data strategy include:

1. Outlining business objectives you want your data to help you accomplish

For example, a financial institution may look to improve regulatory compliance, lower costs, and increase revenues. Stakeholders can identify business use cases for certain data types, such as running data analytics on real-time data as it’s ingested to automate decision-making to drive cost reduction.

2. Taking an inventory of existing data assets and mapping current data flows

This step includes identifying and cataloging all data throughout the organization into a centralized or federated inventory list, thereby removing data silos. The list should detail where each dataset resides and what applications and use cases rely on it. Next, select the data needed for your key use cases and prioritize those data domains that included it.

3. Developing a standardized nomenclature

A naming convention and aligned data format (data classes) for data used throughout the organization helps to ensure data consistency and interoperability across departments (domains) and use cases.

4. Determining what changes must be made to the existing architecture

Decide on the changes that will optimize your data for achieving your business objectives. Researching the different types of modern data architectures, such as a data fabric and data mesh can help you decide on the data structure most suitable to your business requirements.

5. Deciding on KPIs to gauge a data architecture’s effectiveness

Create KPIs and use advanced analytics that link the measure of your architecture’s success to how well it supports data quality.

6. Creating a data architecture roadmap

Companies can develop a rollout plan for implementing data architecture and governance in three to four data domains per quarter.

Data architecture and IBM

A well-designed data architecture creates a foundation for data quality through transparency and standardization that frames how your organization views, uses and talks about data.

As previously mentioned, a data fabric is one such architecture. A data fabric automates data discovery, governance and data quality management and simplifies self-service data access to data distributed across a hybrid cloud landscape. It can encompass the applications that generate and use data, as well as any number of data storage repositories such as data warehouses, data lakes (which store vast amounts of big data), NoSQL databases (which store unstructured data) and relational databases that utilize SQL.

Source: ibm.com

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