How a solid generative AI strategy can improve telecom network operations

Generative AI (gen AI) has transformed industries with applications such as document-based Q&A with reasoning, customer service chatbots and summarization tasks. These use cases have demonstrated the impressive capabilities of large language models (LLMs) in understanding and generating human-like responses, particularly in fields requiring nuanced language understanding and inferencing.

However, in the realm of telecom network operations, the data is different. The observability data comes from proprietary sources and encompasses a wide variety of formats, including alarms, performance metrics, probes and ticketing systems capturing incidents, defects and changes. This data, whether structured or unstructured, is deeply embedded in a domain-specific language. This includes terms and concepts from technologies like 5G, IP-MPLS and other network protocols.

A notable challenge arises from the fact that standard foundational LLMs are not typically trained on this highly specialized and technical data. This needs a careful strategy for integrating gen AI into the telecom operations domain, where operational efficiencies and accuracy are paramount.

Successfully using gen AI for network operations requires tailoring the models to this niche context while addressing unique challenges around data specificity and system integration.

How generative AI addresses network operations challenges

The complexity and diversity of network data, along with rapidly changing technologies, presents several challenges for network operations. Gen AI offers efficient solutions where traditional methods are costly or impractical.

• Time-consuming processes: Switching between multiple systems (such as alarms, performance or traces) delays problem resolution. Generative AI centralizes data into one interface providing natural language experience, speeding up issue resolution by reducing system toggling.
• Data fragmentation: Scattered data across platforms prevents a cohesive view of issues. Generative AI consolidates data from various sources based on the training. It can correlate and present data in a unified view, enhancing issue comprehension.
• Complex interfaces: Engineers spend extra time adapting to various system interfaces (such as UIs, scripts and reports). Generative AI provides a natural language interface, simplifying navigation across complex systems.
• Human error: Manual data consolidation leads to misdiagnoses due to data fragmentation challenges. AI-driven data analysis reduces errors, helping ensure accurate diagnosis and resolution.
• Inconsistent data formats: Varying data formats make analysis difficult. Gen AI model training can provide standardized data output, improving correlation and troubleshooting.

Challenges in applying generative AI in network operations

While gen AI offers transformative potential in network operations, several challenges must be addressed to help ensure effective implementation:

• Relevance and contextual precision: General-purpose language models perform well in nontechnical contexts, but in network-specific use cases, models need to be fine-tuned with domain-specific terminology to deliver relevant and precise results.
• AI guardrails and hallucinations: In network operations, outputs must be grounded in technical accuracy, not just linguistic sense. Strong AI guardrails are essential to prevent incorrect or misleading results.
• Chain-of-thought (CoT) loops: Network use cases often involve multistep reasoning across multiple data sources. Without proper control, AI agents can enter endless loops, leading to inefficiencies due to incomplete or misunderstood data.
• Explainability and transparency: In critical network operations, engineers must understand how AI-derived decisions are made. AI systems must provide clear and transparent reasoning to build trust and help ensure effective troubleshooting, avoiding “black box” situations.
• Continuous model enhancements: Constant feedback from technical experts is crucial for model improvement. This feedback loop should be integrated into model training to keep pace with the evolving network environment.

Implementing a workable strategy to maximize business benefits

Key design principles can help ensure the successful implementation of gen AI in network operations. These include:  

• Multilayer agent architecture: A supervisor/worker model offers modularity, making it easier to integrate legacy network interfaces while supporting scalability.
• Intelligent data retrieval: Using Reflective Retrieval-Augmented Generation (RAG) with hallucination safeguards helps ensure reliable, relevant data processing.
• Directed chain of thought: This pattern helps guide AI reasoning to deliver predictable outcomes and avoid deadlocks in decision-making.
• Transactional-level traceability: Every AI decision should be auditable, ensuring accountability and transparency at a granular level.
• Standardized tooling: Seamless integration with various enterprise data sources is crucial for broad network compatibility.
• Exit prompt tuning: Continuous model improvement is enabled through prompt tuning, ensuring that it adapts and evolves based on operational feedback.

Implementing a gen AI strategy in network operations can lead to significant performance improvements, including:

• Faster mean time to repair (MTTR): Achieve a 30-40% reduction in MTTR, resulting in enhanced network uptime.
• Reduced average handle time (AHT): Decrease the time network operations center (NOC) technicians expenditure addressing field technician queries by 30-40%.
• Lower escalation rates: Reduce the percentage of tickets escalated to L3/L4 by 20-30%.

Beyond these KPIs, gen AI can enhance the overall quality and efficiency of network operations, benefiting both staff and processes.

IBM Consulting, as part of its telecommunications solution offerings, provides reference implementation of the above strategy, helping our clients in applying gen AI-based solutions successfully in their network operations.

Source: ibm.com

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New IBM study: How business leaders can harness the power of gen AI to drive sustainable IT transformation

As organizations strive to balance productivity, innovation and environmental responsibility, the need for sustainable IT practices is even more pressing. A new global study from the IBM Institute for Business Value reveals that emerging technologies, particularly generative AI, can play a pivotal role in advancing sustainable IT initiatives. However, successful transformation of IT systems demands a strategic and enterprise-wide approach to sustainability.

The power of generative AI in sustainable IT

Generative AI is creating new opportunities to transform IT operations and make them more sustainable. Teams can use this technology to quickly translate code into more energy-efficient languages, develop more sustainable algorithms and software and analyze code performance to optimize energy consumption. 27% of organizations surveyed are already applying generative AI in their sustainable IT initiatives, and 63% of respondents plan to follow suit by the end of 2024. By 2027, 89% are expecting to be using generative AI in their efforts to reduce the environmental impact of IT.

Despite the growing interest in using generative AI for sustainability initiatives, leaders must first consider its broader implications, particularly energy consumption.

64% say they are using generative AI and large language models, yet only one-third of those report having made significant progress in addressing its environmental impact. To bridge this gap, executives must take a thoughtful and intentional approach to generative AI, asking questions like, “What do we need to achieve?” and “What is the smallest model that we can use to get there?”

A holistic approach to sustainability

To have a lasting impact, sustainability must be woven into the very fabric of an organization, breaking free from traditional silos and incorporating it into every aspect of operations. Leading organizations are already embracing this approach, integrating sustainable practices across their entire operations, from data centers to supply chains, to networks and products. This enables operational efficiency by optimizing resource allocation and utilization, maximizing output and minimizing waste.

The results are telling: 98% of surveyed organizations that take a holistic, enterprise-wide approach to sustainable IT report seeing benefits in operational efficiency—compared to 50% that do not. The leading organizations also attribute greater reductions in energy usage and costs to their efforts. Moreover, they report impressive environmental benefits, with two times greater reduction in their IT carbon footprint.

Hybrid cloud and automation: key enablers of sustainable IT

Many organizations are turning to hybrid cloud and automation technologies to help reduce their environmental footprint and improve business performance. By providing visibility into data, workloads and applications across multiple clouds and systems, a hybrid cloud platform enables leaders to make data-driven decisions. This allows them to determine where to run their workloads, thereby reducing energy consumption and minimizing their environmental impact.

In fact, one quarter (25%) of surveyed organizations are already using hybrid cloud solutions to boost their sustainability and energy efficiency. Nearly half (46%) of those report a substantial positive impact on their overall IT sustainability. Automation is also playing a key role in this shift. With 83% of leading organizations harnessing its power to dynamically adjust IT environments based on demand.

Sustainable IT strategies for a better tomorrow

The future of innovation is inextricably linked to a deep commitment to sustainability. As business leaders harness the power of technology to drive impact, responsible decision-making is crucial, particularly in the face of emerging technologies such as generative AI. To better navigate this intersection of IT and sustainability, here are a few actions to consider: 

1. Actively manage the energy consumption associated with AI: Optimize the value of generative AI while minimizing its environmental footprint by actively managing energy consumption from development to deployment. For example, choose AI models that are designed for speed and energy efficiency to process information more effectively while reducing the computational power required.

2. Identify your environmental impact drivers: Understand how different elements of your IT estate influence environmental impacts and how this can change as you scale new IT efforts.

3. Embrace sustainable-by-design principles: Embed sustainability assessments into the design and planning stages of every IT project, by using a hybrid cloud platform to centralize control and gain better visibility across your entire IT estate.

Source: ibm.com

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Using AI to conserve the endangered African forest elephant

In the Congo Basin, the second-largest rainforest in the world, the African forest elephant population has been in drastic decline for decades. This decline is the result of habitat loss caused by deforestation and climate change, along with rampant poaching.

We can observe the beneficial environmental effects of these species starting to disappear. As a keystone species in the habitat, the dwindling presence of the elephants has major implications you might not imagine. African forest elephants have been shown to increase carbon storage in their habitats. They are “ecosystem engineers” according to the World Wide Fund for Nature, clearing out lesser vegetation and making room for stronger, more resilient, flora to thrive.

While we know these changes will occur as the elephant population shrinks, actually seeing it happen presents challenges. The World Wide Fund for Nature-Germany aims to track and identify individual elephants to count them. With help from IBM, the WWF will be able to use a system of camera traps connected to software that enables automatic tracking as opposed to manual tracking.

Augmenting our vision with tech

That is where computer vision can serve as a fresh set of eyes. IBM announced earlier this year that it would team with WWF to pair camera traps with IBM Maximo® Visual Inspection (MVI) to help monitor and track individual elephants as they pass by the camera traps.

“MVI’s AI-powered visual inspection and modeling capabilities allow for head- and tusk-related image recognition of individual elephants similar to the way we identify humans via fingerprints,” explained Kendra DeKeyrel, Vice President ESG and Asset Management Product Leader at IBM. 

These capabilities allow for not only counting or spotting the individual elephants, but also tracking some of their behaviors to better understand their movement patterns and impact in the ecosystem. MVI particularly offers help in automating the process of identifying these elephants instead of having staff manually look at the images. Additionally, the AI’s advanced visual recognition capabilities can pull the identity of an elephant from an image that is blurry or incomplete.

“Counting African forest elephants is both difficult and costly,” Dr. Thomas Breuer, WWF’s African Forest Elephant Coordinator, said. “The logistics are complex and the resulting population numbers are not precise. Being able to identify individual elephants from camera trap images with the help of AI has the potential to be a game-changer.”

Strengthening our connection to the natural world

As more about the movement and migration of the African forest elephant is gleaned, more additional information can be pulled from our increased understanding of how the species is behaving and interacting with its environment. “IBM is exploring how to leverage IBM Environmental Intelligence above ground biomass estimates to better predict elephants’ future locations and migration patterns, as well as their impact on a specific forest,” DeKeyrel said.

That includes determining how much the African forest elephants can help with mitigating climate change. It’s understood that the presence of elephants helps to increase the carbon storage capacity of the forest. “African forest elephants play a crucial role in influencing the shape of the forest structure, including helping increase the diversity, density, and abundance of plant and tree species,” Oday Abbosh, IBM Global Sustainability Services Leader, explained. It’s estimated that one forest elephant can increase the net carbon capture capacity of the forest by almost 250 acres, the equivalent of removing a full year’s worth of emissions from 2,047 cars from the atmosphere.

Having a more accurate image of the elephant population allows for performance-based conservation payments, such as wildlife credits. In the future, this could help enable organizations to better assess the financial value of nature’s contributions to people (NCP) provided by African forest elephants, such as carbon sequestration services.

We know the animal kingdom is constantly shaping the planet, and being affected by our own activity even when we can’t see it. Due to continuing breakthroughs in technology, we’re increasingly getting a clearer picture of the world of wildlife that was previously difficult to capture. When we can see it, we can react to it, helping to protect species that need help and strengthening our connection to the natural world.

“Our collaboration with WWF marks a significant step forward in this effort,” Abbosh said, “By combining our expertise in technology and sustainability with WWF’s conservation expertise, we aim to leverage the power of technology to create a more sustainable future.”

Source: ibm.com

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Internet of Animals: A look at the new tech getting animals online

All living things on Earth are connected, in that we all affect one another, directly and indirectly. But more often than not, we don’t see or know what is happening in the lives of animals. Deep in the jungles and forests, far off in the deserts and prairies, many species of animals are seeing their behavior change as the planet warms in ways we can’t see.

Thanks to technological achievements in recent years, we are starting to have a clearer look into these environments that have previously been obscured from our view. Modern breakthroughs have made tracking tools less invasive, easier to manage, and have created the conditions for better seeing and understanding of wildlife, including how they move and behave.

A team of researchers has tapped these innovations to create a global network of animals, tracking the movement of thousands of creatures in a way that reveals never-before-seen activity. Through this data, we’re gaining a new understanding of animal migration, what is causing it, and how different species are adapting to climate change and rapid changes to their ecosystems.

Getting animals online

In 2001—before the Internet of Things was much more than a sci-fi-like fantasy, before even half of the United States was regularly online—professor of ecology and evolutionary biology Martin Wikelski had an idea for a global network of sensors that could provide never-before accessible insight into the activities of animals who live well outside of the human-dominated parts of the planet.

The “Internet of Animals” known as ICARUS (International Cooperation for Animal Research Using Space) went from idea to reality in 2018 when, after nearly two decades of laying groundwork, a receiver was launched to the International Space Station and embedded on the Russian portion of the orbiting science laboratory, where it functioned as a central satellite-style receiver, collecting data from more than 3,500 animals that had been tagged with tiny trackers.

According to Uschi Müller, ICARUS Project Coordinator and member of the Department of Migration Team at the Max Planck Institute of Animal Behavior in Germany, the ICARUS receiver collected the data from the trackers and sent it to a ground station, where the information was then uploaded to Movebank, an open source database that hosts animal sensor data for researchers and wildlife managers to freely access.

The original version of ICARUS was groundbreaking but limited. “The ISS only covers an area up to 55 degrees North and 55 degrees South within its flight path,” explained Müller. Mechanical issues on ISS knocked the network offline in 2020, and Russia’s invasion of Ukraine in 2022 brought the tracking activity to a grinding halt.

Expanding the vision

“The dependence on a single ICARUS payload…demonstrated the vulnerability of the former infrastructure,” Müller said. Animals continued to carry the trackers, a burden that was no longer producing benefits for potentially understanding and protecting them. And the sudden absence in the database that counts on regular updates carried the potential for harmful consequences to scientific research. 

While it’s hard to say getting plunged back into darkness is ever a benefit to those who value data and information, the event was illuminating on its own. It sent the ICARUS team back to the drawing board, which also allowed them to build a system that wouldn’t just get them back online but would offer fail-safes that could mitigate risks of future outages.

“What was initially a shock for all the scientists involved very quickly turned into a euphoric ‘Plan B’ and the development of a new, much more powerful and much cheaper CubeSat system, flanked by a terrestrial observation system,” Müller said. 

The space segment of the new system will include multiple payloads, the first of which will be launched in 2025 in partnership with the University of the Federal Armed Forces in Munich. It will be the first five planned launches, which will send CubeSat satellites, nanosatellites that will hang in polar orbit and provide coverage across the entire planet rather than a limited range. 

They will work in collaboration with a terrestrial “Internet of Things” style network that will be able to generate real-time data from the ground. The result, according to Müller, will be “tagged animals can be observed much more frequently, more reliably and in every part of the world.”

These receivers will be picking up data from upgraded tags, which the ICARUS team has been working tirelessly to shrink down to a size that minimizes invasiveness for the animal. The tags that will be used for the latest version of the ICARUS system will weigh just 0.95 grams, but according to Müller, their transmitters have gotten incredibly small in recent years. 

“Thanks to the continuous technical development of animal transmitters, which now weigh just as little as 0.08 grams and are extremely powerful, even insects such as butterflies and bees as well as the smallest bats can be tagged for the first time,” she said.

Once the new ICARUS system is online, Müller and the team expect to see the clouded vision of the animal kingdom continue to clear up. “The migration routes and the behavior and interactions of animals about which almost nothing is known to date can be researched,” she said of the project. “We continue to expect great interest in the scientific world to use this system and to continuously develop and optimize it.”

Source: ibm.com

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IBM Planning Analytics: The scalable solution for enterprise growth

Companies need powerful tools to handle complex financial planning. At IBM, we’ve developed Planning Analytics, a revolutionary solution that transforms how organizations approach planning and analytics. With robust features and unparalleled scalability, IBM Planning Analytics is the preferred choice for businesses worldwide.

We’ll explore the aspects of IBM Planning Analytics that set it apart in the enterprise performance management landscape. We delve into its architecture, scalability and core technology, highlighting its data handling capabilities and modeling flexibility.

We’ll also showcase its analytics functions and integration possibilities. By the end, you’ll understand why IBM Planning Analytics is the superior choice for your enterprise planning needs.

Platform architecture and scalability

IBM Planning Analytics Architecture

IBM Planning Analytics features a robust and adaptable architecture, powered by a cutting-edge in-memory online analytical processing (OLAP) engine that provides rapid, scalable analytics. The system employs a distributed, multitier architecture centered on the IBM TM1 engine server, enabling seamless integration and connectivity across platforms and clients.

A key strength of IBM Planning Analytics is its multitier architecture, which includes a server component that houses the in-memory OLAP engine, advanced planning and analytics functions, and an intuitive web-based user interface.

Scalability without limits

Planning Analytics offers unmatched scalability, a standout feature in the enterprise planning world. Powered by TM1, a highly efficient in-memory engine, the system easily handles massive data volumes. What’s impressive is the absence of practical restrictions on model size or complexity.

The solution is designed to manage enormous memory capacity, enabling you to build large and complex data models while maintaining smooth performance and usability. Many customers use models with hundreds of thousands or even millions of data points. We’ve seen data models exceed 5 TB in size, and IBM Planning Analytics still delivers excellent performance.

Scalability means that IBM Planning Analytics can grow with your business and adapt to evolving requirements, supporting even the most complex business applications.

Performance that keeps pace with your business

At IBM, we understand that performance is key. IBM Planning Analytics is built for speed, delivering fast results even with enormous data sets and complex calculations. Its in-memory processing helps to ensure that data is ready for quick analysis and reporting, enabling real-time what-if scenarios and reports without lag.

Our solution handles massive multidimensional cubes seamlessly, enabling you to maintain a complete view of your data without sacrificing performance or data integrity. This combination of unlimited scalability and high performance means that your business can expand without outgrowing your planning solution. With IBM Planning Analytics, you’re not just planning for today, you’re future-proofing for tomorrow.

Performance benchmarks

Our in-memory TM1 engine rapidly analyzes big data, delivering real-time insights and AI-powered forecasting for faster, more accurate planning. Here’s how it has made a difference for our clients:

• Solar Coca-Cola: Simulates the impact of stock keeping unit (SKU) price changes on margins and profits in real time, eliminating the need for manual spreadsheets.
• Mawgif: Manages and analyzes data in real time, optimizing revenue and efficiency.
• Novolex: Reduced its 6-week forecasting process by 83%, bringing it down to less than a week.

These benchmarks highlight the power and efficiency of IBM Planning Analytics in transforming complex planning and analytics processes across industries.

Data handling and performance

IBM Planning Analytics Data Handling

IBM Planning Analytics excels in data handling. Built on our powerful TM1 analytics engine, this enterprise performance management tool transcends the limits of manual planning. We store data in in-memory multidimensional OLAP cubes, providing lightning-fast access and processing capabilities.

One of the standout features of IBM Planning Analytics is its ability to handle massive data volumes. With a theoretical limit of 16 million terabytes of memory, our system can create and manage large and complex data models while maintaining excellent performance.

Performance benchmarks

IBM Planning Analytics excels in handling large data volumes, complex calculations and multiple concurrent users, helping to ensure fast and efficient processing as data needs grow. Our TM1 in-memory database rapidly analyzes big data, providing real-time insights for accurate planning across financial planning and analysis (FP&A), sales and supply chain functions.

Data updates are processed instantly, reflecting changes in real time and handling millions of rows per second, so decision-makers have up-to-date information. With no practical limits on cube size or dimensionality, Planning Analytics supports even the most complex models.

Our clients work with massive data sets, including 51 quintillion intersections and environments exceeding 5 TB, all while maintaining seamless performance.

Modeling flexibility and customization

IBM Planning Analytics Modeling

When it comes to modeling flexibility, IBM Planning Analytics stands out. Our solution offers unmatched freedom in design and configuration, supporting any combination of configurations to align with your specific process requirements. There are no practical limitations on the number of dimensions, elements, hierarchies, real-time calculations or defined processes you can implement.

This flexibility enables us to build fully customized solutions tailored to your needs. You start with a blank slate, empowering you to design your entire solution from scratch. While this might seem daunting at first, it enables you to start small and expand your application step by step, helping to ensure that it aligns perfectly with your business processes.

Our approach to modeling is designed to give you complete control over your planning and analytics environment. Whether you’re dealing with simple forecasts or complex, multidimensional models, IBM Planning Analytics provides the tools and flexibility you need to create a solution that works for you.

IBM Planning Analytics combines the best elements of spreadsheets, databases and OLAP cubes, offering unparalleled flexibility, scale and analytical capabilities. Our solution is built to support enterprise-wide integrated planning at scale, addressing the needs of businesses of all sizes.

A key strength of IBM Planning Analytics is its intuitive interface. We’ve simplified users and developers from technical tasks by implementing intuitive configuration options and tools. This creates a system that’s simple to use for both development and maintenance. The work is largely configuration-based, using predefined menus and options, with many rules and calculations created using a graphical user interface.

Customization capabilities

When it comes to customization, IBM Planning Analytics offers unmatched flexibility. Our solution is free of constraints, enabling you to build solutions that adapt to any process or requirement. This level of customization is beneficial for businesses with complex and unique needs. Our modeling flexibility is a key differentiator, providing the tools needed to create solutions tailored to your business processes.

Integration and data connectivity

IBM Planning Analytics Integrations

At IBM, we’ve helped to ensure that IBM Planning Analytics excels when it comes to integration capabilities. We offer embedded tools that make integration seamless for any combination of cloud and on-premises environments.

IBM Planning Analytics provides several integration options:

1. ODBC connection using TM1 Turbo Integrator: This powerful utility enables users to automate data import, manage metadata and perform administrative tasks.
2. Push-pull using flat files: Turbo Integrator supports reading and writing flat files, which is useful for pushing data from TM1 to a relational database.
3. Using the REST API: This increasingly popular option opens up possibilities for a single tool to manage data push-pull operations.
4. Microsoft Office 365 integration: Seamless integration fosters effortless collaboration.
5. ERP system connectivity: Our solution connects with major enterprise resource planning (ERP) systems such as SAP, Oracle and Microsoft Dynamics, helping to ensure smooth financial and operational data flow.
6. Customer relationship management (CRM) integration: Integrations with systems such as Salesforce provide access to crucial sales and customer data.
7. Data warehouses and business intelligence (BI) tools: Our solution interfaces with data warehouses and BI tools, enabling advanced analytics and comprehensive reporting.

Connectivity options

IBM Planning Analytics stands out with its flexible deployment options, offering both cloud and on-premises capabilities to cater to diverse customer needs. Our solution integrates seamlessly with IBM® Cognos® Analytics for advanced reporting and dashboarding, and it connects with various databases and ERP systems, creating a unified planning ecosystem.

Our open application programming interface (API) and extensive integration capabilities enable organizations to connect IBM Planning Analytics with their existing technology stack, creating a cohesive and integrated planning experience that streamlines processes and enhances efficiency.

Experience IBM Planning Analytics

When evaluating a planning and analytics solution, businesses must consider their specific needs, scalability requirements and budget constraints. At IBM, we designed Planning Analytics to provide more flexibility in deployment options and pricing models, often resulting in a lower total cost of ownership for complex, large-scale implementations. We invite you to experience the transformative power of IBM Planning Analytics firsthand. Try the demo to explore how our solution can revolutionize your planning processes. We are confident that IBM Planning Analytics will meet and exceed your organization’s unique requirements and goals in the ever-evolving landscape of business performance management.

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Data observability: The missing piece in your data integration puzzle

Historically, data engineers have often prioritized building data pipelines over comprehensive monitoring and alerting. Delivering projects on time and within budget often took precedence over long-term data health. Data engineers often missed subtle signs such as frequent, unexplained data spikes, gradual performance degradation or inconsistent data quality. These issues were seen as isolated incidents, not systemic ones. Better data observability unveils the bigger picture. It reveals hidden bottlenecks, optimizes resource allocation, identifies data lineage gaps and ultimately transforms firefighting into prevention.

Until recently, there were few dedicated data observability tools available. Data engineers often resorted to building custom monitoring solutions, which were time-consuming and resource-intensive. While this approach was sufficient in simpler environments, the increasing complexity of modern data architectures and the growing reliance on data-driven decisions have made data observability an indispensable component of the data engineering toolkit.

It’s important to note that this situation is changing rapidly. Gartner® estimates that “by 2026, 50% of enterprises implementing distributed data architectures will have adopted data observability tools to improve visibility over the state of the data landscape, up from less than 20% in 2024”.

As data becomes increasingly critical to business success, the importance of data observability is gaining recognition. With the emergence of specialized tools and a growing awareness of the costs of poor data quality, data engineers are now prioritizing data observability as a core component of their roles.

Hidden dangers in your data pipeline

There are several signs that can tell if your data team needs a data observability tool:

• High incidence of incorrect, inconsistent or missing data can be attributed to data quality issues. Even if you can spot the issue, it becomes a challenge to identify the origin of the data quality problem. Often, data teams must follow a manual process to help ensure data accuracy.
• Recurring breakdowns in data processing workflows with long downtime might be another signal. This points to data pipeline reliability issues when the data is unavailable for extended periods, resulting in a lack of confidence among stakeholders and downstream users.
• Data teams face challenges in understanding data relationships and dependencies.
• Heavy reliance on manual checks and alerts, along with the inability to address issues before they impact downstream systems, can signal that you need to consider observability tools.
• Difficulty managing intricate data processing workflows with multiple stages and diverse data sources can complicate the whole data integration process.
• Difficulty managing the data lifecycle according to compliance standards and adhering to data privacy and security regulations can be another signal.

If you’re experiencing any of these issues, a data observability tool can significantly improve your data engineering processes and the overall quality of your data. By providing visibility into data pipelines, detecting anomalies and enabling proactive issue resolution, these tools can help you build more reliable and efficient data systems.

Ignoring the signals that indicate a need for data observability can lead to a cascade of negative consequences for an organization. While quantifying these losses precisely can be challenging due to the intangible nature of some impacts, we can identify key areas of potential loss

There might be financial loss as erroneous data can lead to incorrect business decisions, missed opportunities or customer churn. Oftentimes, businesses ignore the reputational loss where inaccurate or unreliable data can damage customer confidence in the organization’s products or services. The intangible impacts on reputation and customer trust are difficult to quantify but can have long-term consequences.

Prioritize observability so bad data doesn’t derail your projects

Data observability empowers data engineers to transform their role from mere data movers to data stewards. You are not just focusing on the technical aspects of moving data from various sources into a centralized repository, but taking a broader, more strategic approach. With observability, you can optimize pipeline performance, understand dependencies and lineage, and streamline impact management. All these benefits help ensure better governance, efficient resource utilization and cost reduction.

With data observability, data quality becomes a measurable metric that’s easy to act upon and improve. You can proactively identify potential issues within your datasets and data pipelines before they become problems. This approach creates a healthy and efficient data landscape.

As data complexity grows, observability becomes indispensable, enabling engineers to build robust, reliable and trustworthy data foundations, ultimately accelerating time-to-value for the entire organization. By investing in data observability, you can mitigate these risks and achieve a higher return on investment (ROI) on your data and AI initiatives.

In essence, data observability empowers data engineers to build and maintain robust, reliable and high-quality data pipelines that deliver value to the business.

Source: ibm.com

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How Data Cloud and Einstein 1 unlock AI-driven results

Einstein 1 is going to be a major focus at Dreamforce 2024, and we’ve already seen a tremendous amount of hype and development around the artificial intelligence capabilities it provides. We have also seen a commensurate focus on Data Cloud as the tool that brings data from multiple sources to make this AI wizardry possible. But how exactly do the two work together? Is Data Cloud needed to enable Einstein 1? Why is there such a focus on data, anyway?

Data Cloud as the foundation for data unification

As a leader in the IBM Data Technology & Transformation practice, I’ve seen firsthand that businesses need a solid data foundation. Clean, comprehensive data is necessary to optimize the execution and reporting of their business strategy. Over the past few years, Salesforce has made heavy investments in Data Cloud. As a result, we’ve seen it move from a mid-tier customer Data Platform to the Leader position in the 2023 Gartner® Magic Quadrant™. Finally, we can say definitively that Cloudera Data Platform (CDP) is the most robust foundation as a comprehensive data solution inside the Salesforce ecosystem.

Data Cloud works to unlock trapped data by ingesting and unifying data from across the business. With over 200 native connectors—including AWS, Snowflake and IBM® Db2®—the data can be brought in and tied to the Salesforce data model. This makes it available for use in marketing campaigns, Customer 360 profiles, analytics, and advanced AI capabilities.

Simply put, the better your data, the more you can do with it. This requires a thorough analysis of the data before ingestion in Data Cloud: Do you have the data points you need for personalization? Are the different data sources using the same formats that you need for advanced analytics? Do you have enough data to train the AI models?

Remember that once the data is ingested and mapped in Data Cloud, your teams will still need to know how to use it correctly. This might mean to work with a partner in a “two in a box” structure to rapidly learn and apply those takeaways. However, it requires substantial training, change management and willingness to adopt the new tools. Documentation like a “Data Dictionary for Marketers” is indispensable so teams fully understand the data points they are using in their campaigns.

Einstein 1 Studio provides enhanced AI tools

Once you have Data Cloud up and running, you are able to use Salesforce’s most powerful and forward-thinking AI tools in Einstein 1 Studio.

Einstein 1 Studio is Salesforce’s low-code platform to embed AI across its product suite, and this studio is only available within Data Cloud. Salesforce is investing heavily in its Einstein 1 Studio roadmap, and the functions continues to improve through regular releases. As of this writing in early September 2024, Einstein 1 Studio consists of three components:

Prompt builder

Prompt builder allows Salesforce users to create reusable AI prompts and incorporate these generative AI capabilities into any object, including contact records. These prompts trigger AI commands like record summarization, advanced analytics and recommended offers and actions.

Copilot builder

Salesforce copilots are generative AI interfaces based on natural language processing that can be used to both internally and externally to boost productivity and improve customer experiences. Copilot builder allows you to customize the default copilot functions with prompt builder functions like summarizations and AI-driven search, but it also triggers actions and updates through Apex and Flow.

Model builder

The Bring Your Own Model (BYOM) solution allows companies to use Salesforce’s standard large language models. They can also incorporate their own, including SageMaker, OpenAI or IBM Granite™, to use the best AI model for their business. In addition, Model Builder makes it possible to build a custom model based on the robust Data Cloud data.

How do you know which model returns the best results? The BYOM tool allows you to test and validate responses, and you should also check out the model comparison tool here.

Expect to see regular enhancements and new features as Salesforce continues to invest heavily in this area. I personally can’t wait to hear about what’s coming next at Dreamforce.

Salesforce AI capabilities without Data Cloud

If you are not yet using Data Cloud or haven’t ingested a critical mass of data, Salesforce still provides various AI capabilities. These are available across Sales Cloud, Service Cloud, Marketing Cloud, Commerce Cloud and Tableau. These native AI capabilities range from case and call summarization to generative AI content to product recommendations. The better the quality and cohesion of the data, the better the potential for AI outputs.

This is a powerful function, and you should definitely be taking advantage of Salesforce’s AI capabilities in the following areas:

Campaign optimization

Einstein Generative AI can create both subject lines and message copy for marketing campaigns, and Einstein Copy Insights can even analyze the proposed copy against previous campaigns to predict engagement rates. This function isn’t limited to Marketing Cloud but can also propose AI-generated copy for Sales Cloud messaging based on CRM record data.

Recommendations

Einstein Recommendations can be used across the clouds to recommend products, content and engagement strategies based on CRM records, product catalogs and previous activity. The recommendation might come in various flavors, like a next best offer product recommendation or personalized copy based on the context.

Search and self-service

Einstein Search provides personalized search results based on natural language processing of the query, previous interactions and various data points within the tool. Einstein Article Answers can promote responses from a specified knowledge to drive self-service, all built on Salesforce’s foundation of trust and security.

Advanced analytics

Salesforce offers a specific analytics visualization and insights tool called Tableau CRM (formerly Einstein Analytics), but AI-based advanced analytics capabilities have been built into Salesforce. These business-focused advanced analytics are highlighted through various reports and dashboards like Einstein Lead Scoring, sales summaries and marketing campaigns.

CRM + AI + Data + Trust

Salesforce’s focus on Einstein 1 as “CRM + AI + Data + Trust” provides powerful tools within the Salesforce ecosystem. These tools are only enhanced by starting with Data Cloud as the tool to aggregate, unify and activate data. Expect to see this continue to improve over time even further. The rate of change in the AI space has been incredible, and Salesforce continues to lead the way through their investments and approach.

If you’re going to be at Dreamforce 2024, Gustavo Netto and I will be presenting on September 17 at 1 PM in Moscone North, LL, Campground, Theater 1 on “Fueling Generative AI with Precision.” Please stop by and say hello. IBM has over 100 years of experience in responsibly organizing the world’s data, and I’d love to hear about the challenges and successes you see with Data Cloud and AI.

Source: ibm.com

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How digital solutions increase efficiency in warehouse management

In the evolving landscape of modern business, the significance of robust maintenance, repair and operations (MRO) systems cannot be overstated. Efficient warehouse management helps businesses to operate seamlessly, ensure precision and drive productivity to new heights. In our increasingly digital world, bar coding stands out as a cornerstone technology, revolutionizing warehouses by enabling meticulous data tracking and streamlined workflows.

With this knowledge, A3J Group is focused on using IBM Maximo Application Suite and the Red Hat® Marketplace to help bring inventory solutions to a wider audience. This collaboration brings significant advancements to warehouse management, setting a new standard for efficiency and innovation.

To achieve the maintenance goals of the modern MRO program, these inventory management and tracking solutions address critical facets of inventory management by way of bar code technology.

Bar coding technology in warehouse management

Bar coding plays a critical role in modern warehouse operations.Bar coding technology provides an efficient way to track inventory, manage assets and streamline workflows, while providing resiliency and adaptability. Bar coding provides essential enhancements inkey areas such as:

Accuracy of data: Accurate data is the backbone of effective warehouse management. With barcoding, every item can be tracked meticulously, reducing errors and improving inventory management. This precision is crucial for maintaining stock levels, fulfilling orders and minimizing discrepancies.

Efficiency of data and workers: Barcoding enhances data accuracy and boosts worker efficiency. By automating data capture, workers can process items faster and more accurately. This efficiency translates to quicker turnaround times and higher productivity, ultimately improving the bottom line.

Visibility into who, where, and when of the assets: Visibility is key in warehouse management. Knowing the who, where and when of assets helps ensure accountability and control. Enhanced visibility allows managers to track the movement of items, monitor workflows and optimize resource allocation, leading to better decision-making and operational efficiency.

Auditing and compliance: Traditional systems often lack robust auditing capabilities. Modern solutions provide comprehensive auditing features that enhance control and accountability. With these capabilities, every transaction can be recorded, making it easier to identify issues, conduct audits and maintain compliance.

Implementing digital solutions to minimize disruption

Implementing advanced warehouse management solutions can significantly ease operations during stressful times, such as equipment outages or unexpected order surges. When systems are down or demand spikes, having a robust management system in place helps leaders continue operations with minimal disruption.

During equipment outages, quick decision-making and efficient processes are critical. Advanced solutions help leaders manage these scenarios by providing accurate data, efficient workflows and visibility into inventory levels, which enables swift and informed decisions.

Implementing software accelerators to address warehouse needs

Current trends in warehouse management focus on automation, real-time data tracking and enhanced visibility. By adopting these trends, warehouses can remain competitive, efficient and capable of meeting increasing demands.

IBM and A3J Group offer integrated solutions that address the unique challenges of warehouse management. Available on IBM Red Hat Marketplace, these solutions provide comprehensive features to enhance efficiency, accuracy and visibility.

IBM Maximo Application Suite

IBM® Maximo® Manage offers robust functionality for managing assets, work orders and inventory. Its integration with A3J Group’s solutions enhances its capabilities, providing a comprehensive toolkit for warehouse management.

A3J Group accelerators

A3J Group offers several accelerators that integrate seamlessly with IBM Maximo, providing enhanced functionality tailored to warehouse management needs.

MxPickup

MxPickup is a material pickup solution designed for the busy warehouse manager or employee. It is ideal for projects, special orders and nonstocked items. MxPickup enhances the Maximo receiving process with superior tracking and issuing controls, making it easier to receive large quantities of items and materials.

Unlike traditional systems that force materials to be stored in specific locations, MxPickup allows flexibility in placing and tracking materials anywhere, including warehouse locations, bins, any Maximo location, and freeform staging and delivery locations. Warehouse experts can choose to place or issue a portion or all of the received items, with a complete history of who placed the material and when.

MxPickup also enables mass issue of items, allowing warehouse experts to select records from the application list screen and issue materials directly, streamlining the process and saving valuable time.

A3J Automated Label Printing

The Automated Label Printing solution is designed to notify warehouse personnel proactively when items or materials are received through a printed label report. This report includes information about the received items with bar coded fields for easier scanning. Labels can be automatically fixed to received parts or materials, containing all the necessary information for warehouse operations staff to fulfill requests. The bar codes facilitate quick inventory transactions by using mobile applications, enhancing efficiency and accuracy.

Bringing innovative solutions to warehouse management

The collaboration between IBM and A3J Group on Red Hat Marketplace brings innovative solutions to warehouse management. By using advanced bar coding, data accuracy, efficiency and visibility, warehouses can achieve superior operational performance. Implementing these solutions addresses current challenges and prepares warehouses for future demands, supporting long-term success and competitiveness in the market.

Source: ibm.com

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How fintechs are helping banks accelerate innovation while navigating global regulations

Financial institutions are partnering with technology firms—from cloud providers to fintechs—to adopt innovations that help them stay competitive, remain agile and improve the customer experience. However, the biggest hurdle to adopting new technologies is security and regulatory compliance.

While third and fourth parties have the potential to introduce risk, they can also be the solution. As enterprises undergo their modernization journeys, fintechs are redefining digital transformation in ways that have never been seen before. This includes using hybrid cloud and AI technologies to provide their clients with the capabilities they need to modernize securely and rapidly while addressing existing and emerging legislation, such as the Digital Operational Resilience Act (DORA) in the EU.

The financial services industry needs to modernize, but it is challenging to build modern digital solutions on top of existing systems. These digital transformation projects can be costly, especially if not done correctly. It is critical for banks and other financial institutions to partner with a technology provider that can automate enterprise processes and enable them to manage their complex environments while prioritizing resilience, security and compliance. As the January deadline for DORA (which is designed to strengthen the operational resilience of the financial sector) approaches, it is critical that fintechs align their practices to support resilience and business continuity.

How FlowX.AI is modernizing mission-critical workloads with AI

Both IBM® and FlowX.AI have been on a mission to enable our clients to manage mission-critical workload challenges with ease. IBM designed its enterprise cloud for regulated industries with built-in controls and confidential computing capabilities to help customers across highly regulated industries (such as financial services, healthcare, public sector, telco, insurance, life sciences and more) to maintain control of their data and use new technologies with confidence.

FlowX.AI believes that the key to managing increasingly complex environments is AI, and its solution brings multiagent AI to banking modernization. The robust, scalable platform combines deep integration capabilities and connector technology with AI-enabled application development. FlowX.AI is designed to automate enterprise processes and integrate seamlessly with existing systems, from APIs and databases to mainframes. This enables enterprises to build and deploy powerful, secure applications in a fraction of the time traditionally required. Also, as a part of the IBM Cloud for Financial Services® ecosystem, the company is using the IBM Cloud Framework for Financial Services to address risk in the digital supply chain through a common set of security controls.

“Highly regulated industries like financial services are under immense pressure to adapt quickly to shifting market dynamics and deliver exceptional customer experiences, all while navigating increasingly complex regulatory requirements. Our platform is designed to address these challenges head-on, equipping banks with the tools to deliver rapidly and efficiently. By collaborating with IBM Cloud for Financial Services, FlowX.AI aims to simplify the complexity banks face and help them unlock innovation faster, while still prioritizing security and compliance.” – Ioan Iacob, CEO, FlowX.AI

How Swisschain is ushering in the next era of blockchain technology

The financial industry is on the verge of a significant transformation with the digitization of financial assets. In financial services, blockchain technology has made it easier to securely digitize assets to trade currencies, secure loans, process payments and more. However, as banks and other financial institutions are building their blockchain integrations, it can be difficult to maintain security, resilience and compliance.

To meet the evolving needs of the financial industry, Swisschain has developed a hybrid digital asset custody and tokenization platform that can be deployed either on premises or in the cloud. The platform is designed to allow financial institutions to securely manage high-value digital assets and to offer seamless integration with both public and permissioned blockchains. Swisschain’s multilayered security architecture is built to deliver protection of governance over private keys and policies. They offer root-level control, which aims to eliminate single points of failure. This can be a critical feature for institutions using high-value assets.

By using IBM Cloud Hyper Protect Services, Swisschain can tap into IBM’s “keep your own key” encryption capabilities, designed to allow clients exclusive key control over their assets and to help address privacy needs. Swisschain’s solution is designed to offer greater levels of scalability, agility and cost-effectiveness, to help financial institutions navigate the complex digital asset landscape with confidence and efficiency. In collaboration with IBM, Swisschain aims to set a new standard for innovation in the digital asset ecosystem.

“Tokenizing financial assets through blockchain technology is rapidly accelerating the digitization of the financial industry, fundamentally reshaping how we trade and manage assets. By converting traditional asset ownership into digital tokens, we enhance transparency, security and liquidity, making it easier for financial institutions to navigate this new landscape. Our goal is to provide the essential infrastructure that bridges traditional finance with digital assets. Collaborating with IBM Cloud for Financial Services has been a game-changer in our mission to lead the next era of blockchain and digital asset technology.” – Simon Olsen, CEO, Swisschain

Innovating at the pace of change

Modernization efforts vary across the financial services industry, but one thing is for certain: banks need to innovate at the pace of change or risk getting left behind. Having an ecosystem that incorporates fintech, cloud and AI technology will enable large financial institutions to remain resilient, secure and compliant as they serve their customers.

With IBM Cloud for Financial Services, IBM is positioned to help fintechs ensure that their products and services are compliant and adhere to the same stringent regulations that banks must meet. With security and controls built into the cloud platform and designed by the industry, we aim to help fintechs and larger financial institutions minimize risk, stay on top of evolving regulations and accelerate cloud and AI adoption.

Source: ibm.com

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Primary storage vs. secondary storage: What’s the difference?

What is primary storage?

Computer memory is prioritized according to how often that memory is required for use in carrying out operating functions. Primary storage is the means of containing primary memory (or main memory), which is the computer’s working memory and major operational component. The main or primary memory is also called “main storage” or “internal memory.” It holds relatively concise amounts of data, which the computer can access as it functions.

Because primary memory is so frequently accessed, it’s designed to achieve faster processing speeds than secondary storage systems. Primary storage achieves this performance boost by its physical location on the computer motherboard and its proximity to the central processing unit (CPU).

By having primary storage closer to the CPU, it’s easier to both read and write to primary storage, in addition to gaining quick access to the programs, data and instructions that are in current use and held within primary storage. 

What is secondary storage?

External memory is also known as secondary memory and involves secondary storage devices that can store data in a persistent and ongoing manner. Because they can be used with an interruptible power supply, secondary storage devices are said to provide non-volatile storage.

These data storage devices can safeguard long-term data and establish operational permanence and a lasting record of existing procedures for archiving purposes. This makes them the perfect hosts for housing data backups, supporting disaster recovery efforts and maintaining the long-term storage and data protection of essential files.

How computer memory mimics human memory

To further understand the differences between primary storage and secondary storage, consider how human beings think. Each day, people are mentally bombarded by a startling amount of incoming data.

• Personal contacts: The average American makes or receives 6 phone calls per day, as well as sends or receives approximately 32 texts.
• Work data: In addition, most people are also engaged in work activities that involve incoming organizational data via any number of business directives or communiques.
• Advertising: It’s been estimated that the average person is exposed to as many as 10,000 advertisements or sponsored messages per day. Subtracting 8 hours for an average night’s sleep, that equates to a person being exposed to an advertising message every 5.76 seconds that they’re awake.
• News: The advertising figure does not include media-conveyed news information, which we’re receiving in an increasing number of formats. In many current television news programs, a single screen is being used to simultaneously transmit several types of information. For example, a news program might feature a video interview with a newsmaker while a scroll at the bottom of the screen announces breaking news headlines and a sidebar showcases latest stock market updates.
• Social media: Nor does that figure account for the growing and pervasive influence of social media. Through social media websites, messaging boards and online communities, people are absorbing even more data.

Clearly, this is a lot of incoming information to absorb and process. From the moment we awake until we return to sleep, our minds scan through all this possible data, making a near-endless series of minute judgments about what information to retain and what to ignore. In most instances, that decision comes down to utility. If the mind perceives that this information will need to be recalled again, that data is awarded a higher order of mental priority.

These prioritization decisions happen with such rapid frequency that our minds are trained to input this data without truly realizing it, leaving it to the human mind to sort out how primary and secondary memory is allocated. Fortunately, the human mind is quite adept at managing such multitasking, as are modern computers.

An apt analogy exists between how the human mind works and how computer memory is managed. In the mind, a person’s short-term memory is more dedicated to the most pressing and “current” cognitive needs. This might include data such as an access code used for personal banking, the scheduled time of an important medical appointment or the contact information of current business clients. In other words, it’s information of the highest anticipated priority. Similarly, primary storage is concerned with the computer’s most pressing processing needs.

Secondary data storage, on the other hand, offers long-term storage, like a person’s long-term memory. Secondary storage tends to operate with less frequency and can require more computer processing power to retrieve long-stored data. In this way, it mirrors the same retention and processing as long-term memory. Examples of long-term memory for a human could include a driver’s license number, long-retained facts or a spouse’s phone number.

Memory used in primary storage

Numerous forms of primary storage memory dominate any discussion of computer science:

• Random Access Memory (RAM): The most vitally important type of memory, RAM handles and houses numerous key processes, including system apps and processes the computer is currently managing. RAM also serves as a kind of launchpad for files or apps.
• Read-Only Memory (ROM): ROM allows viewing of contents but does not allow viewers to make changes to collected data. ROM is non-volatile storage because its data remains even when the computer is turned off.
• Cache memory: Another key form of data storage that stores data that is often retrieved and used. Cache memory contains less storage capacity than RAM but is faster than RAM.
• Registers: The fastest data access times of all are posted by registers, which exist within CPUs and store data to achieve the goal of immediate processing.
• Flash memory: Flash memory offers non-volatile storage that allows data to be written and saved (as well as be re-written and re-saved). Flash memory also enables speedy access times. Flash memory is used in smartphones, digital cameras, universal serial bus (USB) memory sticks and flash drives.
• Cloud storage: Cloud storage might operate as primary storage, in certain circumstances. For example, organizations hosting apps in their own data centers require some type of cloud service for storage purposes.
• Dynamic Random-Access Memory (DRAM): A type of RAM-based semiconductor memory, DRAM features a design that relegates each data bit to a memory cell that houses a tiny capacitor and transistor. DRAM is non-volatile memory thanks to a memory refresh circuit inside the DRAM capacitor. DRAM is most often used in creating a computer’s main memory.
• Static Random-Access Memory (SRAM): Another type of RAM-based semiconductor memory, SRAM’s architecture is based around a latching, flip-flop circuitry for data storage. SRAM is volatile storage that sacrifices its data when power is removed from the system. However, when it is operational, it provides faster processing than DRAM, which often drives SRAM’s price upward. SRAM is typically used within cache memory and registers.

Memory used in secondary storage

There are three forms of memory commonly used in secondary storage:

• Magnetic storage: Magnetic storage devices access data that’s written onto a spinning metal disk that contains magnetic fields.
• Optical storage: If a storage device uses a laser to read data off a metal or plastic disk that contains grooves (much like an audio LP), that’s considered optical storage.
• Solid state storage: Solid state storage (SSS) devices are powered by electronic circuits. Flash memory is commonly used in SSS devices, although some use random-access memory (RAM) with battery backup. SSS offers high-speed data transfer and high performance, although its financial costs when compared to magnetic storage and optical storage can prove prohibitive.

Types of primary storage devices

Storage resources are designated as primary storage according to their perceived utility and how that resource is used. Some observers incorrectly assume that primary storage depends upon the storage space of a particular storage medium, the amount of data contained within its storage capacity or its specific storage architecture. It’s actually not about how a storage medium might store information. It’s about the anticipated utility of that storage media.

Through this utility-based focus, it’s possible for primary storage devices to take multiple forms:

• Hard disk drives (HDDs)
• Flash-based solid-state drives (SSDs)
• Shared storage area network (SAN)
• Network attached storage (NAS)

Types of secondary storage devices

While some forms of secondary memory are internally based, there are also secondary storage devices that are external in nature. External storage devices (also called auxiliary storage devices) can be easily unplugged and used with other operating systems, and offer non-volatile storage:

• HDDs
• Floppy disks
• Magnetic tape drives
• Portable hard drives
• Flash-based solid-state drives
• Memory cards
• Flash drives
• USB drives
• DVDs
• CD-ROMs
• Blu-ray Discs
• CDs 

Source: ibm.com

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