Empowering the digital-first business professional in the foundation model era

In the fast-paced digital age, business professionals constantly seek innovative ways to streamline processes, enhance productivity and drive growth. Today’s professionals, regardless of their fields, must fluently use advanced artificial intelligence (AI) tools. This is especially important given the application of foundation models and large language models (LLMs) in Open AI’s ChatGPT and IBM’s advances with IBM watsonx.

 

Professionals must keep up with rapid technological changes such as cloud computing and AI, recognizing the integrative power of foundation models, which are increasingly central to AI-based automation. The transition to the foundation model era signifies a substantial change in how professionals use technology to enhance their digital strategies. By using cutting-edge technology, professionals can optimize decision-making processes and enhance operational efficiency. 

For instance, business analysts now play a crucial role in bridging the gap between business and IT but also in integrating these foundational AI models into business strategies, further augmenting and optimizing operations. They translate business needs into solution requirements and propose ways to optimize business operations. 

The next leap: Beyond low-code platforms 

While the rise of low-code platforms has marked a significant evolution in bridging business requirements with IT implementation, the current market trend is veering toward more intuitive, AI-driven solutions. Foundation models urge businesses to look beyond conventional limitations, with their inherent ability to understand, generate and process human-like text, allowing non-technical professionals to interact and build applications by using natural language, marking a shift from conventional programming. By transcending the constraints of low-code platforms, businesses can build more robust, tailored solutions that align closely with their evolving digital strategies. 

Deploying LLMs effectively, like any tool, requires professionals to understand their capabilities and potential biases. Blending creativity and domain-specific expertise with AI’s computational prowess helps ensure technologically sound and contextually relevant solutions. 

From digital assistants to AI assistants

The narrative surrounding digital assistants is evolving. While computer-aided instructions or computer-assisted instructions represented a previous breakthrough, AI platforms like watsonx are elevating the concept. Instead of mere assistants, these AI-based automation platforms act as collaborators, offering insights, handling routine tasks with precision and accuracy, and enhancing decision-making processes for knowledge workers. 

The distinction between traditional robotic process automation (RPA) robots and AI-driven digital collaborators is paramount. The latter not only automates but also comprehends, reasons and learns, providing richer, more dynamic interactions. More importantly, they enable systems and tools to conform to the needs of the users, responding intelligently to users’ natural language requests.  

IBM’s vision: watsonx, watsonx Orchestrate, foundation models and beyond 

IBM strategically innovates by venturing into the world of foundation models with watsonx, demonstrating their dedication to revolutionizing businesses through AI. Their powerful IBM watsonx™ Orchestrate platform equips digital assistants with essential tools to deliver unparalleled value. Simultaneously, IBM complements this ecosystem with its RPA and Process Mining tools, offering a low-code interface for business analysts to unearth and enhance business processes. 

In essence, IBM’s comprehensive suite, centered on watsonx, aims to usher in a new era by empowering businesses to use foundation models. This supercharges their operations, helping to ensure a harmonized dance between human expertise and AI-driven automation. 

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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Three mega-trends shaping the data economy

Data Economy - A European Perspective

I recently had the pleasure of chatting with Vilmos Lorincz, Managing Director of Data and Digital Products for Lloyds Banking Group in the United Kingdom. Data is a fundamental currency in financial services, and so developing new approaches for banking protocols is critical to formulating progressive solutions for both clients and industry colleagues.

In response to a demand by the U.K. government for more transparency in financial services, the Open Banking Implementation Entity (OBIE) was set up in 2017 to deliver architectures that give customers more control of their data within a secure framework.

Lloyds Bank undertook a decisive transformation by moving their big data to the cloud and advancing data literacy for its employees, upgrading their capacity to provide benefit to clients. “We had to design the new agile operating model for more than a thousand colleagues,” said Vilmos, “helping them land in their newly defined roles, making the right technology investment choices, while engaging with more than 20,000 people.”

Vilmos emphasized that ethical behavior is absolutely critical to gaining and maintaining client trust, establishing a company’s brand as honest and responsible partners.

When asked about mega trends that are shaping the data-driven economy, Vilmos suggested three fundamentals.

1. Customer awareness: As citizens become more digitally sophisticated, they are keenly attuned to privacy and security issues. They rightfully want control of their data, expanding their ability to explore and select personal options.

2. Maturing corporations: The corporate world is advancing its ability to adopt new processes that keep pace with emergent technologies to add value to their business models and to benefit their customers.

3. Regulatory bodies: Regulators and governments are playing active roles in adjusting to new market realities, both protecting individual rights and positioning their nation to take full advantage of the opportunities of the rising data economy.

“Organizations are realizing that data is a mission-critical competitive factor and a must-have to meet and exceed customer expectations,” Vilmos explained. “They are becoming much better at deploying machine-learning and artificial-intelligence capabilities as an increasing part of their data estate.”

Vilmos advises business executives to prepare for a fast-evolving future by establishing frameworks that can accommodate the growth of the data economy and by planning how to deliver their products within the data-driven landscape.

Source: ibm.com

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How Canada is growing its data economy

The data economy is booming. In 2021, IDC estimated the value of the data economy in the U.S. at USD 255 billion, and that of the European Union at USD 110 billion. In these and many other regions, growth in the data economy outpaces GDP. IBM has examined Canada’s particular potential for data leadership, with lessons for any other country hoping to compete in the data economy.

Will we get to CAD 1 trillion value of data in Canada before 2030? In mid-2019, Statistics Canada estimated that Canadian investment in “data, databases and data science” has grown over 400% since 2005. At an upper limit, the value of the stock of data, databases and data science in Canada was $217B in 2018, roughly equivalent to the stock of all other intellectual property products (software, research and development, mineral exploration) and equivalent to more than two-thirds the value of the country’s crude oil reserves.

As the world continues to rapidly change around us, ground-breaking opportunities are presenting themselves that will shift the fundamentals of how businesses, governments and citizens function. This shift will be supported by enormous amounts of data, regardless of the part of society in which these transformations take place.

What is the data economy?

The amount of data throughout the world has almost doubled in just two years, with growth expected to triple by the year 2025. With data’s unprecedented growth, important decisions will have to be made about how to use it; and these decisions will determine the commercial success or failure of the digital revolution.

The data economy is the social and economic value attained from data sharing. While data has no inherent value, its use does. When it is organized, categorized and transformed into information that can drive innovation, solve complex problems, create new products, or provide better services its value becomes apparent.

While data can solve critical challenges in our society, most of its value is inaccessible due to the siloed and fragmented nature of most data ecosystems. Governments cannot develop effective policies; business leaders are unable to fully tap their resources; and citizens are prevented from making informed decisions. Leveraging data to benefit society depends upon the amount of connections that we can form between contributors and consumers, among enterprises and governments. A prosperous data economy must be linked to intelligent governance, administered for the good of everyone.

Why does it matter?

1. Citizens can assume more control of their data, ensuring its appropriate use and security while benefiting from new products and services.

2. Businesses can customize their products to align with their clients and better manage regulations.

3. Governments can collaborate on national and international strategies to achieve optimum effectiveness on a global scale.

And what can it do for you?

The profound implications of well-managed global data exchanges illuminate the vision of a better world, opening the window to myriad possibilities:

◉ Fighting disease through shared research on diagnostics and therapeutics

◉ Identifying global threats and reacting to them quickly

◉ Deploying advanced applications to solve organizational issues, unlocking innovation

◉ Harnessing data to promote environmental health, prevent environmental degradation and protect at-risk ecosystems

◉ Coordinating data to benefit industrial sectors such as tourism or agriculture

Canada has the potential to create a world-leading data economy, positioning us to develop innovations that will allow us to compete globally. We have many advantages in our favour: a highly trained workforce strengthened by our skills-based immigration system; our government’s commitment to accountability, security and innovation; and our unique history, geography and public policies.

Our success will depend upon a collective effort to promote engagement and facilitate the transition to a data-driven economy. Together with its financial investment, Canada must focus on cultivating data literacy among its citizens, as businesses increasingly embrace digitized platforms.

Fast-tracked by COVID-19, investment in data science has accelerated, alongside the proliferation of emerging technologies. By leveraging the opportunities in the rising data economy, Canada can unlock a trillion-dollar benefit within the next decade.

Source: ibm.com

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From research to contracts, AI is changing the legal services industry

There are many reasons a person chooses to become a lawyer or work in the legal industry, but hours of paperwork every week isn’t one of them. Often legal professionals spend a lot of time trying to find and properly classify information in their complex and siloed filing systems.

To complete the many tasks they are responsible for, paralegals, attorneys, and compliance and contract specialists need the ability to quickly identify relevant information in an overabundance of big data.

That’s where AI comes in. Natural language processing (NLP) is an AI technique that can help legal professionals quickly surface insights across millions of unstructured data sources, like printed books, legal websites, commercial databases and historical case files. By augmenting manual processes with AI, paralegals and attorneys can focus on more rewarding, higher-level tasks like working with clients.

Let’s explore a few of the major responsibilities of legal professionals and the top AI use cases in the legal field.

Legal research and drafting

While making legal decisions, attorneys and their teams spend time sorting through documents and running database searches to locate and review relevant statutes, laws and precedents. Not only is this a time-consuming and detail-oriented process, but if the correct keywords aren’t used, important resources may not even surface. Legal teams can use AI search tools like NLP to pull up information quickly, identify emerging trends and reveal hidden connections that help perform billable work faster.

Paralegals and attorneys can also use AI to help ensure all relevant facts, laws and statutes are included in legal documentation and follow the tedious standard formatting rules. AI solution provider LegalMation created a domain-specific model focused on legal terminology and concepts. The LegalMation platform helps legal teams craft early-phase response documentation in under two minutes.

Contract lifecycle management

Legal organizations create, update and store a large volume of contracts throughout their entire lifecycle. From initial drafting and negotiations to compliance management, maintaining these hundreds and sometimes millions of contracts — often stored across multiple repositories — represents a huge investment.

To ease the workload of contract review, legal professionals can use AI to help quickly identify and surface contracts in need of renewal before they expire. Teams can also use AI to help minimize the negotiation time frame by suggesting standard updates and renewal opportunities during protracted negotiations.

Legal technology firm ContractPodAi offers an end-to-end contract management solution that can analyze inventories of over 400,000 contracts. Designed to help counsel easily and cost-effectively manage any contract throughout its lifecycle, ContractPodAi clients report over 50% reductions in contract renewal time.

Client service

Law firms are experimenting with digital subscription services, which provide fast, affordable online legal services that can help reduce operational costs and empower teams to serve additional smaller clients without sacrificing quality. Teams can use AI-assisted customer service to offer clients a faster way to get common questions answered automatically. For example, teams can deploy AI-powered chatbots equipped with search capabilities that can surface relevant data, present it to customers and perform other tedious tasks, enabling legal teams to focus on higher-level work.

Affordable legal services provider QNC GmbH built its “digital law firm” subscription service Prime Legal to provide fast, affordable, flat-rate online legal services to small businesses in Germany. Lawyers can now match client questions against the Prime Legal database of 180,000 previously answered questions and typically respond to client inquiries in less than an hour.

Source: ibm.com

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Top 3 Data Job Roles Explained : A Career Guide

Data is the world`s most valuable resource!

Data is not recent, but it is growing at an incredible rate. The increasing interactions between data, algorithms, and analytics of big data, connected data and individuals are opening enormous new prospects. Enterprises and even economies have now started developing products and services based on data-driven analogies. The ability to provide an agile environment to serve the data workload is critical with data powering so many innovative approaches, whether it be artificial intelligence, machine learning or deep learning. Data undoubtedly offers them the chance to enhance or redesign almost every part of their business model.

Engineers, researchers, and marketers of today could be the data scientists of tomorrow

According to data gathered by LinkedIn, Coursera and the World Economic Forum in the Future of Jobs Report 2020, it’s estimated that, by 2025, 85 million jobs may be displaced by a shift in the division of labor between humans and machines. Roles growing in demand include data analysts and scientists, AI and machine learning specialists, robotics engineers, software and application developers, and digital transformation specialists.

Top cross-cutting, specialized skills of the future

Data and AI skills are fast becoming essential digital skills required across all business disciplines.

Jobs of tomorrow

Roles growing in demand include data analysts and scientists, AI and machine learning specialists, robotics engineers, software and application developers, and digital transformation specialists.

Key roles in the Data Ecosystem

The lure of leveraging data for competitive edge is transforming organizations to become more data driven in their operations and decisions, it has resulted in an enormous job opportunity for various data related professions. The key data roles include Data Engineering, Data Analytics and Data Science.

Data Engineering entails managing data throughout its lifecycle and includes the tasks of designing, building, and maintaining data infrastructures. These data infrastructures can include databases – relational and NoSQL, Big Data repositories and processing engines – such as Hadoop and Spark, as well as data pipelines – for transforming and moving data between these data platforms.

Data Analytics involves finding the right data in these data systems, cleaning it for the purposes of the required analysis, and mining data to create reports and visualizations.

Data Scientists take Data Analytics even further by performing deeper analysis on the data and developing predictive models to solve more complex data problems.

A Deep Dive: Skills Needed for Data Professions

In terms of skills required to perform each of these roles, while there are some unique skills for each job role, there also some common skills that all data professionals need, however the level of proficiency required may vary.

IBM and Coursera hosted a very engaging webinar with 4 IBM Subject Matter Experts and discussed each of these roles in depth. The replay of the session can be found here:

Webinar Recording

Webinar Presentation

Your burning Data career questions answered

We received over 300 questions before and during the webinar. Here are some of the most common questions received.

Q1. For someone with no work experience and a fresh graduate, which one is better Data Science or Data Analyst? Also, what advice would you give me to make my resume stand-out since I have no experience.

Both courses are good, but it would be great if you could start with one and then move onto the other one. In terms of making a start with no work-experience that’s a tough one. As a hiring manager, I tend to look for a portfolio in the form of:

1. School or side projects done during school

2. Work done on github with an emphasis on project summary, how clean the code in the notebooks is, what data modeling and visualization techniques have been applied

Basically, pick a passion project and create a portfolio based on it. Also, establish some presence by participating on relevant online forums, attend meetups, compete in hackathons, contribute to open source projects and submit proposals to trade journals and conferences. Finally, round out your resume with a diverse set of verifiable technical and non-technical skills.

Q2. I am mid-career with 10-15 years of experience looking to transition to a role in data. I have taken several online courses and earned badges. However, I am not being given option to pivot into DS roles due to lack of real-life experience. I also don’t want to start from scratch as a rookie. What advice do you have for me?

For these data roles in addition to technical skills and foundational mathematics you also need domain expertise. Since you’ve invested so many years in your career and have deep domain knowledge in your area, you should try to find jobs within related industries.  That way you will not be starting from scratch and be able to leverage your existing skills.

Q3. To get a holistic view of Data science is the knowledge of Data engineering essential. Are Data engineers more technical as compared to Data scientist or Data analytics?

Data engineering is not essential to get a holistic view of Data Science. The key skill that you need as a Data Engineer is a good base knowledge of SQL and the ability to work with databases. You also need to have some basic foundational concepts and knowledge about how data systems work but otherwise the fields are independent. As a data scientist you’ll be working with data engineers and other stakeholders in the corporation, but you don’t necessarily need to have data engineering skills. The truth of the matter is that there’s always the opportunity to start in one role and evolve into another by expanding your knowledge and gaining experiences as you go along.

Q4. I’ve been learning python and have a decent grasp of the basics, but I haven’t tried using any libraries/packages. I also only have basic excel skills. What should I learn next to be able to start applying to data?

You should start learning the basic data science libraries like NumPy and Pandas and try to complete a whole data science pipeline. Load a data set, process the data, do some summary statistics, visualize it and then create a machine learning model. You should also learn to use a Jupyter notebook.

Q5. When it comes to data, there’s so many languages and disciplines to learn such as Python, SQL, R, RPA. Do you suggest learning a little bit of everything or specializing in one or two languages?

SQL is mandatory. Once you’ve mastered SQL, pick either Python or R and see which one you are more comfortable with and stick with it. Then, even if you need to use a different programming language, making the transition will be much simpler as the constructs are basically the same, the nuances and syntax may be different. Master one language and learn how to apply it.

Q6. How are the courses on Coursera going to help me get ready for an entry level job as a Data Engineer?

We’ve designed the program to prepare you for the entry level role in data engineering by not only have teaching you theory but also by applying the concepts learned in hands-on labs and projects.  Every course in the Data Engineering program (and Data Analytics and Data Science) have several hands-on labs, projects and provide exposure to many data sets.  So, by the end of these programs you will have access to a vast variety of data sets and several tools that data engineers use and apply. The projects leverage real databases and have you practicing with RDBMSes, Data Warehouses, NoSQL,big data, Hadoop, Spark etc.

Q7. Will Coursera help me get a job after I have completed a professional certificate? How can I get a job at IBM?

All Professional Certificate completers get access to several career support resources to help them reach their career objectives. They also get access to the Professional Certificate community for peer support and the ability to network with alumni who have successfully made a career change.

Q8. Which courses will help me prepare for the 3 data roles discussed – Data Engineer, Data Analyst and Data Scientist?

There are numerous data courses available in English, Spanish, Arabic, Russian and Brazilian Portuguese. The programs aligned with the 3 job roles discussed in this article are:

IBM Certifications

Source: ibm.com

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The Top 10 storage moments of 2019

2019 was a big year for IBM Storage, with a slew of exciting launches of new solutions, fascinating and valuable reports, and deep dives into the ways in which storage can help your organization continue to innovate and drive value from your oceans of data.

But amongst all that great news, what stands out as the best storage moments of 2019? Read on to find out the top ten, presented in no particular order.

1. Storwize V5000 Launch

In April, IBM launched the new Storwize V5000 family of offerings to complement Storwize V7000 Gen3. The Storwize V5000 models support end-to-end NVMe and include industry acclaimed IBM Spectrum Virtualize software to provide increased performance and enterprise-class functionality, availability, and reliability in an easy-to-buy, easy-to-use, and easy-to-manage entry storage systems. These models demonstrate the IBM storage commitment to simplify your modern IT and hybrid multicloud infrastructure.

>Watch the video

2. DS8900F Launch

Designed for mission critical production systems and with strong synergy with IBM Z and the cloud, the new family of all-flash arrays launched in September. The DS8900F provides enhancements in performance, modern data protection, cyber resiliency, availability and cost-efficiency – all of which are demanded in mission-critical hybrid multicloud mainframe environments – with simple and secure support for data to the cloud.

3. Spectrum Discover Launch

The launch of IBM Spectrum Discover in July provided tools to help enterprises address the challenges of unstructured data across multiple vendors (IBM, Dell/EMC Isilon, and NetApp), the cloud, and, even, backups. IBM Spectrum Discover provides unified metadata management and insights for file and object storage that can be leveraged for storage optimization, data analytics and AI. With extended support and open APIs, IBM Spectrum Discover delivers insight into an expansive universe of file and object data.

4. IBM Storage for Red Hat OpenShift Container Platform Launch

The IBM Storage for Red Hat OpenShift Container Platform, which launched in August, is a reference solution bringing together IBM Solutions and open source technologies. IBM Storage offers new advantages and benefits for enterprises moving into hybrid multicloud operations, including better security, service orchestration, infrastructure agility, performance, and availability. The IBM Storage portfolio supports IBM Cloud Paks as well as standalone applications (including DevOps, database, HPC, analytics and AI workloads).

5. TechQuickie Autonomous Driving Vehicle Collaboration

This exciting video from April explains how self-driving cars work, revealing that an individual vehicle can generate up to 15 terabytes of data every hour. Fortunately, IBM Spectrum Storage, automized for AI and machine learning with industry-leading GPU-accelerated servers, helps automakers manage all of this data to keep those self-driving cars safely on the road.

>Watch the video

6. Elastic Storage System 3000 Launch

The launch of the ESS 3000 in October addressed a critical area of emphasis for AI, big data and analytics workloads – the management and storage of unstructured data.

IBM provides market-leading solutions for the requirements of AI-driven organizations, addressing every stage of the pipeline from insight to ingest. Leveraging IBM’s industry acclaimed IBM Spectrum Scale, the ESS 3000 is the latest in IBM’s line of high-performance, highly flexible scale-out file system solutions engineered to handle the toughest unstructured data challenges with the ultra-low latency and massive throughput advantages offered by Non-Volatile Memory Express (NVMe) flash storage.

7. Gartner 2019 Magic Quadrant Leader in 4 Reports

This year, IBM was honored to be recognized as a Leader in four 2019 Gartner Magic Quadrant reports: Distributed File Systems and Object Storage; Critical Capabilities for Object Storage; Primary Storage; and Data Center Backup and Recovery Solutions.  The Magic Quadrant for Data Center Backup and Recovery Solutions (October 2019) was the eighth report in a row where IBM was been recognized for both its completeness of vision and its ability to execute in the data protection market, driven by the proven ability to provide a highly scalable, efficient and modern data protection platform that safeguards client’s data.

8. IDC’s 2019 Worldwide Datacenter Support Customer Satisfaction Study Rates IBM #1

In November, IBM took the top in IDC’s 2019 World Datacenter Support Customer Satisfaction Study, which looked at twelve leading vendors and found that support and services are a key differentiator that puts IBM at the head of the pack. IBM also received the best NPS rating from respondents of any of the storage vendors in the study, excelling in overall support services contract satisfaction, as well was satisfaction with the ease of doing business with the company.

>Check out the InfoBrief

9. IBM Storage eBooks

Throughout the year, IBM Storage developed resources for enterprises looking to expand their knowledge and capability in various important areas of storage. Subjects covered included AI & big data, cyber resiliency, modern data protection, and NVMe over Fibre Channel.

◉ >Read the AI & Big Data eBook

◉ >Read the Cyber Resiliency eBook

◉ >Read the Modern Data Protection eBook

◉ >Read the NVMe over Fibre Channel for Dummies eBook

10. IT Brand Pulse Flash Leader for NVMeof

IT Brand Pulse selected IBM as Market Leader for the new category of “All Flash NVMe-oF Array,” covering all-flash arrays for block or file storage with NVMe over FC, NVMe over RoCE, or NVMe over TCP interfaces. In addition to being overall Market Leader, IBM was also named Performance Leader, Reliability Leader, Service & Support Leader, and Innovation Leader (in a tie).

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