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I recently met with Dr. Nick Fuller, Vice President, Distributed Cloud, at IBM Research for a discussion about IBM’s long-range plans and strategy for artificial intelligence and machine learning at the edge.

Dr. Fuller is responsible for providing AI and platform–based innovation for enterprise digital transformation spanning edge computing and distributed cloud management. He is an IBM Master Inventor with over 75 patents and co-author of 75 technical publications. Dr. Fuller obtained his Bachelor of Science in Physics and Math from Morehouse College and his PhD in Applied Physics from Columbia University.

Edge In, not Cloud Out

In general, Dr. Fuller told me that IBM is focused on developing an "edge in" position versus a "cloud out" position with data, AI, and Kubernetes-based platform technologies to scale hub and spoke deployments of edge applications.

A hub plays the role of a central control plane used for orchestrating the deployment and management of edge applications in a number of connected spoke locations such as a factory floor or a retail branch, where data is generated or locally aggregated for processing.

“Cloud out” refers to the paradigm where cloud service providers are extending their cloud architecture out to edge locations. In contrast, “edge in” refers to a provider-agnostic architecture that is cloud-independent and treats the data-plane as a first-class citizen.

IBM's overall architectural principle is scalability, repeatability, and full stack solution management that allows everything to be managed using a single unified control plane.

IBM’s Red Hat platform and infrastructure strategy anchors the application stack with a unified, scalable, and managed OpenShift-based control plane equipped with a high-performance storage appliance and self-healing system capabilities (inclusive of semi-autonomous operations).

IBM’s strategy also includes several in-progress platform-level technologies for scalable data, AI/ML runtimes, accelerator libraries for Day-2 AI operations, and scalability for the enterprise.

It is an important to mention that IBM is designing its edge platforms with labor cost and technical workforce in mind. Data scientists with PhDs are in high demand, making them difficult to find and expensive to hire once you find them. IBM is designing its edge system capabilities and processes so that domain experts rather than PhDs can deploy new AI models and manage Day-2 operations.

Why edge is important

Advances in computing and storage have made it possible for AI to process mountains of accumulated data to provide solutions. By bringing AI closer to the source of data, edge computing is faster and more efficient than cloud. While Cloud data accounts for 60% of the world’s data today, vast amounts of new data is being created at the edge, including industrial applications, traffic cameras, and order management systems, all of which can be processed at the edge in a fast and timely manner.

Public cloud and edge computing differ in capacity, technology, and management. An advantage of edge is that data is processed and analyzed at / near its collection point at the edge. In the case of cloud, data must be transferred from a local device and into the cloud for analytics and then transferred back to the edge again. Moving data through the network consumes capacity and adds latency to the process. It’s easy to see why executing a transaction at the edge reduces latency and eliminates unnecessary load on the network.

Increased privacy is another benefit of processing data at the edge. Analyzing data where it originates limits the risk of a security breach. Most of the communications between the edge and the cloud is then confined to such things as reporting, data summaries, and AI models, without ever exposing the raw data.

IBM at the Edge

In our discussion, Dr. Fuller provided a few examples to illustrate how IBM plans to provide new and seamless edge solutions for existing enterprise problems.

Example #1 – McDonald’s drive-thru

Dr. Fuller’s first example centered around Quick Service Restaurant’s (QSR) problem of drive-thru order taking. Last year, IBM acquired an automated order-taking system from McDonald's. As part of the acquisition, IBM and McDonald's established a partnership to perfect voice ordering methods using AI. Drive-thru orders are a significant percentage of total QSR orders for McDonald's and other QSR chains.

McDonald's and other QSR restaurants would like every order to be processed as quickly and accurately as possible. For that reason, McDonald's conducted trials at ten Chicago restaurants using an edge-based AI ordering system with NLP (Natural Language Processing) to convert spoken orders into a digital format. It was found that AI had the potential to reduce ordering errors and processing time significantly. Since McDonald's sells almost 7 million hamburgers daily, shaving a minute or two off each order represents a significant opportunity to address labor shortages and increase customer satisfaction.

Example #2 – Boston Dynamics and Spot the agile mobile robot

According to an earlier IBM survey, many manufacturers have already implemented AI-driven robotics with autonomous decision-making capability. The study also indicated that over 80 percent of companies believe AI can help Excellerate future business operations. However, some companies expressed concern about the limited mobility of edge devices and sensors.

To develop a mobile edge solution, IBM teamed up with Boston Dynamics. The partnership created an agile mobile robot using IBM Research and IBM Sustainability Software AI technology. The device can analyze visual sensor readings in hazardous and challenging industrial environments such as manufacturing plants, warehouses, electrical grids, waste treatment plants and other hazardous environments. The value proposition that Boston Dynamics brought to the partnership was Spot the agile mobile robot, a walking, sensing, and actuation platform. Like all edge applications, the robot’s wireless mobility uses self-contained AI/ML that doesn’t require access to cloud data. It uses cameras to read analog devices, visually monitor fire extinguishers, and conduct a visual inspection of human workers to determine if required safety equipment is being worn.

IBM was able to show up to a 10X speedup by automating some manual tasks, such as converting the detection of a problem into an immediate work order in IBM Maximo to correct it. A fast automated response was not only more efficient, but it also improved the safety posture and risk management for these facilities. Similarly, some factories need to thermally monitor equipment to identify any unexpected hot spots that may show up over time, indicative of a potential failure.

IBM is working with National Grid, an energy company, to develop a mobile solution using Spot, the agile mobile robot, for image analysis of transformers and thermal connectors. As shown in the above graphic, Spot also monitored connectors on both flat surfaces and 3D surfaces. IBM was able to show that Spot could detect excessive heat build-up in small connectors, potentially avoiding unsafe conditions or costly outages. This AI/ML edge application can produce faster response times when an issue is detected, which is why IBM believes significant gains are possible by automating the entire process.

IBM market opportunities

Drive-thru orders and mobile robots are just a few examples of the millions of potential AI applications that exist at the edge and are driven by several billion connected devices.

Edge computing is an essential part of enterprise digital transformation. Enterprises seek ways to demonstrate the feasibility of solving business problems using AI/ML and analytics at the edge. However, once a proof of concept has been successfully demonstrated, it is a common problem for a company to struggle with scalability, data governance, and full-stack solution management.

Challenges with scaling

“Determining entry points for AI at the edge is not the difficult part,” Dr. Fuller said. “Scale is the real issue.”

Scaling edge models is complicated because there are so many edge locations with large amounts of diverse content and a high device density. Because large amounts of data are required for training, data gravity is a potential problem. Further, in many scenarios, vast amounts of data are generated quickly, leading to potential data storage and orchestration challenges. AI Models are also rarely "finished." Monitoring and retraining of models are necessary to keep up with changes the environment.

Through IBM Research, IBM is addressing the many challenges of building an all-encompassing edge architecture and horizontally scalable data and AI technologies. IBM has a wealth of edge capabilities and an architecture to create the appropriate platform for each application.

IBM AI entry points at the edge

IBM sees Edge Computing as a $200 billion market by 2025. Dr. Fuller and his organization have identified four key market entry points for developing and expanding IBM’s edge compute strategy. In order of size, IBM believes its priority edge markets to be intelligent factories (Industry 4.0), telcos, retail automation, and connected vehicles.

IBM and its Red Hat portfolio already have an established presence in each market segment, particularly in intelligent operations and telco. Red Hat is also active in the connected vehicles space.

Industry 4.0

There have been three prior industrial revolutions, beginning in the 1700s up to our current in-progress fourth revolution, Industry 4.0, that promotes a digital transformation.

Manufacturing is the fastest growing and the largest of IBM’s four entry markets. In this segment, AI at the edge can Excellerate quality control, production optimization, asset management, and supply chain logistics. IBM believes there are opportunities to achieve a 4x speed up in implementing edge-based AI solutions for manufacturing operations.

For its Industry 4.0 use case development, IBM, through product, development, research and consulting teams, is working with a major automotive OEM. The partnership has established the following joint objectives:

  • Increase automation and scalability across dozens of plants using 100s of AI / ML models. This client has already seen value in applying AI/ML models for manufacturing applications. IBM Research is helping with re-training models and implementing new ones in an edge environment to help scale even more efficiently. Edge offers faster inference and low latency, allowing AI to be deployed in a wider variety of manufacturing operations requiring instant solutions.
  • Dramatically reduce the time required to onboard new models. This will allow training and inference to be done faster and allow large models to be deployed much more quickly. The quicker an AI model can be deployed in production; the quicker the time-to-value and the return-on-investment (ROI).
  • Accelerate deployment of new inspections by reducing the labeling effort and iterations needed to produce a production-ready model via data summarization. Selecting small data sets for annotation means manually examining thousands of images, this is a time-consuming process that will result in - labeling of redundant data. Using ML-based automation for data summarization will accelerate the process and produce better model performance.
  • Enable Day-2 AI operations to help with data lifecycle automation and governance, model creation, reduce production errors, and provide detection of out-of-distribution data to help determine if a model’s inference is accurate. IBM believes this will allow models to be created faster without data scientists.

Maximo Application Suite

IBM’s Maximo Application Suite plays an important part in implementing large manufacturers' current and future IBM edge solutions. Maximo is an integrated public or private cloud platform that uses AI, IoT, and analytics to optimize performance, extend asset lifecycles and reduce operational downtime and costs. IBM is working with several large manufacturing clients currently using Maximo to develop edge use cases, and even uses it within its own Manufacturing.

IBM has research underway to develop a more efficient method of handling life cycle management of large models that require immense amounts of data. Day 2 AI operations tasks can sometimes be more complex than initial model training, deployment, and scaling. Retraining at the edge is difficult because resources are typically limited.

Once a model is trained and deployed, it is important to monitor it for drift caused by changes in data distributions or anything that might cause a model to deviate from original requirements. Inaccuracies can adversely affect model ROI.

Day-2 AI Operations (retraining and scaling)

Day-2 AI operations consist of continual updates to AI models and applications to keep up with changes in data distributions, changes in the environment, a drop in model performance, availability of new data, and/or new regulations.

IBM recognizes the advantages of performing Day-2 AI Operations, which includes scaling and retraining at the edge. It appears that IBM is the only company with an architecture equipped to effectively handle Day-2 AI operations. That is a significant competitive advantage for IBM.

A company using an architecture that requires data to be moved from the edge back into the cloud for Day-2 related work will be unable to support many factory AI/ML applications because of the sheer number of AI/ML models to support (100s to 1000s).

“There is a huge proliferation of data at the edge that exists in multiple spokes,” Dr. Fuller said. "However, all that data isn’t needed to retrain a model. It is possible to cluster data into groups and then use sampling techniques to retrain the model. There is much value in federated learning from our point of view.”

Federated learning is a promising training solution being researched by IBM and others. It preserves privacy by using a collaboration of edge devices to train models without sharing the data with other entities. It is a good framework to use when resources are limited.

Dealing with limited resources at the edge is a challenge. IBM’s edge architecture accommodates the need to ensure resource budgets for AI applications are met, especially when deploying multiple applications and multiple models across edge locations. For that reason, IBM developed a method to deploy data and AI applications to scale Day-2 AI operations utilizing hub and spokes.

The graphic above shows the current status quo methods of performing Day-2 operations using centralized applications and a centralized data plane compared to the more efficient managed hub and spoke method with distributed applications and a distributed data plane. The hub allows it all to be managed from a single pane of glass.

Data Fabric Extensions to Hub and Spokes

IBM uses hub and spoke as a model to extend its data fabric. The model should not be thought of in the context of a traditional hub and spoke. IBM’s hub provides centralized capabilities to manage clusters and create multiples hubs that can be aggregated to a higher level. This architecture has four important data management capabilities.

  1. First, models running in unattended environments must be monitored. From an operational standpoint, detecting when a model’s effectiveness has significantly degraded and if corrective action is needed is critical.
  2. Secondly, in a hub and spoke model, data is being generated and collected in many locations creating a need for data life cycle management. Working with large enterprise clients, IBM is building unique capabilities to manage the data plane across the hub and spoke estate - optimized to meet data lifecycle, regulatory & compliance as well as local resource requirements. Automation determines which input data should be selected and labeled for retraining purposes and used to further Excellerate the model. Identification is also made for atypical data that is judged worthy of human attention.
  3. The third issue relates to AI pipeline compression and adaptation. As mentioned earlier, edge resources are limited and highly heterogeneous. While a cloud-based model might have a few hundred million parameters or more, edge models can’t afford such resource extravagance because of resource limitations. To reduce the edge compute footprint, model compression can reduce the number of parameters. As an example, it could be reduced from several hundred million to a few million.
  4. Lastly, suppose a scenario exists where data is produced at multiple spokes but cannot leave those spokes for compliance reasons. In that case, IBM Federated Learning allows learning across heterogeneous data in multiple spokes. Users can discover, curate, categorize and share data assets, data sets, analytical models, and their relationships with other organization members.

In addition to AI deployments, the hub and spoke architecture and the previously mentioned capabilities can be employed more generally to tackle challenges faced by many enterprises in consistently managing an abundance of devices within and across their enterprise locations. Management of the software delivery lifecycle or addressing security vulnerabilities across a vast estate are a case in point.

Multicloud and Edge platform

In the context of its strategy, IBM sees edge and distributed cloud as an extension of its hybrid cloud platform built around Red Hat OpenShift. One of the newer and more useful options created by the Red Hat development team is the Single Node OpenShift (SNO), a compact version of OpenShift that fits on a single server. It is suitable for addressing locations that are still servers but come in a single node, not clustered, deployment type.

For smaller footprints such as industrial PCs or computer vision boards (for example NVidia Jetson Xavier), Red Hat is working on a project which builds an even smaller version of OpenShift, called MicroShift, that provides full application deployment and Kubernetes management capabilities. It is packaged so that it can be used for edge device type deployments.

Overall, IBM and Red Hat have developed a full complement of options to address a large spectrum of deployments across different edge locations and footprints, ranging from containers to management of full-blown Kubernetes applications from MicroShift to OpenShift and IBM Edge Application Manager.

Much is still in the research stage. IBM's objective is to achieve greater consistency in terms of how locations and application lifecycle is managed.

First, Red Hat plans to introduce hierarchical layers of management with Red Hat Advanced Cluster Management (RHACM), to scale by two to three orders of magnitude the number of edge locations managed by this product. Additionally, securing edge locations is a major focus. Red Hat is continuously expanding platform security features, for example by recently including Integrity Measurement Architecture in Red Hat Enterprise Linux, or by adding Integrity Shield to protect policies in Red Hat Advanced Cluster Management (RHACM).

Red Hat is partnering with IBM Research to advance technologies that will permit it to protect platform integrity and the integrity of client workloads through the entire software supply chains. In addition, IBM Research is working with Red Hat on analytic capabilities to identify and remediate vulnerabilities and other security risks in code and configurations.

Telco network intelligence and slice management with AL/ML

Communication service providers (CSPs) such as telcos are key enablers of 5G at the edge. 5G benefits for these providers include:

  • Reduced operating costs
  • Improved efficiency
  • Increased distribution and density
  • Lower latency

The end-to-end 5G network comprises the Radio Access Network (RAN), transport, and core domains. Network slicing in 5G is an architecture that enables multiple virtual and independent end-to-end logical networks with different characteristics such as low latency or high bandwidth, to be supported on the same physical network. This is implemented using cloud-native technology enablers such as software defined networking (SDN), virtualization, and multi-access edge computing. Slicing offers necessary flexibility by allowing the creation of specific applications, unique services, and defined user groups or networks.

An important aspect of enabling AI at the edge requires IBM to provide CSPs with the capability to deploy and manage applications across various enterprise locations, possibly spanning multiple end-to-end network slices, using a single pane of glass.

5G network slicing and slice management

Network slices are an essential part of IBM's edge infrastructure that must be automated, orchestrated and optimized according to 5G standards. IBM’s strategy is to leverage AI/ML to efficiently manage, scale, and optimize the slice quality of service, measured in terms of bandwidth, latency, or other metrics.

5G and AI/ML at the edge also represent a significant opportunity for CSPs to move beyond traditional cellular services and capture new sources of revenue with new services.

Communications service providers need management and control of 5G network slicing enabled with AI-powered automation.

Dr. Fuller sees a variety of opportunities in this area. "When it comes to applying AI and ML on the network, you can detect things like intrusion detection and malicious actors," he said. "You can also determine the best way to route traffic to an end user. Automating 5G functions that run on the network using IBM network automation software also serves as an entry point.”

In IBM’s current telecom trial, IBM Research is spearheading the development of a range of capabilities targeted for the IBM Cloud Pak for Network Automation product using AI and automation to orchestrate, operate and optimize multivendor network functions and services that include:

  • End-to-end 5G network slice management with planning & design, automation & orchestration, and operations & assurance
  • Network Data and AI Function (NWDAF) that collects data for slice monitoring from 5G Core network functions, performs network analytics, and provides insights to authorized data consumers.
  • Improved operational efficiency and reduced cost

Future leverage of these capabilities by existing IBM Clients that use the Cloud Pak for Network Automation (e.g., DISH) can offer further differentiation for CSPs.

5G radio access

Open radio access networks (O-RANs) are expected to significantly impact telco 5G wireless edge applications by allowing a greater variety of units to access the system. The O-RAN concept separates the DU (Distributed Units) and CU (Centralized Unit) from a Baseband Unit in 4G and connects them with open interfaces.

O-RAN system is more flexible. It uses AI to establish connections made via open interfaces that optimize the category of a device by analyzing information about its prior use. Like other edge models, the O-RAN architecture provides an opportunity for continuous monitoring, verification, analysis, and optimization of AI models.

The IBM-telco collaboration is expected to advance O-RAN interfaces and workflows. Areas currently under development are:

  • Multi-modal (RF level + network-level) analytics (AI/ML) for wireless communication with high-speed ingest of 5G data
  • Capability to learn patterns of metric and log data across CUs and DUs in RF analytics
  • Utilization of the antenna control plane to optimize throughput
  • Primitives for forecasting, anomaly detection and root cause analysis using ML
  • Opportunity of value-added functions for O-RAN

IBM Cloud and Infrastructure

The cornerstone for the delivery of IBM's edge solutions as a service is IBM Cloud Satellite. It presents a consistent cloud-ready, cloud-native operational view with OpenShift and IBM Cloud PaaS services at the edge. In addition, IBM integrated hardware and software Edge systems will provide RHACM - based management of the platform when clients or third parties have existing managed as a service models. It is essential to note that in either case this is done within a single control plane for hubs and spokes that helps optimize execution and management from any cloud to the edge in the hub and spoke model.

IBM's focus on “edge in” means it can provide the infrastructure through things like the example shown above for software defined storage for federated namespace data lake that surrounds other hyperscaler clouds. Additionally, IBM is exploring integrated full stack edge storage appliances based on hyperconverged infrastructure (HCI), such as the Spectrum Fusion HCI, for enterprise edge deployments.

As mentioned earlier, data gravity is one of the main driving factors of edge deployments. IBM has designed its infrastructure to meet those data gravity requirements, not just for the existing hub and spoke topology but also for a future spoke-to-spoke topology where peer-to-peer data sharing becomes imperative (as illustrated with the wealth of examples provided in this article).

Wrap up

Edge is a distributed computing model. One of its main advantages is that computing, and data storage and processing is close to where data is created. Without the need to move data to the cloud for processing, real-time application of analytics and AI capabilities provides immediate solutions and drives business value.

IBM’s goal is not to move the entirety of its cloud infrastructure to the edge. That has little value and would simply function as a hub to spoke model operating on actions and configurations dictated by the hub.

IBM’s architecture will provide the edge with autonomy to determine where data should reside and from where the control plane should be exercised.

Equally important, IBM foresees this architecture evolving into a decentralized model capable of edge-to-edge interactions. IBM has no firm designs for this as yet. However, the plan is to make the edge infrastructure and platform a first-class citizen instead of relying on the cloud to drive what happens at the edge.

Developing a complete and comprehensive AI/ML edge architecture - and in fact, an entire ecosystem - is a massive undertaking. IBM faces many known and unknown challenges that must be solved before it can achieve success.

However, IBM is one of the few companies with the necessary partners and the technical and financial resources to undertake and successfully implement a project of this magnitude and complexity.

It is reassuring that IBM has a plan and that its plan is sound.

Paul Smith-Goodson is Vice President and Principal Analyst for quantum computing, artificial intelligence and space at Moor Insights and Strategy. You can follow him on Twitter for more current information on quantum, AI, and space.

Note: Moor Insights & Strategy writers and editors may have contributed to this article.

Moor Insights & Strategy, like all research and tech industry analyst firms, provides or has provided paid services to technology companies. These services include research, analysis, advising, consulting, benchmarking, acquisition matchmaking, and speaking sponsorships. The company has had or currently has paid business relationships with 8×8, Accenture, A10 Networks, Advanced Micro Devices, Amazon, Amazon Web Services, Ambient Scientific, Anuta Networks, Applied Brain Research, Applied Micro, Apstra, Arm, Aruba Networks (now HPE), Atom Computing, AT&T, Aura, Automation Anywhere, AWS, A-10 Strategies, Bitfusion, Blaize, Box, Broadcom, C3.AI, Calix, Campfire, Cisco Systems, Clear Software, Cloudera, Clumio, Cognitive Systems, CompuCom, Cradlepoint, CyberArk, Dell, Dell EMC, Dell Technologies, Diablo Technologies, Dialogue Group, Digital Optics, Dreamium Labs, D-Wave, Echelon, Ericsson, Extreme Networks, Five9, Flex, Foundries.io, Foxconn, Frame (now VMware), Fujitsu, Gen Z Consortium, Glue Networks, GlobalFoundries, Revolve (now Google), Google Cloud, Graphcore, Groq, Hiregenics, Hotwire Global, HP Inc., Hewlett Packard Enterprise, Honeywell, Huawei Technologies, IBM, Infinidat, Infosys, Inseego, IonQ, IonVR, Inseego, Infosys, Infiot, Intel, Interdigital, Jabil Circuit, Keysight, Konica Minolta, Lattice Semiconductor, Lenovo, Linux Foundation, Lightbits Labs, LogicMonitor, Luminar, MapBox, Marvell Technology, Mavenir, Marseille Inc, Mayfair Equity, Meraki (Cisco), Merck KGaA, Mesophere, Micron Technology, Microsoft, MiTEL, Mojo Networks, MongoDB, MulteFire Alliance, National Instruments, Neat, NetApp, Nightwatch, NOKIA (Alcatel-Lucent), Nortek, Novumind, NVIDIA, Nutanix, Nuvia (now Qualcomm), onsemi, ONUG, OpenStack Foundation, Oracle, Palo Alto Networks, Panasas, Peraso, Pexip, Pixelworks, Plume Design, PlusAI, Poly (formerly Plantronics), Portworx, Pure Storage, Qualcomm, Quantinuum, Rackspace, Rambus, Rayvolt E-Bikes, Red Hat, Renesas, Residio, Samsung Electronics, Samsung Semi, SAP, SAS, Scale Computing, Schneider Electric, SiFive, Silver Peak (now Aruba-HPE), SkyWorks, SONY Optical Storage, Splunk, Springpath (now Cisco), Spirent, Splunk, Sprint (now T-Mobile), Stratus Technologies, Symantec, Synaptics, Syniverse, Synopsys, Tanium, Telesign,TE Connectivity, TensTorrent, Tobii Technology, Teradata,T-Mobile, Treasure Data, Twitter, Unity Technologies, UiPath, Verizon Communications, VAST Data, Ventana Micro Systems, Vidyo, VMware, Wave Computing, Wellsmith, Xilinx, Zayo, Zebra, Zededa, Zendesk, Zoho, Zoom, and Zscaler. Moor Insights & Strategy founder, CEO, and Chief Analyst Patrick Moorhead is an investor in dMY Technology Group Inc. VI, Dreamium Labs, Groq, Luminar Technologies, MemryX, and Movandi.

Mon, 08 Aug 2022 03:51:00 -0500 Paul Smith-Goodson en text/html https://www.forbes.com/sites/moorinsights/2022/08/08/ibm-research-rolls-out-a-comprehensive-ai-and-ml-edge-research-strategy-anchored-by-enterprise-partnerships-and-use-cases/
Killexams : IBM Acquires Databand.ai to Boost Data Observability Capabilities

IBM is acquiring Databand.ai, a leading provider of data observability software that helps organizations fix issues with their data, including errors, pipeline failures, and poor quality. The acquisition further strengthens IBM's software portfolio across data, AI, and automation to address the full spectrum of observability.

Databand.ai is IBM's fifth acquisition in 2022 as the company continues to bolster its hybrid cloud and AI skills and capabilities.

Databand.ai's open and extendable approach allows data engineering teams to easily integrate and gain observability into their data infrastructure.

This acquisition will unlock more resources for Databand.ai to expand its observability capabilities for broader integrations across more of the open source and commercial solutions that power the modern data stack.

Enterprises will also have full flexibility in how to run Databand.ai, whether as-a-Service (SaaS) or a self-hosted software subscription.

The acquisition of Databand.ai builds on IBM's research and development investments as well as strategic acquisitions in AI and automation. By using Databand.ai with IBM Observability by Instana APM and IBM Watson Studio, IBM is well-positioned to address the full spectrum of observability across IT operations.

"Our clients are data-driven enterprises who rely on high-quality, trustworthy data to power their mission-critical processes. When they don't have access to the data they need in any given moment, their business can grind to a halt," said Daniel Hernandez, general manager for data and AI, IBM. "With the addition of Databand.ai, IBM offers the most comprehensive set of observability capabilities for IT across applications, data and machine learning, and is continuing to provide our clients and partners with the technology they need to deliver trustworthy data and AI at scale."

The acquisition of Databand.ai further extends IBM's existing data fabric solution by helping ensure that the most accurate and trustworthy data is being put into the right hands at the right time—no matter where it resides.

Headquartered in Tel Aviv, Israel, Databand.ai employees will join IBM Data and AI, further building on IBM's growing portfolio of Data and AI products, including its IBM Watson capabilities and IBM Cloud Pak for Data. Financial details of the deal were not disclosed. The acquisition closed on June 27, 2022.

For more information about this news, visit www.ibm.com.


Mon, 11 Jul 2022 01:00:00 -0500 en text/html https://www.dbta.com/Editorial/News-Flashes/IBM-Acquires-Databandai-to-Boost-Data-Observability-Capabilities-153842.aspx
Killexams : Product Information Management (PIM) Market Growing at a CAGR 14.3% | Key Player Oracle, SAP, IBM, Informatica, Pimcore
Product Information Management (PIM) Market Growing at a CAGR 14.3% | Key Player Oracle, SAP, IBM, Informatica, Pimcore

“Oracle (US), SAP (US), IBM (US), Informatica (US), Pimcore (Austria), Akeneo (France), inriver (Sweden), Winshuttle (US), Riversand (US), Salsify (US), Aprimo (US), Stibo Systems (Denmark), Contentserv (Switzerland), Mobius (India), Perfion (Denmark), Profisee (US), Censhare (Germany), Vinculum (India), PIMworks (US), Truecommerce (US), Vimedici (Germany).”

Product Information Management (PIM) Market by Component, Solution (Multi-domain, and Single Domain), Deployment Type, Organization Size, Vertical (Consumer Goods & retail, IT & Telecom, and Media & Entertainment) and Region – Global Forecast to 2027

The global Product Information Management Market size to grow from USD 12.2 billion in 2022 to USD 23.8 billion by 2027, at a Compound Annual Growth Rate (CAGR) of 14.3% during the forecast period. Growing demands for better customer experience, as well as updating product information across all the channels, demand for delivering contextualized user experience, and a raise in digital content across enterprises, are among the major factors boosting the growth of the PIM market. 

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As per verticals, the media & entertainment segment to grow at highest CAGR during the forecast period

PIM market is segmented into BFSI, consumer goods & retail, manufacturing, IT & telecom, media & entertainment, healthcare & life sciences, transportation & logistics, and other verticals (education, and travel & hospitality). As per verticals, the media & entertainment vertical is expected to grow at the highest CAGR during the forecast period. The demand for PIM among media & entertainment industry vertical is rising owing to rising need for data silos, different data management systems, diverse and vast product portfolios, dispersed product information, changing consumer expectations, and growing demand for reducing the time-to-market.

Cloud Segment to grow at the highest CAGR during the forecast period

As per deployment mode, Cloud Segment to grow at the highest CAGR for the cPIM market during the forecast period. The PIM market by deployment type is segmented into on-premises and cloud deployment type. On-premises deployment type is preferred by large enterprises as such organizatios prefer information to be kept on local server as cloud has security issues. However, large enterprises and SMEs have started adopting cloud services and solutions in oreder to reduce their CAPEX. Moreover, cloud-based solutions offerd scalability, flexibility and pay as you go model which helps orgnizations to save cost and access services and solutions anywhere at any time. However, security issues associated with cloud deployment type is expected to impact its adoption during the forecast period.

Request sample Pages: https://www.marketsandmarkets.com/requestsampleNew.asp?id=661489

The PIM solutions enable companies to create, update, and maintain product information to optimize product data synchronization and publishing, ensure faster TTM, increase brand awareness, drive online traffic and sales, and enhance customer experience and satisfaction. Furthermore, emerging technologies, such as AI, and the integration capabilities of PIM solutions with various data platforms, such as ERP and marketing platforms, and merging capabilities, such as DAM and content management, have facilitated better product assortment and improved data syndication in real time therefore, its demand across organizations are increasing

Some of the key players operating in the content services platform market include Oracle (US), SAP (US), IBM (US), Informatica (US), Pimcore (Austria), Akeneo (France), Inriver (Sweden), Winshuttle (US), Riversand (US), Salsify (US), Aprimo (US), Stibo Systems (Denmark), Contentserv (Switzerland), Mobius (India), Perfion (Denmark), Profisee (US), Censhare (Germany), Vinculum (India), Pimworks (US), Truecommerce (US), Vimedici (germany), Magnitude Software (US), Plytix (Denmark) and Syndigo (US). These PIM vendors have adopted various organic and inorganic strategies to sustain their positions and increase their market shares in the global PIM market.

IBM was founded in 1911 and is headquartered in New York, US. The company is listed on the New York Stock Exchange (NYSE) under the ticker symbol IBM. It is one of the leading providers of hardware; software; and a broad range of infrastructure, hosting, cloud, and consulting services in areas ranging from mainframe computers to nanotechnology. It operates through six business segments: cloud and cognitive software, global business services, global technology services, systems, global financing, and others. It offers a diversified product portfolio ranging from software to finances and storage to integrated systems. IBM provides solutions to various verticals, such as IT, healthcare and life sciences, government, telecom, automobile, manufacturing, fast-moving consumer goods, chemicals and petroleum, electronics, energy and power, media and entertainment, mining, retail, BFSI, travel and transportation, and education. It has a presence in more than 175 countries in regions of North America, Europe, Asia Pacific, Middle East & Africa, and Latin America.

In 2009, Pimcore was developed at New Media Solutions GmbH by digital agency elements. In 2010, the company launched its first public beta version. However, the Pimcore GmbH was officially established in Salzburg, Austria, in 2013 and is headquartered in Salzburg, Austria. It is an open-source Digital Experience Platform (DXP) provider and makes it possible to collaborate in a single platform across the entire digital organization. The company’s PIM, MDM, CDP, and DAM modules consolidate any type and amount of digital information – to solve enterprise data issues, such as scattered, siloed, and messy data. Pimcore PIM software centralizes and harmonizes marketing, sales, and technical product information. Its Content Management System (CMS) and digital commerce modules personalize the customer experience, including marketing automation. A fully API-driven architecture enables superior TTM and unmatched connectivity. Enterprises choose Pimcore to solve digital transformation challenges because it ensures flexibility, intellectual property ownership, continuous innovation, and the lowest Total Cost of Ownership (TCO). The open-source software can be used free of charge by organizations of any size or in any industry. Its more than 150 employees provide optional Service Level Agreements (SLAs), training and professional consulting, implementation, and integration services. It has enabled thousands of customers globally, including Burger King, Audi, T-Mobile, and IKEA, to help centralize their data management operations, simplify IT architectures, automate processes, and create stunning digital experiences that provide significant value for their businesses. Furthermore, the company has a prominent geographic presence with offices across North America, Europe, and the Asia Pacific.

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Killexams : IBM Aims to Capture Growing Market Opportunity for Data Observability with Databand.ai Acquisition

Acquisition helps enterprises catch "bad data" at the source

Extends IBM's leadership in observability to the full stack of capabilities for IT -- across infrastructure, applications, data and machine learning

ARMONK, N.Y., July 6, 2022  /CNW/ -- IBM (NYSE: IBM) today announced it has acquired Databand.ai, a leading provider of data observability software that helps organizations fix issues with their data, including errors, pipeline failures and poor quality — before it impacts their bottom-line. Today's news further strengthens IBM's software portfolio across data, AI and automation to address the full spectrum of observability and helps businesses ensure that trustworthy data is being put into the right hands of the right users at the right time.

Databand.ai is IBM's fifth acquisition in 2022 as the company continues to bolster its hybrid cloud and AI skills and capabilities. IBM has acquired more than 25 companies since Arvind Krishna became CEO in April 2020.

As the volume of data continues to grow at an unprecedented pace, organizations are struggling to manage the health and quality of their data sets, which is necessary to make better business decisions and gain a competitive advantage. A rapidly growing market opportunity, data observability is quickly emerging as a key solution for helping data teams and engineers better understand the health of data in their system and automatically identify, troubleshoot and resolve issues, like anomalies, breaking data changes or pipeline failures, in near real-time. According to Gartner, every year poor data quality costs organizations an average $12.9 million. To help mitigate this challenge, the data observability market is poised for strong growth.1

Data observability takes traditional data operations to the next level by using historical trends to compute statistics about data workloads and data pipelines directly at the source, determining if they are working, and pinpointing where any problems may exist. When combined with a full stack observability strategy, it can help IT teams quickly surface and resolve issues from infrastructure and applications to data and machine learning systems.

Databand.ai's open and extendable approach allows data engineering teams to easily integrate and gain observability into their data infrastructure. This acquisition will unlock more resources for Databand.ai to expand its observability capabilities for broader integrations across more of the open source and commercial solutions that power the modern data stack. Enterprises will also have full flexibility in how to run Databand.ai, whether as-a-Service (SaaS) or a self-hosted software subscription.

The acquisition of Databand.ai builds on IBM's research and development investments as well as strategic acquisitions in AI and automation. By using Databand.ai with IBM Observability by Instana APM and IBM Watson Studio, IBM is well-positioned to address the full spectrum of observability across IT operations.

For example, Databand.ai capabilities can alert data teams and engineers when the data they are using to fuel an analytics system is incomplete or missing. In common cases where data originates from an enterprise application, Instana can then help users quickly explain exactly where the missing data originated from and why an application service is failing. Together, Databand.ai and IBM Instana provide a more complete and explainable view of the entire application infrastructure and data platform system, which can help organizations prevent lost revenue and reputation.

"Our clients are data-driven enterprises who rely on high-quality, trustworthy data to power their mission-critical processes. When they don't have access to the data they need in any given moment, their business can grind to a halt," said Daniel Hernandez, General Manager for Data and AI, IBM. "With the addition of Databand.ai, IBM offers the most comprehensive set of observability capabilities for IT across applications, data and machine learning, and is continuing to provide our clients and partners with the technology they need to deliver trustworthy data and AI at scale."

Data observability solutions are also a key part of an organization's broader data strategy and architecture. The acquisition of Databand.ai further extends IBM's existing data fabric solution  by helping ensure that the most accurate and trustworthy data is being put into the right hands at the right time – no matter where it resides.

"You can't protect what you can't see, and when the data platform is ineffective, everyone is impacted –including customers," said Josh Benamram, Co-Founder and CEO, Databand.ai. "That's why global brands such as FanDuel, Agoda and Trax Retail already rely on Databand.ai to remove bad data surprises by detecting and resolving them before they create costly business impacts. Joining IBM will help us scale our software and significantly accelerate our ability to meet the evolving needs of enterprise clients."

Headquartered in Tel Aviv, Israel, Databand.ai employees will join IBM Data and AI, further building on IBM's growing portfolio of Data and AI products, including its IBM Watson capabilities and IBM Cloud Pak for Data. Financial details of the deal were not disclosed. The acquisition closed on June 27, 2022.

To learn more about Databand.ai and how this acquisition enhances IBM's data fabric solution and builds on its full stack of observability software, you can read our blog about the news or visit here: https://www.ibm.com/analytics/data-fabric.

About Databand.ai

Databand.ai is a product-driven technology company that provides a proactive data observability platform, which empowers data engineering teams to deliver reliable and trustworthy data. Databand.ai removes bad data surprises such as data incompleteness, anomalies, and breaking data changes by detecting and resolving issues before they create costly business impacts. Databand.ai's proactive approach ties into all stages of your data pipelines, beginning with your source data, through ingestion, transformation, and data access. Databand.ai serves organizations throughout the globe, including some of the world's largest companies in entertainment, technology, and communications. Our focus is on enabling customers to extract the maximum value from their strategic data investments. Databand.ai is backed by leading VCs Accel, Blumberg Capital, Lerer Hippeau, Differential Ventures, Ubiquity Ventures, Bessemer Venture Partners, Hyperwise, and F2. To learn more, visit www.databand.ai.

About IBM

IBM is a leading global hybrid cloud and AI, and business services provider, helping clients in more than 175 countries capitalize on insights from their data, streamline business processes, reduce costs and gain the competitive edge in their industries. Nearly 3,800 government and corporate entities in critical infrastructure areas such as financial services, telecommunications and healthcare rely on IBM's hybrid cloud platform and Red Hat OpenShift to affect their digital transformations quickly, efficiently, and securely. IBM's breakthrough innovations in AI, quantum computing, industry-specific cloud solutions and business services deliver open and flexible options to our clients. All of this is backed by IBM's legendary commitment to trust, transparency, responsibility, inclusivity, and service. For more information, visit www.ibm.com.

Media Contact:
Sarah Murphy
IBM Communications
[email protected]

1 [1] Source: Smarter with Gartner, "How to Excellerate Your Data Quality," Manasi Sakpal, [July 14, 2021]

GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and is used herein with permission. All rights reserved.

SOURCE IBM

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Killexams : How Wimbledon is keeping its eye on the ball with IBM tech

Vish Gain visited Wimbledon to get a sneak peek of how IBM is using data and AI to help the tennis tournament engage with fans in its ‘pursuit of greatness’.

It’s not every day that you get to visit Wimbledon and walk around its hallowed courts during the tournament. An even rarer cohort of individuals gets to visit the underground bunkers where the behind-the-scenes action happens. I was lucky enough to do both last week.

Walking into the premises of the world’s oldest and most prestigious tennis tournament, I wasn’t sure what to expect. I’d watched Wimbledon matches growing up, but witnessing one live was a different ball game altogether, excuse the pun.

But my trip to Wimbledon wasn’t just about watching the action as it happened, but to dig deeper. And by digging deeper, I mean visiting the underground data rooms run by Wimbledon’s technology partner, IBM.

IBM has been a tech partner of Wimbledon since 1990. Since then, the two have been linked inextricably, trying to innovate new ways of engaging Wimbledon’s worldwide audience and using technology to live up to its motto: ‘In pursuit of greatness.’

Data analysis, automation and artificial intelligence are just some of the technologies developed by IBM and its partners that are being deployed to make watching Wimbledon, both in-person and from afar, a more meaningful experience.

“It all starts with the data,” Kevin Farrar, IBM UK sports partnerships lead, told me. “We’ve built this platform of innovation with the club to turn massive amounts of data into engaging and meaningful insights for the fans.”

Farrar works with a team of experts who, in collaboration with other technology partners, collect and process the immense amounts of data generated throughout the tournament.

“We’re collecting the test stats. There’s the direction of serve, how the ball is returned, backhand or forehand, the rally count, how the point is won, if it’s a forced or unforced error,” he whispered to me in a room full of experts wearing headphones watching the matches closely.

This information is collected from thousands of data points, which are then combined with data from other sources, such as Hawk-Eye’s electronic line-calling technology, to produce meaningful insights that are fed into the Wimbledon website and to global broadcasters.

Wimbledon and the IBM Power Index

The fruit of this behind-the-scenes work by IBM is best displayed on Wimbledon’s official website, where live updates on matches are combined with AI-powered match insights to make the sport exciting for those not within the premises.

This year, for example, has seen the introduction of the IBM Power Index, an AI-powered daily ranking of player momentum before and during Wimbledon. Using Watson, IBM’s powerful natural language processing system, the Power Index analyses player performance, media commentary and other factors to quantify momentum.

“A lot of people just watch tennis once a year – they watch Wimbledon. They’ll know the big names, but they won’t necessarily know the upcoming players. The Power Index gives a mechanism for them to sort of identify players that are hot at the moment,” Farrar said.

Users of the Wimbledon website or smartphone app can view the Power Index and click on any player they find interesting and want to keep an eye on. They can track the player’s progress and get personalised updates based on what or who they’re interested in.

“It’s an algorithm that takes both structured data and unstructured data,” Farrar explained. “The structured data is the scores and match results. But it’s also looking at the media buzz through trusted data sources, to see what the media is saying about the players.”

The Sherlock-like Watson (although named after early IBM CEO Thomas Watson) is also able to use vast amounts of data and expert input to predict which of the two players in any given match has a higher chance of winning. Fans on the app can weigh in too and see how far they stand from the AI estimate.

Serving the fans

Farrar said the reason IBM is doing all this is to engage with fans interested in both technical details as well as the “drama and beauty of it all” through a visual experience. In the 2021 championships, Wimbledon reached approximately 18m people through its digital platforms.

“Sports fans love debate. So, putting something out there in terms of a prediction that Watson has come up with, they’ll have their own views and their own win factors in their mind. It’s about engaging the fans in that social debate and asking them, ‘Well, what do you think?’”

For Deborah Threadgold, IBM Ireland country manager, the relationship between Wimbledon and IBM is a great example of what the company’s strategy is all around.

“When you look at the data piece, when you look at the automation piece, and the security and how it is all sitting on that platform, and how that’s allowing them to innovate, then that’s exactly what IBM brings to all of our clients,” Threadgold told me.

“So even here in Ireland, whether you’re in the sporting industry, or much more broadly, whether you’re in financial services, public sector, whatever it may be, all of those tools and those mechanisms, you can actually reimagine how that works into your own industry.”

Of the four cornerstone annual tennis tournaments, Wimbledon is by far the most traditional with the richest history. It has been played since 1877 at the All England Lawn Tennis and Croquet Club in London.

“Our challenges here is to get that balance right between the tradition and heritage of the club, and the way they present themselves with technology and innovation,” Farrar said. “The brand is very important to them, and we make sure that that remains the case while still innovating every year.”

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Thu, 07 Jul 2022 12:00:00 -0500 en text/html https://www.siliconrepublic.com/machines/wimbledon-2022-results-ibm-watson-technology
Killexams : Key Customer Management BPO Service Market to See Booming Growth | Concentrix, Firstsource, IBM

Advance Market Analytics published a new research publication on “Global Key Customer Management BPO Service Market Insights, to 2027” with 232 pages and enriched with self-explained Tables and charts in presentable format. In the Study you will find new evolving Trends, Drivers, Restraints, Opportunities generated by targeting market associated stakeholders. The growth of the Key Customer Management BPO Service market was mainly driven by the increasing R&D spending across the world.

Major players profiled in the study are:

Sutherland Global Services (United States), Concentrix (United States), Firstsource (India), HGS (India), IBM (United States),

Get Free Exclusive PDF sample Copy of This Research @ https://www.advancemarketanalytics.com/sample-report/111008-global-key-customer-management-bpo-service-market

Scope of the Report of Key Customer Management BPO Service

The companies operating in this marketing are focusing on effective growth, development of operational efficiency and output, achieving high safety standards, and emphasis on maintaining maintainable development. The vendors are concentrating on securing the leading position in this industry. They are endlessly looking for the opportunity to strengthen their competitive advantage.

The Global Key Customer Management BPO Service Market segments and Market Data Break Down are illuminated below:

by Type (Telephony, Business Process as a Service (BPaaS), Email Response Management, Web/Mobile Chat, Knowledge Management for Web and Mobile-Based Self-Service), Application (SME (Small and Medium Enterprises), Large Enterprise), Deployment Mode (Cloud-Based, On-Premises)

Market Opportunities:

  • The advent of Online Services across Every industry

Market Drivers:

  • Rising Number of BPO Services across the Globe

Market Trend:

  • Technology Advancement in Cloud-Based Technologies

What can be explored with the Key Customer Management BPO Service Market Study?

  • Gain Market Understanding
  • Identify Growth Opportunities
  • Analyze and Measure the Global Key Customer Management BPO Service Market by Identifying Investment across various Industry Verticals
  • Understand the Trends that will drive Future Changes in Key Customer Management BPO Service
  • Understand the Competitive Scenarios
    • Track Right Markets
    • Identify the Right Verticals

Region Included are: North America, Europe, Asia Pacific, Oceania, South America, Middle East & Africa

Country Level Break-Up: United States, Canada, Mexico, Brazil, Argentina, Colombia, Chile, South Africa, Nigeria, Tunisia, Morocco, Germany, United Kingdom (UK), the Netherlands, Spain, Italy, Belgium, Austria, Turkey, Russia, France, Poland, Israel, United Arab Emirates, Qatar, Saudi Arabia, China, Japan, Taiwan, South Korea, Singapore, India, Australia and New Zealand etc.

Have Any Questions Regarding Global Key Customer Management BPO Service Market Report, Ask Our [email protected] https://www.advancemarketanalytics.com/enquiry-before-buy/111008-global-key-customer-management-bpo-service-market

Strategic Points Covered in Table of Content of Global Key Customer Management BPO Service Market:

Chapter 1: Introduction, market driving force product Objective of Study and Research Scope the Key Customer Management BPO Service market

Chapter 2: Exclusive Summary – the basic information of the Key Customer Management BPO Service Market.

Chapter 3: Displaying the Market Dynamics- Drivers, Trends and Challenges & Opportunities of the Key Customer Management BPO Service

Chapter 4: Presenting the Key Customer Management BPO Service Market Factor Analysis, Porters Five Forces, Supply/Value Chain, PESTEL analysis, Market Entropy, Patent/Trademark Analysis.

Chapter 5: Displaying the by Type, End User and Region/Country 2016-2021

Chapter 6: Evaluating the leading manufacturers of the Key Customer Management BPO Service market which consists of its Competitive Landscape, Peer Group Analysis, BCG Matrix & Company Profile

Chapter 7: To evaluate the market by segments, by countries and by Manufacturers/Company with revenue share and sales by key countries in these various regions (2022-2027)

Chapter 8 & 9: Displaying the Appendix, Methodology and Data Source

finally, Key Customer Management BPO Service Market is a valuable source of guidance for individuals and companies.

Read Detailed Index of full Research Study at @ https://www.advancemarketanalytics.com/buy-now?format=1&report=111008

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Killexams : IBM report: Middle Eastern consumers pay the price as regional data breach costs reach all-time high

Riyadh, Saudi Arabia: IBM, the leading global technology company, has published a study highlighting the importance of cybersecurity in an increasingly digital age. According to IBM Security’s annual Cost of a Data Breach Report,  the Middle East has incurred losses of SAR 28 million from data breaches  in 2022 alone — this figure already exceeding the total amount of losses accrued in each of the last eight years. 

The latest edition of the Cost of a Data Breach Report — now in its 17th year — reveals costlier and higher-impact data breaches than ever before. As outlined by the study, the global average cost of a data breach has reached an all-time high of $4.35 million for surveyed organizations. With breach costs increasing nearly 13% over the last two years of the report, the findings suggest these incidents may also be contributing to rising costs of goods and services. In fact, 60% of studied organizations raised their product or services prices due to the breach, when the cost of goods is already soaring worldwide amid inflation and supply chain issues.

Notably, the report ranks the Middle East2 among the top five countries and regions for the highest average cost of a data breach. As per the study, the average total cost of a data breach in the Middle East amounted to SAR 28 million in 2022, the region being second only to the United States on the list. The report also spotlights the industries across the Middle East that have suffered the highest per-record costs in millions; the financial (SAR 1,039), health (SAR 991) and energy (SAR 950) sectors taking first, second and third spot, respectively.    

Fahad Alanazi, IBM Saudi General Manager, said: “Today, more so than ever, in an increasingly connected and digital age, cybersecurity is of the utmost importance. It is essential to safeguard businesses and privacy. As the digital economy continues to evolve, enhanced security will be the marker of a modern, world class digital ecosystem.” 

He continued: “At IBM, we take great pride in enabling the people, businesses and communities we serve to fulfil their potential by empowering them with state-of-the-art services and support. Our findings reiterate just how important it is for us, as a technology leader, to continue pioneering solutions that will help the Kingdom distinguish itself as the tech capital of the region.”

The perpetuality of cyberattacks is also shedding light on the “haunting effect” data breaches are having on businesses, with the IBM report finding 83% of studied organizations have experienced more than one data breach in their lifetime. Another factor rising over time is the after-effects of breaches on these organizations, which linger long after they occur, as nearly 50% of breach costs are incurred more than a year after the breach.

The 2022 Cost of a Data Breach Report is based on in-depth analysis of real-world data breaches experienced by 550 organizations globally between March 2021 and March 2022. The research, which was sponsored and analyzed by IBM Security, was conducted by the Ponemon Institute.

Some of the key global findings in the 2022 IBM report include:

  • Critical Infrastructure Lags in Zero Trust – Almost 80% of critical infrastructure organizations studied don’t adopt zero trust strategies, seeing average breach costs rise to $5.4 million – a $1.17 million increase compared to those that do. All while 28% breaches amongst these organizations were ransomware or destructive attacks.
  • It Doesn’t Pay to Pay – Ransomware victims in the study that opted to pay threat actors’ ransom demands saw only $610,000 less in average breach costs compared to those that chose not to pay – not including the cost of the ransom. Factoring in the high cost of ransom payments, the financial toll may rise even higher, suggesting that simply paying the ransom may not be an effective strategy.
  • Security Immaturity in Clouds – Forty-three percent of studied organizations are in the early stages or have not started applying security practices across their cloud environments, observing over $660,000 on average in higher breach costs than studied organizations with mature security across their cloud environments. 
  • Security AI and Automation Leads as Multi-Million Dollar Cost Saver – Participating organizations fully deploying security AI and automation incurred $3.05 million less on average in breach costs compared to studied organizations that have not deployed the technology – the biggest cost saver observed in the study.

“Businesses need to put their security defenses on the offense and beat attackers to the punch. It’s time to stop the adversary from achieving their objectives and start to minimize the impact of attacks. The more businesses try to perfect their perimeter instead of investing in detection and response, the more breaches can fuel cost of living increases.” said Charles Henderson, Global Head of IBM Security X-Force. “This report shows that the right strategies coupled with the right technologies can help make all the difference when businesses are attacked.”

Over-trusting Critical Infrastructure Organizations 

Concerns over critical infrastructure targeting appear to be increasing globally over the past year, with many governments’ cybersecurity agencies urging vigilance against disruptive attacks. In fact, IBM’s report reveals that ransomware and destructive attacks represented 28% of breaches amongst critical infrastructure organizations studied, highlighting how threat actors are seeking to fracture the global supply chains that rely on these organizations. This includes financial services, industrial, transportation and healthcare companies amongst others.

Despite the call for caution, and a year after the Biden Administration issued a cybersecurity executive order that centers around the importance of adopting a zero trust approach to strengthen the nation’s cybersecurity, only 21% of critical infrastructure organizations studied adopt a zero trust security model, according to the report. Add to that, 17% of breaches at critical infrastructure organizations were caused due to a business partner being initially compromised, highlighting the security risks that over-trusting environments pose.

Businesses that Pay the Ransom Aren’t Getting a “Bargain” 

According to the 2022 IBM report, businesses that paid threat actors’ ransom demands saw $610,000 less in average breach costs compared to those that chose not to pay – not including the ransom amount paid. However, when accounting for the average ransom payment, which according to Sophos reached $812,000 in 2021, businesses that opt to pay the ransom could net higher total costs - all while inadvertently funding future ransomware attacks with capital that could be allocated to remediation and recovery efforts and looking at potential federal offenses.

The persistence of ransomware, despite significant global efforts to impede it, is fueled by the industrialization of cybercrime. IBM Security X-Force discovered the duration of studied enterprise ransomware attacks shows a drop of 94% over the past three years – from over two months to just under four days. These exponentially shorter attack lifecycles can prompt higher impact attacks, as cybersecurity incident responders are left with very short windows of opportunity to detect and contain attacks. With “time to ransom” dropping to a matter of hours, it's essential that businesses prioritize rigorous testing of incident response (IR) playbooks ahead of time. But the report states that as many as 37% of organizations studied that have incident response plans don’t test them regularly.

Hybrid Cloud Advantage

The report also showcased hybrid cloud environments as the most prevalent (45%) infrastructure amongst organizations studied. Averaging $3.8 million in breach costs, businesses that adopted a hybrid cloud model observed lower breach costs compared to businesses with a solely public or private cloud model, which experienced $5.02 million and $4.24 million on average respectively. In fact, hybrid cloud adopters studied were able to identify and contain data breaches 15 days faster on average than the global average of 277 days for participants.

The report highlights that 45% of studied breaches occurred in the cloud, emphasizing the importance of cloud security. However, a significant 43% of reporting organizations stated they are just in the early stages or have not started implementing security practices to protect their cloud environments, observing higher breach costs3 . Businesses studied that did not implement security practices across their cloud environments required an average 108 more days to identify and contain a data breach than those consistently applying security practices across all their domains. 

Additional findings in the 2022 IBM report include:

  • Phishing Becomes Costliest Breach Cause – While compromised credentials continued to reign as the most common cause of a breach (19%), phishing was the second (16%) and the costliest cause, leading to $4.91 million in average breach costs for responding organizations.
  • Healthcare Breach Costs Hit Double Digits for First Time Ever– For the 12th year in a row, healthcare participants saw the costliest breaches amongst industries with average breach costs in healthcare increasing by nearly $1 million to reach a record high of $10.1 million.
  • Insufficient Security Staffing – Sixty-two percent of studied organizations stated they are not sufficiently staffed to meet their security needs, averaging $550,000 more in breach costs than those that state they are sufficiently staffed.

Additional Sources

  • To download a copy of the 2022 Cost of a Data Breach Report, please visit: https://www.ibm.com/security/data-breach. 
  • Read more about the report’s top findings in this IBM Security Intelligence blog.
  • Sign up for the 2022 IBM Security Cost of a Data Breach webinar on Wednesday, August 3, 2022, at 11:00 a.m. ET here.
  • Connect with the IBM Security X-Force team for a personalized review of the findings: https://ibm.biz/book-a-consult.

-Ends-

About IBM Security

IBM Security offers one of the most advanced and integrated portfolios of enterprise security products and services. The portfolio, supported by world-renowned IBM Security X-Force® research, enables organizations to effectively manage risk and defend against emerging threats. IBM operates one of the world's broadest security research, development, and delivery organizations, monitors 150 billion+ security events per day in more than 130 countries, and has been granted more than 10,000 security patents worldwide. For more information, please check www.ibm.com/security, follow @IBMSecurity on Twitter or visit the IBM Security Intelligence blog.

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Killexams : IBM, DELTA AIR LINES WPP, 4A'S, IAB, AD COUNCIL AND OTHER INDUSTRY LEADERS ACT TO MITIGATE BIAS IN ADVERTISING TECHNOLOGY IBM, DELTA AIR LINES WPP, 4A'S, IAB, AD COUNCIL AND OTHER INDUSTRY LEADERS ACT TO MITIGATE BIAS IN ADVERTISING TECHNOLOGY

PR Newswire

CANNES, France, June 20, 2022

IBM Delivers Open Source Toolkit to Identify and Mitigate Bias in Advertising Technology

CANNES, France, June 20, 2022 /PRNewswire/ -- Today, leading companies committed to improving fairness in marketing campaigns. The initiative, announced at the Cannes Lions International Festival of Creativity 2022, brought together agencies, brands, and other leaders to generate awareness and take action towards mitigating bias in advertising technology. Committing organizations include IBM (NYSE: IBM), Delta Air Lines, WPP, Mindshare, 4A's, IAB and the Ad Council.

IBM Corporation logo. (PRNewsfoto/IBM)

The action is the most exact effort by IBM to drive education and awareness around the impact of bias in advertising technology. In 2021, the company launched a research initiative to explore the hypothesis that bias can exist in ad technology, which initial findings confirmed. The research also showed that mitigating bias in ad technology was possible using AI tools and resources in marketing processes. More industry participation and data collection are needed to better understand the potential impact of bias on these campaigns, but several industry leaders are demonstrating early activism by raising awareness and taking action via IBM's Advertising Fairness Pledge.

"While the risk of bias in advertising is well known, by making this commitment, these organizations are among the first in the industry to take action," said Bob Lord, IBM Senior Vice President of The Weather Company and Alliances. "Together, we are agreeing to educate ourselves and our companies and ask other industry leaders to join us in helping to mitigate bias in advertising."

Toward that effort, IBM also announced the release of its gratis Advertising Toolkit for AI Fairness 360, an open-source solution deploying 75 fairness metrics and 13 state-of-the-art algorithms to help identify and mitigate biases in discrete data sets. A playbook and sample code are also made available for ease of use. Organizations utilizing the toolkit may gain a better understanding of the presence and impact of bias on their ad campaigns, as well as the makeup of their audiences.

"Used correctly, data can help brands personalize consumer engagement and identify the most relevant touchpoints. However, we know that bias can exist in algorithms or technology, and that's why we're helping our clients to evaluate how and when to use data in a meaningful way that will benefit the customer experience," said Mark Read, CEO of WPP. "Through WPP's GroupM, we've developed the Data Ethics Compass to help clients navigate the challenges of using datasets, while IBM's new Advertising Toolkit for AI Fairness 360 will help us to better understand the potential impact of bias. Consumers rightly expect brands to use their information in a fair way and for the industry to tackle data bias collectively, which can ultimately result in increased engagement and commercial outcomes."

Bias is often unintentional, a result of human assumptions and judgments encoded into algorithms that can result in unfair targeting, exclusion of certain groups, and marketing campaign failures. Organizations taking the pledge can contribute data to ongoing studies that seek to better explain the impact of bias. According to Salesforce's 2022 State of the Connected Customer survey, nearly 62 percent of consumers surveyed reported they are concerned about bias in AI, up from just 54 percent two years prior, emphasizing the imperative for brands and agencies to better understand its impacts.

"As technology and data prevalence accelerates, the risk for bias in advertising compounds. It is our duty to address this head-on," said Adam Gerhart, Global CEO of Mindshare. "We believe the industry needs to take clear and intentional action, which is why we are committing to leverage the Advertising Toolkit for AI Fairness 360."

As the advertising industry continues to face issues related to privacy and transparency, many organizations believe that tackling bias in ad tech could be a next key area of focus for marketers. Nearly $1 trillion was spent on digital advertising globally in 2021, much of which flows through programmatic engines that segment and target specific audiences, sometimes missing large consumer groups in the process. With increasing consumer demand for transparency in how their data is used, marketers must look for new ways to remain effective. Tapping into alternative privacy-forward data sources, such as weather data, can be effective predictors of behavior that could also help rebuild trust with consumers.

"As a global brand, we know that every decision we make, whether it's about a supplier, an employee or an ad campaign, is a reflection of our values and the change we want to see in the world," said Emmakate Young, Delta's Managing Director of Brand Marketing. "We've long been focused on inclusive representation in our campaign creative, this effort allows us to go a step further to bring more inclusive representation to our campaign delivery."

To download the Advertising Toolkit for AI Fairness 360 and the associated playbook, to take the Advertising Fairness Pledge, and to learn more about how bias in advertising can negatively impact businesses and consumers, visit IBM's Bias in Advertising microsite.

To learn more about IBM Watson Advertising solutions and services, visit here.

Statements regarding IBM's future direction and intent are subject to change or withdrawal without notice and represent goals and objectives only.

About IBM
IBM is a leading global hybrid cloud and AI, and business services provider, helping clients in more than 175 countries capitalize on insights from their data, streamline business processes, reduce costs and gain the competitive edge in their industries. Nearly 3,000 government and corporate entities in critical infrastructure areas such as financial services, telecommunications and healthcare rely on IBM's hybrid cloud platform and Red Hat OpenShift to affect their digital transformations quickly and efficiently. IBM's breakthrough innovations in AI, quantum computing, industry-specific cloud solutions and business services deliver open and flexible options to our clients. All of this is backed by IBM's legendary commitment to trust, transparency, responsibility, inclusivity and service. Visit www.ibm.com for more information.

IBM Contacts:

Luca Sesti
luca.sesti@ibm.com

Clare Chachere
Clare.chachere@ibm.com

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SOURCE IBM

Mon, 20 Jun 2022 01:59:00 -0500 en text/html https://www.morningstar.com/news/pr-newswire/20220620ny93276/ibm-delta-air-lines-wpp-4as-iab-ad-council-and-other-industry-leaders-act-to-mitigate-bias-in-advertising-technology
Killexams : IBM Aims to Capture Growing Market Opportunity for Data Observability with Databand.ai Acquisition

Acquisition helps enterprises catch "bad data" at the source

Extends IBM's leadership in observability to the full stack of capabilities for IT -- across infrastructure, applications, data and machine learning

ARMONK, N.Y., July 6, 2022 /PRNewswire/ -- IBM (NYSE: IBM) today announced it has acquired Databand.ai, a leading provider of data observability software that helps organizations fix issues with their data, including errors, pipeline failures and poor quality — before it impacts their bottom-line. Today's news further strengthens IBM's software portfolio across data, AI and automation to address the full spectrum of observability and helps businesses ensure that trustworthy data is being put into the right hands of the right users at the right time.

IBM has acquired Databand.ai, a leading provider of data observability software that helps organizations fix issues with their data, including errors, pipeline failures and poor quality.

Databand.ai is IBM's fifth acquisition in 2022 as the company continues to bolster its hybrid cloud and AI skills and capabilities. IBM has acquired more than 25 companies since Arvind Krishna became CEO in April 2020.

As the volume of data continues to grow at an unprecedented pace, organizations are struggling to manage the health and quality of their data sets, which is necessary to make better business decisions and gain a competitive advantage. A rapidly growing market opportunity, data observability is quickly emerging as a key solution for helping data teams and engineers better understand the health of data in their system and automatically identify, troubleshoot and resolve issues, like anomalies, breaking data changes or pipeline failures, in near real-time. According to Gartner, every year poor data quality costs organizations an average $12.9 million. To help mitigate this challenge, the data observability market is poised for strong growth.1

Data observability takes traditional data operations to the next level by using historical trends to compute statistics about data workloads and data pipelines directly at the source, determining if they are working, and pinpointing where any problems may exist. When combined with a full stack observability strategy, it can help IT teams quickly surface and resolve issues from infrastructure and applications to data and machine learning systems.

Databand.ai's open and extendable approach allows data engineering teams to easily integrate and gain observability into their data infrastructure. This acquisition will unlock more resources for Databand.ai to expand its observability capabilities for broader integrations across more of the open source and commercial solutions that power the modern data stack. Enterprises will also have full flexibility in how to run Databand.ai, whether as-a-Service (SaaS) or a self-hosted software subscription.

The acquisition of Databand.ai builds on IBM's research and development investments as well as strategic acquisitions in AI and automation. By using Databand.ai with IBM Observability by Instana APM and IBM Watson Studio, IBM is well-positioned to address the full spectrum of observability across IT operations.

For example, Databand.ai capabilities can alert data teams and engineers when the data they are using to fuel an analytics system is incomplete or missing. In common cases where data originates from an enterprise application, Instana can then help users quickly explain exactly where the missing data originated from and why an application service is failing. Together, Databand.ai and IBM Instana provide a more complete and explainable view of the entire application infrastructure and data platform system, which can help organizations prevent lost revenue and reputation.

"Our clients are data-driven enterprises who rely on high-quality, trustworthy data to power their mission-critical processes. When they don't have access to the data they need in any given moment, their business can grind to a halt," said Daniel Hernandez, General Manager for Data and AI, IBM. "With the addition of Databand.ai, IBM offers the most comprehensive set of observability capabilities for IT across applications, data and machine learning, and is continuing to provide our clients and partners with the technology they need to deliver trustworthy data and AI at scale."

Data observability solutions are also a key part of an organization's broader data strategy and architecture. The acquisition of Databand.ai further extends IBM's existing data fabric solution by helping ensure that the most accurate and trustworthy data is being put into the right hands at the right time – no matter where it resides.

"You can't protect what you can't see, and when the data platform is ineffective, everyone is impacted –including customers," said Josh Benamram, Co-Founder and CEO, Databand.ai. "That's why global brands such as FanDuel, Agoda and Trax Retail already rely on Databand.ai to remove bad data surprises by detecting and resolving them before they create costly business impacts. Joining IBM will help us scale our software and significantly accelerate our ability to meet the evolving needs of enterprise clients."

Headquartered in Tel Aviv, Israel, Databand.ai employees will join IBM Data and AI, further building on IBM's growing portfolio of Data and AI products, including its IBM Watson capabilities and IBM Cloud Pak for Data. Financial details of the deal were not disclosed. The acquisition closed on June 27, 2022.

To learn more about Databand.ai and how this acquisition enhances IBM's data fabric solution and builds on its full stack of observability software, you can read our blog about the news or visit here: https://www.ibm.com/analytics/data-fabric.

About Databand.ai

Databand.ai is a product-driven technology company that provides a proactive data observability platform, which empowers data engineering teams to deliver reliable and trustworthy data. Databand.ai removes bad data surprises such as data incompleteness, anomalies, and breaking data changes by detecting and resolving issues before they create costly business impacts. Databand.ai's proactive approach ties into all stages of your data pipelines, beginning with your source data, through ingestion, transformation, and data access. Databand.ai serves organizations throughout the globe, including some of the world's largest companies in entertainment, technology, and communications. Our focus is on enabling customers to extract the maximum value from their strategic data investments. Databand.ai is backed by leading VCs Accel, Blumberg Capital, Lerer Hippeau, Differential Ventures, Ubiquity Ventures, Bessemer Venture Partners, Hyperwise, and F2. To learn more, visit www.databand.ai.

About IBM

IBM is a leading global hybrid cloud and AI, and business services provider, helping clients in more than 175 countries capitalize on insights from their data, streamline business processes, reduce costs and gain the competitive edge in their industries. Nearly 3,800 government and corporate entities in critical infrastructure areas such as financial services, telecommunications and healthcare rely on IBM's hybrid cloud platform and Red Hat OpenShift to affect their digital transformations quickly, efficiently, and securely. IBM's breakthrough innovations in AI, quantum computing, industry-specific cloud solutions and business services deliver open and flexible options to our clients. All of this is backed by IBM's legendary commitment to trust, transparency, responsibility, inclusivity, and service. For more information, visit www.ibm.com.

Media Contact:
Sarah Murphy
IBM Communications
Srmurphy@us.ibm.com

1[1] Source: Smarter with Gartner, "How to Excellerate Your Data Quality," Manasi Sakpal, [July 14, 2021]

GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and is used herein with permission. All rights reserved.

IBM Corporation logo. (PRNewsfoto/IBM)

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SOURCE IBM

Wed, 06 Jul 2022 12:22:00 -0500 text/html https://stockhouse.com/news/press-releases/2022/07/06/ibm-aims-to-capture-growing-market-opportunity-for-data-observability-with
Killexams : IBM Aims to Capture Growing Market Opportunity for Data Observability with Databand.ai Acquisition

The MarketWatch News Department was not involved in the creation of this content.

ARMONK, N.Y., Jul 6, 2022 (Canada NewsWire via COMTEX) -- Acquisition helps enterprises catch "bad data" at the source

Extends IBM's leadership in observability to the full stack of capabilities for IT -- across infrastructure, applications, data and machine learning

IBM (NYSE: IBM) today announced it has acquired Databand.ai, a leading provider of data observability software that helps organizations fix issues with their data, including errors, pipeline failures and poor quality -- before it impacts their bottom-line. Today's news further strengthens IBM's software portfolio across data, AI and automation to address the full spectrum of observability and helps businesses ensure that trustworthy data is being put into the right hands of the right users at the right time.

Databand.ai is IBM's fifth acquisition in 2022 as the company continues to bolster its hybrid cloud and AI skills and capabilities. IBM has acquired more than 25 companies since Arvind Krishna became CEO in April 2020.

As the volume of data continues to grow at an unprecedented pace, organizations are struggling to manage the health and quality of their data sets, which is necessary to make better business decisions and gain a competitive advantage. A rapidly growing market opportunity, data observability is quickly emerging as a key solution for helping data teams and engineers better understand the health of data in their system and automatically identify, troubleshoot and resolve issues, like anomalies, breaking data changes or pipeline failures, in near real-time. According to Gartner, every year poor data quality costs organizations an average $12.9 million. To help mitigate this challenge, the data observability market is poised for strong growth.(1)

Data observability takes traditional data operations to the next level by using historical trends to compute statistics about data workloads and data pipelines directly at the source, determining if they are working, and pinpointing where any problems may exist. When combined with a full stack observability strategy, it can help IT teams quickly surface and resolve issues from infrastructure and applications to data and machine learning systems.

Databand.ai's open and extendable approach allows data engineering teams to easily integrate and gain observability into their data infrastructure. This acquisition will unlock more resources for Databand.ai to expand its observability capabilities for broader integrations across more of the open source and commercial solutions that power the modern data stack. Enterprises will also have full flexibility in how to run Databand.ai, whether as-a-Service (SaaS) or a self-hosted software subscription.

The acquisition of Databand.ai builds on IBM's research and development investments as well as strategic acquisitions in AI and automation. By using Databand.ai with IBM Observability by Instana APM and IBM Watson Studio, IBM is well-positioned to address the full spectrum of observability across IT operations.

For example, Databand.ai capabilities can alert data teams and engineers when the data they are using to fuel an analytics system is incomplete or missing. In common cases where data originates from an enterprise application, Instana can then help users quickly explain exactly where the missing data originated from and why an application service is failing. Together, Databand.ai and IBM Instana provide a more complete and explainable view of the entire application infrastructure and data platform system, which can help organizations prevent lost revenue and reputation.

"Our clients are data-driven enterprises who rely on high-quality, trustworthy data to power their mission-critical processes. When they don't have access to the data they need in any given moment, their business can grind to a halt," said Daniel Hernandez, General Manager for Data and AI, IBM. "With the addition of Databand.ai, IBM offers the most comprehensive set of observability capabilities for IT across applications, data and machine learning, and is continuing to provide our clients and partners with the technology they need to deliver trustworthy data and AI at scale."

Data observability solutions are also a key part of an organization's broader data strategy and architecture. The acquisition of Databand.ai further extends IBM's existing data fabric solution by helping ensure that the most accurate and trustworthy data is being put into the right hands at the right time - no matter where it resides.

"You can't protect what you can't see, and when the data platform is ineffective, everyone is impacted -including customers," said Josh Benamram, Co-Founder and CEO, Databand.ai. "That's why global brands such as FanDuel, Agoda and Trax Retail already rely on Databand.ai to remove bad data surprises by detecting and resolving them before they create costly business impacts. Joining IBM will help us scale our software and significantly accelerate our ability to meet the evolving needs of enterprise clients."

Headquartered in Tel Aviv, Israel, Databand.ai employees will join IBM Data and AI, further building on IBM's growing portfolio of Data and AI products, including its IBM Watson capabilities and IBM Cloud Pak for Data. Financial details of the deal were not disclosed. The acquisition closed on June 27, 2022.

To learn more about Databand.ai and how this acquisition enhances IBM's data fabric solution and builds on its full stack of observability software, you can read our blog about the news or visit here: https://www.ibm.com/analytics/data-fabric.

Databand.ai is a product-driven technology company that provides a proactive data observability platform, which empowers data engineering teams to deliver reliable and trustworthy data. Databand.ai removes bad data surprises such as data incompleteness, anomalies, and breaking data changes by detecting and resolving issues before they create costly business impacts. Databand.ai's proactive approach ties into all stages of your data pipelines, beginning with your source data, through ingestion, transformation, and data access. Databand.ai serves organizations throughout the globe, including some of the world's largest companies in entertainment, technology, and communications. Our focus is on enabling customers to extract the maximum value from their strategic data investments. Databand.ai is backed by leading VCs Accel, Blumberg Capital, Lerer Hippeau, Differential Ventures, Ubiquity Ventures, Bessemer Venture Partners, Hyperwise, and F2. To learn more, visit www.databand.ai.

IBM is a leading global hybrid cloud and AI, and business services provider, helping clients in more than 175 countries capitalize on insights from their data, streamline business processes, reduce costs and gain the competitive edge in their industries. Nearly 3,800 government and corporate entities in critical infrastructure areas such as financial services, telecommunications and healthcare rely on IBM's hybrid cloud platform and Red Hat OpenShift to affect their digital transformations quickly, efficiently, and securely. IBM's breakthrough innovations in AI, quantum computing, industry-specific cloud solutions and business services deliver open and flexible options to our clients. All of this is backed by IBM's legendary commitment to trust, transparency, responsibility, inclusivity, and service. For more information, visit www.ibm.com.

Media Contact:Sarah MurphyIBM CommunicationsSrmurphy@us.ibm.com

(1) ([1]) Source: Smarter with Gartner, "How to Excellerate Your Data Quality," Manasi Sakpal, [July 14, 2021]

GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and is used herein with permission. All rights reserved.

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SOURCE IBM

View original content to download multimedia: http://www.newswire.ca/en/releases/archive/July2022/06/c0037.html

SOURCE: IBM

SOURCE: Databand.ai

COMTEX_409761296/2197/2022-07-06T08:00:00

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