Memorize and practice these 000-M70 brain dumps before taking test gives the latest and up in order to date Test Prep with Real 000-M70 Exam Questions plus Answers for most recent subjects of IBM IBM Information Management Optim z/OS Technical Mastery v1 Examination. Practice our 000-M70 Exam Braindumps in order to Improve your understanding and pass your own examination with Higher Marks. We assure your success within the Test Middle, covering each associated with the parts associated with examination and developing your understanding associated with the 000-M70 exam. Complete with our real 000-M70 questions.

Exam Code: 000-M70 Practice exam 2022 by team
IBM Information Management Optim z/OS Technical Mastery v1
IBM Information plan
Killexams : IBM Information plan - BingNews Search results Killexams : IBM Information plan - BingNews Killexams : How Blockchain Brought Solid Level Security To Fortune 500 Companies

Serge Beck is the founder and CEO of Omnibek.

Cybercrime is estimated to cost the world more than $10 trillion annually by 2025. If measured as the GDP of a country, that would represent the third biggest economy in the world after the U.S. and China.

That shows that not only should governmental institutions and banks invest in cybersecurity but private corporations as well. In fact, giants like US Bank, JPMorgan Chase, Bank of America and IBM are some of the early adopters of blockchain technology.

With the technological advancement of cybercrime, cybersecurity must take a step forward. With the implementation of private blockchain OS, cybersecurity reaches a whole new level.

Fortune 500 Blockchain Plans

According to a survey of top fintech and tech companies by Synechron, 94% of companies had plans related to blockchain initiatives in the near future. Some of the world's largest companies are beginning to realize the potential that blockchain can bring.

Samsung implemented blockchain security for the virtual assets of its users. It came up with a series of apps that allows users to store information safely, including crypto addresses and amounts. In addition, Private Share brings encrypted chatting capabilities to the table.

In 2019, Google partnered with Chainlink to create a blockchain project that entailed processing future contracts at a safer level, including enhancing the privacy of transactions.

Of course, there are those that failed. In 2019, for example, Facebook (now Meta) had an idea to air its own cryptocurrency called Libra. It had originally gained the support of financial partners like Visa, PayPal, eBay, Stripe and MasterCard. However, regulators questioned the plan, and it ended up falling through.

Why Is Blockchain Secure?

In 1991, a chain of secure information-containing blocks with the help of cryptography was described in the work of W. Scott Stornetta and Stuart Haber. This event, followed by 2008's breakthrough of Bitcoin, set the ground for blockchain use.

In 2014, the public started realizing what the potential of this technology was. Blockchain technology was successfully separated from the above-mentioned cryptocurrency and began being used for other purposes. The most cherished qualities of a blockchain are now being developed into something new each day.

Fortune 500 companies value blockchain's capabilities mostly because of the following qualities.

• Peer-to-peer sharing. P2P sharing ensures that no data is passed through a single channel. The information is passed through the millions of nodes in the network while using the two unique keys of both the sender and receiver. There is no middle man in blockchain transactions, which is usually the weakest link of any informational exchange. This way, the process is 10 times as secure and much faster.

• Decentralization. Hackers love sensitive data being stored in one place. That makes it easy to target and sets a clear target for them. Blockchain technology breaks information into pieces and spreads it all over its network, and each node stores chunks of information. If a separate node gets corrupted, there wouldn't be a dangerous data leak.

• Encryption and validation. Cybersecurity relies mostly on encryption and validation. All data stored in a blockchain is both encrypted and encoded upon storage. Users can decode the received information with the use of their personal key or a set of keys. Files are also validated with the help of signatures and records, making sure nothing has been altered during the process of receipt.

• Blockchain can be private. Blockchain technology can be both private and public. Limiting access to a blockchain project would greatly increase the security of a network, as it eliminates the majority of the threats. Although private networks have much fewer nodes, they are greatly more secure, and that is the reason why Fortune 500 companies use them. The reason for that is that nobody outside of the network can access it, which eliminates the chance of decoding even singular nodes.

• No DDoS. Blockchain is the single viable solution for the handling of DDoS attacks. This is one of the top reasons why successful companies go for decentralized technology as a form of cybersecurity.

Small Businesses Can Also Benefit

Fortune 500 companies, alongside other successful giants, are stepping up their cybersecurity game with the help of blockchain technology. This puts small businesses right in the crosshair of hackers.

Despite small businesses not considering themselves a target, they're actually the target of 43% of cyberattacks—meaning they could definitely benefit from blockchain security. Transitioning toward blockchain as a small business can bring many benefits, including safer and cheaper cloud storage, new forms of payment, capital raising and smart contracts.

Blockchain technology can offer a way to cut down costs from mediators like attorneys and distributors. Smart contracts are self-enforcing contracts that, with the help of blockchain technology, cannot be manipulated or changed and can be used instead of commercial leases or deals between vendors and suppliers. Accepting new forms of payment from customers could also be one of the next steps in the journey of a small business.

Preparing To Adopt Blockchain As A Small Business

Regardless of whether you need new cloud storage or decide to step up your game with blockchain payments or contracts, you need to prepare your company for adoption.

In order to do so, you need to make sure a wide variety of processes are automated. Your BI, CRM or ERP software solutions need to be properly optimized, and no data in your business processes should be lost or duplicated.

Most importantly, employees should be trained in dealing with blockchain, regardless of their position in the company. Everyone should know how it functions and why it was the chosen way to go.

Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?

Sun, 17 Jul 2022 22:15:00 -0500 Serge Beck en text/html
Killexams : IBM’s Red Hat taps product and technology chief as new leader

IBM’s Red Hat named Matt Hicks, head of products and technologies, as its new leader, solidifying a bet that hybrid-cloud offerings will fuel the company’s growth.

Hicks takes over as the software unit’s chief executive officer and president from Paul Cormier, who will serve as chairman. “Paul and I have planned this for a while,” Hicks said Tuesday in an interview. “There’ll be a lot of similarities in what I did yesterday and what I’ll be doing tomorrow.”

International Business Machine (IBM) acquired Red Hat for about $34bn in 2019 as a central component of chief executive Arvind Krishna’s plan to steer the century-old company into the fast-growing cloud-computing market. As a division, Red Hat’s has seen steady revenue growth near 20 per cent, far outpacing IBM as a whole.

IBM hopes to distinguish itself in the crowded cloud market by targeting a hybrid model, which helps clients store and analyse information across their own data centres, private cloud services and servers run by major public providers such as and Microsoft. IBM has been a rare pocket of stability in the accurate stock market meltdown. The shares have gained 4.1 per cent this year, closing at $139.18 Tuesday in New York, compared with a 28 per cent decline for the tech-heavy Nasdaq 100.

“Together, we can really lead a a new era of hybrid computing,” said Hicks, who joined Red Hat in 2006. “Red Hat has the technology expertise and open source model – IBM has the reach.”

Hicks said demand for hybrid cloud and software services should remain strong despite questions about the global economic outlook, touting accurate deals with General Motors and ABB. The telecommunication and automotive industries are two areas he is targeting for expansion because they require geographically distributed data.

Read: IBM, Saudi’s King Saud University partner to advance skills development

Tue, 12 Jul 2022 17:48:00 -0500 Bloomberg en-US text/html
Killexams : Machine-learning models that can help doctors more efficiently find information in a patient's health record

Physicians often query a patient's electronic health record for information that helps them make treatment decisions, but the cumbersome nature of these records hampers the process. Research has shown that even when a doctor has been trained to use an electronic health record (EHR), finding an answer to just one question can take, on average, more than eight minutes.

The more time physicians must spend navigating an oftentimes clunky EHR interface, the less time they have to interact with patients and provide treatment.

Researchers have begun developing machine-learning models that can streamline the process by automatically finding information physicians need in an EHR. However, training effective models requires huge datasets of relevant medical questions, which are often hard to come by due to privacy restrictions. Existing models struggle to generate authentic questions—those that would be asked by a human doctor—and are often unable to successfully find .

To overcome this data shortage, researchers at MIT partnered with medical experts to study the questions physicians ask when reviewing EHRs. Then, they built a publicly available dataset of more than 2,000 clinically relevant questions written by these medical experts.

When they used their dataset to train a machine-learning model to generate clinical questions, they found that the model asked high-quality and authentic questions, as compared to test questions from medical experts, more than 60% of the time.

With this dataset, they plan to generate vast numbers of authentic medical questions and then use those questions to train a machine-learning model which would help doctors find sought-after information in a patient's record more efficiently.

"Two thousand questions may sound like a lot, but when you look at machine-learning models being trained nowadays, they have so much data, maybe billions of . When you train machine-learning models to work in health care settings, you have to be really creative because there is such a lack of data," says lead author Eric Lehman, a graduate student in the Computer Science and Artificial Intelligence Laboratory (CSAIL).

The senior author is Peter Szolovits, a professor in the Department of Electrical Engineering and Computer Science (EECS) who heads the Clinical Decision-Making Group in CSAIL and is also a member of the MIT-IBM Watson AI Lab. The research paper, a collaboration between co-authors at MIT, the MIT-IBM Watson AI Lab, IBM Research, and the doctors and medical experts who helped create questions and participated in the study, will be presented at the annual conference of the North American Chapter of the Association for Computational Linguistics.

"Realistic data is critical for training models that are relevant to the task yet difficult to find or create," Szolovits says. "The value of this work is in carefully collecting questions asked by clinicians about patient cases, from which we are able to develop methods that use these data and general language models to ask further plausible questions."

Data deficiency

The few large datasets of clinical questions the researchers were able to find had a host of issues, Lehman explains. Some were composed of medical questions asked by patients on web forums, which are a far cry from physician questions. Other datasets contained questions produced from templates, so they are mostly identical in structure, making many questions unrealistic.

"Collecting high-quality data is really important for doing machine-learning tasks, especially in a health care context, and we've shown that it can be done," Lehman says.

To build their dataset, the MIT researchers worked with practicing physicians and medical students in their last year of training. They gave these medical experts more than 100 EHR discharge summaries and told them to read through a summary and ask any questions they might have. The researchers didn't put any restrictions on question types or structures in an effort to gather natural questions. They also asked the medical experts to identify the "trigger text" in the EHR that led them to ask each question.

For instance, a medical expert might read a note in the EHR that says a patient's past medical history is significant for prostate cancer and hypothyroidism. The trigger text "prostate cancer" could lead the expert to ask questions like "date of diagnosis?" or "any interventions done?"

They found that most questions focused on symptoms, treatments, or the patient's test results. While these findings weren't unexpected, quantifying the number of questions about each broad Topic will help them build an effective dataset for use in a real, clinical setting, says Lehman.

Once they had compiled their dataset of questions and accompanying trigger text, they used it to train to ask new questions based on the trigger text.

Then the medical experts determined whether those questions were "good" using four metrics: understandability (Does the question make sense to a human physician?), triviality (Is the question too easily answerable from the trigger text?), medical relevance (Does it makes sense to ask this question based on the context?), and relevancy to the trigger (Is the trigger related to the question?).

Cause for concern

The researchers found that when a model was given trigger text, it was able to generate a good question 63% of the time, whereas a human physician would ask a good question 80% of the time.

They also trained models to recover answers to clinical questions using the publicly available datasets they had found at the outset of this project. Then they tested these trained models to see if they could find answers to "good" questions asked by human .

The models were only able to recover about 25% of answers to physician-generated questions.

"That result is really concerning. What people thought were good-performing models were, in practice, just awful because the evaluation questions they were testing on were not good to begin with," Lehman says.

The team is now applying this work toward their initial goal: building a model that can automatically answer physicians' questions in an EHR. For the next step, they will use their dataset to train a machine-learning model that can automatically generate thousands or millions of good clinical questions, which can then be used to train a new model for automatic question answering.

While there is still much work to do before that model could be a reality, Lehman is encouraged by the strong initial results the team demonstrated with this .

More information: Eric Lehman et al, Learning to Ask Like a Physician. arXiv:2206.02696v1 [cs.CL],

This story is republished courtesy of MIT News (, a popular site that covers news about MIT research, innovation and teaching.

Citation: Machine-learning models that can help doctors more efficiently find information in a patient's health record (2022, July 14) retrieved 18 July 2022 from

This document is subject to copyright. Apart from any fair dealing for the purpose of private study or research, no part may be reproduced without the written permission. The content is provided for information purposes only.

Thu, 14 Jul 2022 05:38:00 -0500 en text/html
Killexams : IBM acquires Databand to bolster its data observability stack

IBM today announced that it acquired Databand, a startup developing an observability platform for data and machine learning pipelines. Details of the deal weren't disclosed, but Tel Aviv-based Databand had raised $14.5 million prior to the acquisition.

Databand employees will join IBM's data and AI division, with the purchase expected to close on July 27.

In a statement, IBM general manager for data and AI Daniel Hernandez said that folding Databand into IBM's broader portfolio would help the latter's customers better identify and fix data issues including errors, pipeline failures and poor quality. The plan is to expand Databand's observability capabilities for integrations across open source and commercial tools, while allowing customers to have "full flexibility" in running Databand either as a service or a self-hosted subscription.

"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," Hernandez said.

Hernandez sees Databand complementing IBM's existing observability tools, namely IBM Observability by Instana APM and IBM Watson Studio. For example, he suggests, Databand could alert engineers when the data they're using to power an analytics system is incomplete, triggering Instana to explain where the missing data originated and why the system is failing.

"With the addition of Databand, IBM ... is continuing to provide our clients and partners with the technology they need to deliver trustworthy data and AI at scale," Hernandez added.

Databand was co-founded in 2018 by Josh Benamram, Victor Shafran and Evgeny Shulman. As my colleague Ingrid Lunden wrote in her profile of the company two years ago, Databand crunches various pipeline metadata including logs, runtime info and data profiles, and presents it in a single platform alongside data from other sources like Airflow, Spark and Snowflake. The goal is to deliver engineers a view of where bottlenecks or anomalies are appearing and the potential reasons why.

Databand managed to attract notable customers including FanDuel, Agoda and Trax Retail. Accel, Blumberg Capital, Lerer Hippeau, Ubiquity Ventures, Differential Ventures and Bessemer Venture Partners were among the early investors.

"You can’t protect what you can’t see, and when the data platform is ineffective, everyone is impacted – including customers," Benamram said in a statement. "That’s why global brands ... already rely on Databand 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.”

Data observability is a burgeoning -- and perhaps even recession-proof -- market. As the volume of data continues to climb, organizations are struggling to manage the health and quality of their datasets (so the vendor narrative goes). Statista estimates that the sector will increase from $12.98 billion in worth in 2020 to $19.38 billion in 2024, buoyed by the growth of startups like Manta, Monte Carlo, Edge Delta and Cribl. Investors poured over half a billion dollars into observability startups in May alone.

In a press release, IBM notes that Databand is its fifth acquisition in 2022. It continues the buying spree Arvind Krishna kicked off when he became CEO two years ago, focused on companies in AI, automation, cloud and IT.

Wed, 06 Jul 2022 00:00:00 -0500 en-US text/html
Killexams : Forget long screen recordings. These tools automate your company’s how-tos. Killexams : Scribe and Tango help train remote workers - Protocol
Thu, 14 Jul 2022 00:05:00 -0500 en text/html
Killexams : IBM Acquires Israeli Data Observability Startup

American tech giant IBM announced on Wednesday that it had acquired its acquisition of Tel-Aviv-based company, a data observability software company that helps organizations with data issues. works to help companies alleviate data errors, pipeline failures, and poor data quality before the company’s bottom line is impacted. By acquiring, IBM hopes to strengthen its software portfolio across artificial intelligence, data, and automation, ultimately ensuring data stays secure at all times.

Founded in 2018 by CEO Josh Benamram, Victor Shafran, and CTO Evgeny Shulman, has developed a unified data pipeline observability solution that’s built for data engineers. has an open and extendable approach that allows data engineering teams to easily integrate and gain observability into their data infrastructure. In partnering with IBM, will be able to expand its data integration capabilities to meet the needs of more commercial data solutions. IBM will also benefit from the acquisition, as’s software will partner with IBM Observability by Instana APM and IBM Watson Studio in addressing the full spectrum of observability across information technologies. marks IBM’s fifth acquisition in 2022. 

IBM’s acquisition comes at a time when during which the volume of data is growing at an unprecedented rate. Now more than ever, organizations are grappling with the challenges of managing healthy and high-quality data sets. Data observability is newly emerging as a prime solution for helping companies and engineers understand the status of their data and efficiently address and troubleshoot issues as they arise.

“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, 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,” he added.  

Thu, 07 Jul 2022 08:10:00 -0500 en-US text/html
Killexams : Francisco Partners Closes Acquisition of IBM’s Healthcare Data and Analytics Assets; Launches Merative

Francisco Partners, a global investment firm that specializes in partnering with technology businesses, completed the acquisition of healthcare data and analytics assets that were part of IBM’s (NYSE: IBM) Watson Health business, previously announced in January.

Under the ownership of Francisco Partners, the new standalone company will be called Merative and will be headquartered in Ann Arbor, Michigan.

Led by seasoned healthcare CEO Gerry McCarthy, Merative will provide offerings that deliver value across the global healthcare ecosystem, serving clients in life sciences, provider, imaging, health plan, employer, and government health and human services sectors. Products will be organized into six product families, including Health Insights; MarketScan; Clinical Development; Social Program Management and Phytel; Micromedex, and Merge Imaging solutions.

The investment, which include True Wind Capital and Sixth Street, will provide Merative with resources and opportunities for new investment, acquisitions, partnerships, and growth.

McCarthy has been in healthcare information technology for 30 years, most recently serving as CEO of eSolutions, a Francisco Partners portfolio company, which exited to Waystar in October 2020. Prior to eSolutions, he was the President of TransUnion Healthcare and an executive at McKesson.

Paul Roma, General Manager of the Watson Health business, will be transitioning to Senior Advisor to Francisco Partners.



Thu, 30 Jun 2022 21:42:00 -0500 FinSMEs en-US text/html
Killexams : IBM beefs up manager roster on custom bond fund for DC plan

International Business Machines Inc., Armonk, N.Y., added four new underlying managers to the custom total bond market fund investment option in its 401(k) plan.

The plan hired Loomis Sayles & Co., Western Asset Management, Pacific Investment Management Co. and R.W. Baird & Co. as additional underlying managers for the total bond market fund in 2021, according to a comparison of the company's new 11-K filing with the SEC on June 17 and last year's filing.

Each managers' individual assets in the plan as of Dec. 31 were $1.2 billion, $572 million, $551 million and $512 million, respectively, according to the new 11-K filing.

According to the plan's prior 11-K filing, Neuberger Berman was the sole underlying manager of the total bond market fund as of Dec. 31, 2020. As of that date, the fund had $4.5 billion in assets in the plan, according to the new 11-K filing.

In the new 11-K filing, Neuberger Berman's share of the Total Bond Market Fund had dropped to $1.9 billion as of Dec. 31.

The reason for the addition of the four managers was not provided in the new 11-K filing.

As of Dec. 31, the IBM 401(k) Plus Plan had $65.6 billion in assets, according to the new 11-K filing.

IBM spokeswoman Sabrina Giglio could not be immediately reached for further information.

Mon, 20 Jun 2022 07:33:00 -0500 en text/html
Killexams : IBM Acquires to Boost Data Observability Capabilities

IBM is acquiring, 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. is IBM's fifth acquisition in 2022 as the company continues to bolster its hybrid cloud and AI skills and capabilities.'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 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, whether as-a-Service (SaaS) or a self-hosted software subscription.

The acquisition of builds on IBM's research and development investments as well as strategic acquisitions in AI and automation. By using 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, 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 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, 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

Mon, 11 Jul 2022 01:02:00 -0500 en text/html
Killexams : Product Analytics Market Growing at a CAGR 21.3% | Key Player Google, IBM, Oracle, Adobe, Salesforce

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

Jul 07, 2022 (AB Digital via COMTEX) -- The global Product Analytics Market size to grow from USD 9.6 billion in 2021 to USD 25.3 billion by 2026, at a Compound Annual Growth Rate (CAGR) of 21.3% during the forecast period. Various factors such as growing need to Excellerate customer behavior management to deliver personalized recommendation of products, increasing demand for advanced analytics tools to ensure market competitiveness, and growing adoption of big data and other related technologies are expected to drive the adoption of product analytics solutions and services.

Download PDF Brochure:

COVID-19’s global impact has shown that interconnectedness plays an important role in international cooperation. As a result, several governments started rushing toward identifying, evaluating, and procuring reliable solutions powered by AI. Advanced analytics and AI are invaluable to organizations managing uncertainty in real-time, but most predictive models rely on historical patterns. The use of advanced analytics and AI has accelerated in the COVID-19 pandemic period. This has helped organizations engage customers through digital channels, manage fragile and complex supply chains, and support workers through disruption to their work and lives. At the same time, leaders have identified a major weakness in their analytics strategy: the reliance on historical data for algorithmic models. From customer behavior to supply and demand patterns, historical patterns, and the assumption of continuity are empowering the predictive models. Technology and service providers have been facing significant disruption to their businesses from COVID-19. It has become important for product managers to evaluate the critical ways in which the pandemic affects their teams so they can mitigate the negative effects and plan for recovery. Product managers serve at the intersection of different functions. They glue together product, engineering, and design. However, as the COVID-19 has been changing the product landscape, these relationships have gone remote and that is not the only problem teams are tackling. As many of the world’s major economies work to address the second wave of COVID-19, it would be an appropriate time to look at how the pandemic has changed product management. Hence, the COVID-19 pandemic has disrupted the global financial markets and has created panic, uncertainty, and distraction in the operations of global corporations.

Scope of the Report

Report Attributes


Market size available for years


Base year considered


Forecast period


Product Analytics Market Size in 2026

USD 25.3 billion

Growth Rate


Segments covered

Component, Mode (Tracking Data, Analyzing Data), End User (Sales & Marketing Professionals, Consumer Engagement), Deployment Mode, Organization Size, Vertical, & Region

Geographies covered

North America, Europe, APAC, MEA, and Latin America

Companies covered

Google (US), IBM (US), Oracle (US), Adobe (US), Salesforce (US), Medallia (US), Veritone (US), LatentView Analytics (US), Mixpanel (US), Amplitude (US), Pendo (US), Kissmetrics (US), Gainsight (US), UserIQ (US), Copper CRM (US), Countly (UK), Heap (US), Plytix (Denmark), Risk Edge Solutions (India), Woopra (US), Piwik PRO (Poland), Smartlook (Czech Republic), LogRocket (US), Auryc (US), Quantum Metric (US), (Germany), Refiner (France), InnerTrends (England), GrowthSimple (US), OmniPanel (US), and Productlift (Canada)

The services segment to hold higher CAGR during the forecast period

Based on components, the product analytics market is segmented into solutions and services. The services segment has been further divided into professional and managed services. These services play a vital role in the functioning of product analytics solutions, as well as ensure faster and smoother implementation that maximizes the value of the enterprise investments. The growing adoption of product analytics solutions is expected to boost the adoption of professional and managed services. Professional service providers have deep knowledge related to the products and enable customers to focus on the core business, while MSPs help customers Excellerate business operations and cut expenses.

Request sample Pages:

As per Heap, product analytics is a robust set of tools that allow product managers and product teams to assess the performance of the digital experiences they build. Product analytics provides critical information to optimize performance, diagnose problems, and correlate a customer activity with a long-term value. The product analytics market comprises product analytics services and solutions embedded with advanced technologies, such as Artificial Intelligence (AI) and Machine Learning (ML) and big data analytics.

Some of the key players operating in the product analytics market include Google (US), IBM (US), Oracle (US), Adobe (US), Salesforce (US), Medallia (US), Veritone (US), LatentView Analytics (US), Mixpanel (US), Amplitude (US), Pendo (US), Kissmetrics (US), Gainsight (US), UserIQ (US), Copper CRM (US), Countly (UK), Heap (US), Plytix (Denmark), Risk Edge Solutions (India), Woopra (US), Piwik PRO (Poland), Smartlook (Czech Republic), LogRocket (US), Auryc (US), Quantum Metric (US), (Germany), Refiner (France), InnerTrends (England), GrowthSimple (US), OmniPanel (US), and Productlift (Canada). These product analytics vendors have adopted various organic and inorganic strategies to sustain their positions and increase their market shares in the global product analytics market.

Oracle was incorporated in 1977 and is headquartered in California, US. The company is a global leader in delivering a broad spectrum of products, solutions, and services designed to meet the requirements of corporate IT environments, such as platforms, applications, and infrastructure. Oracle’s customers include businesses of various sizes, government agencies, educational institutions, and resellers. The company, directly and indirectly, sells its products and services through a worldwide sales force and Oracle Partner Network, respectively. It specializes in developing, manufacturing, and marketing hardware systems, databases, middleware software, and application software. It provides SaaS offerings that are designed to incorporate emerging technologies, such as IoT, AI, ML, and blockchain. It operates through three business segments: cloud and license, hardware, and services, in more than 175 countries and caters to 4,30,000 customers across banking, telecommunications, engineering and construction, financial services, healthcare, insurance, public sector, retail, and utilities verticals. Oracle offers Oracle Analytics Cloud, Oracle Analytics Server, Oracle fusion analytics, and Oracle Essbase in the product analytics market.

IBM is a multinational technology and consulting corporation founded in the year 1911 and is headquartered in New York, US. It offers infrastructure, hosting, and consulting services and operates through five major business segments: cloud and cognitive software, global business services, global technology services, systems, and global financing. IBM’s product portfolio comprises various segments, such as IoT, analytics, security, mobile, social, and Watson. It caters to various industry verticals that include aerospace and defense, education, healthcare, oil and gas, automotive, electronics, insurance, retail and consumer products, banking and finance, energy and utilities, life sciences, telecommunications, media and entertainment, chemicals, government, manufacturing, travel and transportation, construction, and metals and mining. The company has a robust presence in the Americas, Europe, the MEA, and Asia Pacific and clients in more than 175 countries. In the product analytics market, IBM offers IBM Cognos Analytics, IBM Planning Analytics, IBM Spectrum control, IBM Streaming Analytics, and IBM QRadar User Behavior Analytics (UBA).

Media Contact
Company Name: MarketsandMarkets(TM) Research Private Ltd.
Contact Person: Mr. Aashish Mehra
Email: Send Email
Phone: 18886006441
Address:630 Dundee Road Suite 430
City: Northbrook
State: IL 60062
Country: United States


Is there a problem with this press release? Contact the source provider Comtex at You can also contact MarketWatch Customer Service via our Customer Center.

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

Thu, 07 Jul 2022 02:07:00 -0500 en-US text/html
000-M70 exam dump and training guide direct download
Training Exams List