IBM Research’s Deep Search product uses natural language processing (NLP) to “ingest and analyze massive amounts of data—structured and unstructured.” Over the years, Deep Search has seen a wide range of scientific uses, from Covid-19 research to molecular synthesis. Now, IBM Research is streamlining the scientific applications of Deep Search by open-sourcing part of the product through the release of Deep Search for Scientific Discovery (DS4SD).
DS4SD includes specific segments of Deep Search aimed at document conversion and processing. First is the Deep Search Experience, a document conversion service that includes a drag-and-drop interface and interactive conversion to allow for quality checks. The second element of DS4SD is the Deep Search Toolkit, a Python package that allows users to “programmatically upload and convert documents in bulk” by pointing the toolkit to a folder whose contents will then be uploaded and converted from PDFs into “easily decipherable” JSON files. The toolkit integrates with existing services, and IBM Research is welcoming contributions to the open-source toolkit from the developer community.
IBM Research paints DS4SD as a boon for handling unstructured data (data not contained in a structured database). This data, IBM Research said, holds a “lot of value” for scientific research; by way of example, they cited IBM’s own Project Photoresist, which in 2020 used Deep Search to comb through more than 6,000 patents, documents, and material data sheets in the hunt for a new molecule. IBM Research says that Deep Search offers up to a 1,000× data ingestion speedup and up to a 100× data screening speedup compared to manual alternatives.
The launch of DS4SD follows the launch of GT4SD—IBM Research’s Generative Toolkit for Scientific Discovery—in March of this year. GT4SD is an open-source library to accelerate hypothesis generation for scientific discovery. Together, DS4SD and GT4SD constitute the first steps in what IBM Research is calling its Open Science Hub for Accelerated Discovery. IBM Research says more is yet to come, with “new capabilities, such as AI models and high quality data sources” to be made available through DS4SD in the future. Deep Search has also added “over 364 million” public documents (like patents and research papers) for users to leverage in their research—a big change from the previous “bring your own data” nature of the tool.
The Deep Search Toolkit is accessible here.
International Business Machines Corp. (IBM) is a multinational technology company that offers a range of computing solutions and technology consulting services. Big Blue traces its origins back as early as the 1880s, but it was in 1911 that the company was first incorporated as the Computing-Tabulating-Recording Co. (C-T-R) in the state of New York.
C-T-R manufactured and sold a range of machinery, including commercial scales, industrial time recorders, meat and cheese slicers, tabulators, and punched cards. The company formally changed its name to its current name in 1924 and has since grown into a major global corporation focused on software, technology and business consulting, and cloud computing.
IBM generated annual net income of $5.6 billion on revenue of $73.6 billion in 2020. It has a market capitalization of $125.3 billion as of July 13, 2021. It currently operates through five business segments: Cloud & Cognitive Software, Global Business Services, Global Technology Services, Systems, and Global Financing.
IBM’s early history is marked by its focus on the manufacture of machinery and computer hardware products. But beginning around the 1990s, the company began to shift its focus toward computer services and software. It sold off hardware businesses, such as flat-panel displays, disk drives, and personal computers, while simultaneously acquiring services and software companies. In recent years, the company has set its sights on becoming a leader in cloud computing, a strategic push underscored by its 2019 acquisition of Red Hat Inc. To facilitate this shift toward cloud services and artificial intelligence (AI), IBM has announced that it will spin off its infrastructure management business. The new company will be called Kyndryl, and the spin-off is expected to be complete by the end of 2021.
Below, we take a closer look at six of IBM’s more recent acquisitions, all of which have taken place within the past 20 years. The company does not provide a breakdown of how much profit or revenue each acquisition currently contributes, except for quarterly revenue for Red Hat.
Red Hat was founded in 1993 by Marc Ewing, or, as he was known by many in the computer lab during college, “the guy in the red hat.” Ewing had created and was distributing his own version of Linux® on CDs. He later joined forces with small businessman Bob Young and the two launched Red Hat Software in 1995, with Young as CEO. The open source development model on which the company was built was aimed at challenging what its founders saw as the monopolistic tendencies of the technology industry. The company went public in 1999 with a record-breaking initial public offering (IPO).
In 2019, Red Hat was acquired by IBM for approximately $34 billion, which broke the record for the largest software acquisition in history. The acquisition brings together Red Hat’s open hybrid cloud technologies with the scale and depth of IBM’s innovation, industry expertise, and sales leadership. The goal for IBM has been to work with Red Hat in offering a next-generation hybrid multi-cloud platform for its enterprise clients, especially as some of IBM’s legacy businesses are shrinking.
Cognos was founded in 1969 by Alan Rushforth and Peter Glenister in Ottawa, Canada. The firm specialized in developing software for business intelligence and performance management. In 2005, Cognos launched its award winning BI 8 product, which could be used for creating professional reports, data analysis and monitoring, model creation, and more.
The company was acquired by IBM in 2008 for $4.9 billion. At the time, IBM’s intention in acquiring Cognos was to accelerate its cross-company Information on Demand strategy, unlocking the business value of information through information integration, content and data management, and business consulting services. The combination would help companies increase the value of their information, optimize business processes, and maximize performance.
SoftLayer Technologies was founded in 2005 as a hosting services and cloud computing provider. By the time SoftLayer was acquired by IBM in 2013, it had about 21,000 customers and was operating 13 data centers in the United States, Europe, and Asia.
Following the acquisition, SoftLayer became part of IBM’s cloud services division. The aim of the combination was to create a global cloud platform that would make it easier for companies and organizations to adopt the latest cloud services. It was another example of IBM’s push to accelerate the growth of its cloud computing business since it began making more than a dozen cloud-related acquisitions starting in 2007. SoftLayer, now called IBM Cloud, currently has more than 60 data centers in 19 countries.
PricewaterhouseCoopers, a global network of firms offering assurance, tax, and consulting services, was created out of a 1998 merger between Price Waterhouse and Coopers & Lybrand. In 2002, the company sold its consulting services business—PwC Consulting—to IBM for approximately $3.5 billion.
IBM integrated the consulting service into a new global business unit called IBM Business Consulting Services, extending the reach of its Global Services Business. At the time, it was the largest consulting services organization in the world, having operations in more than 160 countries. The acquisition allowed IBM to combine business consulting with technology solutions, which many of its clients were demanding at the time.
Healthcare data and analytics services company Truven Health Analytics was formerly the healthcare unit of Thomson Reuters Corp. (TRI). Veritas Capital Fund Management LLC bought the healthcare unit from Thomson Reuters for $1.25 billion in 2012 and rebranded it as Truven Health Analytics. Several years later, Truven became a target in IBM’s aggressive push into the healthcare industry. IBM bought the healthcare analytics company in 2016 after making several acquisitions of medical technology companies totaling more than $4 billion over the previous year.
The Truven acquisition provided IBM with a massive new body of data, enabling IBM to expand the capabilities of its Watson AI system. AI machine learning systems such as Watson require large amounts of data from which they are trained to identify and extract useful patterns. The deal also was expected to double the size of IBM’s healthcare business. Truven has since become fully integrated into IBM Watson Health, which provides solutions to the healthcare industry.
Turbonomic was founded in 2009 with the goal of developing AI software to manage information technology (IT) systems. The company provides an analytics engine to oversee resourcing decisions and facilitate communication among systems. The company has grown to serve thousands of customers, including a third of Fortune 500 companies.
IBM announced the closing of its acquisition of Turbonomic in June 2021 as part of its new focus on cloud services and AI. This acquisition comes amid a flurry of deals around other cloud and AI companies, including Nordcloud and Instana.
Over the past decade, artificial intelligence (AI) has emerged as an engine of discovery by helping to unlock information from large repositories of previously inaccessible data. The cloud has expanded computer capacity exponentially by creating a global network of remote and distributed computing resources. And quantum computing has arrived on the scene as a game changer in processing power by harnessing quantum simulation to overcome the scaling and complexity limits of classical computing.
In parallel to these advances in computing, in which IBM is a world leader, the healthcare and life sciences have undergone their own information revolution. There has been an explosion in genomic, proteomic, metabolomic and a plethora of other foundational scientific data, as well as in diagnostic, treatment, outcome and other related clinical data. Paradoxically, however, this unprecedented increase in information volume has resulted in reduced accessibility and a diminished ability to use the knowledge embedded in that information. This reduction is caused by siloing of the data, limitations in existing computing capacity, and processing challenges associated with trying to model the inherent complexity of living systems.
IBM Research is now working on designing and implementing computational architectures that can convert the ever-increasing volume of healthcare and life-sciences data into information that can be used by scientists and industry experts the world over. Through an AI approach powered by high-performance computing (HPC)—a synergy of quantum and classical computing—and implemented in a hybrid cloud that takes advantage of both private and public environments, IBM is poised to lead the way in knowledge integration, AI-enriched simulation, and generative modeling in the healthcare and life sciences. Quantum computing, a rapidly developing technology, offers opportunities to explore and potentially address life-science challenges in entirely new ways.
“The convergence of advances in computation taking place to meet the growing challenges of an ever-shifting world can also be harnessed to help accelerate the rate of discovery in the healthcare and life sciences in unprecedented ways,” said Ajay Royyuru, IBM fellow and CSO for healthcare and life sciences at IBM Research. “At IBM, we are at the forefront of applying these new capabilities for advancing knowledge and solving complex problems to address the most pressing global health challenges.”
Innovation in the healthcare and life sciences, while overall a linear process leading from identifying drug targets to therapies and outcomes, relies on a complex network of parallel layers of information and feedback loops, each bringing its own challenges (Fig. 1). Success with target identification and validation is highly dependent on factors such as optimized genotype–phenotype linking to enhance target identification, improved predictions of protein structure and function to sharpen target characterization, and refined drug design algorithms for identifying new molecular entities (NMEs). New insights into the nature of disease are further recalibrating the notions of disease staging and of therapeutic endpoints, and this creates new opportunities for improved clinical-trial design, patient selection and monitoring of disease progress that will result in more targeted and effective therapies.
Powering these advances are several core computing technologies that include AI, quantum computing, classical computing, HPC, and the hybrid cloud. Different combinations of these core technologies provide the foundation for deep knowledge integration, multimodal data fusion, AI-enriched simulations and generative modeling. These efforts are already resulting in rapid advances in the understanding of disease that are beginning to translate into the development of better biomarkers and new therapeutics (Fig. 2).
“Our goal is to maximize what can be achieved with advanced AI, simulation and modeling, powered by a combination of classical and quantum computing on the hybrid cloud,” said Royyuru. “We anticipate that by combining these technologies we will be able to accelerate the pace of discovery in the healthcare and life sciences by up to ten times and yield more successful therapeutics and biomarkers.”
Developing new drugs hinges on both the identification of new disease targets and the development of NMEs to modulate those targets. Developing NMEs has typically been a one-sided process in which the in silico or in vitro activities of large arrays of ligands would be tested against one target at a time, limiting the number of novel targets explored and resulting in ‘crowding’ of clinical programs around a fraction of validated targets. recent developments in proteochemometric modeling—machine learning-driven methods to evaluate de novo protein interactions in silico—promise to turn the tide by enabling the simultaneous evaluation of arrays of both ligands and targets, and exponentially reducing the time required to identify potential NMEs.
Proteochemometric modeling relies on the application of deep machine learning tools to determine the combined effect of target and ligand parameter changes on the target–ligand interaction. This bimodal approach is especially powerful for large classes of targets in which active-site similarities and lack of activity data for some of the proteins make the conventional discovery process extremely challenging.
Protein kinases are ubiquitous components of many cellular processes, and their modulation using inhibitors has greatly expanded the toolbox of treatment options for cancer, as well as neurodegenerative and viral diseases. Historically, however, only a small fraction of the kinome has been investigated for its therapeutic potential owing to biological and structural challenges.
Using deep machine learning algorithms, IBM researchers have developed a generative modeling approach to access large target–ligand interaction datasets and leverage the information to simultaneously predict activities for novel kinase–ligand combinations1. Importantly, their approach allowed the researchers to determine that reducing the kinase representation from the full protein sequence to just the active-site residues was sufficient to reliably drive their algorithm, introducing an additional time-saving, data-use optimization step.
Machine learning methods capable of handling multimodal datasets and of optimizing information use provide the tools for substantially accelerating NME discovery and harnessing the therapeutic potential of large and sometimes only minimally explored molecular target spaces.
Electronic health records (EHRs) and insurance claims contain a treasure trove of real-world data about the healthcare history, including medications, of millions of individuals. Such longitudinal datasets hold potential for identifying drugs that could be safely repurposed to treat certain progressive diseases not easily explored with conventional clinical-trial designs because of their long time horizons.
Turning observational medical databases into drug-repurposing engines requires the use of several enabling technologies, including machine learning-driven data extraction from unstructured sources and sophisticated causal inference modeling frameworks.
Parkinson’s disease (PD) is one of the most common neurodegenerative disorders in the world, affecting 1% of the population above 60 years of age. Within ten years of disease onset, an estimated 30–80% of PD patients develop dementia, a debilitating comorbidity that has made developing disease-modifying treatments to slow or stop its progression a high priority.
IBM researchers have now developed an AI-driven, causal inference framework designed to emulate phase 2 clinical trials to identify candidate drugs for repurposing, using real-world data from two PD patient cohorts totaling more than 195,000 individuals2. Extracting relevant data from EHRs and claims data, and using dementia onset as a proxy for evaluating PD progression, the team identified two drugs that significantly delayed progression: rasagiline, a drug already in use to treat motor symptoms in PD, and zolpidem, a known psycholeptic used to treat insomnia. Applying advanced causal inference algorithms, the IBM team was able to show that the drugs exert their effects through distinct mechanisms.
Using observational healthcare data to emulate otherwise costly, large and lengthy clinical trials to identify repurposing candidates highlights the potential for applying AI-based approaches to accelerate potential drug leads into prospective registration trials, especially in the context of late-onset progressive diseases for which disease-modifying therapeutic solutions are scarce.
One of the main bottlenecks in drug discovery is the high failure rate of clinical trials. Among the leading causes for this are shortcomings in identifying relevant patient populations and therapeutic endpoints owing to a fragmented understanding of disease progression.
Using unbiased machine-learning approaches to model large clinical datasets can advance the understanding of disease onset and progression, and help identify biomarkers for enhanced disease monitoring, prognosis, and trial enrichment that could lead to higher rates of trial success.
Huntington’s disease (HD) is an inherited neurodegenerative disease that results in severe motor, cognitive and psychiatric disorders and occurs in about 3 per 100,000 inhabitants worldwide. HD is a fatal condition, and no disease-modifying treatments have been developed to date.
An IBM team has now used a machine-learning approach to build a continuous dynamic probabilistic disease-progression model of HD from data aggregated from multiple disease registries3. Based on longitudinal motor, cognitive and functional measures, the researchers were able to identify nine disease states of clinical relevance, including some in the early stages of HD. Retrospective validation of the results with data from past and ongoing clinical studies showed the ability of the new disease-progression model of HD to provide clinically meaningful insights that are likely to markedly Excellerate patient stratification and endpoint definition.
Model-based determination of disease stages and relevant clinical and digital biomarkers that lead to better monitoring of disease progression in individual participants is key to optimizing trial design and boosting trial efficiency and success rates.
IBM has established its mission to advance the pace of discovery in healthcare and life sciences through the application of a versatile and configurable collection of accelerator and foundation technologies supported by a backbone of core technologies (Fig. 1). It recognizes that a successful campaign to accelerate discovery for therapeutics and biomarkers to address well-known pain points in the development pipeline requires external, domain-specific partners to co-develop, practice, and scale the concept of technology-based acceleration. The company has already established long-term commitments with strategic collaborators worldwide, including the recently launched joint Cleveland Clinic–IBM Discovery Accelerator, which will house the first private-sector, on-premises IBM Quantum System One in the United States. The program is designed to actively engage with universities, government, industry, startups and other relevant organizations, cultivating, supporting and empowering this community with open-source tools, datasets, technologies and educational resources to help break through long-standing bottlenecks in scientific discovery. IBM is engaging with biopharmaceutical enterprises that share this vision of accelerated discovery.
“Through partnerships with leaders in healthcare and life sciences worldwide, IBM intends to boost the potential of its next-generation technologies to make scientific discovery faster, and the scope of the discoveries larger than ever,” said Royyuru. “We ultimately see accelerated discovery as the core of our contribution to supercharging the scientific method.”
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Open Source Services market report provides a detailed study of global market scope, regional and country-level market size, segmentation, growth, share, competitive Landscape, sales analysis, impact of domestic and global market players, value chain optimization, trade regulations, recent developments, opportunities analysis, strategic market growth analysis, product launches, area marketplace expanding and technological innovations.
The open-source services focus on emphasizing open-source technology through the whole technology spectrum. This includes a server to data integration software and critical business solutions such as customer relationship management (CRM) and big data. The open-source services are custom made as per companies requirements around the open-source software and it is made through basic offerings such as traditional IT service offerings.
Open Source Services market is divided by Type and Application. For the period 2022-2030, the growth among segments provides accurate calculations and forecasts for revenue by Type and Application. This analysis can help you expand your business by targeting qualified place market
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As the world becomes increasingly data-driven, businesses must find suitable solutions to help them achieve their desired outcomes. Data lake storage has garnered the attention of many organizations that need to store large amounts of unstructured, raw information until it can be used in analytics applications.
The data lake solution market is expected to grow rapidly in the coming years and is driven by vendors that offer cost-effective, scalable solutions for their customers.
Learn more about data lake solutions, what key features they should have and some of the top vendors to consider this year.
A data lake is defined as a single, centralized repository that can store massive amounts of unstructured and semi-structured information in its native, raw form.
It’s common for an organization to store unstructured data in a data lake if it hasn’t decided how that information will be used. Some examples of unstructured data include images, documents, videos and audio. These data types are useful in today’s advanced machine learning (ML) and advanced analytics applications.
Data lakes differ from data warehouses, which store structured, filtered information for specific purposes in files or folders. Data lakes were created in response to some of the limitations of data warehouses. For example, data warehouses are expensive and proprietary, cannot handle certain business use cases an organization must address, and may lead to unwanted information homogeneity.
On-premise data lake solutions were commonly used before the widespread adoption of the cloud. Now, it’s understood that some of the best hosts for data lakes are cloud-based platforms on the edge because of their inherent scalability and considerably modular services.
A 2019 report from the Government Accountability Office (GAO) highlights several business benefits of using the cloud, including better customer service and the acquisition of cost-effective options for IT management services.
Cloud data lakes and on-premise data lakes have pros and cons. Businesses should consider cost, scale and available technical resources to decide which type is best.
Read more about data lakes: What is a data lake? Definition, benefits, architecture and best practices
It’s critical to understand what features a data lake offers. Most solutions come with the same core components, but each vendor may have specific offerings or unique selling points (USPs) that could influence a business’s decision.
Below are five key features every data lake should have:
Data lakes that offer diverse interfaces, APIs and endpoints can make it much easier to upload, access and move information. These capabilities are important for a data lake because it allows unstructured data for a wide range of use cases, depending on a business’s desired outcome.
ML engineers, data scientists, decision-makers and analysts benefit most from a centralized data lake solution that stores information for easy access and availability. This characteristic can help data professionals and IT managers work with data more seamlessly and efficiently, thus improving productivity and helping companies reach their goals.
Imagine a data lake with large amounts of information but no sense of organization. A viable data lake solution must incorporate generic organizational methods and search capabilities, which provide the most value for its users. Other features might include key-value storage, tagging, metadata, or tools to classify and collect subsets of information.
Security and access control are two must-have features with any digital tool. The current cybersecurity landscape is expanding, making it easier for threat actors to exploit a company’s data and cause irreparable damage. Only certain users should have access to a data lake, and the solution must have strong security to protect sensitive information.
More organizations are growing larger and operating at a much faster rate. Data lake solutions must be flexible and scalable to meet the ever-changing needs of modern businesses working with information.
Also read: Unlocking analytics with data lake and graph analysis
Some data lake solutions are best suited for businesses in certain industries. In contrast, others may work well for a company of a particular size or with a specific number of employees or customers. This can make choosing a potential data lake solution vendor challenging.
Companies considering investing in a data lake solution this year should check out some of the vendors below.
The AWS Cloud provides many essential tools and services that allow companies to build a data lake that meets their needs. The AWS data lake solution is widely used, cost-effective and user-friendly. It leverages the security, durability, flexibility and scalability that Amazon S3 object storage offers to its users.
The data lake also features Amazon DynamoDB to handle and manage metadata. AWS data lake offers an intuitive, web-based console user interface (UI) to manage the data lake easily. It also forms data lake policies, removes or adds data packages, creates manifests of datasets for analytics purposes, and features search data packages.
Cloudera is another top data lake vendor that will create and maintain safe, secure storage for all data types. Some of Cloudera SDX’s Data Lake Service capabilities include:
Other benefits of Cloudera’s data lake include product support, downloads, community and documentation. GSK and Toyota leveraged Cloudera’s data lake to garner critical business intelligence (BI) insights and manage data analytics processes.
Databricks is another viable vendor, and it also offers a handful of data lake alternatives. The Databricks Lakehouse Platform combines the best elements of data lakes and warehouses to provide reliability, governance, security and performance.
Databricks’ platform helps break down silos that normally separate and complicate data, which frustrates data scientists, ML engineers and other IT professionals. Aside from the platform, Databricks also offers its Delta Lake solution, an open-format storage layer that can Excellerate data lake management processes.
Domo is a cloud-based software company that can provide big data solutions to all companies. Users have the freedom to choose a cloud architecture that works for their business. Domo is an open platform that can augment existing data lakes, whether it’s in the cloud or on-premise. Users can use combined cloud options, including:
Domo offers advanced security features, such as BYOK (bring your own key) encryption, control data access and governance capabilities. Well-known corporations such as Nestle, DHL, Cisco and Comcast leverage the Domo Cloud to better manage their needs.
Google is another big tech player offering customers data lake solutions. Companies can use Google Cloud’s data lake to analyze any data securely and cost-effectively. It can handle large volumes of information and IT professionals’ various processing tasks. Companies that don’t want to rebuild their on-premise data lakes in the cloud can easily lift and shift their information to Google Cloud.
Some key features of Google’s data lakes include Apache Spark and Hadoop migration, which are fully managed services, integrated data science and analytics, and cost management tools. Major companies like Twitter, Vodafone, Pandora and Metro have benefited from Google Cloud’s data lakes.
Hewlett Packard Enterprise (HPE) is another data lake solution vendor that can help businesses harness the power of their big data. HPE’s solution is called GreenLake — it offers organizations a truly scalable, cloud-based solution that simplifies their Hadoop experiences.
HPE GreenLake is an end-to-end solution that includes software, hardware and HPE Pointnext Services. These services can help businesses overcome IT challenges and spend more time on meaningful tasks.
Business technology leader IBM also offers data lake solutions for companies. IBM is well-known for its cloud computing and data analytics solutions. It’s a great choice if an operation is looking for a suitable data lake solution. IBM’s cloud-based approach operates on three key principles: embedded governance, automated integration and virtualization.
These are some data lake solutions from IBM:
With so many data lakes available, there’s surely one to fit a company’s unique needs. Financial services, healthcare and communications businesses often use IBM data lakes for various purposes.
Microsoft offers its Azure Data Lake solution, which features easy storage methods, processing, and analytics using various languages and platforms. Azure Data Lake also works with a company’s existing IT investments and infrastructure to make IT management seamless.
The Azure Data Lake solution is affordable, comprehensive, secure and supported by Microsoft. Companies benefit from 24/7 support and expertise to help them overcome any big data challenges they may face. Microsoft is a leader in business analytics and tech solutions, making it a popular choice for many organizations.
Companies can use Oracle’s Big Data Service to build data lakes to manage the influx of information needed to power their business decisions. The Big Data Service is automated and will provide users with an affordable and comprehensive Hadoop data lake platform based on Cloudera Enterprise.
This solution can be used as a data lake or an ML platform. Another important feature of Oracle is it is one of the best open-source data lakes available. It also comes with Oracle-based tools to add even more value. Oracle’s Big Data Service is scalable, flexible, secure and will meet data storage requirements at a low cost.
Snowflake’s data lake solution is secure, reliable and accessible and helps businesses break down silos to Excellerate their strategies. The top features of Snowflake’s data lake include a central platform for all information, fast querying and secure collaboration.
Siemens and Devon Energy are two companies that provide testimonials regarding Snowflake’s data lake solutions and offer positive feedback. Another benefit of Snowflake is its extensive partner ecosystem, including AWS, Microsoft Azure, Accenture, Deloitte and Google Cloud.
Companies that spend extra time researching which vendors will offer the best enterprise data lake solutions for them can manage their information better. Rather than choose any vendor, it’s best to consider all options available and determine which solutions will meet the specific needs of an organization.
Every business uses information, some more than others. However, the world is becoming highly data-driven — therefore, leveraging the right data solutions will only grow more important in the coming years. This list will help companies decide which data lake solution vendor is right for their operations.
Read next: Get the most value from your data with data lakehouse architecture
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AUSTIN, Texas, (BUSINESS WIRE) -- FalconStor Software, Inc. (OTCQB: FALC), a trusted data protection leader modernizing disaster recovery and backup for the hybrid cloud world, today announced financial results for its second quarter 2022, which ended on June 30, 2022.
“Our shift to recurring revenue-based hybrid cloud data protection solutions continued to progress in the quarter as we secured our first several hybrid cloud customers under the IBM reseller relationship we announced on May 11, 2022,” said Todd Brooks, FalconStor CEO. “IBM’s hybrid cloud push has been a centerpiece of its corporate strategy, as highlighted in its first and second quarter 2022 results. Through our expanding relationship with IBM, enterprises can now leverage new joint hybrid cloud solutions from FalconStor and IBM. These solutions are especially important to the tens of thousands of companies around the globe that leverage IBM i and AIX environments, as they now have the ability to securely backup and restore to the cloud, as well as migrate their IBM i and AIX workloads to IBM Power VS Cloud with secure backup and recovery on an on-going basis.”
“Our aggressive focus on advancing critical hybrid cloud relationships and our efforts to realign to a subscription- and monthly consumption-based recurring revenue model continue to challenge our year-over-year revenue growth as second quarter revenue was $2.4 million, compared to $3.3 million in the second quarter of 2021. However, from a sequential quarter perspective, total revenue increased to $2.4 million, compared to $2.0 million in the first quarter of 2022,” stated Brooks. “To more closely align with our current quarterly revenue level, we decreased operating expenses quarter-over-quarter by 8.8% in second quarter, and are making additional expense adjustments this quarter. Our sales pipeline for the second half of 2022 is growing, especially as it relates to our hybrid cloud initiatives. As a result, we expect sequential quarter-over-quarter revenue to continue growing over the next two quarters.”
Second Quarter 2022 Financial Results
Given Q1 and Q2 2022 results, we are reducing full-year 2022 guidance as follows. Revised guidance reflects continued sequential quarterly GAAP total revenue growth, and GAAP Net Income positive results in Q3 and Q4 2022:
|Guidance (in $ millions)||Low||High|
|Adjusted EBITDA *||
*Adjusted EBITDA adds back Depreciation, Amortization, Restructuring, Severance, Board expenses, Stock Based Compensation as well as Non-Operating Expenses including Income Taxes and Interest & Other Income Expenses
Please click this link to see the accompanying financial charts.
Conference Call and Webcast Information
WHO: Todd Brooks, Chief Executive Officer, FalconStor and Vincent Sita, Chief Financial Officer, FalconStor
WHEN: Wednesday, August 3, 2022, 4:00 PM Central; 5:00 PM Eastern
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Non-GAAP Financial Measures
The non-GAAP financial measures used in this press release are not prepared in accordance with generally accepted accounting principles and may be different from non-GAAP financial measures used by other companies. The Company’s management refers to these non-GAAP financial measures in making operating decisions because they provide meaningful supplemental information regarding the Company’s operating performance. In addition, these non-GAAP financial measures facilitate management’s internal comparisons to the Company’s historical operating results and comparisons to competitors’ operating results. We include these non-GAAP financial measures (which should be viewed as a supplement to, and not a substitute for, their comparable GAAP measures) in this press release because we believe they are useful to investors in allowing for greater transparency into the supplemental information used by management in its financial and operational decision-making. The non-GAAP financial measures exclude (i) depreciation, (ii) amortization, (iii) restructuring expenses, (iv) severance expenses, (v) board expenses, (vi) stock based compensation, (vii) non-operating expenses (income) including income taxes and interest & other expenses (income). For a reconciliation of our GAAP and non-GAAP financial results, please refer to our Reconciliation of Net income (Loss) to Adjusted EBITDA presented in this release.
About FalconStor Software
FalconStor is the trusted data protection software leader modernizing disaster recovery and backup operations for the hybrid cloud world. The Company enables enterprise customers and managed service providers to secure, migrate, and protect their data while reducing data storage and long-term retention costs by up to 95%. More than 1,000 organizations and managed service providers worldwide standardize on FalconStor as the foundation for their cloud first data protection future. Our products are offered through and supported by a worldwide network of leading managed service providers, systems integrators, resellers, and original equipment manufacturers.
FalconStor and FalconStor Software are trademarks or registered trademarks of FalconStor Software, Inc., in the U.S. and other countries. All other company and product names contained herein may be trademarks of their respective holders.
Links to websites or pages controlled by parties other than FalconStor are provided for the reader's convenience and information only. FalconStor does not incorporate into this release the information found at those links nor does FalconStor represent or warrant that any information found at those links is complete or accurate. Use of information obtained by following these links is at the reader's own risk.
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SOURCE: FalconStor Software, Inc.
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Global Open Source Security Consulting Services Size, Status and Forecast 2022-2028
This report studies the Open Source Security Consulting Services with many aspects of the industry like the market size, market status, market trends and forecast, the report also provides brief information of the competitors and the specific growth opportunities with key market drivers. Find the complete Open Source Security Consulting Services analysis segmented by companies, region, type and applications in the report.
New vendors in the market are facing tough competition from established international vendors as they struggle with technological innovations, reliability and quality issues. The report will answer questions about the current market developments and the scope of competition, opportunity cost and more.
Some of the key players’ Analysis in Global Open Source Security Consulting Services @ HCL,IBM,Accenture,Atos,Infosys,Wipro,HPE,Red Hat,Cisco Systems,Oracle
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It is our aim to provide our readers with report for Global Open Source Security Consulting Services, which examines the industry during the period 2022 – 2028. One goal is to present deeper insight into this line of business in this document. The first part of the report focuses on providing the industry definition for the product or service under focus in the Global Open Source Security Consulting Services report. Next, the document will study the factors responsible for hindering and enhancing growth in the industry. After covering various areas of interest in the industry, the report aims to provide how the Global Open Source Security Consulting Services will grow during the forecast period.
One of the crucial parts of this report comprises Global Open Source Security Consulting Services industry key vendor’s discussion about the brand’s summary, profiles, market revenue, and financial analysis. The report will help market players build future business strategies and discover worldwide competition. A detailed segmentation analysis of the market is done on producers, regions, type and applications in the report.
On the basis of geographically, the market report covers data points for multiple geographies such as United States, Europe, China, Japan, Southeast Asia, India, and Central& South America
Analysis of the market:
Other important factors studied in this report include demand and supply dynamics, industry processes, import & export scenario, R&D development activities, and cost structures. Besides, consumption demand and supply figures, cost of production, gross profit margins, and selling price of products are also estimated in this report.
Frequently Asked Questions
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The conclusion part of their report focuses on the existing competitive analysis of the market. We have added some useful insights for both industries and clients. All leading manufacturers included in this report take care of expanding operations in regions. Here, we express our acknowledgment for the support and assistance from the High-speed and Intercity Trains industry experts and publicizing engineers as well as the examination group’s survey and conventions. Market rate, volume, income, demand and supply data are also examined.
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Table of contents:
Open Source Security Consulting Services Global Market Research Report 2021
1 Market Overview
2 Manufacturers Profiles
3 Global Open Source Security Consulting Services Sales, Revenue, Market Share and Competition by Manufacturer
4 Globa Open Source Security Consulting Services Analysis by Regions
5 North America Open Source Security Consulting Services by Country
6 Europe Open Source Security Consulting Services by Country
7 Asia-Pacific Open Source Security Consulting Services by Country
8 South America Open Source Security Consulting Services by Country
9 Middle East and Africa Open Source Security Consulting Services by Countries
10 Global Open Source Security Consulting Services Segment by Type
11 Global Open Source Security Consulting Services Segment by Application
12Open Source Security Consulting Services Forecast (2021-2027)
13 Sales Channel, Distributors, Traders and Dealers
14 Research Findings and Conclusion
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Our marketing research reports comprise of the best market analysis along with putting the right statistical and analytical information on the markets, applications, industry analysis, market shares, technology and technology shifts, important players, and the developments in the market. If you require any specific company, then our company reports collection has countless profiles of all the key industrial companies. All these reports comprise of vital information including the company overview, the company history, the business description, the key products & services, the SWOT analysis, the crucial facts, employee details, the locations and subsidiaries to name a few.
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Pune, Aug 05, 2022 (GLOBE NEWSWIRE via COMTEX) -- Pune, Aug. 05, 2022 (GLOBE NEWSWIRE) -- Healthcare Analytics Market by Vendor Assessment, Technology Assessment, Partner & Customer Ecosystem, type/solution, service, organization size, end-use verticals, and Region - Global Healthcare Analytics Market Forecast to 2030, published by Market Data Centre, The Healthcare Analytics Market is projected to grow at a solid pace during the forecast period. The presence of key players in the ecosystem has led to a compsetitive and diverse market. The advancement of digital transformation initiatives across multiple industries is expected to drive the worldwide Healthcare Analytics Market during the study period.
This COVID-19 analysis of the report includes COVID-19 IMPACT on the production and, demand, supply chain. This report provides a detailed historical analysis of the global Healthcare Analytics Market from 2017-to 2021 and provides extensive market forecasts from 2022-to 2030 by region/country and subsectors. The report covers the revenue, sales volume, price, historical growth, and future perspectives in the Healthcare Analytics Market.
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On the basis of Geography, the Global Healthcare Analytics Market is segmented into North America, Europe, Asia-Pacific, and the Rest of the World (RoW). North America is expected to hold a considerable share in the global Healthcare Analytics Market. Due to increasing investment for research and development process and adoption of solutions in the region whereas Asia-Pacific is expected to grow at a faster pace during the forecasted period.
The growing number of Healthcare Analytics Market players across regions is expected to drive market growth further. Moreover, increasing investments by prominent vendors in product capabilities and business expansion is expected to fuel the market during the study period. Many market players are finding lucrative opportunities in emerging economies like China and India, where the large populations are coupled with new innovations in numerous industries.
List of Companies Covered in this Report are:
|Market Assessment||Technology Assessment||Vendor Assessment|
|Market Dynamics||Key Innovations||Product Breadth and Capabilities|
|Trends and Challenges||Adoption Trends and Challenges||Technology Architecture|
|Drivers and Restrains||Deployment Trends||Competitive Differentiation|
|Regional and Industry Dynamics||Industry Applications||Price/Performance Analysis|
|Regulations and Compliance||Latest Upgrardation||Strategy and Vision|
In deep ToC includes
233 - Tables
45 - Figures
300 - Pages
Get Table Of Content of the Report @ https://www.marketdatacentre.com/toc/13537
Table of Contents
1.1. Market Definition
1.2. Market Segmentation
1.3. Geographic Scope
1.4. Years Considered: Historical Years - 2017 & 2020; Base Year - 2021; Forecast Years - 2022 to 2030
1.5. Currency Used
2. RESEARCH METHODOLOGY
2.1. Research Framework
2.2. Data Collection Technique
2.3. Data Sources
2.3.1. Secondary Sources
2.3.2. Primary Sources
2.4. Market Estimation Methodology
2.4.1. Bottom-Up Approach
2.4.2. Top-Down Approach
2.5. Data Validation and Triangulation
2.5.1. Market Forecast Model
2.5.2. Limitations/Assumptions of the Study
3. ABSTRACT OF THE STUDY
4. MARKET DYNAMICS ASSESSMENT
5. VALUE CHAIN ANALYSIS
6. PRICING ANALYSIS
7. SUPPLY CHAIN ANALYSIS
8. MARKET SIZING AND FORECASTING
8.1. Global - Healthcare Analytics Market Analysis & Forecast, By Region
8.2. Global - Healthcare Analytics Market Analysis & Forecast, By Segment
8.2.1. North America Healthcare Analytics Market, By Segment
8.2.2. North America Healthcare Analytics Market, By Country
8.2.3. Europe Healthcare Analytics Market, By Segment
8.2.4. Europe Healthcare Analytics Market, By Country
126.96.36.199. Rest of Europe (ROE)
8.2.5. Asia Pacific Healthcare Analytics Market, By Segment
8.2.6. Asia Pacific Healthcare Analytics Market, By Country
188.8.131.52. Rest of Asia Pacific (RoAPAC)
8.2.7. Rest of the World (ROW) Healthcare Analytics Market, By Segment
8.2.8. Rest of the World (ROW) Healthcare Analytics Market, By Country
184.108.40.206. Latin America
220.127.116.11. Middle East & Africa
ToC can be modified as per clients' business requirements*
Read Overview of the Report @https://www.marketdatacentre.com/healthcare-analytics-market-13537
Key Questions Answered in This Report:
Vendor assessment includes a deep analysis of how vendors are addressing the demand in the Healthcare Analytics Market. The MDC CompetetiveScape model was used to assess qualitative and quantitative insights in this assessment. MDC's CompetitiveScape is a structured method for identifying key players and outlining their strengths, relevant characteristics, and outreach strategy. MDC's CompetitiveScape allows organizations to analyze the environmental factors that influence their business, set goals, and identify new marketing strategies. MDC Research analysts conduct a thorough investigation of vendors' solutions, services, programs, marketing, organization size, geographic focus, type of organization and strategies.
Technology dramatically impacts business productivity, growth and efficiency.Technologies can help companies develop competitive advantages, but choosing them can be one of the most demanding decisions for businesses. Technology assessment helps organizations to understand their current situation with respect to technology and offer a roadmap where they might want to go and scale their business. A well-defined process to assess and select technology solutions can help organizations reduce risk, achieve objectives, identify the problem, and solve it in the right way. Technology assessment can help businesses identify which technologies to invest in, meet industry standards, compete against competitors.
Business Ecosystem Analysis
Advancements in technology and digitalization have changed the way companies do business; the concept of a business ecosystem helps businesses understand how to thrive in this changing environment. Business ecosystems provide organizations with opportunities to integrate technology in their daily business operations and Excellerate research and business competency. The business ecosystem includes a network of interlinked companies that compete and cooperate to increase sales, Excellerate profitability, and succeed in their markets. An ecosystem analysis is a business network analysis that includes the relationships amongst suppliers, distributors, and end-users in delivering a product or service.
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Regions and Countries Covered
North America (US, Canada), Europe (Germany, UK, France, Spain, Italy, and Rest of Europe), Asia-Pacific (Japan, China, Australia, India, Rest of Asia-Pacific), and Rest of the World (RoW).
Healthcare Analytics Market Dynamics, Covid-19 Impact on the Healthcare Analytics Market, Vendor Profiles, Vendor Assessment, Strategies, Technology Assessment, Product Mapping, Industry Outlook, Economic Analysis, Segmental Analysis, Healthcare Analytics Market Sizing, Analysis Tables.
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Market Data Centre offers complete solutions for market research reports in miscellaneous businesses.These decisions making process depend on wider and systematic extremely important information created through extensive study as well as the most recent trends going on in the industry.The company also attempts to offer much better customer-friendly services and appropriate business information to achieve our clients' ideas.
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