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This top-notch market research report has been structured by skilful and experienced team players who work hard to accomplish an absolute growth and success in the business. The report performs market study and analysis to provide market data by considering new product development from beginning to launch. The key research methodology used throughout this report by DBMR research team is data triangulation which entails data mining, analysis of the impact of data variables on the market, and primary validation. Adopting such this market research report is always beneficial for any company, whether it is a small scale or large scale, for marketing its products or services.

A market research conducted in this report aids in improving and modifying the products so that future products present more satisfaction to your valuable customers. A comprehensive discussion about numerous market-related topics in the report is sure to assist the client in studying the market on competitive landscape. The data collected to structure this report is based on the data collection modules with large sample sizes. This market research report is prepared with detailed statistics and market research insights that results in sharp growth and thriving sustainability in the market for the businesses.

Perimeter security marketwill grow at a CAGR of 10.63% in the forecast period of 2020 to 2027. Increased need to reduce manpower cost is an essential factor driving the perimeter security market.

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Some of the Leading Key Companys Covered for this Research are:

The major players covered in perimeter security market report are Harper Chalice Group Ltd., United Technologies, Godrej.com, Senstar Corporation, Future Fiber Technologies, PureTech Systems Inc., Detection Technologies, Schneider Electric, Honeywell International Inc., Cias Elettronica Srl, DeTekion Security Systems Inc., RBtech Perimeter Security Systems, Jacksons Fencing, Johnson Controls, Southwest Microwave Inc., SightLogix, Heras, Axis Communications AB, D-Fence, Fiber SenSys Inc., Aventura Technologies Inc., Anixter Inc., ADVANCED PERIMETERE SYSTEMS LTD., SORHEA, FLIR Systems Inc among other domestic and global players. Market share data is available for global, North America, Europe, Asia-Pacific (APAC), Middle East and Africa (MEA) and South America separately. DBMR analysts understand competitive strengths and provide competitive analysis for each competitor separately.

Competitive Landscape

**The report encourages high end commercialization and profit steering possibilities, also considering market dimensions and volatility of market size.

**To ensure thoroughly impeccable investor participation and noteworthy growth prospects, this section of the report also dwells over accurately decoding the competitive landscape with astute high-end identification of frontline players, complete with an in-depth analytical study of their business choices and investment discretion.

**Details pertaining to portfolio advances, regional footprint and other relevant market details have been well gauged in this report to ensure smooth ride and optimistic business returns despite several odds and unprecedented challenges.

Radical conclusions of the report:

Key Market Segmentation

Global Perimeter Security Market, By Component (Video Surveillance, Intrusion Detection, Access Control, Signaling System and Services), Vertical (Industrial, Commercial and Services, Government, Infrastructure, Military & Defense, Residential, Educational, Religious Building), Solutions(Access Control, Intrusion Detection Sensors, Video Surveillance, Mass Notification), Country (U.S., Canada, Mexico, Brazil, Argentina, Rest of South America, Germany, Italy, U.K., France, Spain, Netherlands, Belgium, Switzerland, Turkey, Russia, Rest of Europe, Japan, China, India, South Korea, Australia, Singapore, Malaysia, Thailand, Indonesia, Philippines, Rest of Asia-Pacific, Saudi Arabia, U.A.E, South Africa, Egypt, Israel, Rest of Middle East and Africa) Industry Trends and Forecast to 2027

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Fundamentals of Table of Content:

1 Report Overview

1.1 Study Scope

1.2 Key Market Segments

1.3 Players Covered

1.4 Market Analysis by Type

1.5 Market by Application

1.6 Study Objectives

1.7 Years Considered

2 Global Growth Trends

2.1 Perimeter Security Market Size

2.2 Perimeter Security Growth Trends by Regions

2.3 Industry Trends

3 Market Share by Key Players

3.1 Perimeter Security Market Size by Manufacturers

3.2 Perimeter Security Key Players Head office and Area Served

3.3 Key Players Perimeter Security Product/Solution/Service

3.4 Date of Enter into Perimeter Security Market

3.5 Mergers & Acquisitions, Expansion Plans

4 Breakdown Data by Product

4.1 Global Perimeter Security Sales by Product

4.2 Global Perimeter Security Revenue by Product

4.3 Perimeter Security Price by Product

5 Breakdown Data by End User

5.1 Overview

5.2 Global Perimeter Security Breakdown Data by End User

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Key Influence of this Market:

Comprehensive assessment of all opportunities and risk in Perimeter Security Market

This Market recent innovations and major events

Detailed study of business strategies for growth of the this Market-leading players

Conclusive study about the growth plot of the Market for forthcoming years

In-depth understanding of this Market-particular drivers, constraints and major micro markets

Favourable impression inside vital technological and market latest trends striking Perimeter Security Market

To provide historical and forecast revenue of the market segments and sub-segments with respect to four main geographies and their countries- North America, Europe, Asia, and Rest of the World (ROW)

To provide country level analysis of the market with respect to the current market size and future prospective

NOTE: Our report highlights the major issues and hazards that companies might come across due to the unprecedented outbreak of COVID-19.

About Data Bridge Market Research, Private Ltd

Data Bridge Market ResearchPvtLtdis a multinational management consulting firm with offices in India and Canada. As an innovative and neoteric market analysis and advisory company with unmatched durability level and advanced approaches. We are committed to uncover the best consumer prospects and to foster useful knowledge for your company to succeed in the market.

Data Bridge Market Research is a result of sheer wisdom and practice that was conceived and built-in Pune in the year 2015. The company came into existence from the healthcare department with far fewer employees intending to cover the whole market while providing the best class analysis. Later, the company widened its departments, as well as expands their reach by opening a new office in Gurugram location in the year 2018, where a team of highly qualified personnel joins hands for the growth of the company. Even in the tough times of COVID-19 where the Virus slowed down everything around the world, the dedicated Team of Data Bridge Market Research worked round the clock to provide quality and support to our client base, which also tells about the excellence in our sleeve.

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Global Perimeter Security Market Revenue Overview, Business Analysis by leading Players with its Application and Forecast KSU | The Sentinel...

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PLANO, Texas, Feb. 10, 2021 (GLOBE NEWSWIRE) -- INTRUSION Inc. (NASDAQ: INTZ), a leading provider of entity identification, high speed data mining, cybercrime and advanced persistent threat detection services, announces Katrinka McCallum as the newest member of its Board of Directors.

We are beyond thrilled to have Katrinka join INTRUSIONs Board of Directors and welcome her to our team. Her outstanding executive experience and board involvement brings great perspective and fresh ideas to our company, said Tony LeVecchio, Chairman of the Board. Katrinkas background and skillsets make her a natural fit for this seat.

Most recently, Katrinka McCallum was Vice President of Customer and Product Experience at Red Hat, a leading provider of enterprise open source solutions, which was acquired by IBM in 2019. During her career that spans more than two decades in enterprise software, McCallum led business units, sales and marketing organizations as well as engineering and operations teams. She developed a reputation for driving strategy into actions that intelligently aligned the operational backbone and accelerated the business.

McCallum has served on corporate boards including Micromuse, Inc. and Round Pond, a subsidiary board of Red Hat, Inc. In addition, she was a member of the executive committee for the North Carolina Technology Association board.

I am passionate about my work scaling organizations for growth within the technology industry and hope to further strengthen the impressive INTRUSION team, added McCallum. My focus has been on operating excellence and delivering results that benefit customers, ecosystems and shareholders. I look forward to continuing that work within the cybersecurity sphere.

The appointment of Katrinka McCallum comes at an exciting time for INTRUSION, as the awareness of INTRUSION Shield is rapidly growing now that businesses know it can protect from zero-day attacks such as those that breached SolarWinds.

About INTRUSION Inc.

INTRUSION, Inc. is a global provider of entity identification, high speed data mining, cybercrime and advanced persistent threat detection solutions. INTRUSIONs family of solutions includes Shield, a combination of plug-n-play hardware, software, global data, and real-time Artificial Intelligence (AI) services that provide organizations with the most robust cybersecurity defense possible, TraceCop for identity discovery and disclosure, and Savant for network data mining and advanced persistent threat detection. INTRUSIONs solutions help protect critical information assets by quickly detecting, protecting, analyzing and reporting attacks or misuse of classified, private and regulated information for government and enterprise networks. For more information, please visitwww.intrusion.com.

Cautionary Statement Regarding Forward Looking Information

This release may contain certain forward-looking statements, including, without limitations, statements about the performance of protections provided by our Shield products, or other statements which reflect management's expectations regarding future events and operating performance and speak only as of the date hereof. These forward- looking statements involve a number of risks and uncertainties, including that the Company may not benefit from these changes in its executive management team. These statements are made under the "safe harbor" provisions of the Private Securities Litigation Reform Act of 1995 and involve risks and uncertainties which could cause actual results to differ materially from those in the forward-looking statements, including, risks that we have detailed in the Company's most recent reports on Form 10-K and Form 10-Q, particularly under the heading Risk Factors.

ContactJulia Kramer jkramer@intrusion.comP: 972-301-3635

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INTRUSION Inc. Announces Appointment of Katrinka McCallum to its Board of Directors - GlobeNewswire

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To capitalize on the promise of artificial intelligence and alternative data, boards need to anticipate and mitigate various risks.Takeaways

Alternative data and artificial intelligence (AI) have generated tremendous excitement in the business world. The technology offers the potential for faster, more efficient and more reliable decisions. Banks and fintech platforms already use them to make credit decisions, and they show promise in other areas, from fraud prevention to hiring decisions.

But the surprising predictive success of AI-based decision models is precisely what makes them tricky legally. Often, the developers of such models cannot explain the relation between a variable and its predictive value. Anything from where you shop to the type of mobile phone you use or the first letter of your last name may prove to be a predictor of, say, the likelihood you will default on a loan or that you will perform well in a job you are applying for.

To the companies that use such models, this may seem like brilliant data mining unearthing nonobvious predictors that outperform conventional ones. But to regulators and those harmed by AI-based decisions whose rationales cannot be fully explained, the process may seem capricious.

AI models are the subject of particularly lively debate in the lending sphere, both because lending is a heavily regulated activity and because the technology may turn out to be more reliable than traditional credit bureau factors (number of tradelines, average balance, debt-to-income, etc.). The latter have proven less effective in predicting defaults over the past year, particularly during the pandemic.

Alternative data may also benefit consumers who have not established the kind of borrowing track record typically relied on by credit bureaus. It could therefore expand the population qualifying for credit. Similar benefits and problems arise in recruiting and other areas where AI models based on alternative data are being explored.

The appeal of AI is that the technology can make predictions using offbeat data that humans cannot make or explain. But the fact that AI uncovers new and surprising predictors by poring through hundreds of types of data poses a basic problem: The most valuable variables may have no obvious relation to the thing being predicted, such as a borrowers ability to pay its debts.

In the worst cases, outcomes of these models may be both surprising and problematic. For example, one AI-based recruiting model for software developers relied on the success of previous hires. They were almost entirely male, however, and the model turned out to have a strong bias against women so strong that it excluded any candidate from two womens colleges. That could violate employment nondiscrimination laws in jurisdictions where its not necessary to show discriminatory intent.

This sort of unforeseen bias is on the minds of bank regulators, because it could violate fair lending rules. This hiring example was cited by Federal Reserve Bank Governor Lael Brainard in a recent speech about AI in financial services. Regulators may demand proof that a similar nonobvious variable is not a proxy for another, forbidden factor such as the race or gender of the applicant.

The hiring example also underscores that companies cannot blindly accept AI-based recommendations as technical wizardry. The predictions based on novel data may be quite explainable if you dig deep enough.

An AI models black box quality itself poses a problem, apart from any biases. To illustrate this, assume one variable in a lenders underwriting model is whether the applicant uses an Apple or a Samsung mobile phone, because that (hypothetically) has been shown to be highly predictive of an applicants risk of default.

If predictive value were the only factor, the brand variable might satisfy safety and soundness bank rules. But regulations often require more than predictive value. U.S. banking regulators require that financial models be conceptually sound. And banking rules in the U.S., EU and Hong Kong all generally require lenders to be able to explain to an applicant why credit was denied.

Hence, explainability the ability to articulate the relationship between a variable and the attribute being predicted has become a buzzword in AI, and is particularly central to fintech regulation and the growth of AI in finance. In the U.S., regulators may also ask if a prospective borrower could anticipate that a lender would consider a certain factor in its decision, so the applicant can take action to avoid being denied credit. If the model uses, say, the first letter of an applicants surname, the consumer would have few options. (And, of course, if the phone brand proved to be correlated with race, gender or some other factor lenders cannot consider, that would pose fair lending and other problems.)

Finally, reputational risk needs to be weighed. If it becomes public that an institution makes decisions based on complex black box models relying on puzzling alternative data, it could lead to bad publicity. Several years ago, a lender drew criticism for scoring applicants based in part on the chain stores where they shopped. As a result, the company stopped using that factor.

Explain yourself In many cases, the best approach will be the common sense one: Make sure your business can explain the relationship between each type of data used and the decisions that result. That will be necessary to satisfy regulators, customers and the public at large.

Key steps companies can take to identify and mitigate the risks of using alternative data and artificial intelligence models:

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Why Does the Brand of My Phone Affect My Credit Rating? - JD Supra

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Pune, Feb. 08, 2021 (GLOBE NEWSWIRE) -- Market AnalysisMarket Research Future (MRFR) predicts the global data analytics market size to reach USD 132,903.8 million at a 28.9% CAGR from 2016 to 2026 (forecast period).

Data can be used by a variety of organizations to boost their marketing strategies, increase their bottom line, personalize their content, and better understand their customers. Data analytics is the science of analyzing raw data to extract meaningful insights. Many data analytics processes and techniques are automated into algorithms and mechanical processes to operate on raw data for better decision-making processes. Data analytics is a broad concept that includes many different forms of data analysis. It refers to the process of analyzing data sets to draw conclusions about the information it holds.

Key factors driving the global data analytics market are the widespread adoption of advanced technologies for business operations and the rising demand for data analytics for faster decision-making processes and cost reduction. The implementation of data analytics techniques improves the efficiency and productivity of business operations and strengthens the organizational workforce. Data analytical techniques help identify and fix the errors in data sets with the help of data filtration tools, which further improve the quality of data to benefit both consumers and institutions, including insurance firms, banks, and finance companies. These advantages are responsible for the exponential growth of the global data analytics market. In addition, the rising volume and complexity of data due to increasing mobile data traffic, increasing adoption and development of technologies like AI and IoT, and cloud computing traffic, are fueling the growth of the global data analytics industry.

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COVID-19 Impact on the Global Data Analytics MarketThe COVID-19 pandemic has accelerated the adoption of data analytics solutions and services. Researchers have developed predictive analytics models to track COVID-19 surges in various countries. These factors lead to a major rise in the global data analytics market.

Market SegmentationThe global data analytics industry has been segmented based on type, solution, application, deployment, organization size, function, and vertical.

By type, the global market has been segregated into predictive analytics, prescriptive analytics, customer analytics, descriptive analytics, and others.

By solution, the global market has been segregated into data management, data mining, fraud & security intelligence, and data monitoring.

Based on application, the global market has been segregated into enterprise resource planning, database management, supply chain management, human resource management, and others.

By deployment, the global market has been segregated into cloud and on-premises.

By organization size, the global market has been segregated into large enterprises and small & medium enterprises.

By function, the data analytics market has been segregated into marketing analytics, sales analytics, operational analytics, accounting & finance analytics, HR analytics, and others.

By vertical, the global market has been segregated into BFSI, IT & Telecom, manufacturing, retail & e-commerce, energy & power, healthcare, transport & logistics, media & entertainment, and others. The BFSI segment earned the largest market share of 22.3% in 2019, with a market value of USD 5.127.7 million, and is projected to have the highest CAGR of 30.9% in the assessment period. The IT & Telecom segment was valued at USD 4.444.9 million in 2019 and is expected to have a CAGR of 30.6%.

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Regional AnalysisGeographically, the global data analytics industry has been segmented into North America, Europe, Asia Pacific, and the rest of the world.

North America to Lead Global MarketNorth America captured the highest market share of 43.6% in 2019, with a market valuation of USD 10,037.1 million; the market is projected to have a CAGR of 26.4% over the forecast period. The US earned the largest market share of 86.7% in 2019, with a valuation of USD 8.701.4 million; it is projected to register a 26.2% CAGR during the forecast period. Canada was the second-largest market in 2019, estimated at USD 773.6 million; it is projected to post the highest CAGR of 27.9%. While the Mexican region recorded a market share of 5.6%, reported USD 562.1 million in 2019, and is expected to hit USD 3.068.0 million at CAGR 27.8% by the end of 2026.

Europe to Hold the Second SpotEurope held the second-largest position in 2019, estimated at USD 6,428.0 million; the market is expected to have a CAGR of 29.8% over the forecast period. The UK held the largest market share of 38.1% in 2019, with a market value of USD 2.447.8 million; it is expected to register a CAGR of 29.7% during the forecast period. Germany was the second-largest market in 2019, estimated at USD 1,249.1 million; it is expected to have the highest CAGR of 28.2% over the forecast period.

Competitive LandscapeWith the presence of a large number of global and regional players, the global data analytics market is moderately fragmented and competitive. Market players are actively involved in technological advancement, geographic expansion, and mergers and acquisitions in order to retain their position in the international market.

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The key players in the global data analytics market are:

Industry NewsIn 2020, IBM built new cloud and AI-powered technologies to help researchers across a broad range of scientific disciplines accelerate the delivery process. It aims to help scientists, doctors, and health researchers improve the processing of the Covid-19 drug, from collecting insights to applying new virus genomic information.

In 2019, Oracle revealed the addition of Pre-Packaged Enterprise Analytics to applications. The new package is built for the fusion cloud enterprise resource planning applications that companies use to run their financial processes.

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About Market Research Future:At Market Research Future (MRFR), we enable our customers to unravel the complexity of various industries through our Cooked Research Report (CRR), Half-Cooked Research Reports (HCRR), Raw Research Reports (3R), Continuous-Feed Research (CFR), and Market Research & Consulting Services.

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Data Analytics Market to Hit USD 132.90 Billion by 2026 | North America Region to Spearhead the Global Data Analytics Industry with the Projected CAGR...

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