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A2180-270 - Assessment- IBM Business Process Manager Advanced V7.5, Integration Develop - Dump Information

Vendor : IBM
Exam Code : A2180-270
Exam Name : Assessment- IBM Business Process Manager Advanced V7.5, Integration Develop
Questions and Answers : 56 Q & A
Updated On : October 19, 2018
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A2180-270 Questions and Answers

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A2180-270 Assessment- IBM Business Process Manager Advanced V7.5, Integration Develop

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A2180-270 exam Dumps Source : Assessment- IBM Business Process Manager Advanced V7.5, Integration Develop

Test Code : A2180-270
Test Name : Assessment- IBM Business Process Manager Advanced V7.5, Integration Develop
Vendor Name : IBM
Q&A : 56 Real Questions

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IBM Assessment- IBM Business Process

IBM files formal JEDI protest a day earlier than bidding procedure closes | killexams.com Real Questions and Pass4sure dumps

IBM announced yesterday that it has filed a formal protest with the U.S. govt Accountability workplace over the structure of the Pentagon’s winner-take-all $10 billion, 10-year JEDI cloud contract. The protest came only a day earlier than the bidding system is scheduled to shut. As IBM put it in a weblog put up, they took concerns with the one vendor method. they're under no circumstances by myself.

nearly each seller in need of Amazon, which has remained mainly quiet, has been complaining about this strategy. IBM definitely faces a tricky combat going up against Amazon and Microsoft.

IBM doesn’t cover the undeniable fact that it thinks the contract has been written for Amazon to win and that they agree with the one-vendor method comfortably doesn’t make feel. “No business on earth would build a cloud the style JEDI would after which lock in to it for a decade. JEDI turns its back on the preferences of Congress and the administration, is a bad use of taxpayer greenbacks and was written with just one business in intellect.” IBM wrote within the weblog submit explaining why it turned into protesting the deal before a call changed into made or the bidding become even closed.

For the list, DOD spokesperson Heather Babb informed TechCrunch last month that the bidding is open and no dealer is appreciated. “The JEDI Cloud remaining RFP reflects the enjoyable and important needs of DOD, employing the greatest practices of competitive pricing and protection. No carriers had been pre-selected,” she stated.

an awful lot like Oracle, which filed a protest of its personal returned in August, IBM is a standard seller that turned into late to the cloud. It begun a adventure to construct a cloud company in 2013 when it purchased Infrastructure as a carrier seller SoftLayer and has been using its checkbook to purchase application functions so as to add on properly of SoftLayer ever on account that. IBM has concentrated on constructing cloud capabilities round AI, safety, large information, blockchain and other rising applied sciences.

each IBM and Oracle have a problem with the one-supplier strategy, exceptionally one that locks within the government for a ten-year duration. It’s value mentioning that the contract definitely is an initial two-year contend with two further three yr options and a ultimate two yr choice. The DOD has left open the possibility this could not go the whole 10 years.

It’s additionally worth placing the contract in viewpoint. whereas 10 years and $10 billion is nothing to sneeze at, neither is it as market altering as it could appear, now not when some are predicting the cloud may be $100 billion a year market very quickly.

IBM uses the blog post as a form of revenue pitch as to why it’s a sensible choice, while on the equal time pointing out the issues in the single vendor strategy and complaining that it’s geared toward a single unnamed dealer that we all comprehend is Amazon.

The bidding system closes today, and except some thing adjustments on account of these protests, the winner could be selected next April


Healthcare enterprise system Outsourcing Market is likely to experience an enormous boom in close Future | Eli world, Gebbs, Genpact, IBM | killexams.com Real Questions and Pass4sure dumps

October 04, 2018 --

"Healthcare business system Outsourcing Market"

latest analysis look at from HTF MI with title international Healthcare enterprise procedure Outsourcing through manufacturers, regions, class and software, Forecast to 2023. The analysis file presents a complete evaluation of the market and contains Future fashion, existing increase components, attentive opinions, records, old information, and statistically supported and trade validated market statistics. The look at is segmented by using products classification, utility/conclusion-clients.

The research look at offers estimates for international Healthcare enterprise method Outsourcing Forecast until 2023.

if you're concerned in the Healthcare company method Outsourcing trade or intend to be, then this study will deliver you finished outlook. It�s a must-have you preserve your market expertise up thus far segmented by applications Analytics and fraud administration features, Billing and money owed management services, Claims management features, HR features, integrated entrance-conclusion services and back office operations, Member management services & issuer administration capabilities, Product types similar to Manufacturing services, Non-scientific features & R&D functions and a few main players in the business. if you have a unique set of avid gamers/manufacturers in accordance with geography or wants regional or country segmented reports we will provide customization in keeping with your requirement.�

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Key corporations/avid gamers:�Accenture, R1 RCM, Allscripts, Capgemini, Cognizant, Dynamic Healthcare techniques, Eli international, Gebbs, Genpact, IBM, Infosys BPM, Lonza, Parexel, IQVIA, Sutherland, Tata Consultancy capabilities, Truven health, UnitedHealth, Wipro & Xerox.�

utility:�Analytics and fraud administration services, Billing and money owed administration services, Claims administration functions, HR features, built-in entrance-conclusion capabilities and back workplace operations, Member management features & issuer administration capabilities,�Product category:�Manufacturing services, Non-clinical capabilities & R&D functions.�

The analysis covers the latest & Future�market dimension of the global Healthcare company method Outsourcing�market and its boom prices according to 5 year heritage facts. It also covers various kinds of segmentation similar to through geography united states, Europe, China, Japan, Southeast Asia, India & important & South america. The market competition is normally starting to be greater with the rise in technological innovation and M&A activities in the industry. additionally, many native and regional vendors are offering particular application products for distinct end-users. On the foundation of attributes similar to enterprise overview, recent tendencies, thoughts adopted via the market leaders to make certain boom, sustainability, economic overview and up to date trends.�

live up-to-date with Healthcare company method Outsourcing market research offered by HTF MI. assess how key tendencies and rising drivers are shaping this industry growth as the analyze avails you with market qualities, dimension and increase, segmentation, regional breakdowns, competitive landscape, shares, vogue and strategies for this market. in the global Healthcare company technique Outsourcing Market analysis & Forecast 2018-2023, the income is valued at USD XX million in 2017 and is expected to attain USD XX million by the conclusion of 2023, growing to be at a CAGR of XX% between 2018 and 2023. The production is estimated at XX million in 2017 and is forecasted to attain XX million via the end of 2023, starting to be at a CAGR of XX% between 2018 and 2023.�

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Key questions answered during this report - world Healthcare company procedure Outsourcing Market measurement, status and Forecast 2018-2025�

what is going to the market measurement be in 2023 and what's going to the growth expense be�What are the key market tendencies�what is riding international Healthcare enterprise system Outsourcing Market?�What are the challenges to market boom?�who're the important thing vendors in Healthcare company process Outsourcing Market space?�What are the key market tendencies impacting the boom of the international Healthcare business procedure Outsourcing Market ?�What are the key outcomes of the five forces analysis of the global Healthcare business technique Outsourcing Market?�What are the market opportunities and threats faced via the providers in the international Healthcare company process Outsourcing market? Get in-depth details about components influencing the market shares of the Americas, APAC, and EMEA?�

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There are 15 Chapters to monitor the world Healthcare enterprise method Outsourcing market.�

Chapter 1, to describe Definition, specifications and Classification of world Healthcare enterprise method Outsourcing, functions of Healthcare company manner Outsourcing, Market phase by way of areas;�Chapter 2, to investigate the Manufacturing charge constitution, uncooked cloth and Suppliers, Manufacturing technique, business Chain constitution;�Chapter three, to monitor the Technical records and Manufacturing flora evaluation of , ability and industrial production Date, Manufacturing flowers Distribution, Export & Import, R&D status and expertise supply, raw substances Sources evaluation;�Chapter 4, to exhibit the overall Market evaluation, ability analysis (business phase), income analysis (business section), income rate evaluation (company segment);�Chapter 5 and 6, to show the Regional Market evaluation that contains united states, Europe, China, Japan, Southeast Asia, India & vital & South the us, Healthcare business technique Outsourcing section Market evaluation (via classification);�Chapter 7 and eight, to analyze the Healthcare enterprise process Outsourcing phase Market evaluation (via utility Analytics and fraud administration features, Billing and accounts administration functions, Claims administration capabilities, HR capabilities, built-in entrance-conclusion capabilities and back workplace operations, Member administration services & issuer management functions) major producers evaluation;�Chapter 9, Market trend evaluation, Regional Market fashion, Market fashion by Product category Manufacturing capabilities, Non-medical services & R&D functions, Market fashion by way of software Analytics and fraud administration services, Billing and bills management services, Claims management services, HR features, integrated front-conclusion services and lower back workplace operations, Member administration features & provider administration features;�Chapter 10, Regional advertising type evaluation, overseas alternate classification analysis, provide Chain evaluation;�Chapter eleven, to investigate the consumers evaluation of global Healthcare business process Outsourcing by means of place, category and utility ;�Chapter 12, to describe Healthcare business manner Outsourcing research Findings and Conclusion, Appendix, methodology and records source;�Chapter 13, 14 and 15, to describe Healthcare enterprise manner Outsourcing income channel, distributors, merchants, buyers, analysis Findings and Conclusion, appendix and facts supply.�

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This record gives pin-factor analysis for changing aggressive dynamics�It gives a ahead looking viewpoint on various factors using or restraining market increase�It gives a 5-year forecast assessed on the groundwork of how the market is anticipated to develop�It helps in understanding the key product segments and their future�It gives pin element evaluation of altering competitors dynamics and continues you forward of opponents�It helps in making informed business choices by using having comprehensive insights of market and via making in-depth analysis of market segments�

Thanks for analyzing this text; you could additionally get individual chapter clever part or location smart document edition like North the usa, Europe or Asia.�

About writer:�HTF Market file is a totally owned brand of HTF market Intelligence Consulting private confined. HTF Market file international research and market intelligence consulting firm is uniquely positioned to now not handiest identify boom opportunities but to additionally empower and encourage you to create visionary boom options for futures, enabled by way of our superb depth and breadth of concept management, analysis, equipment, activities and journey that support you for making goals right into a reality. Our knowing of the interplay between business convergence, Mega developments, applied sciences and market traits provides our customers with new business models and expansion alternatives. we're concentrated on picking the �accurate Forecast� in every business we cowl so our purchasers can reap the merits of being early market entrants and may accomplish their �desires & objectives�.�

Media ContactCompany identify: HTF Market Intelligence Consulting deepest LimitedContact person: Craig FrancisEmail: [email protected]cellphone: 2063171218Address:Unit No. 429, Parsonage RoadCity: EdisonState: New JerseyCountry: United StatesWebsite: www.htfmarketreport.com/stories/1398538-world-healthcare-enterprise-system-outsourcing-market

source: www.abnewswire.com

linked keywords:business,computers & application,information & existing Affairs,technology,website & weblog,

supply:Copyright (c) AB Digital, Inc. All Rights Reserved


large information Market 2018 by foremost Key players: overseas enterprise Machines (IBM) corporation, Oracle service provider, Microsoft organization | killexams.com Real Questions and Pass4sure dumps

This press release changed into orginally allotted with the aid of SBWire

Pune, India -- (SBWIRE) -- 10/16/2018 -- massive records business Overview

The huge information market analysis look at relies upon a combination of primary in addition to secondary research.  The expertise-driven ambiance is the huge ingredient in the back of the era of the big amount of statistics. as an instance multiplied adoption of technologies like social networking sites, cell devices have turn into the core ingredient at the back of the records explosion. This huge amount of facts generated is also called large statistics which is increasing exponentially with time. speedy advancement within the container of technology has led the users to shift from analog to digital technologies. This kinds the fundamental driver in boosting the large facts market. multiplied demand for the cell purposes has resulted within the growth of cell clients thereby contributing in propelling the market growth.youngsters, safety and privateness challenge might also slow the market boom, because of inefficient data administration. The safety know-how fails in managing the dynamic and big datasets. extended adoption of cloud-based know-how especially, due to the pliability offered when it comes to cost and storage has resulted in the generation of new market alternatives.

Get a pattern PDF report: https://www.garnerinsights.com/massive-records-Market—global-Market-Synopsis-boom-factors-business-Segmentation-Regional-evaluation-and-aggressive-analysis-2017—2025#request-pattern

Scope Of The file:latest analysis file on world Animal Husbandry Biologicals testing Market brought by way of Garner Insights which covers Market Overview, Future economic influence, competitors by way of producers, provide (construction), and Consumption evaluation

fundamental Key avid gamers of the big information Market are:international company Machines (IBM) service provider, Oracle organisation, Microsoft organisation, Hewlett-Packard enterprise ,SAP SE, Amazon internet services, SAS Institute,    Dell, Inc., Teradata agency, Splunk.

major types of huge information lined are:- Structured records- Semi-Structured facts-  Unstructured statistics

essential applications of massive records coated are:- Banking and Finance- Healthcare- IT and Telecom- Manufacturing

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A2180-270 Assessment- IBM Business Process Manager Advanced V7.5, Integration Develop

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Assessment- IBM Business Process Manager Advanced V7.5, Integration Develop

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What is IAM? Identity and access management explained | killexams.com real questions and Pass4sure dumps

IAM definition

Identity and access management (IAM) in enterprise IT is about defining and managing the roles and access privileges of individual network users and the circumstances in which users are granted (or denied) those privileges. Those users might be customers (customer identity management) or employees (employee identity management. The core objective of IAM systems is one digital identity per individual. Once that digital identity has been established, it must be maintained, modified and monitored throughout each user’s “access lifecycle.” 

Thus, the overarching goal of identity management is to “grant access to the right enterprise assets to the right users in the right context, from a user’s system onboarding to permission authorizations to the offboarding of that user as needed in a timely fashion,” according to Yassir Abousselham, senior vice president and chief security officer for Okta, an enterprise identity and access management provider. 

IAM systems provide administrators with the tools and technologies to change a user’s role, track user activities, create reports on those activities, and enforce policies on an ongoing basis. These systems are designed to provide a means of administering user access across an entire enterprise and to ensure compliance with corporate policies and government regulations. 

IAM tools

Identity and management technologies include (but aren’t limited to) password-management tools, provisioning software, security-policy enforcement applications, reporting and monitoring apps and identity repositories. Identity management systems are available for on-premises systems, such as Microsoft SharePoint, as well as for cloud-based systems, such as Microsoft Office 365. 

In its Tech Tide: Identity and Access Management, Q4 2017, Forrester Research identified six IAM technologies with low maturity, but high current business value:

API security enables IAM for use with B2B commerce, integration with the cloud, and microservices-based IAM architectures. Forrester sees API security solutions being used for single sign-on (SSO) between mobile applications or user-managed access. This would allow security teams to manage IoT device authorization and personally identifiable data.

Customer identity and access management (CIAM) allow "comprehensive management and authentication of users; self-service and profile management; and integration with CRM, ERP, and other customer management systems and databases," according to the report.

Identity analytics (IA) will allow security teams to detect and stop risky identity behaviors using rules, machine learning, and other statistical algorithms.

Identity as a service (IDaaS) includes "software-as-a-service (SaaS) solutions thatoffer SSO from a portal to web applications and native mobile applications as well as some level of user account provisioning and access request management," according to the report

Identity management and governance (IMG) provides automated and repeatable ways to govern the identity life cycle. This is important when it comes to compliance with identity and privacy regulations.

Risk-based authentication (RBA) solutions "take in the context of a user session and authentication and form a risk score. The firm can then prompt high-risk users for 2FA and allow low-risk users to authenticate with single factor (e.g., username plus password) credentials," according to the report. (For more on authentication, see "Ready for more secure authentication? Try these password alternatives and enhancements.")

IAM systems must be flexible and robust enough to accommodate the complexities of today’s computing environment. One reason: An enterprise’s computing environment used to be largely on-premises, and identity management systems authenticated and tracked users as they worked on-premises, says Jackson Shaw, vice president of product management for identity and access management provider One Identity. “There used to be a security fence around the premises,” Shaw noted. “Today, that fence isn’t there anymore.” 

As a consequence, identity management systems today should enable administrators to easily manage access privileges for a variety of users, including domestic on-site employees and international off-site contractors; hybrid compute environments that encompass on-premise computing, software as a service (SaaS) applications and shadow IT and BYOD users; and computing architectures that include UNIX, Windows, Macintosh, iOS, Android and even internet of things (IoT) devices.

Ultimately, the identity and access management system should enable centralized management of users “in a consistent and scalable way across the enterprise,” says Abousselham.

In recent years, identity-as-a-service (IDaaS) has evolved as a third-party managed service offered over the cloud on a subscription basis, providing identity management to a customers’ on-premises and cloud-based systems.

Why do I need IAM? 

Identity and access management is a critical part of any enterprise security plan, as it is inextricably linked to the security and productivity of organizations in today’s digitally enabled economy. 

Compromised user credentials often serve as an entry point into an organization’s network and its information assets. Enterprises use identity management to safeguard their information assets against the rising threats of ransomware, criminal hacking, phishing and other malware attacks. Global ransomware damage costs alone are expected to exceed $5 billion this year, up 15 percent from 2016, Cybersecurity Ventures predicted.

In many organizations, users sometimes have more access privileges than necessary. A robust IAM system can add an important layer of protection by ensuring a consistent application of user access rules and policies across an organization.    

Identity and access management systems can enhance business productivity. The systems’ central management capabilities can reduce the complexity and cost of safeguarding user credentials and access. At the same time, identity management systems enable workers to be more productive (while staying secure) in a variety of environments, whether they’re working from home, the office, or on the road. 

What IAM means for compliance management

Many governments require enterprises to care about identity management. Regulations such as Sarbanes-Oxley, Gramm-Leach-Bliley, and HIPAA hold organizations accountable for controlling access to customer and employee information. Identity management systems can help organizations comply with those regulations. 

The General Data Protection Regulation (GDPR) is a more recent regulation that requires strong security and user access controls. GDPR mandates that organizations safeguard the personal data and privacy of European Union citizens. Effective May 2018, the GDPR affects every company that does business in EU countries and/or has European citizens as customers.

On March 1, 2017, the state of New York’s Department of Financial Services (NYDFS) new cybersecurity regulations went into effect. The regulations prescribe many requirements for the security operations of financial services companies that operate in New York, including the need to monitor the activities of authorized users and maintain audit logs—something identity management systems typically do. 

By automating many aspects of providing secure user access to enterprise networks and data, identity management systems relieve IT of mundane but important tasks and help them stay in compliance with government regulations. These are critical benefits, given that today, every IT position is a security position; there’s a persistent, global cybersecurity workforce shortage; and penalties for not being compliant with relevant regulations can cost an organization millions or even billions of dollars. 

What are the benefits of IAM systems  

Implementing identity and access management and associated best practices can give you a significant competitive advantage in several ways. Nowadays, most businesses need to give users outside the organization access to internal systems. Opening your network to customers, partners, suppliers, contractors and, of course, employees can increase efficiency and lower operating costs. 

Identity management systems can allow a company to extend access to its information systems across a variety of on-premises applications, mobile apps, and SaaS tools without compromising security. By providing greater access to outsiders, you can drive collaboration throughout your organization, enhancing productivity, employee satisfaction, research and development, and, ultimately, revenue. 

Identity management can decrease the number of help-desk calls to IT support teams regarding password resets. Identity management systems allow administrators to automate these and other time-consuming, costly tasks. 

An identity management system can be a cornerstone of a secure network, because managing user identity is an essential piece of the access-control picture. An identity management system all but requires companies to define their access policies, specifically outlining who has access to which data resources and under which conditions they have access. 

Consequently, well-managed identities mean greater control of user access, which translates into a reduced risk of internal and external breaches. This is important because, along with the rising threats of external threats, internal attacks are all too frequent. Approximately 60 percent of all data breaches are caused by an organization’s own employees, according to IBM’s 2016 Cyber Security Intelligence Index. Of those, 75 percent were malicious in intent; 25 percent were accidental. 

As mentioned previously, IAM system can bolster regulatory compliance by providing the tools to implement comprehensive security, audit and access policies. Many systems now provide features designed to ensure that an organization is in compliance. 

How IAM works

In years past, a typical identity management system comprised four basic elements: a directory of the personal data the system uses to define individual users (think of it as an identity repository); a set of tools for adding, modifying and deleting that data (related to access lifecycle management); a system that regulates user access (enforcement of security policies and access privileges); and an auditing and reporting system (to verify what’s happening on your system). 

Regulating user access has traditionally involved a number of authentication methods for verifying the identity of a user, including passwords, digital certificates, tokens and smart cards. Hardware tokens and credit-card-sized smart cards served as one component in two-factor authentication, which combines something you know (your password) with something you have (the token or the card) to verify your identity. A smart card carries an embedded integrated circuit chip that can be either a secure microcontroller or equivalent intelligence with internal memory or a memory chip alone. Software tokens, which can exist on any device with storage capability, from a USB drive to a cell phone, emerged in 2005. 

In today’s complex compute environments, along with heightened security threats, a strong user name and password doesn’t cut it anymore. Today, identity management systems often incorporate elements of biometrics, machine learning and artificial intelligence, and risk-based authentication. 

At the user level, recent user authentication methods are helping to better protect identities. For example, the popularity of Touch ID-enabled iPhones has familiarized many people with using their fingerprints as an authentication method. Newer Windows 10 computers offer fingerprint sensors or iris scanning for biometric user authentication. The next iPhone, due out later this year, is rumored to include iris scanning or facial recognition to authenticate users instead of fingerprint scanning. 

The move to multi-factor authentication

Some organizations are moving from two-factor to three-factor authentication, says Abousselham, combining something you know (your password), something you have (a smartphone), and something you are (facial recognition, iris scanning or fingerprint sensors). “When you go from two-factor to three, you have more assurance that you’re dealing with the correct user,” he says. 

At the administration level, today’s identity management systems offer more advanced user auditing and reporting, thanks to technologies such as context-aware network access control and risk-based authentication (RBA). 

Context-aware network access control is policy-based. It predetermines an event as well as its outcome based on various attributes, says Joe Diamond, Okta’s director of products. For example, if an IP address isn’t whitelisted, it may be blocked. Or if there isn’t a certificate that indicates a device is managed, then context-aware network access control might step-up the authentication process. 

By comparison, RBA is more dynamic and is often enabled by some level of AI. With RBA, “you’re starting to open up risk scoring and machine learning to an authentication event,” Diamond says.   

Risk-based authentication dynamically applies various levels of strictness to authentication processes according to the current risk profile. The higher the risk, the more restrictive the authentication process becomes for a user. A change in a user’s geographic location or IP address may trigger additional authentication requirements before that user can access the company’s information resources. 

What is federated identity management? 

Federated identity management lets you share digital IDs with trusted partners. It’s an authentication-sharing mechanism that allows users to employ the same user name, password or other ID to gain access to more than one network 

Single sign-on (SSO) is an important part of federated ID management. A single sign-on standard lets people who verify their identity on one network, website or app carry over that authenticated status when moving to another. The model works only among cooperating organizations—known as trusted partners—that essentially vouch for each other’s users. 

Are IAM platforms based on open standards?

Authorization messages between trusted partners are often sent using Security Assertion Markup Language (SAML). This open specification defines an XML framework for exchanging security assertions among security authorities. SAML achieves interoperability across different vendor platforms that provide authentication and authorization services. 

SAML isn’t the only open-standard identity protocol, however. Others include OpenID, WS-Trust (short for Web Services Trust) and WS-Federation (which have corporate backing from Microsoft and IBM), and OAuth (pronounced “Oh-Auth”), which lets a user’s account information be used by third-party services such as Facebook without exposing the password. 

What are the challenges or risks of implementing IAM?

Dimensional Research released a report, Assessment of Identity and Access Management in 2018, in October 2018 based on a survey of more than 1,000 IT security professionals. Sponsored by IAM solution provider One Identity, the report asked those professionals about their biggest IAM challenges.

Not surprisingly, 59 percent said that data protection was their biggest concern about their organization using IAM. Only 15 percent said they were completely confident their organization would not be hacked due to their access control system.

IAM systems hold the keys to some of a company’s most valuable assets and critical systems, so the consequences of an IAM system failing are great. Specific concerns include disgruntled employees sharing sensitive data (27 percent), the CIO is interviewed on TV because of a data breach due to bad IAM, and finding their username/password lists posted to the dark web.

“The concept of putting all your eggs in one basket is scary,” says One Identity's Shaw,  "but if you don’t unify the fundamentals of IAM you will never reduce risk. So the correct path is to arrive at a single approach (not necessarily a single solution) that provides all the scope, security and oversight you need (and were probably struggling to get with older projects) across everything, all user types, and all access scenarios.”

Security professionals are also concerned about integrating IAM with legacy systems (50 percent),  moving to the cloud (44 percent), and employees using unapproved technology (43 percent).

Much of that concern stems not from the current IAM technology itself, but with their organization’s ability to implement it well, believes Shaw. “People have always been doing IAM (i.e., authentication, authorization and administration). It’s just that now they are beginning to realize that doing those things poorly puts them at heightened risk and leaves the door open to bad actors doing bad things,” he says.

“The biggest challenge is that old practices that were put in place to secure legacy systems simply don’t work with newer technologies and practices,” Shaw adds, “so often people have to reinvent the wheel and create duplicate workloads and redundant tasks. If the legacy practice was done poorly, trying to reinvent it on a newer paradigm will go poorly as well.”

Shaw sees confidence and trust in IAM growing as companies gain experience administering the solutions, but that depends on how well that administration is executed. “Organizations are more-and-more learning that they can actually unify their administration approach, streamline operations, remove much of the workload from IT and place it in the hands of the line-of-business, and place themselves in an audit-ready stance rather than a reactive stance,” he says.

A successful implementation of identity and access management requires forethought and collaboration across departments. Companies that establish a cohesive identity management strategy—clear objectives, stakeholder buy-in, defined business processes—before they begin the project are likely to be most successful. Identity management works best “when you have human resources, IT, security and other departments involved,” says Shaw. 

Often, identity information may come from multiple repositories, such as Microsoft Active Directory (AD) or human resources applications. An identity management system must be able to synchronize the user identity information across all these systems, providing a single source of truth. 

Given the shortage of IT people today, identity and access management systems must enable an organization to manage a variety of users in different situations and computing environments—automatically and in real-time. Manually adjusting access privileges and controls for hundreds or thousands of users isn’t feasible.   

For example, de-provisioning access privileges for departing employees can fall through the cracks, especially when done manually, which is too often the case. Reporting an employee’s departure from the company and then automatically de-provisioning access across all the apps, services and hardware he or she used requires an automated, comprehensive identity management solution. 

Authentication must also be easy for users to perform, it must be easy for IT to deploy, and above all it must be secure, Abousselham says. This accounts for why mobile devices are “becoming the center of user authentication,” he added, “because smartphones can provide a user’s current geolocation, IP address and other information that can be leveraged for authentication purposes.” 

One risk worth keeping in mind: Centralized operations present tempting targets to hackers and crackers. By putting a dashboard over all of a company’s identity management activities, these systems reduce complexity for more than the administrators. Once compromised, they could allow an intruder to create IDs with extensive privileges and access to many resources. 

What IAM terms  should I know? 

Buzzwords come and go, but a few key terms in the identity management space are worth knowing: 

  • Access management: Access management refers to the processes and technologies used to control and monitor network access. Access management features, such as authentication, authorization, trust and security auditing, are part and parcel of the top ID management systems for both on-premises and cloud-based systems. 
  • Active Directory (AD): Microsoft developed AD as a user-identity directory service for Windows domain networks. Though proprietary, AD is included in the Windows Server operating system and is thus widely deployed. 
  • Biometric authentication: A security process for authenticating users that relies upon the user’s unique characteristics. Biometric authentication technologies include fingerprint sensors, iris and retina scanning, and facial recognition. 
  • Context-aware network access control: Context-aware network access control is a policy-based method of granting access to network resources according to the current context of the user seeking access. For example, a user attempting to authenticate from an IP address that hasn’t been whitelisted would be blocked. 
  • Credential: An identifier employed by the user to gain access to a network such as the user’s password, public key infrastructure (PKI) certificate, or biometric information (fingerprint, iris scan). 
  • De-provisioning: The process of removing an identity from an ID repository and terminating access privileges. 
  • Digital identity: The ID itself, including the description of the user and his/her/its access privileges. (“Its” because an endpoint, such as a laptop or smartphone, can have its own digital identity.) 
  • Entitlement: The set of attributes that specify the access rights and privileges of an authenticated security principal. 
  • Identity as a Service (IDaaS): Cloud-based IDaaS offers identity and access management functionality to an organization’s systems that reside on-premises and/or in the cloud. 
  • Identity lifecycle management: Similar to access lifecycle management, the term refers to the entire set of processes and technologies for maintaining and updating digital identities. Identity lifecycle management includes identity synchronization, provisioning, de-provisioning, and the ongoing management of user attributes, credentials and entitlements. 
  • Identity synchronization: The process of ensuring that multiple identity stores—say, the result of an acquisition—contain consistent data for a given digital ID. 
  • Lightweight Directory Access Protocol (LDAP): LDAP is open standards-based protocol for managing and accessing a distributed directory service, such as Microsoft’s AD 
  • Multi-factor authentication (MFA): MFA is when more than just a single factor, such as a user name and password, is required for authentication to a network or system. At least one additional step is also required, such as receiving a code sent via SMS to a smartphone, inserting a smart card or USB stick, or satisfying a biometric authentication requirement, such as a fingerprint scan. 
  • Password reset: In this context, it’s a feature of an ID management system that allows users to re-establish their own passwords, relieving the administrators of the job and cutting support calls. The reset application is often accessed by the user through a browser. The application asks for a secret word or a set of questions to verify the user’s identity. 
  • Privileged account management:  This term refers to managing and auditing accounts and data access based on the privileges of the user. In general terms, because of his or her job or function, a privileged user has been granted administrative access to systems. A privileged user, for example, would be able set up and delete user accounts and roles.Provisioning: The process of creating identities, defining their access privileges and adding them to an ID repository. 
  • Risk-based authentication (RBA): Risk-based authentication dynamically adjusts authentication requirements based on the user’s situation at the moment authentication is attempted. For example, when users attempt to authenticate from a geographic location or IP address not previously associated with them, those users may face additional authentication requirements. 
  • Security principal: A digital identity with one or more credentials that can be authenticated and authorized to interact with the network. 
  • Single sign-on (SSO): A type of access control for multiple related but separate systems. With a single username and password, a user can access a system or systems without using different credentials.
  • User behavior analytics (UBA): UBA technologies examine patterns of user behavior and automatically apply algorithms and analysis to detect important anomalies that may indicate potential security threats. UBA differs from other security technologies, which focus on tracking devices or security events. UBA is also sometimes grouped with entity behavior analytics and known as UEBA.  
  • IAM vendors

    The identity and access management vendor landscape is a crowded one, consisting of both pureplay providers such as Okta and OneLogin and large vendors such as IBM, Microsoft and Oracle.  Below is a list of leading players based on Gartner’s Magic Quadrant for Access Management, Worldwide, which was published in June 2017.

  • Atos (Evidan)
  • CA Technologies
  • Centrify
  • Covisint
  • ForgeRock
  • IBM Security Identity and Access Assurance
  • I-Spring Innovations
  • Micro Focus
  • Microsoft Azure Active Directory
  • Okta
  • OneLogin
  • Optimal idM
  • Oracle Identity Cloud Service
  • Ping
  • SecureAuth
  • Related video:


    Predictive Analytics in 2018: What’s Possible, Who’s Doing It, and How | killexams.com real questions and Pass4sure dumps

    In 2009, Netflix offered $1 million to anyone who could improve the quality of its recommendation engine by 10%. It took two years, but a team finally won. Netflix paid the bounty—then ignored the code.

    As it turned out, the enhanced algorithms “did not seem to justify the engineering effort needed to bring them into a production environment.”

    Not only did the winning prediction engine fail to scale economically, it also addressed an outdated problem: The shift from mail to streaming during that same two-year window gave Netflix all the data it needed to develop newer, better algorithms.

    Predictive analytics, in other words, wasn’t a panacea. Nor, in the decade since, has it become one. But, in 2018, incremental gains no longer cost $1 million either:

  • You have more data;
  • Storage is cheap; and
  • Cloud computing is almost infinitely scalable.
  • This post details those changes and shows how several businesses—and not just behemoths—have cultivated the predictive analytics landscape.

    What’s changed in the last decade? 1. More data, more storage, more computing power

    Massive, cloud-based repositories of customer interactions, often called data lakes, are the raw source material for predictive analytics applications.

    Many companies have taken advantage of cheap cloud storage to stow away data for years—without even considering its potential use. (How many neglected data points do you have in Google Analytics, Google Ads, MailChimp, Marchex, Stripe, and similar services?)

    That dual growth in scale—of data collected and accessibility to it—has solved two primary challenges of predictive analytics implementation.

    Historically, raw computing power has been the other. As Andrew Pearson of Intelligencia notes, “Without significant hardware investments, predictive analytics programs either weren’t possible or too slow to be useful.”

    That, Pearson continued, has also changed: “Cloud-based analytics systems have added massive computer power into the mix.” Increasingly powerful systems cracked open the door for real-time predictive analytics.

    2. A world of real-time predictions

    For some, the age of “real-time” predictive analytics is here. Judah Phillips, the co-founder and CTO of Vizadata and founder of SmartCurrent, explained:

    We already live in a world of “real-time” predictive analytics. A simple predictive analysis is your arrival time in Waze. A more complex real-time prediction occurs billions of times worldwide every millisecond in matching certain types of digital advertising.

    Further, companies like Mintigo and Versium now offer real-time solutions for lead scoring, showing that the transition is technically possible. Possible, however, doesn’t mean perfect. Sam Underwood, a vice president at Futurety, acknowledged the complexity of necessary integrations:

    Especially in the mid-market world, the tools that gather data to turn into predictive modeling—CRM systems, social media aggregators, logistics, and purchasing systems—often do not have friendly APIs or other easy mechanisms with which to quickly gather and interpret data.

    That disconnect still thwarts even the most fundamental business cases for real-time predictive analytics. David Longstreet, the chief data scientist at FanThreeSixty, offered an example:

    In our world of sports and entertainment, for example, most sports teams do not know how many people are in a stadium for a game. Teams know how many tickets were distributed; however, they do not know in “real time” how many people are in the venue or stadium during the event.

    That knowledge gap hampers efforts to staff and stock the stadium appropriately. It’s also why interest in predictive analytics is almost universal, even if it vastly outpaces adoption.

    3. Slow adoption but soaring interest

    So how many businesses are actively using predictive analytics? According to research from Dresner Advisory Services, about 23%, a figure essentially unchanged from the prior year.

    predictive analytics adoption

    Less than a quarter of businesses are using predictive analytics—though almost all aspire to do so. (Image source)

    Interest, however, exceeds implementation. The same research suggests that 90% of businesses “attach, at minimum, some importance to advanced and predictive analytics.”

    So which questions are those 23% answering with predictive analytics? Let’s take a look.

    Which questions can marketers answer with predictive analytics?

    “They want to predict everything,” according to Underwood. And who wouldn’t want to know the exact foot (or web) traffic by month, day, and hour to streamline staffing (or allocate server resources)?

    But, Underwood continued, he tries to focus clients on “the one thing that, if we could predict it for you, would revolutionize your business.”

    In digital marketing, Phillips outlined myriad use cases for predictive analytics, including the capability to predict:

  • which advertising will be most effective—however you define effective.
  • which marketing campaigns, channels, touches, behaviors, and demographics are contributing to a business outcome, a form of “machine learning–based attribution.”
  • which segment, test, or personalization a user is most likely to respond to.
  • the probability of users to click on an ad, to download a whitepaper, to respond to an email, to respond to an offer, and other customer response you define.
  • which leads will convert—however you define conversion.
  • which customers will buy one or more products for a cross-sell or upsell.
  • the number of purchases or revenue that will occur in the future.
  • which customers will have high/medium/low lifetime value.
  • customer churn.
  • The novel opportunity of predictive analytics, then, is not what you can predict but the fact that you can predict. The historical data you currently analyze can probably become a prediction.

    Just make sure you have the data.

    What do you need to get started with predictive analytics?

    Data, data, and data. “Priority 1A and 1B are data sources,” stated Underwood. That’s true whether you plan to license software or hire an outside organization. (Both options are detailed later.)

    All uses require training data. That training data, in turn, is used to build a predictive model to apply to current data. “The only limitation we’ve run into,” Phillips noted, “is a company’s available data for training.”

    How much data is enough? According to Phillips:

    A few thousand records with a sufficient amount of positive and negative outcomes can be sufficient for marketing, sales, and product prediction.

    Not all data is created (or stored) equally

    “You have to understand—I grew up tearing tickets.”

    FanThreeSixty’s Longstreet has heard that same explanation from venue managers who have spent countless hours counting stacks of stubs after games. It’s a reason why vital data sources may not be easily accessible, or accessible at all.

    In stadiums, Longstreet explained, point-of-sale machines and ticket scanners exist for a single purpose—to complete transactions quickly and keep lines moving. Those systems do not store data efficiently for extraction, nor can they handle incessant server requests (unless hungry fans don’t mind waiting).

    For Underwood, clients tend to fall into one of two buckets, with half in each:

  • “The ideal client has an internal database set up and ready to go. We pull in the data, build the model, and are off and running.”
  • The other half have a mix of data sources, which inevitably include an offshore SQL database (or ten) managed by an external vendor whom no one can track down.
  • Stitching data sources together is a major development project that may require creating custom connectors, setting up third-party FTP drops, and other complex but thankless tasks. That work, however, is necessary: Models and their predictions are only as accurate as the data they’re built upon.

    Don’t forget external data sources

    Not all data comes from internal sources, either. External data sources, like weather reports, are often a critical addition to data lakes, especially for small businesses. As Underwood explained:

    Restaurants may use analytics to trigger email sends; for example, we can set up the email platform to sync with National Weather Service data to send an email about iced tea when the temperature in a given metro area is above 90 degrees.

    Likewise, we can trigger an email to send to customers in a given city if the system detects wind gusts of 40+ MPH. Both of these use cases reach consumers in a key moment of need, negating downstream ad spend and beating competitors to the punch.

    So you have a large, well-organized dataset. What do you do with it?

    How do you turn data into predictions?

    While the limitation of insufficient data has faded, another remains:

    Companies require either a dedicated team of data scientists to parse through these sets, or a software suite powerful enough to do so rapidly. For most small and medium-sized businesses, this usually means settling for subpar software, or forgoing it entirely.

    For businesses of all sizes, solutions branch into two options:

  • Purchase software and create predictions in-house.
  • Pay an outside vendor to develop models and visualizations for you.
  • 1. Predictive analytics software

    The marketplace for predictive analytics software has ballooned: G2Crowd records 92 results in the category. Pricing varies substantially based on the number of users and, in some cases, amount of data, but generally starts around $1,000 per year, though it can easily scale into six figures.

    G2Crowd lists both IBM’s SPSS Statistics and SAS’s Advanced Analytics as market leaders at the enterprise level. Along with RStudio, the pair are also tagged as leaders for mid-market companies; only IBM retains a place in the “Leaders” quadrant for small businesses.

    Historically, however, even industry-leading predictive analytics software hasn’t been a simple, jump-right-in experience. Take these two examples from IBM’s SPSS Statistics and RapidMiner:

    ibm spss statistics screenshot

    rapid miner screenshot

    While these platforms are powerful, users must format data files, link nodes, and develop visualizations. Learning how to do this—and having the time to do it—is a specialized, full-time job. (To believe otherwise is to expect a Microsoft Word license to write your Great American Novel.)

    Not surprisingly, the market is shifting. RapidMiner has rolled out a SaaS beta that, with a bit of manual adjustment, translates an Excel sheet full of, say, employment data to a prediction of employee retention:

    rapid miner saas excel sheet

    A dataset in Excel—a starting point in which most marketers are already comfortable. rapid miner intake

    RapidMiner parses the Excel file prior to crunching the numbers. rapidminer predictive visualization

    The resulting visualization shows the tool’s prediction performance and correlations between datasets and retention.

    Some companies, like Vizadata’s Phillips, see the user-friendly SaaS model as the future:

    We are democratizing data science, so that people with limited or no data science or engineering skills can predict. You simply upload your data and click next. We do all the heavy lifting.

    Our intelligence determines your dependent and independent variables and the type of analysis to run. You can go with our selections or override them—from regression, where we can do forecasting and optimization, to both binary and multiclass classification, where we can predict the probability of outcomes.

    vizadata screenshot

    User-friendly SaaS models make predictive analytics more accessible to marketing teams without data scientists.

    Like Vizadata, MIT’s Endor pursues this path. The platform uses a query-builder to allow anyone to ask questions like “Where should we open our next store?” or “Who is likely to try product X?” It then mines targeted datasets to provide answers, often in a matter of minutes.

    The inclusion of tangential datasets that fall outside consideration—or feasibility—for human observers is a recurring advantage of predictive analytics. Endor’s creators offer an example:

    A marketing department for a bank asks, “Who is going to get a mortgage in the next six months?” Machine-learning engines may detect a pool of, say, 5,000 customers who have a bank credit card and a high credit score, and are married—many of which may be false positives.

    Endor detects more specific clusters of, say, couples about to get married or going through a divorce, founders who recently sold their startups to Facebook, or customers who recently graduated from a local real-estate course.

    Of course, if you want to outsource the process entirely, outside vendors can organize your data, build models, and visualize predictions for you.

    2. Outside vendors Agencies offering bespoke solutions

    For most clients, Futurety starts by identifying the key business question—not a specific metric or visualization. Clients may come in for one-off projects, annual re-runs of their data, or ongoing work.

    Recommended for You

    Webcast, October 17th: How to Solve Your Marketing Data Nightmare

    “The end result is not always clear at the beginning,” Underwood explained. “When we’re delivering to someone close to the outcome, like a marketing manager, they’re typically happy with the model, the finding, and the math behind it.”

    The “end result” could be several things:

  • Integration with a third-party platform, like an email client, to automate predictive messaging.
  • Plain-text predictive answers to guide practitioners.
  • Robust visualizations to demonstrate the process and value to the C-Suite.
  • At the end of each engagement, Futurety delivers the model back to the client for management and maintenance.

    Predictive analytics at work
  • Futurety has a small business client that helps aspiring performing arts majors gain admission to their dream college. But few high schoolers have broad knowledge of good programs. More often than not, they know only one name: Julliard.
  • Futurety trained its model on three years of placement data. Then, using new student data entered into a common portal, predicted where students would get accepted and succeed academically.
  • The predictive analytics model, which Futurety updates annually, delivers a simple list of recommended schools for students based on factors like grades and exposure to different musical or artistic styles.
  • The model takes into account whether past placements graduated or won awards.
  • All-in-one niche providers

    FanThreeSixty serves a narrow market: sporting venues. Because they work with a comparatively consistent dataset—season ticket, concession, and souvenir sales—they know the range of business questions, data outcomes, and relevant visualizations.

    This consistency incentivizes niche vendors like FanThreeSixty to develop proprietary dashboards to roll out to all clients.

    The interface allows Longstreet’s team to keep data science in the background: “The secret of machine learning is when you’re being prompted behind the scenes.”

    Distilled fully, FanThreeSixty’s goal (and Longstreet’s explanation of his role at dinner parties) is to “help teams sell more tickets and hot dogs.”

    Predictive analytics at work
  • FanThreeSixty mines historical data to see which concessions are most commonly purchased with a hot dog at a Major League Soccer venue.
  • If a customer purchases a hot dog, concession staff are prompted to ask whether a customer would like to add the most popular accompaniment. That recommendation—a prediction of fan desire—changes based on other variables.
  • Predictions consider more than 20 datasets—everything from the home location of season ticket holders to the weather—to tailor messaging before, during, and after matches.
  • During cold-weather games, for example, FanThreeSixty can automate push notifications with tailored coupons, like buy-three-get-one-free hot chocolate for a family of four.
  • Whether solutions are internally or externally managed, they‘ve long been common in enterprise businesses.

    Predictive analytics use cases at the enterprise level

    Marketing departments in large organizations have used predictive analytics for years:

  • AutoTrader. AutoTrader uses data from its 40 million monthly visitors to better understand the sometimes lengthy customer journey. They built propensity models based on search behavior and created high-value lookalike audiences.
  • Editialis. The French publisher uses predictive analytics in its email campaigns to “anticipate engagement at an individual level.” As a result, they’ve seen click-through-rates increase “dramatically.”
  • Predictive analytics can also coordinate offline and online interactions, with two clear use cases for marketers whose companies have physical products or storefronts:

  • Improved pricing. Smartphone data registers in-store browsing habits to improve online or offline marketing targeting, approximating the advantages enjoyed by ecommerce companies.
  • Inventory management. Full warehouses cost money; empty shelves cost money. Folding online data, such as search patterns, into sales data can better manage inventory, especially at a regional and local level.
  • In addition to external marketing campaigns, predictive analytics also supports internal project management. Large marketing campaigns have many moving parts—a new ad campaign needs new creative, new copywriting, new landing pages, etc.

    Coordinating the involvement of those teams and accurately estimating the time-to-launch is complex. Many fail to get it right, sometimes at great expense.

    whiteboard planning

    Marketers and software businesses may use predictive analytics for internal project management in addition to external campaigns.

    Predictive algorithms, as McKinsey notes, use a wider lens that captures historical patterns and unique project elements in a single frame:

    While every development project is unique, the underlying complexity drivers across projects are similar and can be quantified. If companies understand the complexity involved in a new project, they can estimate the effort and resources required to complete it.

    Predictive analytics models “take into account not only the complexity of the project (both the functional and implementation aspects) but also the complexity of the team environment.”

    Predictive analytics at work:

    More accurate internal project management, in an example McKinsey offers, can have a major impact:

  • A company initially planned a product update to take roughly 300 person-weeks of effort, an estimate based on the limited number of changes between the current product and a new design.
  • However, that estimate failed to take into account the fact that planned updates would affect many different teams. Predictive analytics models did take it into account and estimated that the project would take three to four times as long.
  • As a result, the company limited the work to the original product team, enabling them to deliver the update on time.
  • In addition to helping companies solve internal and external challenges, predictive analytics is also the foundation for some businesses.

    Building a business on predictive analytics

    Ken Lazarus, CEO of the recruiting platform Scout Exchange, has an advantage—the company has been around for only five years.

    That means that the company’s data sources are already primed for extraction into its predictive models that pair companies with the right recruiter.

    scout exchange processScout Exchange’s predictions pair companies with the right recruiter.

    The single best predictor of job placement, Lazarus and his team have found, is the track record of job recruiters. In contrast, pairing the right job description with the right resume remains exceedingly difficult.

    “Job specs are horrible,” he lamented. “The data isn’t on the paper. CVs are pretty horrible, too.” (Data augmentation, such as skills testing and video interview decoding, Lazarus noted, offer potential improvements.)

    Nonetheless, holes remain. Candidates will never disclose negatives on their resume, and important information might forever remain “non-data,” such as whether a candidate is a good “culture fit.”

    Scaling data gathering

    Scout Exchange has honed its predictions by focusing on enterprise customers—its algorithms feast on hundreds or thousands of openings from Fortune 500 clients.

    As a result, the platform takes in roughly 1 million data points monthly, with each new job posting yielding an additional 50 data points.

    scout exchange platform

    Higher employer ratings indicate an employer is more responsive than his or her peers. Likewise, higher recruiter ratings suggest a recruiter is more likely to succeed in submitting acceptable candidates than his or her peers.

    Still, human assessment by a recruiter—and their client—is necessary. Lazarus drew a parallel: “Would you let machine learning pick your wife? No. But would you let it pick the right matchmaker to help you find a spouse? Yes.”

    Those who are trying to solve the most complex human issues aren’t even in the business world.

    Predictive analytics with life or death consequences

    The greatest challenges for predictive analytics are those that deal with complex, individualized human behavior, such as the likelihood that a patient or crisis-line texter will commit suicide.

    Because success or failure is measured in human lives, these challenges are also the most urgent. And while these projects operate beyond the scope of marketing and business, they suggest the potential for predictive analytics as it evolves.

    “REACH VET is not about trying to find the veteran who’s sitting in the car in a parking lot with a gun in his lap,” Aaron Eagan, Veteran Affairs deputy director for innovation told a Washington conference.

    “What we found,” Eagan continued, “is that veterans at highest risk of suicide [also have] significantly increased rates of all-cause mortality, accident morality, overdoses, violence, [and] opioids.” Proactive alerts that trigger physician check-ins have improved primary-care appointment attendance and reduced hospital admissions for mental health issues.

    crisis text line

    The project is similar to a collaboration between Periscope Data and Crisis Text Line, a text-based suicide hotline.

    Leaning on natural language processing and predictive analytics, the program analyzed conversations, forecasted trends, and trained more than 13,000 volunteers. The results?

  • Wait times decreased to less than 5 minutes, an operational goal.
  • Capacity increased by 10% during peak periods.
  • Responses were prioritized based on machine-identified urgency.
  • Endor’s technology has taken on similarly serious challenges. Using 15 million data points from 50 known ISIS supporters, Endor identified 80 lookalike accounts in less than half an hour, with only 35 false positives—expert investigation was still necessary yet feasible.

    In a collaborative project with the U.S. Defense Advanced Research Project Agency, the platform also analyzed mobile data to identify patterns to predict future riots.

    Conclusion

    Predictive analytics is not immune to criticism: GDPR rebuffs some of the same collection methods that swell data lakes. And not all predictions, even the most accurate, are well-received. (Famously, Target unwittingly informed a father of his teenage daughter’s pregnancy based on seemingly benign shopping habits.)

    Predictive analytics experts point out that their algorithms search for patterns among values, not the values themselves. Regardless, insufficient data is unlikely to hold back the expansion of the industry—the IoT, wearables, and other data collectors already supplement traditional web and app analytics.

    User-friendly SaaS platforms are still an emerging opportunity. For most businesses, creating models and predictions from historical data still requires a dedicated employee to navigate complex software solutions or the outsourcing of that work to a vendor.

    For those postponing predictive analytics projects until the SaaS options are more mature, you would be wise to keep filling your data lake.


    Onapsis Joins IBM Security App Exchange Community | killexams.com real questions and Pass4sure dumps

    BOSTON--(BUSINESS WIRE)--Onapsis, a global expert in business-critical application security, today announced that the Onapsis Security Platform integrates with IBM security intelligence technology to provide customers with improved visibility into their network security. Utilizing QRadar to consolidate different sources of network security, this integration with the Onapsis Security Platform (OSP) will extend the existing visibility of QRadar, as well as security process and workflows to include results from OSP.

    The new application is freely available to the security community through IBM Security App Exchange, a marketplace where developers across the industry can share applications based on IBM Security technologies. As threats are evolving faster than ever, collaborative development amongst the security community will help organizations adapt quickly and speed innovation in the fight against cybercrime.

    The Onapsis Security Platform leverages IBM Security QRadar, the company’s security intelligence platform which analyzes data across an organization’s IT infrastructure in real-time to identify potential security threats. Leveraging QRadar’s new open application programming interfaces (API), the Onapsis Security Platform allows Onapsis and IBM customers to have extended coverage for real-time monitoring and reporting of advanced tacks targeting SAP business systems.

    “With this new integration, subscribers of the Onapsis Security Platform can now send OSP events to their IBM QRadar SIEM for increased visibility into their company’s network security. Alarm activity designated by the user is transferred in real-time to QRadar for comparison with information gathered from other sources within the network. This includes things such as including information flows or traffic, and data about ports and protocols, asset context, events from third-party devices, feeds, and other sources to provide your company with a complete security posture assessment,” said Alex Horan, Director of Product Management, Onapsis.

    About Onapsis

    Onapsis is the pioneer in cybersecurity and compliance solutions for cloud and on-premise ERP and business-critical applications. As the proven market leader, global enterprises trust Onapsis to protect the essential information and processes that run their businesses.

    Headquartered in Boston, MA, Onapsis serves over 200 customers including many of the Global 2000. Onapsis’ solutions are also the de-facto standard for leading consulting and audit firms such as Accenture, Deloitte, E&Y, IBM, KPMG and PwC.

    Onapsis solutions include the Onapsis Security Platform, which is the most widely-used SAP-certified cyber-security solution in the market. Unlike generic security products, Onapsis’ context-aware solutions deliver both preventative vulnerability and compliance controls, as well as real-time detection and incident response capabilities to reduce risks affecting critical business processes and data. Through open interfaces, the platform can be integrated with leading SIEM, GRC and network security products, seamlessly incorporating enterprise applications into existing vulnerability, risk and incident response management programs. These solutions are powered by the Onapsis Research Labs which continuously provide leading intelligence on security threats affecting SAP and Oracle enterprise applications. Experts of the Onapsis Research Labs were the first to lecture on SAP cyber-attacks and have uncovered and helped fix hundreds of security vulnerabilities to-date affecting SAP Business Suite, SAP HANA, SAP Cloud and SAP Mobile applications, as well as Oracle JD Edwards and Oracle E-Business Suite platforms.

    Onapsis has been issued U.S. Patent No. 9,009,837 entitled “Automated Security Assessment of Business-Critical Systems and Applications,” which describes certain algorithms and capabilities behind the technology powering the Onapsis Security Platform™ and Onapsis X1™ software platforms. This patented technology is recognized industry wide and has gained Onapsis the recognition as a 2015 SINET 16 Innovator.

    For more information, please visit www.onapsis.com, or connect with us on Twitter, Google+, or LinkedIn.

    Onapsis and Onapsis Research Labs are registered trademarks of Onapsis, Inc. All other company or product names may be the registered trademarks of their respective owners.

    About IBM Security

    IBM’s security platform provides the security intelligence to help organizations holistically protect their people, data, applications and infrastructure. IBM offers solutions for identity and access management, security information and event management, database security, application development, risk management, endpoint management, next-generation intrusion protection and more. IBM operates one of the world’s broadest security research and development, and delivery organizations. For more information, please visit www.ibm.com/security, follow @IBMSecurity on Twitter or visit the IBM Security Intelligence blog.



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    IBM A2180-270 Exam (Assessment- IBM Business Process Manager Advanced V7.5, Integration Develop) Detailed Information



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