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A2040-412 - Assessment: IBM Notes Traveler Administration - BrainDump Information

Vendor Name : IBM
Exam Code : A2040-412
Exam Name : Assessment: IBM Notes Traveler Administration
Questions and Answers : 118 Q & A
Updated On : October 22, 2018
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A2040-412 exam Dumps Source : Assessment: IBM Notes Traveler Administration

Test Code : A2040-412
Test Name : Assessment: IBM Notes Traveler Administration
Vendor Name : IBM
Q&A : 118 Real Questions

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IBM Assessment: IBM Notes Traveler

iPhone now in IBM Lotus Notes | killexams.com Real Questions and Pass4sure dumps

through IBT personnel Reporter 05/13/09 AT 10:28 AM

IBM is releasing the supply of Lotus Notes and Domino eight.5.1 application with iPhone support.

The beta unencumber of Lotus Notes wit Domino application will provide capabilities for electronic mail and calendaring tools to the Apple iPhone in addition to assisting Microsoft's change ActiveSync.

although iPhone users can already access Notes e mail by way of the IBM Lotus iNotes ultralight customer, the brand new ActiveSync guide will mean push entry to the time-honored utility, including the skill to view Notes mail, calendar and contacts records when now not related on the iPhone.

featuring Microsoft exchange ActiveSync aid by way of the Lotus Notes tourist software is part of IBM's pledge to deliver firms with the option and flexibility to opt for the cellular instruments and carriers that most excellent go well with their wants, spoke of Kevin Cavanaugh, vice president of messaging and collaboration at IBM. this will allow personnel 'on the go' to work smarter.

The free up will additionally encompass edition 1.three of IBM's free Lotus Symphony doc, spread sheet and presentation functions.

I appear forward to change ActiveSync aid. Johnson Controls fitness care and schooling industry shoppers use Apple structures, and we have begun to officially give software support on those. mobile messaging is regarded a vital utility, mentioned Jim Marshall, Director of international structure for Johnson Controls, Inc., a Lotus Notes customer.

The application already supports BlackBerry business Server version 5.0, which changed into announced last week.

IBM has trawled lots of of blogs and Twitter websites attempting to find remarks from users on its application, and has been making alterations to contend with commonplace problems and customer annoyances.

To check pressure IBM Lotus Notes 8.5.1 software beginning on may 15, talk over with http://greenhouse.lotus.com.


IBM Launches Lotus Notes visitor for the iPad | killexams.com Real Questions and Pass4sure dumps

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IBM supports commercial enterprise iPad Adoption with Lotus Notes visitor for iPad | killexams.com Real Questions and Pass4sure dumps

IBM released this week the iPad edition of Lotus Notes vacationer, that includes two-way encrypted synchronization of email, calendar and contacts with IBM Lotus Domino servers. The app also features per-message e-mail encryption.

IBM is having a bet on wide commercial enterprise adoption of the iPad. The business’s press unlock rates John Roling, IT director at IBM customer and enterprise associate Czarnowski, announcing he can see the iPad taking the region of the laptop for executives.

in accordance with the press unlock, “a few businesses in a wide variety of industries globally have begun using Lotus software on the iPad.”

As we’ve mentioned, many companies are assisting the iPad, so IBM’s powerful and early help for the equipment may well be a fine play for the enterprise, which is struggling to benefit mindshare in a market dominated by Microsoft trade (which the iPad helps natively).

Lotus Notes traveller is also obtainable for BlackBerry, iPhone, Symbian, and home windows mobile. IBM introduced an Android edition of Lotus Notes visitor in January, but it isn't yet accessible.

picture by way of IBM</>

Disclosure: IBM is a sponsor of ReadWriteWeb.




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A2040-412 exam Dumps Source : Assessment: IBM Notes Traveler Administration

Test Code : A2040-412
Test Name : Assessment: IBM Notes Traveler Administration
Vendor Name : IBM
Q&A : 118 Real Questions

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Which will have a study and put together for my A2040-412 test, I used killexams.Com QA and exam simulator. All thanks to this specially remarkable Killexams.Com. Thanks for assisting me in clearing my A2040-412 check.


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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.

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    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.


    Workforce Management Software market detailed in new research report | killexams.com real questions and Pass4sure dumps

    The Global Workforce Management Software Market Report includes a comprehensive analysis of the present market. The report starts with the basic Global Workforce Management Software Market overview and then goes into each and every detail.

    One of the latest trends that will gain traction in the global workforce management software market is the surging use of social media. The widespread popularity of social media in the human resource management industry allows companies to receive information from various sources and make an assessment of the employees and potential candidates.

    The features of the software including easy referrals, increased chances of finding the suitable candidates, optimum employee engagement, and mutually-beneficial two-way communication are some of the key factors driving the social media-based workforce management process. Due to such benefits of social media integration with workforce management software, the vendors will start offering a consolidated solution.

    The WFM market size is estimated to grow from USD 4.49 Billion in 2016 to USD 7.37 Billion by 2023, at a Compound Annual Growth Rate (CAGR) of +10%.

    Report: www.researchnreports.com/request_sample.php?id=140125

    According to the research report, the growing Workforce Management Software market is likely to boost the global market substantially over the forthcoming years. Apart from this, the increasing number of driving is projected to add to the growth of this market significantly in the near future.

    On the geographical front, the global market is classified into Europe, Asia-Pacific, Middle East & Africa, North America, and Latin America. The leading region of this global market and the region which is projected to continue its dominance over the forthcoming years is given in the study.

    The key driving force behind the growth of this market in the near future is also presented.

    Report: www.researchnreports.com/.php?id=140125

    Top key vendors:

     IBM (U.S.), Oracle (U.S.), SAP AG (Europe), KRONOS (U.S.), Workforce Software (U.S.), ADP LLC (U.S.), and  WORKDAY (U.S.) among others.

    A competitive analysis of the global Workforce Management Software market has also been provided in this research report, wherein the profiles of the key market players have been reviewed thoroughly to determine the market’s hierarchy. As per the research report, the market is highly fragmented and competitive due to the presence of a number of participants.

    The worldwide market is analyzed on the basis of the various segments and the geographical reach of this market. How the market's segments are propelling the market in the market scenario is mentioned in this report.

    The continual rising factors boosting the demand for Workforce Management Software market notes the research study.

    Report: www.researchnreports.com/.php?id=140125

    Table of Content:

    Global Workforce Management Software Market Research Report 2018-2023

    Chapter 1: Workforce Management Software market Overview

    Chapter 2: Workforce Management Software market Economic Impact

    Chapter 3: Competition by Manufacturer

    Chapter 4: Production, Revenue (Value) by Region (2018-2023)

    Chapter 5: Supply (Production), Consumption, Export, Import by Regions (2018-2023)

    Chapter 6: Production, Revenue (Value), Price Trend by Type

    Chapter 7: Workforce Management Software market Analysis by Application

    Chapter 8: Workforce Management Software Market by Manufacturing Cost Analysis

    Chapter 9: Industrial Chain, Sourcing Strategy and Downstream Buyers

    Chapter 10: Marketing Strategy Analysis, Distributors/Traders

    Chapter 11: Workforce Management Software Market Effect Factors Analysis

    Chapter 12: Workforce Management Software Market Forecast (2018-2023)

    Chapter 13: Appendix

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    Baltimore officials will pay consultant $176,800 to help police department maintain Lotus Notes system | killexams.com real questions and Pass4sure dumps

    Baltimore’s spending panel agreed Wednesday to pay $176,800 to a computer software consultant to help the city’s Police Department as it continues to address some of its rampant technology problems.

    The decision by the Board of Estimates to renew the city’s contract with Marriottsville-based Computer & Network Consultants Inc. comes two months after an internal review detailed widespread failures in the police agency’s technology systems.

    The technology assessment, required under the city’s ongoing consent decree and released in June, found “significant issues,” including problems with the decades-old software the consulting group is tasked with maintaining, Lotus Notes.

    Baltimore aims to fill the most 'challenging police chief job in the country.' So who would want it?

    As a leading consultant to the nation’s largest law enforcement agencies, Chuck Wexler has helped cities across the country find new police chiefs.

    Baltimore, he said, is different.

    “I don’t think there is a more challenging police chief job in the country right now,” said Wexler, the longtime...

    Lotus Notes, designed as a customizable email and database system, has been the Police Department’s main system for detective case management since 1996.

    Officers tap into its databases to track criminal investigations, check arrest data, log ballistic test results and identify “troubled officers,” along with various other uses. There are millions of records and roughly 150 databases built into the system, each designed to address different unit and personnel needs.

    The technology report identified problems with how the agency uses the system. It found that the “siloed nature” of the Lotus Notes databases made it difficult for officers to match, verify or search for information. It also found that various systems may contain conflicting information about the same case, or may not reflect the most complete information. Downloading or querying data is made “difficult, if not impossible” because it isn’t inputted in a standard way across databases.

    Detectives add to and update their cases in Lotus Notes, including information on the victim and suspect, along with their notes.

    “At the same time, detectives continue compiling and using paper case folders,” the report stated. “Depending on the unit and the detective, the appropriate Lotus Notes database and/or hard copy case folder system may or may not be up-to-date, and the systems may or may not match.”

    Computer & Network Consultants has worked with Baltimore police to keep up with the IBM software since 1996, when it was first implemented. This latest agreement between the city and the consulting group will last two years, with the option of renewing for another one-year term.

    City Councilman Brandon Scott, chair of the public safety committee, said the city’s choice to continue spending money on Lotus Notes “doesn’t line up,” especially after looking at the technology report.

    “It’s intriguing to me that we’d be investing so much into Lotus Notes when we know it’s an outdated system and we know through the technology study that we need to update Lotus Notes, along with a bunch of other systems,” he said. “I'll be asking the Baltimore Police Department, ‘Why we are still with Lotus Notes? And when will we make an investment in a new, 21st century system?’”

    Study finds 'significant issues' with Baltimore Police Department's technology resources

    The Baltimore Police Department’s technology systems are in such disarray that officers sometimes don’t know what tools are available to them and come up with their own fixes that only deepen the chaos, according to an internal review released Friday.

    The department worked with the National Police...

    Computer & Network Consultants’s David Alonge, who is responsible for programming and maintaining all of the Police Department’s Lotus Notes applications, told The Sun that the software is “working wonderfully for the police.” He said it runs so smoothly that it isn’t necessary to bring in someone else to help manage it.

    “They'd do nothing all day long,” he said.

    Alonge disagreed with the assertion that Lotus Notes databases exist in silos. Dozens of them “talk to each other” throughout the day, he said.

    The technology report does provide examples of how some Lotus Notes databases are seamlessly interconnected.

    “The homicide database has been updated to connect with the crime lab’s trace analysis database,” the technology report states. “When the crime lab has updated information on a bullet or other evidence, the information is automatically sent to the related incident in the Homicide Lotus Notes database where the detective can view the update.”

    The city entered into a federal consent decree in 2017 after a U.S. Justice Department investigation found officers routinely violated people’s constitutional rights. The investigation also found that the Police Department lacked “adequate technology infrastructure and tools that are common in many similar-sized law enforcement agencies, such as in-car computers.”

    One year into consent decree, Baltimore Police reforms move forward slowly and behind the scenes

    One morning last month, U.S. District Judge James K. Bredar met with officials from the Baltimore Police Department and the U.S. Department of Justice and demanded an update on the sweeping police reforms required under Baltimore’s federal consent decree.

    Bredar said he had been watching the corruption...

    Since the Justice Department investigation, Baltimore Police have instituted changes to Lotus Notes aimed at better tracking sexual assault investigations. Mayor Catherine Pugh also committed her support for implementing new police technology as part of the city’s crime fight.

    T.J. Smith, the police department's chief spokesman, said in a statement Thursday morning that the agency will be moving away from Lotus Notes in the future.

    “However, until such time, we must manage and maintain the product that we currently use which is Lotus Notes,” he said.

    The consultant is charged with system upkeep until the department finishes crafting “a 21st century holistic technology platform,” he said. The final technology plan will be delivered to the court on Dec.1.

    University of Baltimore criminologist Jeffrey Ian Ross said the Police Department’s software choice left him with questions.

    “What do Police Departments who close criminal cases in an expeditious manner, what do they use?” he asked. “What is the standard?”

    The Commission on Accreditation for Law Enforcement Agencies doesn’t tell agencies which software to use.

    “We’re vendor agnostic,” said executive director W. Craig Hartley Jr.

    But when asked about Lotus Notes, Hartley remarked: “I haven't heard that word in a long time.

    “That’s not a reflection of it being good or bad,” he continued. “I just haven't seen that in a long time.”

    trichman@baltsun.com

    twitter.com/TaliRichman



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