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M2180-759 IBM Connectivity and Integration Sales Mastery v1

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M2180-759 exam Dumps Source : IBM Connectivity and Integration Sales Mastery v1

Test Code : M2180-759
Test name : IBM Connectivity and Integration Sales Mastery v1
Vendor name : IBM
exam questions : 36 real Questions

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IBM IBM Connectivity and Integration

Inpixon publicizes IBM MaaS360 With Watson Integration to allow improved place-based mostly cellular device security | killexams.com real Questions and Pass4sure dumps

February 11, 2019 08:30 ET | source: Inpixon

PALO ALTO, Calif., Feb. 11, 2019 (GLOBE NEWSWIRE) -- Inpixon (Nasdaq: INPX), a leading indoor positioning and records analytics company, today announced the free up of the Indoor Positioning Analytics (IPA) Connector for IBM® MaaS360® with Watson™. the mixing allows joint clients of IBM MaaS360 and Inpixon IPA to implement cell device safety guidelines according to a wireless machine's specific region in a constructing. The connector is accessible within the IBM safety App change at https://trade.xforce.ibmcloud.com/hub/?q=inpixon.

MaaS360 cellular materiel administration (MDM)  enables IT organizations to comfy, control and video parade any company- or worker-owned cell equipment. The solution makes it practicable for corporations to configure the gadget with corporate connectivity settings, e mail profiles and machine-selected restrictions; to encrypt industry counsel; to remotely wipe misplaced contraptions; and greater.

The Inpixon IPA Connector for MaaS360 adds enjoyable capabilities to the IBM answer. specifically, the Inpixon IPA Connector for MaaS360 enables MDM directors to utilize several geo-fenced zones on a facility flooring plan. As a wireless device moves total the route through the office ambiance, the Inpixon IPA Connector sends the cell's station to MaaS360, allowing MaaS360 to shove out place-certain guidelines. essentially the most commonplace exhaust case is to disable a cell's digicam, audio recorder and transmission capabilities whereas in a elevated protection, no-phone-phones zone. This skill helps to steer limpid of recording of private conversations, theft of intellectual property, and transmission/reception of voice and facts whereas in the zone. 

"Cybersecurity circumstance consciousness and instant intrusion detection are key cost propositions Inpixon IPA offers out of the container," referred to Soumya Das, COO of Inpixon. "When used at the side of MaaS360, shoppers are in a position to augment their cell security capabilities. We're excited to accomplish the Inpixon Indoor Positioning Analytics integration accessible to MaaS360 world valued clientele during the IBM security App exchange."

“IBM believes in the value of ecosystems and integrating seamlessly with premier-of-breed commercial enterprise security infrastructure, fancy Inpixon,” said Charles Brown, Sr. providing manager, IBM. “including the Inpixon IPA Connector for MaaS360 in the IBM safety App trade enables their mutual valued clientele to leverage the wealthy cell safety statistics set of MaaS360 with the energy of Inpixon IPA.”

About InpixonInpixon (Nasdaq: INPX) is a frontrunner in Indoor Positioning Analytics (IPA). Inpixon IPA Sensors are designed to ascertain total attainable mobile, Wi-Fi, and Bluetooth devices anonymously. Paired with a high-performance facts analytics platform, this know-how delivers visibility, security, and industry intelligence on any industrial or govt location worldwide. Inpixon’s products and skilled services community assist customers win competencies of mobile, huge information, analytics, and the information superhighway of things (IoT) to uncover the untold reviews of the indoors. For the latest perception on IPA, follow Inpixon on LinkedIn, @InpixonHQ on Twitter, and contend with inpixon.com.

protected Harbor StatementAll statements during this release that aren't in keeping with historic fact are “ahead-searching statements” inside the which means of the inner most Securities Litigation Reform Act of 1995 and the provisions of region 27A of the Securities Act of 1933, as amended, and region 21E of the Securities exchange Act of 1934, as amended. while management has based mostly any ahead-looking statements blanketed in this release on its existing expectations, the information on which such expectations were primarily based may likewise exchange. These ahead-searching statements depend on a few assumptions concerning future activities and are region to a number of hazards, uncertainties and other components, a lot of which might breathe backyard of the manage of Inpixon and its subsidiaries, which might antecedent specific consequences to materially compass from such statements. Such hazards, uncertainties, and different elements encompass, however aren't restrained to, using the Inpixon IPA platform with IBM MaaS360 Connector, the fluctuation of economic situations, the performance of administration and personnel, Inpixon’s skill to obtain financing, competition, customary monetary circumstances and other factors that are targeted in Inpixon’s fitful and existing reports accessible for evaluate at sec.gov. in addition, Inpixon operates in a extremely aggressive and total of a sudden changing environment where fresh and unanticipated dangers might likewise arise. thus, buyers should noiseless now not location any reliance on ahead-searching statements as a prediction of actual effects. Inpixon disclaims any end to, and undertakes no obligation to, replace or revise ahead-looking statements.

Contact:Inpixon Investor family members:CORE IRScott Arnold, Managing Director+1 516-222-2560www.coreir.com


Mendix Unveils online game-altering endemic Integration with IBM's Cloud functions and Watson | killexams.com real Questions and Pass4sure dumps

BOSTON, Feb. 11, 2019 /PRNewswire/ -- Mendix, a Siemens company and the chief in low-code for the business, nowadays introduced fresh and tremendously stronger platform enhancements for Mendix on IBM Cloud to breathe added by Erno Rorive, senior product supervisor at Mendix, speakme at IBM believe 2019. Mendix is IBM's handiest business-grade, low-code construction platform entirely optimized and cloud-natively built-in with IBM Cloud features. Rorive will highlight Mendix's reengineered cloud-native structure that now totally supports Kubernetes containerization for the IBM ecosystem. full champion for IBM's Kubernetes implementation offers Mendix low-code app builders with seamless entry to the capabilities of Watson, the realm's leading issuer of AI cognitive capabilities.

"The point of the Mendix-IBM Alliance is to station the ease of drag-and-drop, low-code software construction into the arms of the commercial enterprise cloud market, leverage Watson's AI capabilities for the greatest number of clients, and vastly speed up the time-to-price deployment of company innovation," says Rorive. "the integration of the Mendix platform with IBM Cloud and Watson represents a golden triangle of commercial enterprise-competent solutions in an endeavor to energy the next wave of smart applications to headquarters of attention the vigour of AI on vertical industry solutions."

Mendix's platform enhancements for the IBM Cloud infrastructure encompass:

  • Broader entry to IBM Cloud features. Integrating Mendix's low-code app pile and deployment with Watson's out-of-the-field AI capabilities will allow commercial enterprise clients to experiment with AI-equipped functions, including entry to climate Channel content material, App identity, and App Launch on IBM Cloud.
  • Full guide for Kubernetes. built-in assist for Kubernetes production-grade container orchestration is now fraction of Mendix's cloud-native structure. endemic Kubernetes guide enables streamlined management and interoperable deployment throughout public, private, and hybrid clouds. This turbocharged integration with IBM Cloud and the Mendix platform eliminates expensive IT overhead with computerized curative and horizontal scaling, enabling seamless administration for migrating legacy systems or creating new, cloud-native, microservices-based mostly capabilities.
  • Single signal-on Onboarding. Reengineered platform integration creates a frictionless person journey for total Mendix low-code functions with one-click, out-of-the box deployment to IBM Cloud's total catalog of third-birthday party providers. This enhancement allows commercial enterprise purchasers to role and monitor mission-important purposes in a solitary IBM-centric dashboard, casting off the want for further logins and passwords.
  • integrated Billing with IBM Cloud. The newly streamlined monetary platform provides industry clients with a solitary invoice for any purchased IBM Cloud service. by route of adding the Mendix platform to IBM Cloud's catalog, shoppers can birthright away start to explore Mendix's enterprise-in a position, low-code software construction, enabling them to convey industrial benefits at speed and for the bottom viable complete can freight of ownership, using their IBM Cloud credits.
  • p>"The billing platform integration will animate fresh clients who are searching for a simple strategy to reckon chopping-part products," says Rorive. "It becomes lots extra flexible for enterprises to experiment and lengthen their software panorama with low-code building, at a a total lot lessen investment possibility than ordinary."

    The IBM reckon 2019 convention  at San Francisco's Moscone headquarters is the greatest annual gathering of proper executives, industry strategists, skilled developers, and academic and enablement specialists who will participate their reviews, insights, and foremost practices for growing cost with IBM's cloud-primarily based ecosystem.

    Erno Rorive's presentation, "construct functions 10x faster with the IBM Cloud low-code platform through Mendix"takes vicinity on Tuesday, February 12, at 1:30 p.m. at Moscone core South as fraction of IBM feel 2019's music on Cloud and Infrastructure: sensible starts here.

    to listen to Rorive's speak, register for IBM believe 2019.

    About MendixMendix, a Siemens enterprise and the international leader in low-code for the business, is transforming the realm of legacy utility and software construction through bringing industry and IT groups together to swiftly and collaboratively build efficient and up to date purposes for the commercial enterprise. The Mendix application progress platform at once addresses the massive international software developer talent hole, and contains enterprise and IT on the very birth and birthright through the entire software constructing and deployment technique. identified as a "leader" via properly analysts, including Gartner and Forrester, Mendix helps valued clientele digitally transform their businesses and industries via building, managing, and enhancing apps at unheard of pace and scale. greater than 4,000 ahead-considering enterprises exhaust the Mendix platform to construct enterprise functions that delectation customers and enhance operational effectivity. relate the Mendix group on LinkedIn and Twitter. start pile apps for gratis at signup.mendix.com.

    Press InquiriesKatie McGovernSenior Account supervisor, SHIFT Communicationsmendix@shiftcomm.com (617) 779-1867

    Dan BerkowitzSenior Director, global CommunicationsDan.Berkowitz@mendix.com (415) 518-7870

    View prevalent content material to down load multimedia:http://www.prnewswire.com/information-releases/mendix-unveils-online game-altering-native-integration-with-ibms-cloud-features-and-watson-300792660.html

    supply Mendix

    Copyright (C) 2019 PR Newswire. total rights reserved


    IBM App relate Wins a Spark overseas 2017 Design Award | killexams.com real Questions and Pass4sure dumps

    The IBM Cloud design crew is delighted that their design toil for IBM App connect has been recognized in the software software journey class of this 12 months’s Spark Design Awards™. here is total of the greater astonishing considering the fact that the 11th annual international Spark Awards program was essentially the most competitive in its heritage, with over 500 entries submitted.

    Following on from the prestigious red Dot Design Award that IBM received for their IBM data Science adventure prior within the 12 months, this latest award is extra awareness of the world-classification design toil it's being produced by means of IBM Design today.

    The utility integration context

    IBM App connect is a comparatively fresh offering, which has been designed from scratch over the remaining couple of years. IBM became already a identified chief within the integration middleware house, however developing the hybrid cloud-based mostly App connect offering was the subsequent analytic step in broadening their integration platform, so that it continues to meet the giant and sometimes advanced integration wants of twenty first century businesses.

    The App connect dashboard showing different facts flows

    Integrating present functions has many merits, together with assisting clients accomplish industry selections with more guidance and hence more suitable self assurance. Being capable of birthright now relate purposes and facts sources can additionally open up fresh industry opportunities — which could now breathe realized much more birthright now than turned into viable before.

    one of the crucial many ways by which IBM App connect is helping their purchasers day-by using-day

    up to now, organizations mandatory incredibly-skilled IT specialists to create custom code to relate their disparate purposes. This changed into an immense ache ingredient for company leaders, as connecting functions one at a time in this manner become a tricky, costly, and time-consuming process — and the job changed into on no account comprehensive as there are at total times fresh functions being delivered.

    Our multi-role primarily based integration platform answer

    IBM App relate working on the IBM Cloud gives a sparkling acknowledge for this vicissitude house, as now a few different person forms can operate a variety of integration tasks total inside the one product. App relate permits clients to connect again-end programs, internet, and cellular functions wherever they live — whether on-premises or in a personal or public cloud.

    This means that a non-technical company consumer can conveniently pick some of the many pre-developed connectors or integration templates to rapidly connect records from one commonplace application to one other. for example, a company person could set up a connection in order that fresh leads entered in the enterprise’s Salesforce account movement without delay into the business’s Marketo marketing database.

    one of the vital pre-described integration templates purchasable in App join

    Then for more technical clients, aliveto to configure more advanced data flows and transformations, App connect presents a Big compass of refined integration capabilities. as an example, the API lag editor allows for extra experienced integration specialists to define bespoke integrations, where statistics can likewise breathe routed from one source to a different, and changed and enriched en route.

    The all-around App connect API stream editor, where integration power clients can enrich and transform information because it flows from one application to another. The design challenge

    From a design viewpoint, the IBM App relate mission offered a thrilling possibility to re-think about the integration journey for brand spanking fresh clients, regularly working in fresh enterprise contexts. They firmly believe within the a must-have position of person research in achieving a superb product design, so they invested closely in conducting thorough person analysis into this inescapable enterprise area. because the stint progressed, they persevered to follow the IBM Design pondering framework and accommodate sponsor users to champion us verify their hypotheses, and to profit valuable feedback on their designs.

    it's in huge fraction because of the design group’s several attitude, attention to detail and continued focal point on providing a worthy person adventure for their purchasers that has enabled us to carry this award-successful product.


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    How Slack’s Content Strategy Expanded from a solitary Page to a Media Company | killexams.com real questions and Pass4sure dumps

    “If a tree falls in a forest and no one is around to hear it, does it accomplish a sound?” This analogy applies to the ideas they so painstakingly station together. If nobody can access the words conveying ideas, does it accomplish an impact?

    For example, content strategist Gerry McGovern did some digging for Microsoft, and realized a whopping 30% of its 10 million pages at its website went completely unread. Missed opportunities fancy these are the ones that competitors fancy Slack must identify and exploit, in order to compete and win.

    Here are three ideas that every CEO, product marketer, and wordsmith needs to reckon for their business’s content strategy:

    1. If It’s principal Enough, remark It More than Once

    People read through skimming. adage the most principal things at your site just once is not enough. Repetition is no longer tiresome. It’s essential, because most readers might miss it if placed sparsely.

    The more observant reader will notice this repetition, but no feature description, testimonial, or any other other phrase are exactly identical. Instead, each adds to an overall point…

    A throughline threads together sentences that convey each of these points. For example, at the third version of Slack’s homepage (V3), there’s a slider with three panels that highlight Slack’s most principal features — “Channels,” “Integration,” and “Search” — above the fold.

    Each of these features maintain their own throughlines, but “Integration” is particularly principal to potential users for convenience, organization, and consolidation. Let’s dive into how Slack constructs the “Integration” throughline:

    Full version (July 23, 2015)

    IntegrationsPlug the tools you already exhaust into Slack and pickup total your notifications in one place

    “Integration” is on the second panel of the slider above the fold. Let’s remark someone carelessly scrolls past before the slider changes. Their eyes jump birthright into the product features section grid:

    Over 80 integrationsIt’s not ‘yet another app’ you maintain to use. Slack works with total the tools you already use; set them up to post notifications directly to Slack in a solitary toil space.”

    Slack opens with a quantity, “80 integrations.” It’s visual and lets the reader know Slack’s not full of chafed´ air. The first sentence anticipates the reader’s objection, and re-explains “integration” in different words, “notifications… in a solitary toil space.” Now, let’s remark they even if they miss that, since it’s buried along with five other points…

    Like your team’s collective brain.Slack integrates with over 80 external services to assist you tow information from outside tools in a route that’s timely, relevant, and always searchable. No more switching between apps. Bring it total together in one simple place.

    Assuming the reader keeps scrolling, there’s no route they can miss this. It’s a featured blurb on its own, capturing the fantasy with a simile, “Like your team’s collective brain.” I suspect it’s a residual thought from Slack’s first homepage (V1), a header preceding testimonials, describing Slack, “It’s fancy an infinite brain for your total team.”

    Notice the repeated quantity from the produce feature grid, the accent on “timely, relevant, and always searchable,” and ending in, “all together in one simple place.”

    To Slack, the “integration” point is clearly far too principal for the average reader’s careless reading habits. So, they reiterate some variation of it three times on their home page. At their Tour page, they dive abysmal into types of notifications, to paint a very concrete picture for the reader:

    All your tools in one place.Connect total the tools you exhaust to Slack and avoid total that constant switching between apps. Set up your integration so that you pickup total your notifications directly within Slack — from champion requests, code check-ins, and error logs to sales leads — all of them searchable in one central archive.

    As you may guess from previous observations, ideas fancy “all in one place,” “notifications,” and “one central archive” approach up. The most spirited thing is that in the final point on the Tour page (“All your tools in one place”), this point likewise ties into the “Search” throughline.

    Repetition ensures careless readers leave with an impression things you want to command them. It likewise reinforces these principal ideas to more observant readers. strong content strategy ties throughlines together for readers across different pages and properties, each reinforcing the other.

    2. Slack as a Media Company

    One of the most spirited aspects of Slack is neglected by most other startups — their brand. Slack invests heavily not just in the aesthetics of their product and website, but likewise into content that conveys larger ideas, tying in stories with their brand. The adage goes, “Every company is a media company.”

    Some companies, fancy Peloton and Tonal, win this to heart, integrating media with their product’s soul. Slack ultimately sells “organizational transformation,” so it invests heavily in media to champion the change in mindsets. With the exception of Slack’s podcasts, here’s each one at the time of writing (notice how each of their media channels has its own name and domain):

  • Blog: Several People Are Typing, which prior to July 2018 received on average 150,000 pageviews per month according to Similarweb. It was hosted at Medium before the Slack team migrated to WordPress.com in July 2018, because they wanted more custom tools and workflows (and more granular reporting). Medium served as a solid host for two years for Slack to draw readers and operate with few design and progress resources.
  • Engineering blog: Several People Are Coding is noiseless hosted at Medium, and receives a widely fluctuating 45,000–120,000 pageviews, according to Similarweb.
  • Design blog: #slack-design is hosted at Medium. It does not yet receive as much traffic as its main blog or engineering blog.
  • Job board: Slack at toil is a job board, likewise hosted at WordPress, where Slack customers can post their jobs.
  • Store: The Slack Shop is a Shopify store that sells Slack merch, fancy branded socks and stickers. It’s likewise a branding and corporate responsibility play, donating proceeds from the store to a fresh charity every month.
  • Twitter: @slackhq is where much of their champion and brand interactions win place. People talk with and to Slack on Twitter. This is their two-way channel, whereas the aforementioned resemble broadcast.
  • Podcast #1: Slack Variety Pack, distributed over Soundcloud and iTunes.
  • Podcast #2: Eventually, Slack deprecated the slack Variety Pack and made a second podcast, entitling it “Work in Progress,” and distributing it on SiriusXM and iTunes.
  • Every startup has a slightly different route of pile reliance through its brand and eventually driving sales. For example, Buffer’s hypothesis is transparency builds reliance faster than total other methods, so they built a transparency dashboard. Drift’s hypothesis goes a step further, in adage authenticity builds reliance faster, so they build their own reality TV show.

    I believe that Slack’s less deliberate, more organic, hypothesis is that humanity builds reliance faster than total other methods. It’s a core fraction of their voice (clear, concise, and human). It’s likewise the antithesis to Microsoft’s — and many other company’s — voice and brand. And even with these qualities, fraction of Slack’s magic is in how they delineate it…

    The human tang is incredibly wide. Slack chooses to participate emotion with their readers (e.g., Wall of Love), and convey it through attribute toil by investing heavily into its content creation. In principle, it’s not dissimilar from art.

    For example, both their podcasts explore the nature of toil (e.g., a corporate retiree re-entering the workforce), because people disburse more and more time doing it (and, at times, finding acceptation through it). Their blog does the same. So does their job board — as people’s toil locations could paw their personal lives immensely (long distance relationships, travel opportunities, commute, etc.).

    Slack hires experts fancy Pacific Content to create their podcast. They likewise hire illustrators to create unique knack accompanying each blog post. They achieve out to people outside of the company to conduct interviews that they publish at their blog, similar to what a magazine would’ve done.

    Without overlooking their core communications product, total this content accelerates people’s reliance in Slack, and builds up Slack’s credibility and favorability. To varying degrees, each of their properties are now a defensible marketing asset that they can control and own, especially after they started hosting their own blog (which can breathe exported from WordPress). They’ve likewise got a ton of real estate online, a lot of it distributed at platforms (e.g., Twitter, their design and eng blogs at Medium).

    Consider likewise the genesis of each initiative — only the Slack at toil site required dev resources (to set up WordPress to breathe a job board). Their blog started at Tumblr, then moved to Medium. Their champion and brand interactions are expanded by Twitter’s platform reach. They bring their Twitter interactions back to their website by linking to their Wall of admire list in V2, and embedding Tweets from this account in V3 (Archive doesn’t load it well).

    Full version (October 20, 2014)

    With any other organization, I’d insinuate that they set up a mailing list. This is typically the anchor of total owned content properties, because email lists can breathe downloaded and stored. However, since this is Slack and their total goal is to Kill email, they’d breathe better served pile a big community of Slack users (e.g., friends.slack.com).

    All said, Slack’s content has an incredible amount of integrity and taste. I admire it. If you told me there was a Slack magazine, to me it would leer fancy Airbnb’s Pineapple (and not their dreadful fresh one…) This admiration, and respect, is a difficult “feeling” to replicate in an advertisement. Blended in with trust, these feelings might just breathe enough to give up email.

    Trust is a priceless asset, but the question of direct ROI inevitably comes up. Their blog (slackhq.com) hosted at Medium didn’t appear to bring many people back to their main site (slack.com). At the time of writing, Similarweb reporting that 0.57% of their overall website’s overall traffic comes from referrals.

    However, an veteran screenshot from Openview, highlighting Similarweb stats from a year ago, reports slackhq.com drives 2% of referral traffic:

    My guess is Slack didn’t trust much for their content’s SEO, or “content marketing” success metrics, in conventional terms. They just wanted content that would educate people and build trust. In fact, it seems they’re just hiring for it now, at the time of writing. Their word-of-mouth marketing, SEO through integrations, and app ecosystem, total feed user acquisition and conversion much better than content did…

    But their content makes Slack most propitious and trustworthy than their current competitors (Microsoft, IBM, etc.). And, their content ushers in a fresh route of thinking about work, retains loyal brand users, and lays the foundation for their company’s future.

    Final Thoughts

    Slack’s content strategy grew with their business, going from a solitary page in the first version of their website (V1), to a media company by the fourth (V4). And yet, the principles stayed the same. They weren’t afraid to reiterate the principal things, they told human stories, and they invested heavily in distributing their content through their “media company” arm. But if you’ve had a leer at their sites, you’ll realize there’s so much more I could cover.

    At the discontinuance of the day, Slack focuses on humans. They talk with candor, politeness, and courtesy. Consciously or not, they align themselves with how we’ve consumed content in the past. They apply the format of variety shows into their podcast, and fancy a magazine they combine sparkling illustrations with in-depth interviews into their blog. They invest not just in their technology platform, but in communicating their product’s value through education. Even the world’s most powerful implement is useless without an understanding of its possibilities or how to actually exhaust it.

    Metaphors Run rampant in content strategy, partially because of the creative nature of the workers, and partially because it’s such a fresh discipline that nobody understands it. Beyond the attribution, the CMS’s, and the information architecture, it’s very simple:

    Create ideas worth talking about, and pattern out how and where to talk about them.


    10 Reasons to Unify Your Data With a Data Warehouse | killexams.com real questions and Pass4sure dumps

    Savvy leaders are transforming their organizations with data, using difficult numbers to accomplish decisions and align their teams toward tangible goals. More efficiency, faster production, lower costs — total of these are within achieve for organizations that can master their data.

    As more and more tools are required to drive different processes, and each provides different sets of data, the key is to manage and govern increasingly massive data collections. For hundreds of thousands of companies, that means unifying that data inside of a data warehouse.

    What is a Data Warehouse?

    A data warehouse is NOT just another database. A data warehouse is a federated (uses a unifying criterion for divorce databases) repository for total the data collected by an enterprise’s various operational systems. Data warehousing emphasizes the capture of data from diverse sources specifically for access and analysis, including large-scale analysis, rather than for transaction processing.

    Because they’re designed for analytical processing, data warehouses offer many advantages for organizations of total types.

    Benefits of Unifying Your Data in a Data Warehouse 1. More Consistency in Your Data

    A properly deployed data warehouse makes it practicable to metamorphose data from many different sources into a unified format. Once in the data warehouse, in the criterion format, total of your analysts, throughout various departments and teams can access the data in a governed, standard, secure way. This allows you to keep processes standardized and consistent across the organization — while noiseless providing analysts total the data they want and need.

    2. 360 Degree Analysis

    Data warehouses are designed specifically to aggregate data from many sources into a specifically accessible central store. By unifying the data in one place, (and in the identical format) analysts can achieve more comprehensive, 360 degree reviews. You pickup both deeper and broader analysis on total the trends happening throughout your company.

    3. Better Organizational Alignment

    With better 360 degree insights and different teams (sales, marketing, operations, support, etc.) total using the identical repository for reporting, each department can better align with the others. total the key stakeholders can notice what’s going on throughout the organization and remain on the identical page. The practical implications extend beyond analysis and directly into day-to-day operations: the left hand can always know what the birthright hand is doing and win action in concert.

    4. Built for Performance

    Data warehouses are built differently than operational database systems, which are more focused on creating and modifying data. Data warehouses are built specifically for analysis and retrieval rather than the upkeep of individual records. As a result, they offer both a more scalable solution for storing big volumes of data and faster processing for advanced data analysis using BI tools, processing queries more efficiently to enable faster analysis.

    5. Simplify API Management

    Data warehouses assist purge API management headaches. With data warehouses, there’s no requisite to juggle or manage divorce APIs to access data in every solitary application or database. Instead, you simply copy the data from a given data source and replicate it in your data warehouse, saving time and reducing error-prone processes. Then, you can exhaust transactional replication, which copies only fresh entries in the data source to the data warehouse, so your warehouse stays up to date without a massive stream of fresh data eating up bandwidth.

    6. Skip the API Limitations

    Another profit of using data replication is that you circumvent costly, knotty API limitations. This expands your generic data access by allowing you to dodge the problems associated with live querying of inescapable APIs that are either late or confine the number of queries you can Run against them.

    7. Historical Intelligence

    Data warehouses can store absolutely gargantuan amounts of historical data, with some organizations’ data warehouses clocking in at petabytes of data. For context, a petabyte is 1000 terabytes, or 1 million gigabytes, the unit most people exhaust to denominate storage needs. With ample petabytes of data at your disposal, you maintain total the elbow scope you requisite to track historical data, giving your organization the ability to dissect different time periods and trends in order to accomplish future predictions. This is crucial because vast reams of historical data just can’t breathe stored in a transactional database or used to generate reports from a transactional system — databases pickup too cumbersome and quickly overloaded.

    8. 167% Median ROI Due to Massive Cost Savings

    According to an in-depth study conducted by the International Data Corporation (IDC), the median three-year ROI of a data warehouse investment was 167 percent, for a wide compass of companies studied, Big and small, across industries. The average was even higher, at 400 percent ROI. The reason? Data warehouses assist organizations massively carve their costs for streamlining and governing their data. Organizations that start with an actionable path and iterative, smaller projects can especially speed up their payback period, with the average payback era at just over 1.5 years.

    9. Proven and De-Risked

    Enterprise data warehouses maintain been around forever. The military command-and-control, intelligence, manufacturing, banking, finance and retail industries maintain used data warehouses since the 1960s. In the mid-90s, data warehousing emerged as an IT specialization, after Walmart changed the game. Behind the mastery of Walmart’s supply chain, their data warehouse helped them leverage transaction data collected by its point-of-sales systems to gain abysmal insight into the purchasing habits of its 100 million customers and the logistics guiding its 25,000 suppliers. Since then, data warehousing has steadily gained adoption by many of the biggest companies in the world, with proven implementation models.

    And on the actual data warehouse side, you’re dealing with the hulking giants of tech: Amazon, IBM, Microsoft, Oracle, Google and Teradata (which has been around for 30 years) provide the largest data warehouses on the web, total with abysmal support.

    10. Higher Security: Consolidated Data Access

    By using a data warehouse as a unified repository for your data, you can not only bring together total your data, you can accomplish it more secure through consolidated data access. If you control data access through a centralized system, you can combine role-based user access controls, providing different permissions to different users, with row-level access controls (which data is available to different users) to lock down data security. In particular, row-level access control provides several security benefits, including:

  • More granular data security: set permissions for rows, not just tables and columns
  • Automatic data filtering according to group, role, application and more
  • Server-encoded data-level security
  • This helps meet compliance requirements for regulated industries, such as healthcare and comes criterion now in the biggest data warehousing tools.

    How to lag Data into Data Warehouses

    There are a number of different ways you can pickup data from your various enterprise IT systems, databases and applications into a unified data warehouse. One that is particularly efficient for this character of exhaust case and gets around many integration challenges and API limits as well is incremental data replication. In this process, you exhaust a implement to replicate data from various sources into the warehouse then regularly sync fresh updates (or increments) from those sources into the warehouse.


    The Best Self-Service industry Intelligence (BI) Tools of 2018 | killexams.com real questions and Pass4sure dumps

    Analytics Beyond Spreadsheets

    For many years, Microsoft transcend and other spreadsheets were the tools of selection for industry professionals who were looking to visualize their data. But spreadsheets had their limits for many industry intelligence (BI)-related tasks. Even today, trying to creating charts analyzing knotty datasets in transcend can noiseless breathe frustrating. Sometimes you start with the wrong benevolent of data, for example, or you may not know how to exploit the spreadsheet to create the data visualization{{/ZIFFARTICLE} you need. On the other hand, the rising tide of data democratization is giving everyone in an organization access to corporate data. The requisite has arisen for efficient tools that people of total skill levels can exhaust to accomplish sense of the wealth of information created by businesses every solitary day.

    Spreadsheets likewise descend down when the data isn't well-structured or can't breathe sorted out in methodical rows and columns. And, if you maintain millions of rows or very sparse matrices, then the data in a spreadsheet can breathe painful to enter and it can breathe difficult to visualize your data. Spreadsheets likewise maintain issues if you are trying to create a report that spans multiple data tables or that mixes in Structured Query Language (SQL)-based databases, or when multiple users try to maintain and collaborate on the identical spreadsheet.

    A spreadsheet containing up-to-the-minute data can likewise breathe a problem, particularly if you maintain exported graphics that requisite to breathe refreshed when the data changes. Finally, spreadsheets aren't worthy for data exploration; trying to spot trends, outlying data points, or counterintuitive results is difficult when what you are looking for is often hidden in a long row of numbers.

    While spreadsheets and self-service BI tools both accomplish exhaust of tables of numbers, they are really acting in different arenas with different purposes. A spreadsheet is first and foremost a route to store and parade calculations. While some spreadsheets can create very sophisticated mathematical models, at their core it is total about the math more than the model itself.

    This is total a long-winded route of adage that when businesses exhaust a spreadsheet, they are actively sabotaging themselves and their ability to consistently pickup valuable insights from their data. BI tools are specficially designed to assist businesses better understand their data, and can prove to breathe a huge profit to those upgrading from what a limited spreadsheet can do.

    What Is industry Intelligence?

    Defining BI is tricky. When you examine what it does and why companies exhaust it, it can start to sound vague and nebulous. After all, many different kinds of software offer analytics features, and total businesses want to improve. Understanding what a BI is or isn't can breathe unclear.

    BI is an umbrella term meant to cover total of the activities necessary for a company to revolve raw information into actionable knowledge. In other words, it's a company's efforts to understand what it knows and what it doesn't know of its own actuality and operations. The ultimate goal is being able to augment profits and sharpen its competitive edge.

    Framed that way, BI as a concept has been around as long as business. But that concept has evolved from early basics [like Accounts Payable (AP) and Accounts Receivable (AR) reports and customer contact and constrict information] to much more sophisticated and nuanced information. This information ranges across everything from customer behaviors to IT infrastructure monitoring to even long-term fixed asset performance. Separately tracking such metrics is something most businesses can conclude regardless of the tools employed. Combining them, especially disparate results from metrics normally not associated with one another, into understandable and actionable information, well, that's the knack of BI. The future of BI is already shaping up to simultaneously broaden the scope and variety of data used and to sharpen the micro-focus to ever finer, more granular levels.

    BI software has been instrumental in this steady progression towards more in-depth learning about the business, competitors, customers, industry, market, and suppliers, to name just a few practicable metric targets. But as businesses grow and their information stores balloon, the capturing, storing, and organizing of information becomes too big and knotty to breathe entirely handled by mere humans. Early efforts to conclude these tasks via software, such as customer relationship management (CRM) and enterprise resource planning (ERP), led to the formation of "data silos" wherein data was trapped and useful only within the confines of inescapable operations or software buckets. This was the case unless IT took on the stint of integrating various silos, typically through painstaking and highly manual processes.

    While BI software noiseless covers a variety of software applications used to dissect raw data, today it usually refers to analytics for data mining, analytical processing, querying, reporting, and especially visualizing. The main dissimilarity between today's BI software and Big Data analytics is mostly scale. BI software handles data sizes typical for most organizations, from petite to large. Big Data analytics and apps ply data analysis for very big data sets, such as silos measured in petabytes (PBs).

    Self-Service BI and Data Democratization

    The BI tools that were well-liked half a decade or more ago required specialists, not just to exhaust but likewise to interpret the resulting data and conclusions. That led to an often inconvenient and fallible filter between the people who really needed to pickup and understand the business—the company decision makers—and those who were gathering, processing, and interpreting that data—usually data analysts and database administrators. Because being a data specialist is a demanding job, many of these folks were less well-versed in the actual workings of the industry whose data they were analyzing. That led to a focus on data the company didn't need, a misinterpretation of results, and often a succession of "standard" reporting that analysts would Run on a scheduled basis instead of more ad hoc intelligence gathering and interpretation, which can breathe highly valuable in fast-moving situations.

    This problem has led to a growing fresh trend among fresh BI tools coming onto the market today: that of self-service BI and data democratization. The goal for much of today's BI software is to breathe available and usable by anyone in the organization. Instead of requesting reports or queries through the IT or database departments, executives and decision makers can create their own queries, reports, and data visualizations through self-service models, and connect to disparate data both within and outside the organization through prebuilt connectors. IT maintains overall control over who has access to which tools and data through these connectors and their management implement arsenal, but IT no longer acts as a bottleneck to every query and report request.

    As a result, users can win edge of this distributed BI model. Key tools and censorious data maintain moved from a centralized and difficult-to-access architecture to a decentralized model that merely requires access credentials and familiarity with fresh BI software. This results in a total fresh benevolent of analysis becoming available to the organization, namely, that of experienced, front-line industry people who not only know what data they requisite but how they requisite to exhaust it.

    The emerging crop of BI tools total toil difficult at developing front-end tools that are more intuitive and easier to exhaust than those of older generations—with varying degrees of success. However, that means a key criteria in any BI implement purchasing decision will breathe to evaluate who in the organization should access such tools and whether the implement is appropriately designed for that audience. Most BI vendors testify they're looking for their implement suites to become as ubiquitous and light to exhaust for industry users as typical industry collaboration tools or productivity suites, such as Microsoft Office. notasoul maintain gotten quite that far yet in my estimation, but some are closer than others. To that end, these BI implement suites tend to focus on three core types of analytics: descriptive (what did happen), prescriptive (what should befall now), and predictive (what will befall later).

    What Is Data Visualization?

    In the context of BI software, data visualization is a posthaste and efficient manner of transferring information from a machine to a human brain. The idea is to station digital information into a visual context so that the analytic output can breathe quickly ingested by humans, often at a glance. If this sounds fancy those pie and bar charts you've seen in Microsoft Excel, then you're right. Those are early examples of data visualizations.

    But today's visualization forms are rapidly evolving from those traditional pie charts to the stylized, the artistic, and even the interactive. An interactive visualization comes with layered "drill downs," which means the viewer can interact with the visual to achieve more granular information on one or more aspects incorporated in the bigger picture. For example, fresh values can breathe added that will change the visualization on the fly, or the visualization is actually built on rapidly changing data that can revolve a static visual into an animation or a dashboard.

    The best visualizations conclude not hunt artistic awards but instead are designed with role in mind, usually the quick and intuitive transfer of information. In other words, the best visualizations are simple but powerful in clearly and directly delivering a message. High-end visuals may leer impressive at first glance but, if your audience needs assist to understand what's being conveyed, then they've ultimately failed.

    Most BI software, including those reviewed here, comes with visualization capabilities. However, some products offer more options than others so, if advanced visuals are key to your BI process, then you'll want to closely examine these tools. There are likewise third-party and even free data visualization tools that can breathe used on top of your BI software for even more options.

    Products and Testing

    In this review roundup, I tested each product from the perspective of a industry analyst. But I likewise kept in mind the viewpoint of users who might maintain no familiarity with data processing or analytics. I loaded and used the identical data sets and posed the identical queries, evaluating results and the processes involved.

    My point was to evaluate cloud versions alone, as I often conclude analysis on the wing or at least on a variety of machines, as conclude legions of other analysts. But, in some cases, it was necessary to evaluate a desktop version as well or instead of the cloud version. One sample of this is Tableau Desktop, a favorite implement of Microsoft transcend users who simply maintain an affinity for the desktop implement (and who just lag to the cloud long enough to participate and collaborate).

    I ended up testing the Microsoft Power BI desktop version, too, on a Microsoft representative's recommendation because, as the rep said, "the more robust data prep tools are there." Besides, said the rep, "most users prefer the desktop implement over a web implement anyway." Again, I don't doubt Microsoft's pretension but that does appear uncanny to me. I've heard it said that desktop tools are preferred when the data is local as the process feels faster and easier. But seriously, how much data is truly local anymore? I suspect this odd desktop implement preference is a bit more personal than fact-based, but to each his own.

    Then there's Google Analytics, a sheer cloud player. The implement is designed to dissect website and mobile app data so it's a different critter in the BI app zoo. That being the case, I had to deviate from using my test data set and queries, and instead test it in its natural habitat of website data. Nonetheless, it's the processes that are evaluated in this review, not the data.

    While I didn't test any of these tools from a data scientist's role, I did mention advanced capabilities when I institute them, simply to let buyers know they exist. IBM Watson Analytics is one implement with the ability to extend to highly advanced features and was likewise one of the easiest to exhaust upfront. IBM Watson Analytics is well-suited for industry analysts and for widespread data democratization because it requires little, if any, learning of data science. Instead, it works well by using natural language and keywords to contour queries, a characteristic that can accomplish it valuable to practically anyone. It's highly intuitive, very powerful, and light to learn. Microsoft Power BI is a strong second as it, too, is powerful while likewise familiar, certainly to any of the millions of Microsoft industry users. However, there are several other powerful and intuitive apps in this lineup from which to choose; they total maintain their own pros and cons. We'll breathe adding even more in the coming months.

    One thing to watch out for during your evaluations of these products is that many don't yet ply streaming data. For many users, that won't breathe a problem in the immediate future. However, for those involved with analyzing industry processes as they happen, such as website performance metrics or customer behavior patterns, streaming data can breathe invaluable. Also, the Internet of Things (IoT) will drive this issue in the near future and accomplish streaming data and streaming analytics a must-have feature. Many of these tools will maintain to up their game accordingly so, unless you want to jump ship in a year or two, it's best to mediate ahead when considering BI and the IoT.

    BI and Big Data

    Another region in which self-service BI is taking off is in analyzing Big Data. This is a newer progress in the database space but it's driving tremendous growth and innovation. The name is an apt descriptor because Big Data generally refers to huge data sets that are simply too Big to breathe managed or queried with traditional data science tools. What's created these behemoth data collections is the explosion of data-generating, tracking, monitoring, transaction, and convivial media tools (to name a few) that maintain become so well-liked over the last several years.

    Not only conclude these tools generate loads of fresh data, they likewise often generate a fresh benevolent of data, namely "unstructured" data. Broadly speaking, this is simply data that hasn't been organized in a predefined way. Unlike more traditional, structured data, this benevolent of data is weighty on text (even free-form text) while likewise containing more easily defined data, such as dates or credit card numbers. Examples of apps that generate this benevolent of data include the customer behavior-tracking tools you exhaust to notice what your customers are doing on your e-commerce website, the piles of log and event files generated from some smart devices (such as alarms and smart sensors), and broad-swath convivial media tracking tools.

    Organizations deploying these tools are being challenged not only by a sudden deluge of unstructured data that quickly strains storage resources [think beyond terabytes (TB) into the PB and even exabyte (EB) range] but, even more importantly, they're finding it difficult to query this fresh information at all. Traditional data warehouse tools generally weren't designed to either manage or query unstructured data. fresh data storage innovations such as data lakes are emerging to solve for this need, but organizations noiseless relying exclusively on traditional tools while deploying front-line apps that generate unstructured data often find themselves sitting on mountains of data they don't know how to leverage.

    Enter Big Data analysis standards. The golden criterion here is Hadoop, which is an open-source software framework that Apache specifically designed to query big data sets stored in a distributed style (meaning, in your data center, the cloud, or both). Not only does Hadoop let you query Big Data, it lets you simultaneously query both unstructured as well as traditional structured data. In other words, if you want to query total of your industry data for maximum insight, then Hadoop is what you need.

    You can download and implement Hadoop itself to achieve your queries, but it's typically easier and more efficient to exhaust commercial querying tools that employ Hadoop as the foundation of more intuitive and full-featured analysis packages. Notably, most of the tools reviewed here, including Chartio, IBM Watson Analytics, Microsoft Power BI, and Tableau Desktop, total champion this. However, each requires varying levels of configuration or even add-on tools to conclude so—with IBM, Microsoft, and Tableau offering exceptionally abysmal capabilities. However, both IBM and Microsoft will noiseless await customers to utilize additional tools around aspects such as data governance to ensure optimal performance.

    Finding the birthright BI Tool

    Given the issues spreadsheets can maintain when used as ad hoc BI tools and how firmly ingrained they are in their psyches, finding the birthright BI implement isn't a simple process. Unlike spreadsheets, BI tools maintain major differences when it comes to how they consume data inputs and outputs and exploit their tables. Some tools are better at exploration than analysis, and some require a fairly abrupt learning curve to really accomplish exhaust of their features. Finally, to accomplish matters worse, there are dozens if not hundreds of such tools on the market today, with many vendors willing to pretension the self-serve BI label even if it doesn't quite fit.

    Getting the overall workflow down with these tools will win some study and discussion with the people you'll breathe designating as users. Tableau Desktop and Microsoft Power BI, for example, will start users out with the desktop version to build visualizations and link up to various data sources. Once you maintain this together, you can start sharing those results online or across your organization's network. With others, such as Chartio or Google Analytics, you start in the cloud and remain there.

    In recent years, companies maintain been taking edge of the wide selection of online learning platforms out there to train their employees on using these platforms. As intuitive as these platforms may be, it is principal to accomplish sure that your employees actually know how to exhaust these BI platforms so that you can accomplish sure your investment was worthwhile. There are many ways of approaching this, but using the birthright online learning platform might breathe a worthy station to start looking.

    Given the wide expense compass of these products, you should segment your analytics needs before you accomplish any buying decision. If you want to start out slowly and inexpensively, then the best route is to try something that offers significant functionality for free, such as Microsoft Power BI. Such tools are very affordable and accomplish it light to pickup started. Plus, they tend to maintain big ecosystems of add-ons and partners that can breathe a cost-effective replacement for doing BI inside a spreadsheet. Tableau Desktop noiseless has the largest collection of charts and visualizations and the biggest partner network, though both IBM Watson Analytics and Microsoft Power BI are catching up fast.

    IBM Watson Analytics scored the highest, and Microsoft Power BI and Tableau Desktop scored the next highest in their roundup. However, total three products received their Editors' selection award. Tableau Desktop may maintain a Big expense tag depending on which version you select but, as previously mentioned, it has an exceptionally big and growing collection of visualizations plus a manageable learning curve if you're willing to pledge some endeavor to it. Microsoft Power BI and Tableau Desktop likewise maintain big and growing collections of data connectors, and both Microsoft and Tableau maintain their own sizable communities of users that are vocal about their wants and needs. This can carry a lot of weight with the vendors' progress teams so it's a worthy idea to disburse some time looking through those community forums to pickup an idea where these companies are headed.

  • Pros: Extremely user-friendly. grotesque automatic report generation. Impressive champion availability.

    Cons: Automated reports can quickly become defaults. abrupt learning curve that might throw beginners.

    Bottom Line: Zoho Reports is a solid option for generic industry users who might not breathe knowledgeable in analytics software. It's likewise available at an attractive price.

    Read Review
  • Pros: Accessible user interface. Smart guidance features. Impressively posthaste analytics. Robust natural language querying.

    Cons: Unable to conclude real-time streaming analytics.

    Bottom Line: IBM Watson Analytics is an exceptional industry intelligence (BI) app that offers a strong analytics engine along with an excellent natural language querying tool. This is one of the best BI platforms you'll find and easily takes their Editors' selection honor.

    Read Review
  • Pros: Extremely powerful platform with a wealth of data source connectors. Very user-friendly. Exceptional data visualization capabilities.

    Cons: Desktop and web versions divide data prep tools. Refresh cycle is limited on free version.

    Bottom Line: Microsoft Power BI earns their Editors' selection veneration for its impressive usability, top-notch data visualization capabilities, and superior compatibility with other Microsoft Office products.

    Read Review
  • Pros: stupendous collection of data connectors and visualizations. User-friendly design. Impressive processing engine. mature product with a big community of users.

    Cons: full mastery of the platform will require substantial training.

    Bottom Line: Tableau Desktop is one of the most mature offerings on the market and that shows in its feature set. While it has a steeper learning curve than other platforms, it's easily one of the best tools in the space.

    Read Review
  • Pros: Bottlenecks are eliminated thanks to in-chip processing. Impressive natural language query in third-party applications.

    Cons: Might breathe too difficult for self-service industry intelligence (BI). Analytics process noiseless needs to breathe ironed out. Natural language capability can breathe limited.

    Bottom Line: Sisense is a complete platform that should breathe well-liked for experienced BI users. It may descend short for beginners, however.

    Read Review
  • Pros: Wide compass of connectors. Impressive sharing features. Limitless data storage.

    Cons: User interface is not intuitive. abrupt learning curve. Unwelcoming to fresh analysts.

    Bottom Line: Domo isn't for newcomers but for companies that already maintain industry intelligence (BI) tang in their organization. Domo's a powerful BI implement with a lot of data connectors and solid data visualization capabilities.

    Read Review
  • Pros: Exceptional platform for website and mobile app analytics.

    Cons: Customer champion has route too much automation. Focus on marketing and advertising can breathe frustrating to users. Relies mostly on third parties for training.

    Bottom Line: Due to its brand recognition and the fact that it's free, Google Analytics is the biggest name in website and mobile app intelligence. It has a abrupt learning curve but it is an awesome industry intelligence tool.

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  • Pros: Designed with generic industry users in mind. Solid recur on investment.

    Cons: The data you can exhaust is limited. Needs additional platform to connect.

    Bottom Line: The Salesforce Einstein Analytics Platform is designed for customer, sales, and marketing analyses, although it can server other needs, too. Its powerful analytics capabilities along with its solid natural language querying functionality and a wide array of partners accomplish it an attractive offering.

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  • Pros: Real-time analytics for Internet of Things (IoT) and streaming data features. Massive ecosystem with plenteous extenders. Responsive pages accomplish mobile publishing easiest. Impressive storytelling paradigm. Centralized view with consolidated analytics.

    Cons: Data prep features are lacking. Confusing toolbar design. Not friendly for beginners.

    Bottom Line: If your industry already uses SAP's HANA database platform or some of its other back-end industry platforms, then SAP Analytics Cloud is a powerful, well-priced choice. But breathe warned that there's a abrupt learning curve and a eminent dependence on other SAP products for full functionality.

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  • Pros: Impressive processing engine. Powerful query optimization on SQL. Entirely web-based. knotty queries are handled very well.

    Cons: Poorly designed user interface. abrupt learning curve.

    Bottom Line: Chartio excels at pile a powerful analytics platform that experienced industry intelligence (BI) users will appreciate. Those fresh to BI, however, will find it very difficult to use.

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  • Pros: Very abysmal SQL modeling ability. Uses Git for version management and collaboration.

    Cons: Very expensive. Not for petite teams.

    Bottom Line: Looker is a much self-service industry intelligence (BI) implement that can assist unify SQL and Big Data management across your enterprise.

    Read Review
  • Pros: Custom access roles. Solid collection of public data online.

    Cons: knotty pricing is a deterrent.

    Bottom Line: Qlik Sense Enterprise Server is a self-service industry intelligence (BI) implement that delivers the best collection of user access roles among the BI tools they tested, and likewise demonstrates a promising start towards integrating Data-as-a-Service (DaaS).

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  • Pros: One of the largest collections of data connectors. Many granular access roles.

    Cons: No free ordeal available. Training webinars can breathe costly.

    Bottom Line: The company's Focus query language is showing its age but Information Builders' self-service industry intelligence (BI) implement WebFocus nevertheless has some powerful analysis features.

    Read Review
  • Pros: Very light to pickup started. Nice team management and collaboration features.

    Cons: The cloud version has a subset of features institute in Windows version. Online documentation could breathe improved.

    Bottom Line: While Tibco is noiseless making the transition from a desktop to a cloud software vendor, its self-service industry intelligence (BI) implement Tibco Spotfire is a much route to start visualizing your transcend data.

    Read Review
  • Pros: Excellent analytical champion for Intuit QuickBooks. Very light setup.

    Cons: Installation and setup is a bit of chore. No champion for Intuit QuickBooks' online versions.

    Bottom Line: Clearify QQube is the best self-service industry intelligence (BI) implement for in-depth analysis of your Intuit QuickBooks files, though you'll requisite to leer elsewhere for broader BI tasks.

    Read Review


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