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M2180-759 - IBM Connectivity and Integration Sales Mastery v1 - Dump Information

Vendor : IBM
Exam Code : M2180-759
Exam Name : IBM Connectivity and Integration Sales Mastery v1
Questions and Answers : 36 Q & A
Updated On : February 21, 2019
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M2180-759 Questions and Answers

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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
Q&A : 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 equipment administration (MDM)  enables IT organizations to comfy, control and video display any company- or worker-owned cell equipment. The solution makes it possible for corporations to configure the gadget with corporate connectivity settings, e mail profiles and machine-selected restrictions; to encrypt business 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 distinct geo-fenced zones on a facility flooring plan. As a wireless device moves all the way through the office ambiance, the Inpixon IPA Connector sends the cell's place to MaaS360, allowing MaaS360 to push out place-certain guidelines. essentially the most commonplace use case is to disable a cell's digicam, audio recorder and transmission capabilities whereas in a high protection, no-phone-phones zone. This skill helps to steer clear 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 increase their cell security capabilities. We're excited to make 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, like Inpixon,” said Charles Brown, Sr. providing manager, IBM. “including the Inpixon IPA Connector for MaaS360 in the IBM safety App trade enables our 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 discover all attainable mobile, Wi-Fi, and Bluetooth devices anonymously. Paired with a high-performance facts analytics platform, this know-how delivers visibility, security, and business intelligence on any industrial or govt location worldwide. Inpixon’s products and skilled services community assist customers take 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 discuss 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 area 27A of the Securities Act of 1933, as amended, and area 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 also exchange. These ahead-searching statements depend on a few assumptions concerning future activities and are area to a number of hazards, uncertainties and other components, a lot of which might be backyard of the manage of Inpixon and its subsidiaries, which might cause specific consequences to materially range 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, usual financial circumstances and other factors that are targeted in Inpixon’s periodic and existing reports accessible for evaluate at sec.gov. in addition, Inpixon operates in a extremely aggressive and all of a sudden changing environment where new and unanticipated dangers might also arise. thus, buyers should still now not location any reliance on ahead-searching statements as a prediction of actual effects. Inpixon disclaims any intention 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 Native 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 new and tremendously stronger platform enhancements for Mendix on IBM Cloud to be 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 support 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 aim of the Mendix-IBM Alliance is to place 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 effort to energy the next wave of smart applications to center of attention the vigour of AI on vertical business solutions."

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

  • Broader entry to IBM Cloud features. Integrating Mendix's low-code app building 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 help for Kubernetes production-grade container orchestration is now part of Mendix's cloud-native structure. Native 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 all 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 function and monitor mission-important purposes in a single IBM-centric dashboard, casting off the want for further logins and passwords.
  • integrated Billing with IBM Cloud. The newly streamlined financial platform provides business clients with a single invoice for any purchased IBM Cloud service. by way of adding the Mendix platform to IBM Cloud's catalog, shoppers can right 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 charge of ownership, using their IBM Cloud credits.
  • p>"The billing platform integration will encourage new clients who are searching for a simple strategy to consider 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 whole lot lessen investment possibility than ordinary."

    The IBM consider 2019 convention  at San Francisco's Moscone center is the greatest annual gathering of proper executives, business strategists, skilled developers, and academic and enablement specialists who will share 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 part 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 business and IT groups together to swiftly and collaboratively build effective and up to date purposes for the commercial enterprise. The Mendix application development platform at once addresses the massive international software developer talent hole, and contains enterprise and IT on the very birth and right 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 use the Mendix platform to construct enterprise functions that pleasure customers and enhance operational effectivity. join the Mendix group on LinkedIn and Twitter. start building 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 normal 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. All rights reserved


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

    The IBM Cloud design crew is delighted that our design work for IBM App connect has been recognized in the software software journey class of this 12 months’s Spark Design Awards™. here is all 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 our IBM data Science adventure prior within the 12 months, this latest award is extra awareness of the world-classification design work it's being produced by means of IBM Design today.

    The utility integration context

    IBM App connect is a comparatively new 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 logical step in broadening our 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 make business selections with more guidance and hence more suitable self assurance. Being capable of right now join purposes and facts sources can additionally open up new business opportunities — which could now be realized much more right now than turned into viable before.

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

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

    Our multi-role primarily based integration platform answer

    IBM App join working on the IBM Cloud gives a beautiful answer for this difficulty house, as now a few different person forms can operate a variety of integration tasks all inside the one product. App join 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 new 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, eager to configure more advanced data flows and transformations, App connect presents a big range of refined integration capabilities. as an example, the API move editor allows for extra experienced integration specialists to define bespoke integrations, where statistics can also be routed from one source to a different, and changed and enriched en route.

    The versatile 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 join mission offered a thrilling possibility to re-think about the integration journey for brand spanking new clients, regularly working in new enterprise contexts. We firmly believe within the a must-have position of person research in achieving a superb product design, so we invested closely in conducting thorough person analysis into this certain enterprise area. because the task progressed, we persevered to follow the IBM Design pondering framework and contain sponsor users to support us verify our hypotheses, and to benefit valuable feedback on our designs.

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


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    How Slack’s Content Strategy Expanded from a Single 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 make a sound?” This analogy applies to the ideas we so painstakingly put together. If nobody can access the words conveying ideas, does it make 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 like these are the ones that competitors like Slack must identify and exploit, in order to compete and win.

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

    1. If It’s Important Enough, Say It More than Once

    People read through skimming. Saying the most important 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 careful 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 important features — “Channels,” “Integration,” and “Search” — above the fold.

    Each of these features have their own throughlines, but “Integration” is particularly important 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 use into Slack and get all your notifications in one place

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

    Over 80 integrationsIt’s not ‘yet another app’ you have to use. Slack works with all the tools you already use; set them up to post notifications directly to Slack in a single work space.”

    Slack opens with a quantity, “80 integrations.” It’s visual and lets the reader know Slack’s not full of hot air. The first sentence anticipates the reader’s objection, and re-explains “integration” in different words, “notifications… in a single work space.” Now, let’s say 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 help you pull information from outside tools in a way that’s timely, relevant, and always searchable. No more switching between apps. Bring it all together in one simple place.

    Assuming the reader keeps scrolling, there’s no way they can miss this. It’s a featured blurb on its own, capturing the imagination 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 like an infinite brain for your whole team.”

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

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

    All your tools in one place.Connect all the tools you use to Slack and avoid all that constant switching between apps. Set up your integration so that you get all your notifications directly within Slack — from support 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 like “all in one place,” “notifications,” and “one central archive” come up. The most interesting thing is that in the final point on the Tour page (“All your tools in one place”), this point also ties into the “Search” throughline.

    Repetition ensures careless readers leave with an impression things you want to tell them. It also reinforces these important ideas to more careful 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 interesting 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 also into content that conveys larger ideas, tying in stories with their brand. The adage goes, “Every company is a media company.”

    Some companies, like Peloton and Tonal, take this to heart, integrating media with their product’s soul. Slack ultimately sells “organizational transformation,” so it invests heavily in media to support 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 development resources.
  • Engineering blog: Several People Are Coding is still 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 Work is a job board, also hosted at WordPress, where Slack customers can post their jobs.
  • Store: The Slack Shop is a Shopify store that sells Slack merch, like branded socks and stickers. It’s also a branding and corporate responsibility play, donating proceeds from the store to a new charity every month.
  • Twitter: @slackhq is where much of their support and brand interactions take 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 way of building trust through its brand and eventually driving sales. For example, Buffer’s hypothesis is transparency builds trust faster than all other methods, so they built a transparency dashboard. Drift’s hypothesis goes a step further, in saying authenticity builds trust faster, so they build their own reality TV show.

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

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

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

    Slack hires experts like Pacific Content to create their podcast. They also hire illustrators to create unique art accompanying each blog post. They reach 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, all this content accelerates people’s trust 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 be exported from WordPress). They’ve also 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 also the beginning of each initiative — only the Slack at Work site required dev resources (to set up WordPress to be a job board). Their blog started at Tumblr, then moved to Medium. Their support 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 Love 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 suggest that they set up a mailing list. This is typically the anchor of all owned content properties, because email lists can be downloaded and stored. However, since this is Slack and their whole goal is to kill email, they’d be better served building a large community of Slack users (e.g., friends.slack.com).

    All said, Slack’s content has an incredible amount of integrity and taste. I love it. If you told me there was a Slack magazine, to me it would look like Airbnb’s Pineapple (and not their dreadful new one…) This admiration, and respect, is a difficult “feeling” to replicate in an advertisement. Blended in with trust, these feelings might just be 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 seem 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 old screenshot from Openview, highlighting Similarweb stats from a year ago, reports slackhq.com drives 2% of referral traffic:

    My guess is Slack didn’t care 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, all feed user acquisition and conversion much better than content did…

    But their content makes Slack most favorable and trustworthy than their current competitors (Microsoft, IBM, etc.). And, their content ushers in a new way 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 single 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 repeat the important things, they told human stories, and they invested heavily in distributing their content through their “media company” arm. But if you’ve had a look at their sites, you’ll realize there’s so much more I could cover.

    At the end of the day, Slack focuses on humans. They speak 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 like a magazine they combine beautiful 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 tool is useless without an understanding of its possibilities or how to actually use it.

    Metaphors run rampant in content strategy, partially because of the creative nature of the workers, and partially because it’s such a new 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 figure 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 hard numbers to make decisions and align their teams toward tangible goals. More efficiency, faster production, lower costs — all of these are within reach 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 standard for separate databases) repository for all 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 all types.

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

    A properly deployed data warehouse makes it possible to convert data from many different sources into a unified format. Once in the data warehouse, in the standard format, all 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 still providing analysts all 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 same format) analysts can perform more comprehensive, 360 degree reviews. You get both deeper and broader analysis on all the trends happening throughout your company.

    3. Better Organizational Alignment

    With better 360 degree insights and different teams (sales, marketing, operations, support, etc.) all using the same repository for reporting, each department can better align with the others. All the key stakeholders can see what’s going on throughout the organization and stay on the same page. The practical implications extend beyond analysis and directly into day-to-day operations: the left hand can always know what the right hand is doing and take 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 large 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 help eliminate API management headaches. With data warehouses, there’s no need to juggle or manage separate APIs to access data in every single 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 use transactional replication, which copies only new entries in the data source to the data warehouse, so your warehouse stays up to date without a massive stream of new data eating up bandwidth.

    6. Skip the API Limitations

    Another benefit of using data replication is that you circumvent costly, complex API limitations. This expands your general data access by allowing you to dodge the problems associated with live querying of certain APIs that are either slow or limit 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 use to denominate storage needs. With spacious petabytes of data at your disposal, you have all the elbow room you need to track historical data, giving your organization the ability to analyze different time periods and trends in order to make future predictions. This is crucial because vast reams of historical data just can’t be stored in a transactional database or used to generate reports from a transactional system — databases get 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 range of companies studied, big and small, across industries. The average was even higher, at 400 percent ROI. The reason? Data warehouses help organizations massively cut 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 period at just over 1.5 years.

    9. Proven and De-Risked

    Enterprise data warehouses have been around forever. The military command-and-control, intelligence, manufacturing, banking, finance and retail industries have 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 deep 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, all with deep 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 all your data, you can make 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 standard now in the biggest data warehousing tools.

    How to Move Data into Data Warehouses

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


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

    Analytics Beyond Spreadsheets

    For many years, Microsoft Excel and other spreadsheets were the tools of choice for business professionals who were looking to visualize their data. But spreadsheets had their limits for many business intelligence (BI)-related tasks. Even today, trying to creating charts analyzing complex datasets in Excel can still be frustrating. Sometimes you start with the wrong kind of data, for example, or you may not know how to manipulate 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 need has arisen for effective tools that people of all skill levels can use to make sense of the wealth of information created by businesses every single day.

    Spreadsheets also fall down when the data isn't well-structured or can't be sorted out in neat rows and columns. And, if you have millions of rows or very sparse matrices, then the data in a spreadsheet can be painful to enter and it can be hard to visualize your data. Spreadsheets also have 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 same spreadsheet.

    A spreadsheet containing up-to-the-minute data can also be a problem, particularly if you have exported graphics that need to be refreshed when the data changes. Finally, spreadsheets aren't good 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 make use of tables of numbers, they are really acting in different arenas with different purposes. A spreadsheet is first and foremost a way to store and display calculations. While some spreadsheets can create very sophisticated mathematical models, at their core it is all about the math more than the model itself.

    This is all a long-winded way of saying that when businesses use a spreadsheet, they are actively sabotaging themselves and their ability to consistently get valuable insights from their data. BI tools are specficially designed to help businesses better understand their data, and can prove to be a huge benefit to those upgrading from what a limited spreadsheet can do.

    What Is Business Intelligence?

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

    BI is an umbrella term meant to cover all of the activities necessary for a company to turn 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 existence and operations. The ultimate goal is being able to increase 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 contract 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 do 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 art 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 knowledge about the business, competitors, customers, industry, market, and suppliers, to name just a few possible metric targets. But as businesses grow and their information stores balloon, the capturing, storing, and organizing of information becomes too large and complex to be entirely handled by mere humans. Early efforts to do 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 certain operations or software buckets. This was the case unless IT took on the task of integrating various silos, typically through painstaking and highly manual processes.

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

    Self-Service BI and Data Democratization

    The BI tools that were popular half a decade or more ago required specialists, not just to use but also to interpret the resulting data and conclusions. That led to an often inconvenient and fallible filter between the people who really needed to get 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 business whose data they were analyzing. That led to a focus on data the company didn't need, a misinterpretation of results, and often a series of "standard" reporting that analysts would run on a scheduled basis instead of more ad hoc intelligence gathering and interpretation, which can be highly valuable in fast-moving situations.

    This problem has led to a growing new trend among new 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 be 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 tool arsenal, but IT no longer acts as a bottleneck to every query and report request.

    As a result, users can take advantage of this distributed BI model. Key tools and critical data have moved from a centralized and difficult-to-access architecture to a decentralized model that merely requires access credentials and familiarity with new BI software. This results in a whole new kind of analysis becoming available to the organization, namely, that of experienced, front-line business people who not only know what data they need but how they need to use it.

    The emerging crop of BI tools all work hard at developing front-end tools that are more intuitive and easier to use than those of older generations—with varying degrees of success. However, that means a key criteria in any BI tool purchasing decision will be to evaluate who in the organization should access such tools and whether the tool is appropriately designed for that audience. Most BI vendors indicate they're looking for their tool suites to become as ubiquitous and easy to use for business users as typical business collaboration tools or productivity suites, such as Microsoft Office. None have gotten quite that far yet in my estimation, but some are closer than others. To that end, these BI tool suites tend to focus on three core types of analytics: descriptive (what did happen), prescriptive (what should happen now), and predictive (what will happen later).

    What Is Data Visualization?

    In the context of BI software, data visualization is a fast and effective method of transferring information from a machine to a human brain. The idea is to place digital information into a visual context so that the analytic output can be quickly ingested by humans, often at a glance. If this sounds like 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 reach more granular information on one or more aspects incorporated in the bigger picture. For example, new values can be added that will change the visualization on the fly, or the visualization is actually built on rapidly changing data that can turn a static visual into an animation or a dashboard.

    The best visualizations do not seek artistic awards but instead are designed with function 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 look impressive at first glance but, if your audience needs help 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 also third-party and even free data visualization tools that can be 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 business analyst. But I also kept in mind the viewpoint of users who might have no familiarity with data processing or analytics. I loaded and used the same data sets and posed the same queries, evaluating results and the processes involved.

    My aim was to evaluate cloud versions alone, as I often do analysis on the fly or at least on a variety of machines, as do 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 example of this is Tableau Desktop, a favorite tool of Microsoft Excel users who simply have an affinity for the desktop tool (and who just move to the cloud long enough to share 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 tool over a web tool anyway." Again, I don't doubt Microsoft's claim but that does seem weird 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 tool preference is a bit more personal than fact-based, but to each his own.

    Then there's Google Analytics, a pure cloud player. The tool is designed to analyze 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 found them, simply to let buyers know they exist. IBM Watson Analytics is one tool with the ability to extend to highly advanced features and was also one of the easiest to use upfront. IBM Watson Analytics is well-suited for business analysts and for widespread data democratization because it requires little, if any, knowledge of data science. Instead, it works well by using natural language and keywords to form queries, a characteristic that can make it valuable to practically anyone. It's highly intuitive, very powerful, and easy to learn. Microsoft Power BI is a strong second as it, too, is powerful while also familiar, certainly to any of the millions of Microsoft business users. However, there are several other powerful and intuitive apps in this lineup from which to choose; they all have their own pros and cons. We'll be 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 handle streaming data. For many users, that won't be a problem in the immediate future. However, for those involved with analyzing business processes as they happen, such as website performance metrics or customer behavior patterns, streaming data can be invaluable. Also, the Internet of Things (IoT) will drive this issue in the near future and make streaming data and streaming analytics a must-have feature. Many of these tools will have to up their game accordingly so, unless you want to jump ship in a year or two, it's best to think ahead when considering BI and the IoT.

    BI and Big Data

    Another area in which self-service BI is taking off is in analyzing Big Data. This is a newer development 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 be 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 social media tools (to name a few) that have become so popular over the last several years.

    Not only do these tools generate loads of new data, they also often generate a new kind 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 kind of data is heavy on text (even free-form text) while also containing more easily defined data, such as dates or credit card numbers. Examples of apps that generate this kind of data include the customer behavior-tracking tools you use to see 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 social 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 new information at all. Traditional data warehouse tools generally weren't designed to either manage or query unstructured data. New data storage innovations such as data lakes are emerging to solve for this need, but organizations still 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 standard here is Hadoop, which is an open-source software framework that Apache specifically designed to query large data sets stored in a distributed fashion (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 all of your business data for maximum insight, then Hadoop is what you need.

    You can download and implement Hadoop itself to perform your queries, but it's typically easier and more effective to use 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, all support this. However, each requires varying levels of configuration or even add-on tools to do so—with IBM, Microsoft, and Tableau offering exceptionally deep capabilities. However, both IBM and Microsoft will still expect customers to utilize additional tools around aspects such as data governance to ensure optimal performance.

    Finding the Right BI Tool

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

    Getting the overall workflow down with these tools will take some study and discussion with the people you'll be 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 have 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 stay there.

    In recent years, companies have been taking advantage 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 important to make sure that your employees actually know how to use these BI platforms so that you can make sure your investment was worthwhile. There are many ways of approaching this, but using the right online learning platform might be a good place to start looking.

    Given the wide price range of these products, you should segment your analytics needs before you make 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 make it easy to get started. Plus, they tend to have large ecosystems of add-ons and partners that can be a cost-effective replacement for doing BI inside a spreadsheet. Tableau Desktop still 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 our roundup. However, all three products received our Editors' Choice award. Tableau Desktop may have a big price tag depending on which version you choose but, as previously mentioned, it has an exceptionally large and growing collection of visualizations plus a manageable learning curve if you're willing to devote some effort to it. Microsoft Power BI and Tableau Desktop also have large and growing collections of data connectors, and both Microsoft and Tableau have their own sizable communities of users that are vocal about their wants and needs. This can carry a lot of weight with the vendors' development teams so it's a good idea to spend some time looking through those community forums to get an idea where these companies are headed.

  • Pros: Extremely user-friendly. Fantastic automatic report generation. Impressive support availability.

    Cons: Automated reports can quickly become defaults. Steep learning curve that might confuse beginners.

    Bottom Line: Zoho Reports is a solid option for general business users who might not be knowledgeable in analytics software. It's also available at an attractive price.

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

    Cons: Unable to do real-time streaming analytics.

    Bottom Line: IBM Watson Analytics is an exceptional business 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 our Editors' Choice 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 our Editors' Choice honor for its impressive usability, top-notch data visualization capabilities, and superior compatibility with other Microsoft Office products.

    Read Review
  • Pros: Enormous collection of data connectors and visualizations. User-friendly design. Impressive processing engine. Mature product with a large 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 be too difficult for self-service business intelligence (BI). Analytics process still needs to be ironed out. Natural language capability can be limited.

    Bottom Line: Sisense is a complete platform that should be popular for experienced BI users. It may fall short for beginners, however.

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

    Cons: User interface is not intuitive. Steep learning curve. Unwelcoming to new analysts.

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

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

    Cons: Customer support has way too much automation. Focus on marketing and advertising can be 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 steep learning curve but it is an awesome business intelligence tool.

    Read Review
  • Pros: Designed with general business users in mind. Solid return on investment.

    Cons: The data you can use 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 make it an attractive offering.

    Read Review
  • Pros: Real-time analytics for Internet of Things (IoT) and streaming data features. Massive ecosystem with plentiful extenders. Responsive pages make 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 business already uses SAP's HANA database platform or some of its other back-end business platforms, then SAP Analytics Cloud is a powerful, well-priced choice. But be warned that there's a steep learning curve and a noted dependence on other SAP products for full functionality.

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

    Cons: Poorly designed user interface. Steep learning curve.

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

    Read Review
  • Pros: Very deep SQL modeling ability. Uses Git for version management and collaboration.

    Cons: Very expensive. Not for small teams.

    Bottom Line: Looker is a great self-service business intelligence (BI) tool that can help unify SQL and Big Data management across your enterprise.

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

    Cons: Complex pricing is a deterrent.

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

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

    Cons: No free trial available. Training webinars can be costly.

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

    Read Review
  • Pros: Very easy to get started. Nice team management and collaboration features.

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

    Bottom Line: While Tibco is still making the transition from a desktop to a cloud software vendor, its self-service business intelligence (BI) tool Tibco Spotfire is a great way to start visualizing your Excel data.

    Read Review
  • Pros: Excellent analytical support for Intuit QuickBooks. Very easy setup.

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

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

    Read Review


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    IBM M2180-759 Exam (IBM Connectivity and Integration Sales Mastery v1) Detailed Information



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