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Vendor : IBM
Exam Code : M2180-759
Exam Name : IBM Connectivity and Integration Sales Mastery v1
Questions and Answers : 36 Q & A
Updated On : April 25, 2019
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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

IBM and Salesforce enhance Partnership | killexams.com Real Questions and Pass4sure dumps

some of the international leaders in CRM - Salesforce and IBM lately announced an additional expansion of their partnership: one that brings together IBM Cloud and Watson capabilities with Salesforce Quip and Salesforce provider Cloud Einstein. this could permit users (corporations) to have superior connect with their patrons as well a deeper collaboration teamed with prosperous insights.

Salesforce named IBM a favored cloud features company and IBM has named Salesforce its favorite consumer engagement platform for earnings and service with this enlargement.

Ginni Rometty, chairman, president, and CEO, IBM turned into quoted in a press release, “Naming IBM as a Salesforce favored cloud features company demonstrates the energy of the IBM Cloud to assist companies essentially exchange the manner they do enterprise, this extended partnership builds on the combined vigour of Watson and Einstein to aid corporations make smarter company decisions.”

Marc Benioff, chairman and CEO, Salesforce talked about, “The success of our clients drives everything we do at Salesforce, including our strategic partnership with IBM, the mixture of IBM Cloud and Watson functions with Salesforce Einstein and Quip will deliver even more innovation to empower businesses to connect with their clients in an entire new approach, leveraging the vigour of the cloud and AI.”

release the 3 the reason why B2B entrepreneurs should still use Predictive Analytics in 2018, supply your business the energy of insights.

This strategic partnership will compile the energy of IBM Watson and repair Cloud Einstein to convey a brand new latitude of AI-pushed ideas. With AI pushed predictive analytics, agencies can be capable of create a greater personalised verbal exchange to build better connections with consumers.


IBM marries on-premises, inner most and public-cloud facts | killexams.com Real Questions and Pass4sure dumps

Cloud

(photograph: Stockfresh)

Cloud Integration Platform allows shoppers to improved combine statistics securely no be counted where it resides in on-premises, private, hybrid or public cloud.

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read greater: cloud computing hybrid cloud IBM deepest cloud public cloud

IBM has taken the wraps off of a new multi-cloud integration platform it hopes will help purchasers manipulate, comfy and combine records no matter the place it resides – on-premises, deepest-cloud or public-cloud functions.

business consumers are confronted with the daunting assignment of bridging legacy functions with newest cloud carrier, and many can not simply carry and shift, referred to Juan Carlos Soto, IBM vp of hybrid cloud integration. On precise of that many corporations are already making an attempt to manipulate five or greater cloud environments, commonly from numerous companies, and that they can't keep up, he noted.

IBM’s Institute for enterprise cost estimates that by using 2021, 98% of firms plan to adopt hybrid architectures, however simply 38% may have the tactics and equipment they deserve to operate that environment.

notably the ICP runs on huge Blue’s IBM Cloud private ambiance that can control Kubernetes and other containers. The ICP contains a number of existing IBM choices that encompass here.

IBM MQ is software that simplifies and speeds up the mixing of applications and business information across distinctive structures. It makes use of message queues to permit the exchanges of tips and presents a single messaging equipment for cloud, mobile, internet of issues (IoT) and on-premises environments. IBM says with the aid of connecting just about every thing from an easy pair of applications to probably the most complicated company environments, MQ helps businesses increase company responsiveness, manage costs, cut back risk and profit real-time consciousness from cell, IoT and sensor facts.

enterprise-gradeEnterprise-grade Kafka: above all IBM experience Streams is a platform developed on the general open supply Apache Kafka large facts know-how and a key open-supply know-how that underpins responsive and clever purposes. experience Streams helps firms center of attention on writing functions rather than deploying middleware. It permits purchasers to set up mission-crucial workloads and helps them join new functions to latest infrastructure, IBM says.

The platform also comprises aid for integrating siloed application on premises and in the cloud with support for key APIs similar to rest, IBM observed.

Coding timeThe conception is that with all the integration components of the platform purchasers can cut coding time and costs by means of reusing property they are regular with, Soto mentioned.

offering a multi-cloud, portable hybrid integration answer is critical for IBM in a few approaches, said Cassandra Mooshian, a senior analyst with expertise enterprise research. It greatly reduces the belief of supplier or platform lock-in, which on earth of hybrid it is fascinating, Mooshian mentioned.

“It underscores that IBM is inclined to play in a multivendor world (rather than promoting IBM IaaS as the know-how underpinning ICP and solutions atop it), it may well support carry IBM to the table more frequently in business and midmarket establishments now that it's ‘taking part in pleasant’ extra commonly with peers, and it addresses a primary pain aspect that IT departments are facing, linking on-prem apps and information to cloud apps and data such that strategies can become extra efficient and valued clientele can get essentially the most company value,” Mooshian mentioned.

The Cloud Integration Platform turned into just one of a couple of IBM cloud announcements that emanated from its think 2019 gathering this week in San Francisco.  Others protected:

Watson AI for all: beforehand IBM’s Watson ran handiest on huge Blue’s Cloud inner most device however that modified this week. The enterprise announced that customers operating Amazon, Google, Microsoft and other cloud capabilities might be capable of take skills of Watson’s intelligence by being capable of run IBM Watson services, together with Watson Assistant and Watson OpenScale.  IBM mentioned with Watson, customers can be capable of follow AI to data wherever it's hosted and with its set of applications, building equipment, laptop-studying fashions and administration capabilities. Watson can assist enterprises mine facts, predict outcomes and automate time- and useful resource-delicate procedures. corporations can also advance conversational services such as digital customer-service brokers, IBM referred to.

Hyper-safety: built on IBM LinuxONE know-how, which points pervasive encryption support, IBM Cloud Hyper give protection to capabilities is geared toward securing large Blue’s public-cloud offering. The service will give encryption-key management with a committed cloud hardware security module (HSM) constructed on FIPS a hundred and forty-2 stage 4-based mostly expertise.

Cloud at your service: IBM rolled out a service valued clientele can seek advice from to build hybrid-cloud environments. IBM capabilities is organising dedicated teams of consultants certified within the newest functions and technologies across dissimilar cloud platforms.

Multi-cloud management: The enterprise has introduced IBM capabilities for Multicloud management to deliver a single equipment to support agencies control IT materials across dissimilar cloud suppliers, on-premises environments and personal clouds. The provider lets valued clientele tune fees and monitor provider degrees among different initiatives.

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study more: cloud computing hybrid cloud IBM private cloud public cloud

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IBM Counts On Its Cloud And Platform For The Turnaround | killexams.com Real Questions and Pass4sure dumps

No effect found, are attempting new keyword!It launched the IBM cloud integration platform that gives a standard technique to integrate services and applications across multiple cloud environments. IBM's cloud business is also expected to get ...

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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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