Popular Alternatives to Power BI for Office 365 for Mac. Explore 20 Mac apps like Power BI for Office 365, all suggested and ranked by the AlternativeTo user community. Windward AutoTag is a free-form report design tool for creating exactly the report layout you want. This important component of the Windward reporting solution lets you. Here is a roundup of some of the best self-serve analytics and BI platforms out there for enterprise customers. These tools are designed for a variety of users, from C-level executives to social media managers and data scientists, and put simplicity at the core of their product.
Analytics Beyond Spreadsheets
If you're still relying on Microsoft Excel as your primary parser for corporate data, then you're officially well behind the curve. Today's organizations are being flooded with new data from all directions and executives are expected to make smarter decisions with that information. Spreadsheets, however, are blunt and insufficient to deal with this kind of volume, which is why there's a new crop of business intelligence (BI) tools to fill the gap. These tools combine all of the sophisticated data hooks on the back end with a new style of front end that combines ease of use with things such as natural language querying to make using BI accessible to anyone. These tools also provide new data visualization capabilities that let you turn your insights into clear and easily parsed graphics to help co-workers understand your discoveries.
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.
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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.
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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. For example, Microsoft Power BI 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. 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, 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 Microsoft and and Tableau offering exceptionally deep capabilities. However, Microsoft will still expects 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 Microsoft Power BI is catching up fast.
Microsoft Power BI and Tableau Desktop both scored the highest in our roundup, and both 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.
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Featured Self-Service Business Intelligence Reviews:
Zoho Reports Review
MSRP: $25.00Pros: Decent price. Quick, simple automatic report generation. Easy-to-follow interface.Cons: Frustrating reporting features. Steep learning curve.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 ReviewMicrosoft Power BI Review
MSRP: $0.00Pros: 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 ReviewTableau Desktop Review
MSRP: $70.00Pros: 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 ReviewSisense Review
MSRP:Pros: Solid natural language query in third-party applications. In-chip processing resolves bottlenecks.Cons: Perhaps a bit complex for a self-service business intelligence (BI) tool. Analytics process needs work. Natural language features have limitations.Bottom Line: Sisense will easily appeal to seasoned BI users with its comprehensive features, but it may frustrate novice users.Read ReviewDomo Review
MSRP: $2000.00Pros: 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 ReviewGoogle Analytics Review
MSRP: $0.00Pros: 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 ReviewSalesforce Einstein Analytics Platform Review
MSRP: $75.00Pros: 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 ReviewSAP Analytics Cloud Review
MSRP: $21.00Pros: 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 ReviewChartio Review
MSRP: $400.00Pros: 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 ReviewLooker Review
MSRP: $3000.00Pros: 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 ReviewQlik Sense Enterprise Server Review
MSRP: $1500.00Pros: 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 self-service business intelligence (BI) tool WebFocus nevertheless has some powerful analysis features.Read ReviewTibco Spotfire Review
MSRP: $650.00Pros: 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 ReviewClearify QQube Review
MSRP: $425.00Pros: 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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- ProsExtremely powerful platform with a wealth of data source connectors. Very user-friendly. Exceptional data visualization capabilities.
- ConsDesktop and web versions divide data prep tools. Refresh cycle is limited on free version.
- Bottom LineMicrosoft Power BI earns our Editors' Choice honor for its impressive usability, top-notch data visualization capabilities, and superior compatibility with other Microsoft Office products.
Microsoft Power BI is a prime example of Redmond's stellar offerings in the self-service business intelligence (BI) space. When a platform is this strong, however, the product must match expectations, and Microsoft Power BI does exactly that. Microsoft Power BI does a fantastic job of combining power analytics with a user-friendly user interface (UI) and remarkable data visualization capabilities. Customers have the choice between a limited free version or the Professional version (which begins at $9.99 per user per month). The free service is designed for individual users and offers just 1 gigabyte (GB) of storage along with daily refresh cycles. Enterprises will want to go with the Professional version, which has more data storage (10 GB), faster data fresh cycles (hourly), streaming data consumption (1 million rows per hour compared to the 10,000 rows per hour offered in the free tier), and collaboration features. It's one of the best BI tools on the market and is one of our three Editors' Choice recipients, along with IBM Watson Analytics and Tableau Desktop.
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Today, Microsoft Power BI has 74 data connectors and it's adding more at a regular clip, which is a standout number even when compared to the more mature competition. Data connectors let you point your BI tool at a particular app or data set and incorporate that data into your queries. So, for example, Microsoft Power BI includes a connector for MailChimp, which is an email marketing app, as well as a connector for Salesforce. Enabling both of those means you would be able to form singular queries aimed at each app or more complex queries that would source data from both apps simultaneously. Microsoft does a good job of making such queries easy to do even for general users.
Even with this focus on ease of use, I felt it was a bit weird that I needed to use the desktop tool for data prep and then move to the web user UI to publish. This might be good for data scientists who want to work with truly humongous data sets directly on the Microsoft Power BI service, but business analysts and everyday users are going to want to do all of the data prep work in the desktop tool. If you opt to use Microsoft Power BI, then go ahead and set up both the desktop and the web service.
The folks at Microsoft tell me that, at least for now, most users prefer the desktop software over the web tool anyway. Tableau told me that same story. According to both vendors, most business analysts find it simpler and faster to use a desktop tool when most of the data they're working with is local. Then again, that's coming from two vendors whose traditional model has long been desktop-installed software. Look at long-time web-oriented tools such as Google Analytics, for example, and your perspective may change. Personally, I don't like downloading a bunch of stuff (data or desktop tools) to my devices and prefer robust cloud tools. I think many business analysts will feel the same way, especially in the near future. In any case, while individual preferences vary, there's no doubt that the current desktop tool is more robust for data prep work.
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In July, Microsoft announced new capabilities for Microsoft Power BI, including the ability to integrate Big Data directly in the Power BI web service.Users can now share data across Power BI models, dashboard and boards. This capability ensures that data can be easily reused. In addition, Microsoft said that it would unify access to data between Power BI and Azure Data Lake Storage Gen2, a data lake that lets enterprises run large-scale analytics workloads in the cloud.
The UI is highly intuitive and will be familiar to Microsoft users. That makes sense considering Microsoft Power BI was originally an add-on to other Microsoft products. The familiarity and otherwise intuitive design means the learning curve is very short, even for beginning business analysts.
Getting Started
Loading data is a simple affair. I downloaded CSV files from a Dropbox account (the same test data I used to review all of the products in this BI category), and then simply selected the CSV/text connector under the Data Source button. A screen came up showing a Preview of the data and an Edit button showed up, too, in case I needed to clean up my data before doing the analysis. Then, it's just a matter of clicking the Load button and the data is ready to go.
Data files show on the right-hand side of the screen, and anything I want to do from there is largely a matter of drag and drop, or, alternately, of checking boxes. However, because Microsoft has several development teams working with Microsoft Power BI, there are also several additional modules you might consider. For example, there's the Power BI Gateway - Personal, which is an add-on that provides secure data transfer between the cloud-based Microsoft Power BI service and any on-premises data you might be using. Users interested in data visualization will want to check out Power BI Publish to web . Using Publish to web , you can publish reports and visualizations created in Microsoft Power BI to the web and related targets like emails or social media posts. A critical thing to remember here—and Microsoft emphasizes this—is that Publish to web means exactly that: the World Wide Web, not some private HTTP server you've got sitting behind your firewall. Visualizations uploaded via Publish to web are public and can be viewed by anyone. That might throw you if you're primarily involved with private data like your company's financial data, for example. But for many users, like journalists or professional content developers, it's a great way to get additional exposure for your work. Publish to web is a free download available to registered Microsoft Power BI users of either the desktop or online versions.
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The Discovery Process
Users can select data on the right-hand side of the screen, which will then show the fields within. Then fields can be selected by checking the box by them or by dragging them to the panel. Then, choose a visualization and, in seconds, the output is on the screen. Click an empty space on the dashboard to create another visualization of other insights by following the same process and choosing different fields. Save the dashboard when you're done, and click publish to the web to share it. The simplicity of the UI and its overall presentation is elegant and belies the complexity of the analytics engine underneath.
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Complex queries can be performed, too, as can joining data sets and other tasks highly experienced data and business analysts are likely to do. Buttons to manage relationships and to produce solution templates ease and speed those tasks, too. I especially appreciated the template for tracking sentiment in Twitter data so I didn't have to build that from scratch. All told, it's a powerful and beautifully elegant tool that makes the work nearly effortless, especially when compared to more complex competitors, such as Chartio.
Data Visualizations
I consider Microsoft Power BI to be the king of visualizations. There is the standard fare of stock visualization formats from which to choose on the UI, but there's also a plethora of custom visualizations on the Office Store for free, many sourced from other customers using Publish to web (see above). To show off visualizations uploaded via Publish to web , Microsoft has created a gallery page, dubbed 'Power BI custom visuals.' Here you can search and browse a large and constantly growing gallery of custom data visualizations created by the Microsoft Power BI community. These are fully accessible for reuse as long as you follow Microsoft's community rules. I'm particularly partial to the 3D interactive map but maybe that's just me.
While it's important not to distract viewers from the message with convoluted visuals, giving information a fresh perspective can be extremely valuable and is a core part of the self-service BI value proposition: more people looking at more and different data in new ways. That's why I like having plenty of choices in visualizations. You're telling a data story visually, after all, and it's not always good to present it as if it's the same data story viewers saw last week, last month, or last year. For now at least, Microsoft simply can't be beat in visualizations—either in the simplicity in building them or in the selection of options. That makes it a superb choice both for experienced data analysts as well as newcomers, and well worth an Editors' Choice award in the self-service BI tools category.
Microsoft Power BI
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.
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