Should You Use Power BI and Azure Analysis Services?

Power BI and Azure Analysis Services are dynamic visualization and modeling tools that can help you generate key insights for your business. But are they the best systems for the job? In this guide, we’ll discuss the pros and cons of using these two systems in tandem, and offer essential tips on choosing the best data analysis tools for your business.

Power BI and Azure Analysis Services

What is Azure Analysis Services? 

Azure Analysis Services is a platform as a service (PaaS) offered by Microsoft. You can use it to generate and manage data models in the cloud. At its core, the service provides a semantic layer for your organization: It takes complex sets of data from a server or other storage source and breaks it down into models that are easy for a layperson to understand.

The service works by connecting to a data source, usually the SQL Server, a Microsoft-operated data management system. From here, it creates custom models based on what you would like to test or predict. Microsoft offers three different tiers of Azure Analysis Services that each connect with a different SQL Server and have a different uses for your business:

  • Developer tier: This connects with the SQL Server Developer edition. You can use it to develop basic test models. However, you’re limited by low processing power and reduced Query Processing Units (QPUs)—a metric for measuring current compute load capacity. You’re also only alloted three gigabytes of memory.
  • Basic tier: This connects with the SQL Server Standard edition. Use it to create robust tabular models, ideal for production-focused data analysis. Within this tier, you can choose between a B1 or B2 service. The first offers 10 Gb of memory and the second offers 20 Gb of memory. Besides the memory limits, it also doesn’t support multiple partitions or perspectives.
  • Standard tier: This is used for advanced data modeling, and connects with the SQL Server Enterprise edition. It can handle flexible data models, real time updates, and multiple users. This tier includes six subtiers that vary in cost and complexity. The higher the sub-tier is, the faster the processing power and larger the memory limit. The highest sub-tier, S9, supports 400 Gb of memory.

All of these tiers can also connect with Microsoft’s Power BI, a powerful data visualization tool. To generate the most detailed and user-friendly reports, you should use Power BI and Azure Analysis Services together.

Connecting Power BI to Azure Analysis Services 

When you connect Power BI with Azure Analysis Services, you’ll generate engaging visuals from Azure’s complex data models. There are four basic steps to this process:

  1. Create a new model in the Azure Analysis Services platform;
  2. Sign into your Power BI desktop account;
  3. Add the name of your Azure server in the Power BI desktop settings; and
  4. Start generating new visuals based on the connected Azure Analysis Services model directly from the Power BI desktop app.

The combined services let you generate powerful visuals and share them with others quickly, without showing them the internal structure of the original data model.

Data models generated from Azure Analysis Services are both detailed and easy to understand. However, they’re not as visually-appealing or elegant as Power BI’s custom visuals. Your IT team or managers can use Azure Analysis Services to organize the data and generate accurate models. When you’re ready to share your results with your team or support important business decisions in a meeting with shareholders, you’ll use Power BI to break the model down into visual pieces that are even easier to digest.

Although Power BI and Azure Analysis Services help you find out precisely what your data means, the systems also have a few limitations.

The Cost of Power BI and Azure Analysis Services  

The main problem with using both Power BI and Azure Analysis Services is cost. While Power BI can be free, the Pro and Premium versions cost an additional $9.99 per user per month and $4,995 per resource per month, respectively.

As for Azure Analysis Services, pricing is assessed by the hour. Generally, you’ll pay an estimated: 

  • $96 per month for the Developer tier;
  • Between $313 and $627 per month for the Basic tier; and
  • Between $591 to $5,920 per month for the Standard tier.

Your precise costs depend on which sub-tier you purchase, how often you use the system, and where you are located. 

If you also sign up for a SQL Server license, your monthly costs may increase even more. Developer and Express SQL Servers are both free. However, you can only use them for testing and development. If you want full data intelligence capabilities, including data governance and greater cloud storage security, then you have to buy an Enterprise or Standard SQL Server license. 

To do so, you must license the server and buy a separate Client Access License (CAL) for every individual user. The Standard server license costs $931 and each CAL costs $209. Or, you can license processor cores, with a minimum of four cores per server. Just one Enterprise processor core costs $7,128. One Standard core costs $1,859. 

It all adds up quickly, particularly if you’re planning on buying Power BI Pro or Premium along with Standard Azure Analysis Services and an Enterprise SQL Server license. While this would give you full control over your data and let you generate powerful insights, it’s not the most cost-effective choice for many businesses.

Should You Use Power BI and Azure Analysis Services?

Using Power BI and Azure Analysis Services is the right choice for businesses that can afford the upper-tier licenses or only need to use the low-cost versions of each tool (e.g. the Developer tier of Azure Analysis Services or Power BI Pro) for just a handful of users. It may be out of the price range for smaller businesses or those that require advanced data analysis and increased data security. 

You can get all of the same advanced data analytics by hiring a third-party data analysis provider instead. These data analytics firms provide:

  • Data modeling, including support for tabular models and real time updates;
  • Detailed and beautiful visualizations that connect to these data models; 
  • A user-friendly portal from which you can generate models, visuals, and reports; 
  • Cloud storage and computing from an off-premise server; and
  • Reliable data security, including encryption and user authorizations.

All of this comes at a comparably lower cost, depending on the vendor you choose and your organization’s specific needs. You also won’t have to buy core processors or manage any servers yourself.

Another benefit of hiring a third-party firm is that the process is less complicated and time-consuming compared with using Power BI and Azure Analysis Services in tandem. A third-party firm saves you from having to find the right system for your needs. Sifting through the various versions and tiers of Power BI, Azure Analysis Services, and the SQL Server is a challenge for many business owners, especially those without in-depth data science knowledge. Letting specialists do the job means no one needs to learn all of these separate systems or connect them manually.

When you hire a data analytics firm, you simply tell them what you want to learn from your data and their team of data scientists will create the right models, visuals, and dashboards for your needs. They’ll also help you migrate your data storage system to the cloud safely and seamlessly. With their help, you’ll create insightful data models and compelling visuals at the touch of a button.

To create the most accurate data models and visuals, contact Tek Leaders. We provide our clients with all of the tools they need to gain valuable insights from their data, including advanced data analytics tools, predictive modeling, visualizations, reports, data governance, secure storage, and more. If you have more questions about how our services compare with Power BI and Azure Analysis Services, you can reach us by email directly.

Author: Shashank Reddy Tummala.

Shashank is the COO of Tek Leaders inc.He helps SMB’s to achieve their goals in their journey of Digital Transformation.

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Dashboard Data Visualization Infographic: An Example

What makes a data visualization dashboard effective?

They’re not the easiest tools to design from scratch. They need to display information and insights in the clearest way possible, and be interactive so that users can filter data and generate new insights. With so many factors to consider, it’s easy to get it wrong. 

To help, we’ve made a series of detailed dashboard data visualization infographic examples to help you create the most efficient and user-friendly dashboards for your data. Using this guide, you’ll know precisely what goes into creating a custom dashboard for your organization’s needs.

What Does an Effective Dashboard Database Visualization Look Like?  

To make a custom dashboard, you should first consider the role that dashboards play in your organization. A dashboard is an information hub that collects and displays data and visuals in one place, either on a desktop computer or a mobile device. Like other types of web and mobile applications, users can interact with the dashboard to display different types of information. For example, you can toggle between data filters to analyze the data from multiple angles

The most effective dashboards look simple, but display very detailed information. They focus on just one area of business like mortgage production reportscommercial loan reports, or sales performance by product. 

Narrowing the scope of your data visualization dashboard allows you to zero in on the insights that matter. It also makes the dashboard easier to read and use. Most organizations create multiple dashboards for every aspect of their businesses to keep their data organized and track vital metrics.

To create an attractive, informative interactive display, take a look at a few examples of effective dashboard data visualization infographics.

Examples of Dashboard Data Visualization Infographics 

In the dashboard data visualization infographic below, you will see an example of an effective design layout that displays all of the information you need to identify patterns in a given dataset.

This particular dashboard focuses on mortgage loans as they relate to various demographics. Banks and lenders that use this dashboard can quickly see median loan amounts by demographic and more detailed breakdowns of loans by each dataset. This helps lenders serve their customers better and verify that they are following Home Mortgage Disclosure Act (HMDA) guidelines. 

What makes this dashboard effective is that it follows three simple layout rules. 

#1 The Main Visual: In the top left corner of the dashboard, we created a series of bar graphs that represent median loan amounts by demographic. Even if users only glance at this visual, they will understand the overall pattern that the entire dashboard is designed to track. This visual aid works as a summary of information. When you design your own dashboard, follow this basic layout: Put the most important and informative visual at the top of the page, preferably in the top left, since that’s where users usually look first. 

#2 Filters and Interactive Components: Place data filters near or at the top of the dashboard alongside the main visual. Having these options at the top of the page is important for two reasons. First, users can immediately see that they have some control over the information being displayed. It encourages them to interact with the data. Second, it makes it easier to see how each filter affects the main visual and all of the other visuals underneath it. 

#3 Supporting Visuals: Place all supporting visuals underneath the data filter controls and the main visual. Supporting visuals are any charts or graphs that show detailed information about a specific aspect of the data. For example, in the dashboard data visualization infographic above, we created visuals for: 

  • The most common reasons why loans were denied. 
  • The number of applications received in each location. 
  • The number of applications received per race. 
  • The median income of loan applicants by county.

None of these visuals are comprehensive enough to be the main visual at the top of the page. They only show you part of the bigger picture. However, having them in the dashboard is still important because you can use them to generate insights, such as why certain demographics receive more mortgage approvals or denials than others. You can then improve your business products and services in response to this data. 

For example, you can see that Tarrant County (the green bar in the center visual) receives a higher number of applicants than average. Using this information, you may look into why so many applicants live in this county and what you can do to increase application rates elsewhere. 

Every dashboard is different. The specific visual aids that you choose for your dashboard depend on a number of factors including your business strategy and the type of data you collect. Experienced data scientists and dashboard designers can help you identify the best visuals to use. 

How to Create Dashboard Data Visualizations

The main challenge of creating a dashboard is selecting the right visuals for each dataset. To do this, you must think carefully about your business strategy and the goals of the dashboard. 

In the dashboard data visualization infographic above, the visuals are designed to track every aspect of HDMA guidelines, including income, race, location, and reasons for application denials. Every visual has a specific role in this process.

  • Applications visual works best as a bar chart because users need to quickly see how many applications they’ve received from each county and whether this is below or above average (marked by the thin grey line).
  • Applications by race visual works best as a pie chart because this makes it easy to see which groups submit the highest and lowest percentage of total applications.
  • Loan denial reasons visual features boxes that vary in size and hue to signify how many applications were denied for each specific reason. In this case, you’ll see that debt-to-income ratios are the most common reasons for denial across all counties.
  • Median income by race visual is shown as a heat map graph, allowing you to see which groups have the highest and lowest income in each county.

Having more than one type of visual in the dashboard makes it more engaging and helps you sift through data more effectively. However, understanding how to display each dataset or metric involves some trial and error, especially if you’re not experienced with data analytics. 

This is where a knowledgeable dashboard designer can help. When you hire a data analytics firm to create custom dashboards and visuals for your business, you won’t have to guess which visual aids to use. These firms often already have basic templates that track metrics related to your industry that they adjust to fit your organization’s workflow and preferences.

They can also design a new dashboard from scratch. With experienced data scientists on staff, the firm will know exactly which types of visuals are most effective. They also have advanced data analytics and visualization tools. This is a major benefit for small businesses or those that have limited budgets, with access to the world’s best visuals at a much lower cost. Partnering with the best dashboard consultants will help you connect more strongly to your data and find solutions to your organization’s most pressing issues.

Contact Tek Leaders to design your own custom dashboard today. Our detailed and interactive dashboards harness the power of your insights and get immediate results. If you have more questions about our dashboard services, you can reach us by email directly.

Author: Shashank Reddy Tummala.

Shashank is the COO of Tek Leaders inc.He helps SMB’s to achieve their goals in their journey of Digital Transformation.

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