Embedded Analytics with Power BI and Azure

Business Intelligence (BI) has, by tradition, been a long and drawn out process. In the early days of computers, BI tools such as SAP/Crystal Reports and IBM/Cognos came along to speed things up. Both, however, need extensive development and IT involvement from a few knowledgeable people, to transform functional data into useful data. While there is a valid argument for enterprises to use traditional BI methods, particularly stronger reporting functionality, the total cost commitment is prohibitive.

More recently, real-time BI tools have started to appear. These tools gather and provide access to live data. Data – gathered through various means including analytics tools, surveys, customer issue reports and more – is continuously updated and stored in a data warehouse. By harnessing the power of the masses through the use of these intake methods, and integrating this data into a cloud-hosted data warehouse, the cost of real-time BI plummets.

Once a company decides that they are giving up the power to a few to the power of many, is when real-time BI offers many savings. Companies specializing in real-time BI are popping up, many with customized pricing models that suit business' output needs. So, will this end traditional BI? The answer isn't simple, but when a company decides that centralizing their data may not be cost-effective, then the power of real-time BI shines.

If a company decides to turn their data over to a larger group of users, they must understand that the results may not have the impact of a more traditional BI stance. The cause of this is that the data is constantly changing. Traditional BI is static, and you can gather a chunk of data for a period in the past and show your findings. Real-time BI, well that's happening now, and may not look as impactful as a fixed window of data.

If you're ready to take the plunge, a good start is Power BI and Microsoft Azure.

Power BI (http://www.powerbi.com) is one of the leading business intelligence tools on the market. With its ease of use and its powerful connection modeling, using the power of using Stream Analytics. Power BI can offer a unique transition to real-time BI.

Microsoft's Azure (http://www.azure.com) allows users to harness the power of Azure Stream Analytics (https://azure.microsoft.com/en-us/services/stream-analytics/) and incorporate this data into Power BI. This streamlines the development time to publish.

Stream Analytics and Power BI are an elegant combination of real-time data analysis and fluid data. With these two tools together, information entered within an organization is streamed from Azure Stream Analytics directly into a Power BI analysis. This low latency production environment is especially useful in time sets within an organization. Embedding the results into a company tool is the next step in real-time BI.

Embedding Power BI is not challenging. Though the user should be aware that it is only intended for external use. To use this service the user will have to consider the offerings of the Power BI Premium services. The user will also have to register an Active Azure Directory application and set up their Azure Active Directory Tenant. This is in addition to needing a Power BI Pro account.

There are two different ways to think of embedding with Power BI, organizationally or to a customer base. Each of these options is unique in pricing and in structure. Once the user has decided who their audience is, the embedding process is as simple as selecting where they want the analysis to go and which audience they want to view the data.

Integrating data via Azure into Power BI and embedding it within an organizational mainframe hits the sweet-spot for most companies looking to visualize their data. Azure offers cloud-based data warehousing, which increases the speed a company can take their internal/external data into the visualization phase and offers a unique strategic growth opportunity for a company. The combination of Azure and Power BI’s ability to render interactive designs using REST APIs’ and SDK, free up time for the designer, so they don’t need to redesign each output.

Azure SQL has the built-in security measures and advanced tier firewall processes that go far beyond many companies current structure. With this service a company now can secure their data in the cloud and can expand into petabytes system data storage off-site, thus lowering their overall reliance on on-site costs.

Azure HDInsight and Power BI are complimentary as an enterprise solution. Enterprise data warehouses can be complex, with the various datasets housed within many server locations. By combining the Azure HDInsight’s 99.9% SLA and Power BI, an enterprise can feel confident they have a BI solution that encompasses their entire business model.

The combination of using Azure and Power BI harnesses the full power of creating stunning real-time dashboards. Using the Power BI Cloud and Azure Streamline Analytics together decreases the time that the development process takes compared to other BI models. The complimentary tools, their incredible embedding processes, and connectivity makes this combination a robust BI solution.

Getting started with both Power BI, and Azure requires the creation of a Microsoft account. To do this, log in to Azure (https://www.azure.com/) and click on the "Start Free" button. Follow the prompts to sign up for an account, you will need to provide a credit card for ID and age verification. You will need this account to sign up for Power BI as well.

To sign up for a Power BI account, access the Power BI website (https://www.powerbi.com/) and click on the "Start Free" button. You will need to provide an email ID associated with a Microsoft account. This would be the same email you provided when signing up for Azure above.

Using the accounts together starts with loading the user’s data, either from their servers, files, or possible off-premise data servers. Then, Azure builds, manages, and deploys datasets through Microsoft datacenters in the cloud. There are numerous options with Azure to analyze, store, and monitor data within its robust functionality. As a BI choice, these solutions paired together with a cloud-based backbone, offer a fast solution to analyze data quickly.

While all of this is great for BI needs, there’s one small catch: Azure and Power BI don’t self-optimize. Panoply, and AWS Redshift do.

What does this mean?

It means that Power BI and Azure still require some hand-holding from an experienced Business Analyst, and a small IT staff to get your data loaded, analyzed and then optimized to efficiently process your data, and then render it out to your audience, be they internal or external to your company.

Panoply (https://www.panoply.io) and AWS Redshift, allow you to take in your data through a variety of pre-built means, and even new undeveloped means. Our library of supported Data Sources is already large, with over 50 supported data sources, and is always expanding. The best part, you can roll your own interface using our SDKs for NodeJS, Ruby, and Python, or you can request that we build out a completely fresh data source for you, and we'll do it as part of your service.

The advantage? Reducing your needed IT staff to intake and support your data.

After we have your data, our algorithms will start evaluating and optimizing your data. With each fresh batch that comes in new optimizations are put into place to make accessing your data quicker and easier than ever.

Finally, connecting Panoply to a dashboard tool, such as Tableu, allows you to create visualizations that are suitable for internal or external consumption, with output from your data in real-time.

The advantage? Reducing the workload on your Business Analyst that just had their third cup of coffee trying to stay awake looking at your data.


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