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The Future Of Enterprise Analytics Is Power BI And Azure

Updated: Aug 17, 2023

Many of Microsoft's services are built on Azure, but the company is increasingly giving Azure services to clients as a way to extend and customize their offerings.

Data is saved in Azure Data Lake when you use dataflows to extract, clean, and convert data before loading it into Power BI. You may also utilize it with Azure Databricks or Azure SQL Data Warehouse for analytics, which you can do through the Azure portal or interact with the Power BI Desktop software.

The AutoML tool from Azure Machine Learning automates machine learning in Power BI by looking at what you're trying to predict and what data you have available, then iterating through different machine-learning algorithms to see which one produces the most significant score. You can also use Azure Cognitive Services to analyze data in images and text or create and execute your machine-learning models.

Key Influencers, a built-in AI-powered visualization in Power BI, executes multiple statistical analyses on the data, such as logistical regression or classification, to extract the key factor linked with a specific outcome. You drag the components into the visualization you believe are essential, and Power BI ranks them. It keeps re-running the model as you add more factors you think are relevant, or drill down into a single area, to see if further data reveals anything new.

So, as an example, if you're looking to see which visitors return to your hotel, the Key Influencer might be their nation of origin. However, if you choose guests in a specific age range, the model will only run on that slice of data, with the Key Influencer being whether they ate in the hotel restaurant or received a spa treatment. When it comes to shipping delays, you may look at which division sent the item, what factory it originated from, and what location it was sent from to discover what has the biggest impact on what arrives on time and what arrives late.

Two new AI visualizations have been released. Distribution Change investigates what distinguishes one data distribution from another. The Decomposition Tree sends numerous queries to the Power BI model and then connects them to click on a measure in visualization to see what's behind it. Then, it keeps scrolling down to the different levels of data to comprehend it fully. That way, you can observe if the 500 purchases in one city are driven by a single client group or by a diverse set of customers who share a common interest.

All of this can be used to power Power BI's visualizations, dashboards, and natural-language Q&A features, as well as the new paginated reports that used to require SQL Server. When you use automated machine learning, for example, the forecast for each row includes information about what causes led to the prediction, so you might add the explanation in a report to explain where the figures came from and what factors appear to be involved.

Depending on whether you're a data scientist who wants to share your work with the rest of the company or an analyst who wants to apply machine learning but doesn't have the abilities to do so, Power BI provides several paths for you to take.

By extracting keywords, performing sentiment analysis, or determining what's in a photograph, data scientists can add steps to a dataflow to extract information from unstructured data such as photographs or text from tweets or reviews. Cognitive Services power this, but you can add picture and text analytics to the dataflow instead of writing code to access the API.

Power BI will add more of these functions as new Cognitive Services are released. The most recent developments include extracting text from photos, handwriting recognition, and entity recognition, which involves removing keywords and categorizing what they refer to. Suppose you're a hotel owner reading online reviews. In that case, entity recognition can tell you if a reviewer used the word "cycling" to describe a pleased client who stayed while on a cycling trip or an unhappy guest who complained about the air conditioning cycling on and off all night.

Suppose you create your machine-learning models in Azure Machine Learning and publish them as a web service. In that case, you can grant role-based access to them through the Azure portal, and they'll appear as models for Power BI analysts to utilize in the same way as Cognitive Services. If you want to analyze the photos in those hotel evaluations, you'll probably need to train a specialized image recognition model to recognize photos of hotel items. In a hotel review, photos of air conditioners, light bulbs, windows, and elevators are probably a bad omen. Yet, the standard image recognition model may not recognize these as crucial objects.

You can now upload your machine-learning models to Azure Machine Learning to manage or tune them further if you're building your machine-learning model and using Python and R to integrate it into Power BI, or if you're using AutoML in Power BI to have it discover which machine-learning algorithm works best with your data. As a result, business analysts may use the automated option, and if it shows beneficial, a data scientist could take it over and improve it.

And you can put all of these ideas to work in various ways. Although Power BI's interactive dashboards and visualizations are powerful, sometimes all business customers want is a simple report that they can print, read, or email to a client or supplier. Power BI now supports the same paginated reports with headers and footers as SQL Server Reporting Services and table, chart, and matrix formats (with a new Report Builder tool to create them). Paginated reports are included in Power BI Premium, but they're also compatible with Power BI Report Server on-premises.

So, suppose you want to switch from SQL Server Reporting Services to Power BI. In that case, you can build an enterprise business intelligence system that gives you the full range of business analytics, from the reports your organization probably already relies on to machine learning that tries to find insights in data that isn't necessarily structured or numerical. If Power BI doesn't meet your needs on its own, the goal is to make extending it with Azure so simple that business users can do it themselves.

If you wish to understand more about Azure AI training, Power BI visualization training and Data Scientist training, you may check out these links or should you intend to get yourself validated for your Power BI expertise, do check out the Microsoft Power BI training (Microsoft Certified: Power BI Data Analyst Associate) certification.


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