Future of Advanced Analytics with Power BI

1. Future of Future of Analytics with Power BI Analytics with Power BI 1

2. TABLEOFCONTENTS Introduction ............................................................................................................................................ 3 Predictive Analysis .................................................................................................................................. 4 Integration with Azure and R Featuring Quick Insights Segmentation and Cohort Analysis ......................................................................................................... 9 Grouping and Binning Data Streaming ..................................................................................................................................... 12 Visualization using Power BI ................................................................................................................. 13 Data Modelling: Loading and Shaping Data .......................................................................................... 14 DAX ....................................................................................................................................................... 16 Conclusion ............................................................................................................................................ 17 2

3. INTRODUCTION Manipulation of enormous volumes of available data to create insights and make business decisions is becoming more difficult as data becomes more accessible. Business executives at all levels must become data literate, meaning they must be able to comprehend data and analytical ideas that may have seemed inaccessible in the past, such as statistical methodologies, machine learning, and data manipulation. Despite the fact that there are other tools for extracting accurate data, Power BI is quickly establishing itself as a leader in Data Visualization and Business Intelligence. It has recently taken a significant leap, and the pattern does not appear to be repeating itself. The demand for BI (Business Intelligence) and Decision Making is unlikely to wane very soon. In the next ten years, no other BI tool will be able to match Power BI's capabilities. Furthermore, Power BI is simple to use and provides excellent data visualization. With the development of data literacy comes the powerful ability to make well-informed business decisions based on data rather than an individual's opinion. Previously, these jobs were highly difficult and were delegated to engineers. Business executives may now dive into their own analytics and unearth remarkable insights using the tools available today. Furthermore, the item is a Microsoft product. What makes it even more exciting is that a number of Microsoft products have become global leaders. Combination with Power BI is not only necessary, but it also bodes good for the brand's clientele, as the company ensures it is in it for the long haul, adding features at an arbitrary rate each week. Power BI's advanced analytic features, such as predictive analytics, data visualizations, R integration, and data analysis expressions, will be covered in this white paper. 3

4. 1.PREDICTIVE ANALYSIS Despite the fact that predictive analytics has been around for decades, it is a technology that has now reached its peak. Predictive analytics is increasingly being used by businesses to improve their bottom line and gain a competitive advantage. Why are you doing this now? Predictive analytics is no longer only the domain of mathematicians and statisticians, thanks to the increasing availability of interactive and user-friendly software. These technologies are also used by business analysts and line-of- business professionals. Increasing volumes and varieties of data, as well as a growing interest in leveraging data to generate useful insights. Computers that operate faster and less expensive. Software that is easier to use. A necessity for competitive differentiation as a result of tougher economic conditions. 4

5. Here are few of the modern advantages of integrating POWER BI for predictive analysis into your business. 1.Detecting and preventing fraud: Combining analytics methods can aid in the discovery of patterns and the prevention of illegal activity. As cybersecurity becomes a growing concern, high-performance behavioral analytics is commonly used to review all network activity in real time in order to spot trends that could indicate fraud, zero-day vulnerabilities, and advanced persistent attacks. 2.Marketing campaigns are being optimized: 3.Operations are being improved: Many businesses employ predictive models to forecast inventory and manage resources. Airlines employ predictive analytics to set ticket prices. Hotels strive to forecast the number of guests for any given night in order to maximize occupancy and revenue. Businesses can function more efficiently with the help of predictive analytics. 4.Taking steps to reduce danger: Credit scores, for example, are used to assess a buyer's chance of defaulting on a transaction. A credit score is a number derived from a predictive algorithm that considers all relevant information regarding a person's creditworthiness. Two other risk-related applications are insurance claims and collections. Client reactions and purchases, as well as cross-sell chances, are predicted using predictive analytics. Predictive models can help businesses find, keep, and expand their most profitable customers. 5

6. 1.1INTEGRATION WITH AZURE AND R The ability to visualize the output of an Azure Machine Learning model inside Power BI has picked the interest of the analytics community. To add to the difficulty, operationalizing Azure ML models using the Power BI service would be fantastic. Imagine being able to have Power BI bring in the most recent output of your fraud model or the sentiment for recent Tweets about your items on a regular basis. To do so in Power BI, first extract data from Azure SQL that hasn't been scored by the machine learning model using R. Then, using R, transmit the unscored data to the Azure Machine Learning web service. Write the Azure Machine Learning model's output back into To read scored data into Power BI, utilize SQL and R. The Power BI file should then be published to the Power BI service. Finally, use the Personal Gateway to schedule a data refresh, which will cause the R script to repeat and bring in the new forecasts. R is the most frequently used statistical programming language in the world, utilized by statisticians, data scientists, and data analysts. The R language is now integrated into Power BI at every stage of the insight generation process. Users can execute R scripts directly in Power BI and import the generated data sets into a Power BI data model using the R connection. Advanced data cleansing and preparation queries, including as outlier detection and missing values completion, are performed by R in Power Query. R graphics in Power BI give you limitless flexibility and superior analytics depth when it comes to visualizing data. Once you've finished creating the visuals, you can embed them in your reports and dashboards, where they'll be interactive and cross- filterable. Source: microsoft.com 6

7. Users of Power BI do not need to be familiar with R to take advantage of R's capabilities, including as prediction, clustering, association rules, and decision trees. R custom graphics allow users to harness R's capability without having to write a single line of code. Simply drag your data to update your report after importing a custom R graphic. Source: microsoft.com R reports can be shared and viewed by anybody, even if they don't have R installed, because R is run directly in the Power BI service. 1.2FEATURING QUICK INSIGHTS Power BI's Quick Insights feature is based on a growing set of complex analytical algorithms created in collaboration with Microsoft Research, allowing users to uncover new and natural methods to find insights in their data. Quick Insights in Power BI explores different subsets of your data set using a collection of advanced algorithms to find potentially intriguing insights with a single click. In a certain period of time, Power BI scans as much of a data collection as feasible. Source: microsoft.com 7

8. TO USE QUICK INSIGHTS IN POWER BI, FOLLOW THE BELOW STEPS. 1.Select the ellipses (...) in the left navigation pane under Data sets, and then Quick Insights. 2.Power BI searches your data collection for trends using a variety of methods. 3.Your insights are ready in seconds. To see visualizations, go to View Insights. 2.Alternatively, pick the ellipses (...) in the left navigation bar and then View Insights. 3.NOTE: Because the data isn't statistically significant, some data sets can't yield insights. See Optimize your data for quick insights for additional information. 4.The visualizations are displayed in a unique Quick Insights canvas that can hold up to 32 insight cards. Each card includes a graph or chart as well as a brief summary. To know more about the Implementation of Quick Insights, Refer this Link 8

9. 2.SEGMENTATION AND COHORT ANALYSIS Segmentation and cohort analysis are a quick and easy approach to look at data and find out where deviations from the norm exist. Breaking down or sifting data into meaningful groups, then comparing those groups to find significant relationships in your data, is what segmentation and cohort analysis are all about. It's usually utilized to form a hypothesis about your data and pinpoint areas for additional investigation. Clustering, grouping, and binning are just a few of the capabilities available in Power BI to aid this process. Clustering helps you to quickly locate groups of related data points in a sample of your data using machine learning algorithms. Custom visualizations in Power BI provide further analysis and evaluation of clusters after you've established a cluster field of data. To show the aggregate of each measure for each cluster, you might utilize the cluster column and each of the corresponding measurements in a radar chart. You could alternatively plot the distribution of values for one of the metrics in each cluster using the cluster column and one of the measures in a box and whiskers plot. This might assist you in determining the measure's minimum, maximum, and median values within each cluster. Source: freepik.com 9

10. 2.1GROUPING AND BINNING Two types of data points within a field may be better discussed together and should be consolidated into a single category at times. This method of grouping data points can help you see, evaluate, and understand data and trends in images more effectively. Binning relates to continuous fields such as date fields and numeric values, whereas grouping applies to category fields. These are recognized as continuous fields by Power BI, which opens a dialogue box where you may bin the data by specifying the size of each bin. Grouping manually collects data points into groups and is typically used in the explore phase of an analysis project. These groups become part of the data model and apply to new or refreshed data automatically. Individual data is sorted into categorized traits and dimensions using the grouping and binning approach. This feature aids in the interpretation of data and allows for the grouping of continuous values into a reduced number of bins. While grouping and binning are similar in that they use the same basic notion, they are not identical. Source: microsoft.com 10

11. Grouping: When it comes to dimensions, grouping is used. As an example, let's imagine I have a sales user table that represents all of the organization's 30 sellers. Other users who do not work in sales roles are included in that listing of 30 salespeople. I'd like to develop income and bookings reports broken down per route for both sellers and non-sellers. I'm ready to give this report to my leadership once I've channeled this information. The leadership then requests a report broken down by region and team. So, I go back to the drawing board and examine my user table of salespeople to determine which salespeople are assigned to each team. I can use the button in the dimension column to open the Wizard, then move down to create a new group. I can drag and highlight any number or subset of those sales users once I'm in the Wizard. I can easily establish a new group and then drag those persons into it. When creating a report based on regions, you can use the same method. Binning: Binning is based on the same principle. Binning, on the other hand, focuses on applying buckets to properties, such as numeric values or dates. Binning is advantageous since it allows you to construct buckets based on a range of values ranging from $1.00 to $1,000. These buckets enable you to create a visualization based on several ranges, which will display averages on deals and aid in the management of crucial business choices. Let's pretend a corporation wants to view a wide range of sales and how they link to various marketing initiatives. I can see my sales table, as well as the dates on which those sales were completed. From there, I can create a bucket that will allow me to observe the sales immediately after the marketing promotion. If we have four major promotions every year, I'll select those weeks, apply the Marketing Department's dates, and build a bin using the Wizard. You now have a visual representation of the dollar value of promotions and non-promotions that you may offer. To learn more step by step. Please refer Grouping and Binning 11

12. 3.DATA STREAMING Dashboards that are updated in real time Power BI makes it simple to visualize and analyze real-time data, allowing your company to gain immediate insights from time-sensitive data. As social media initiatives go viral, keep an eye on them. Streaming video can be displayed on your dashboards. IoT sensor readouts will bring your dashboards to life. Power BI's extensive capability, paired with the speed of real-time data, will change the way you do business. Real-time data is all around us, and Power BI capabilities are designed to make it as easy as possible to access it. Power BI can access real- time data from anywhere thanks to integrations with streaming data providers like Azure Event Hub, Pub Nub, and Temboo. Furthermore, Power BI's connection with Microsoft Azure Stream Analytics and Azure Machine Learning allows you to delve deeply into your real-time data. Predictive intelligence can help you stay on track by allowing you to take proactive actions, while stream analytics can help you shape and aggregate your data. For historical analysis and visualization, data streams are kept on the cloud. Source: microsoft.com Real-time streaming data sets setup You can stream data and update dashboards in real time using Power BI real-time streaming. Any Power BI visual or dashboard that can display and update real-time data and visuals may also be used to display and update real-time data and visuals. Factory sensors, social media, service usage metrics, and anything else that can collect or transmit time-sensitive data can be used as devices and sources of streaming data. In Power BI, streaming data can be consumed in two ways: as tiles with streaming data graphics, or as data sets built from streaming data that persist in Power BI. 12

13. 4.VISUALIZATION USING POWER BI Data visualizations enable you to engage with your data to uncover business insights. Choose from a list of available visuals, add a custom visualization that you make yourself, or choose from the community gallery's growing collection of visualizations. Visualizations created by the public Visualizations in R You may download and use many valuable visualizations developed by both the community and Microsoft in your Power BI reports by visiting the visuals gallery in Power BI. Visit the Power BI site's visuals library to add a community-sourced visualization to your report. Browse the galleries on the Welcome to Power BI custom visualizations page. Select a visual tile to see more information about that visual, and download the visual that you want to use. The R Showcase in Power BI allows you to use the community R Script Showcase to leverage R scripts in Power BI to generate new or use existing advanced analytics in R visualizations. You can apply complicated algorithms, visualizations, scripts, and more with a single click using the R Showcase. To use the R custom visuals, you don't even need to know anything about R. Simply drag the data into a R custom visual in your report, and the R power will be deployed without writing a single line of R code. Visualizations made to order Custom visualization in Power BI allows you to generate fully customized graphics that you can use in reports or share with the Power BI community. You can design and test a custom visual using Power BI developer tools, which allow you to write custom visual TypeScript code and create CSS. You may export your own visual to your Power BI dashboard or submit it to the Power BI visuals collection once you've tried it. Source: microsoft.com 13

14. 5.DATA MODELLING:LOADING AND SHAPING DATA Connecting to Azure Services You can turn your data processing efforts into analytics and reports that deliver real-time insights into your organization using Azure services and Power BI. Whether your data processing is simple or complicated, single-sourced or massively scaled, warehoused or real-time, Azure and Power BI offer the built-in connectivity you need, and integration to help you get the most out of your business intelligence efforts. Source: microsoft.com There are many Azure connections to choose from, and the business intelligence solutions you may build using Azure services are as unique as your company. You can connect just one or a few Azure data sources, then shape and modify your data to create bespoke reports. Source: microsoft.com 14

15. DirectQuery DirectQuery enables you to create visualizations over very big data sets, where importing all of the data would be impossible. Historically, fundamental data changes have necessitated a data refresh. The necessity to display current data in some reports can necessitate huge data transfers, making re- importing data impossible. Users can circumvent this problem by querying the data source live with DirectQuery. Using the R connector to get data Users can execute R scripts directly in Power BI Desktop and import the generated data sets into a Power BI Desktop data model using the R connection. You can execute R scripts and develop a data model in just a few steps. To run an R script in Power BI Desktop, first, write it in your local R development environment and verify that it works. When utilizing DirectQuery, keep in mind the performance and load of your data set, as well as the security and supporting capabilities. Because all DirectQuery requests are forwarded to the source database, the time it takes to refresh a visual is determined by how long it takes the back-end source to react with the query's results. Power BI strives to construct queries that are as efficient as possible. However, there are times when a generated query is insufficiently efficient, causing performance to suffer or the refresh to fail. Using columns with a cardinality of less than 1 million can help to prevent this problem. Then, in Power BI Desktop, go to Get Data and look for the R Script data connector. Select Get Data and then More to run your R script. After that, choose Other, then R Script. Data Shaping with R using Power Query With R integrated into Query Editor, you can use R to cleanse data before applying advanced data shaping and analytics to data sets. Completing missing data, making predictions, and clustering are some examples to mention a few. R is a strong programming language that can be used to develop your data model and generate reports in Query Editor. When utilizing DirectQuery in the Power BI service, you need also to think about security. All users that consume a published report utilize the credentials entered after the report was published to the Power BI service to connect to the back-end data source. This is the same situation as when data is imported: regardless of any security controls established in the back- end source, all users view the same data. To learn more Data Loading and Shaping 15

16. 6.DAX-DATA ANALYSIS EXPRESSIONS Data Analysis Expressions (DAX) is a set of functions, operators, and constants that can be used to calculate and return one or more values in a formula or expression. DAX allows you to generate new data from existing data in your model. You'll get the most out of your data if you learn how to construct effective DAX formulas. Creating a new Power BI Desktop file and importing data into it is simple. You can even generate reports that display useful information without using any DAX formulae. But what if you need to compare growth percentages for multiple product categories and time periods? Or perhaps you need to compare year-over-year growth to market trends? This functionality, as well as a slew of others, is provided by DAX formulas. You'll get the most out of your data if you learn how to construct effective DAX formulas. You can start solving genuine business challenges that affect your bottom line after you have the information you need. This is Power BI's true potential, and DAX can help you get there. DAX adds more advantages to Power BI Even if DAX can only be used in a supported environment, the talent of knowing how to utilize it goes much beyond that. Because DAX is based on a system of tiered filter contexts where efficiency is paramount, it alters your perspective on tables and data filtering. You might be able to increase the efficiency of some Python code you created earlier in the afternoon by creating some clever DAX code in the morning. To put it another way, understanding DAX will improve your ability to merge, filter, select, and manipulate data more efficiently. If you invest the effort to study DAX, your newly earned talent does not have to be limited to a Power BI environment. DAX can be used in Microsoft tabular products such as: Power BI is a business intelligence tool that allows you to Analysis Services by Microsoft Microsoft Excel Power Pivot Not to mention that the DAX syntax is extremely similar to Excel formulas, so the information may be applied to this widely used piece of software as well. To learn more Data Analysis Expressions in Power BI DAX Function Cheat Sheet 16

17. 7.CONCLUSION Users at all levels of a company can use Power BI business analytics tools to examine data and communicate insights. Power BI gives a 360-degree view of your most essential metrics through dashboards—all in one place, updated in real-time, and accessible from any device. Use straightforward tools to swiftly obtain answers and examine data with just one click. Discover new information Power BI enables you to delve deeply into your data while being productive and innovative with your creations. Power BI makes it simple to use advanced analytics to effectively articulate your message and manage business difficulties, with more than 20 built-in graphics and a portfolio of dynamic custom visualizations. Excited to start/ boost up your Business with End-to-End Power BI mastery? Now is the right time to train yourself and your Employees to quickly kick start your learning and implement wonders in the world of Data Analytics. Start your learning with Irizpro Learning Solutions 17