Do you know that predictive analytics is the cornerstone of organizations accelerating decision-making and boosting employee productivity? It has become pivotal to optimize internal operations while catering to the increasing challenging demands of clients.
According to Research and Markets, the market for predictive analytics is forecast to grow at a CAGR of 16.7%. From a value of $11.1 billion in 2022, businesses can expect predictive analytics to reach $23.9 billion by 2027.
However, it all depends on how organizations adapt to the analytics framework. Power BI is one of today’s most influential and comprehensive business intelligence tools. Let’s understand how businesses can leveragepredictive forecasting using Power BI.
Time series forecasting using Power BI
Before moving to predictive analytics, it’s important that you understand time series forecasting. Time series forecasting gives you the ability to see ahead of time and make business plans accordingly. Time series is the collection of data in terms of hours, days, months, and years—at regular intervals. Time series forecasting—a machine learning technique analyzes data and time sequences to predict future events. These assumptions based on historical time-series data about future trends is near accurate.
Time series allows you to analyze patterns such as trends, seasonality, irregularity, and cyclicity that finds its application across several business use cases including stock market analysis, economic forecasting, pattern recognition, census analysis, and so on.
How does Power BI handle predictive analytics?
Although Power BI is commonly used for data visualization purposes, it contains powerful predictive forecasting options, especially in the area of time series forecasting. The tool draws up information pertaining to a particular parameter (such as revenue figures) from organizations’ historical data and extrapolates it on a timeline to predict future numbers. The organization needs to specify the variables and other datasets the model will work with to obtain time-bound observations.
Let’s understand this with an example.
Time series models are helpful for businesses whose sales vary from season to season. The time series-based TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA errors, Trend, and Seasonal components) model is used by Power BI for predictive analytics, allowing organizations to map the complex seasonal behavior of sales on a timeline. While the vertical axis shows the quanta of sales, the horizontal axis represents the number of days.
Power BI allows organizations to choose these seasonal trends—daily, weekly, monthly, or any specific period they are targeting. Each seasonal variation will enable companies to visualize unique trendlines for the same datasets, giving more profound insights into sales projections with the following trends:
- The most significant benefit of the TBATS visualization model in Power BI is that the predictions and forecasts are not merely based on logarithmic or simple linear projections.
- You can differentiate the predicted data on the trendline through distinct visualizations from Power BI.
- You can differentiate predicted values using a different color on the trendline, allowing professionals to easily discern historical, present, and forecasted data on the visualization.
Power BI also allows companies to perform predictive analytics with a confidence percentage level. The forecasted values that the Power BI predictive model generates can be specified to fall into a predetermined confidence zone. For example, if the confidence interval is set at 50%, the statistical model would be 50% confident that the generated predictions would follow close to the actual values.
Using external tools with Power BI
Power BI integration into the statistical language R can give users even more flexibility to access insights through visuals and predictive models. Since R is an open source statistical programming language with many custom packages built specifically for data cleansing, visualization, and predictive analytics, R scripts can be used in Azure ML and can enhance Power BI’s capabilities to create powerful visuals for predictive models.
Power BI provides you two ways to create visuals using R. Just as you do with any other data source, you can use R scripts as a source and build your visualization in Power BI. Or otherwise you can do all your development and visualization in R and import visuals into Power BI—you can get an extremely customizable visualization experience. Importing visualizations into Power BI (instead of viewing them in R) is to take Power BI’s ability to cross-filter visuals and select data subsets. In fact, R’s integration into Power BI can fetch you very specific visuals that you can’t accomplish using Power BI prebuilt visuals.
Demonstrating predictive analytics using Power BI
PreludeSys uses Power BI with other powerful Microsoft tools (such as Azure) to deliver positive community-level impacts.
PreludeSys demonstrated the epitome of predictive analytics by helping a leading nutrition company deliver customized organic solutions to a wide range of customers by optimizing sample tracking and solution delivery using Power BI predictive analytics.
The nutrition company focuses on delivering high-quality enteral formulae to its clients with varying stages of chronic illnesses. The formulae are specific and targeted and are nut-, dairy-, gluten-, soy-, and corn-free. With a massive scale of demand and distribution, the client faced trouble with the following aspects:
- Integration setbacks with Jitterbit occurred frequently.
- The order processing system for sample requests was manual, which took time.
- Since their products cater to customers with various stages of chronic illnesses, it took time to track the samples at each stage.
To help streamline operations, the client wished to integrate their ERPs with Microsoft Azure and achieve customized automation of workflows that would suit their business operations. In addition, they wanted to extract their reporting function from Enterprise Data Warehouse for better insights.
PreludeSys identified that the client’s data migration from Salesforce CRM to the McKesson platform could resolve the issue, and they achieved it using Azure Data Factory. Additionally, they employed Power BI at various levels to streamline the reporting and analytics of the samples.
- They beamed all the sample requests to Azure Synapse for analysis and insight delivery.
- They broke down bulk data into batches for processing.
- They used Power BI reports to track the sample statuses and delivery forecasting details.
The new systems helped the client streamline the following aspects of their business operation:
- The client no longer needed to process CSV files manually, saving precious time and resources.
- Visual insights aided better decision-making.
- The comprehensive dashboards allowed a 360° view into any dataset and situation.
Get the Power BI edge with PreludeSys
For business leaders looking to stay ahead of the competition in today’s rapidly-shifting global economy, predictive analytics can make a world of difference. By leveraging the power of data and proactively predicting events before they occur, businesses can optimize their operations and resources with unprecedented accuracy.
Predictive analytics is now at the front and center of business operations, impacting internal processes (resource use) just as much as external ones (such as customer data collection). For an organization to function optimally, it is essential to implement the right predictive analytics tool. It is even more crucial to work with a reliable implementation partner.
PreludeSys delivers a seamless Power BI implementation—customized to a business’s needs. In addition, our custom dashboards and reports ensure that organizations always remain at the top of their game.
Achieve the power of predictive analytics and forecasting with Power BI, implemented flawlessly by PreludeSys. To learn more about Power BI implementation, visit this page or connect with our experts.