Core Power BI Functions

Raw Data

Data is everywhere and with Power BI, you can harness its power to gain insights into your company’s performance. The data source determines what kind or format of information will be consumed by the service; this way it always has value for decision makers who need real-time updates on their operations!

Shape Data

Transform your data in seconds with Power BI and let it do all the heavy lifting. No need to worry about complicated formulas or schedules; this easy-to-use software does everything for you!

Model Data

Data modeling is a powerful tool that allows you to create connections between different pieces of data. You can model relationships, such as having one source publish information about another type entirely through these models – which means no more endless scrolling on your report!


Interactive dashboards will provide actionable insights, while immersive reports can drive business results to help grow revenue. The dashboard is a single page that tells the story through visuals and how they’re impacting your business or organization as whole.

Core Component of Analytics

It is important to understand the process of data analysis, as this will help in better diagnose any problems with company’s information system. The three main components are cleaning up or organizing raw numbers so they make sense; modeling data to make connections between raw data & finally visualizing these insights using charts and graphs which can provide useful takeaways for decision makers looking at numbers. To help analyze data, there are five important components that make up the core of analytics.


Descriptive analytics is a technique that summarizes large historical datasets to answer questions about what has happened in the past. Descriptions are short summaries that outline large datasets to describe outcomes for stakeholders so they can make informed decisions with their time, money and resources. It helps answer questions about what has happened based on past records. E.g. ROI (Return on Investment) KPI.


Diagnostic analytics use the findings from basic descriptive techniques to discover why events happened. They supplement this information with additional, more in-depth data gathering and analysis that helps find causes for changes over time or between different circumstances – like what caused an improvement one day but not another? It helps answer questions about why events happened.


The future is uncertain, but with predictive analytics you can be more confident in your predictions. These techniques use historical data to identify trends and determine if they’re likely to recur. It helps answer questions about what will happen in the future?


To make data-driven decisions, businesses can rely on insights from predictive analytics. This technique allows them to take into account uncertainty in their environment and come up with a plan accordingly. The use of machine learning in prescriptive analytics is an innovative technique that can identify patterns from large datasets. It help answer questions about which actions should be taken to achieve a desired outcome. 


The goal of cognitive analytics is to draw inferences from existing data and patterns, derive conclusions based on what’s already known about a subject matter. These findings then get added back into the knowledge base for future observations/interpretations  establishing itself as an active self-learning feedback loop which can constantly update its own understanding according to the changing circumstances or new information discovered

Storage Mode


Enhances performance of reports and dashboards.

Direct Query

Provides the most up-to-date information available. Due to security reasons, some Organization may not permit  to import local copies of the data into your reports . Direct Query storage mode will be useful in such cases. Useful for large Datasets.


Reduces refresh time by caching only the data that’s necessary.

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