PowerBI Archives - Collective Intelligence

Building a Future-Ready Data Estate

Michelle Driscoll — Wed, 07 May 2025 18:21:57 +0000

Have you ever wondered how some companies seem to effortlessly manage and leverage vast amounts of data? Meanwhile, others struggle to keep up. In contrast, imagine a bustling city where each piece of data is a building block. The city’s success depends on how well these blocks are organized and utilized. Businesses consider a well-designed future-ready data estate essential for effective modern data management. Below you will discover critical elements and strategies for building a future-ready data estate.

Key Components of a Data Estate

To begin with, several key components are involved in building a robust data estate:

Data Sources

Business Applications: ERP systems, CRM systems, and other enterprise applications generate valuable data for organizations.
Legacy Systems: Many organizations still rely on legacy systems for critical business operations. These systems often contain valuable historical data that needs to be integrated into the modern data estate. Integrating legacy systems can be challenging due to outdated technology and data formats, but it is essential for a comprehensive data strategy.
IoT Devices: Data is collected by Internet of Things (IoT) devices from various sensors and connected equipment. These devices provide real-time insights for businesses.
External Data Sources: Market data, social media feeds, and public data sets enrich internal data assets.

Storage Solutions

On-Premises Storage: Traditional data centers and local servers offer direct control over data within physical infrastructure.
Cloud Storage: Solutions such as Microsoft Azure, AWS, and Google Cloud offer scalability and flexibility without large infrastructure investments.
Hybrid Storage: On-premises and cloud storage approaches are combined here. This offers the benefits from both storage methodologies.

Data Processing and Analytic Tools

ETL Tools: Extract, Transform, Load tools integrate data from various sources, transforming raw data into usable formats for analysis.
Data Warehouses: Central repositories for structured data storage are served here. Data warehouses organize information for efficient querying and reporting.
Data Lakes: These repositories hold vast amounts of raw data in native format. Data lakes accommodate structured, semi-structured, and unstructured information.
Analytics Platforms: Comprehensive analysis is enabled by tools like Microsoft Power BI, Tableau, and Databricks. Analytics platforms transform raw data into visual insights. These visualizations simplify complex data, revealing patterns and trends that might be missed in raw formats, leading to a deeper understanding and better use of data. Furthermore, visual insights foster collaboration, enhance retention of information, and can even improve productivity.

Data Quality Management

Importantly, maintaining high-quality data is fundamental to deriving value from your data estate. Without proper quality controls, even the most sophisticated analytics tools will produce unreliable results—remember the adage “garbage in, garbage out.” Implementing comprehensive data quality management ensures your organization can trust the insights generated from your data assets.

Quality Assurance Processes

Data Profiling: Examine data to understand its structure, content, and quality. Use statistical analysis to identify anomalies, outliers, and patterns that may indicate quality issues.
Standardization: Establish consistent formats for names, addresses, dates, and other common data elements across all systems and sources.
Deduplication: Identify and eliminate duplicate records that can skew analytics and waste storage resources. Implement matching algorithms to detect similar records that might represent the same entity.

Monitoring and Maintenance

Quality Metrics: Define key performance indicators (KPIs) for data quality, such as completeness, accuracy, consistency, timeliness, and validity.
Automated Validation: Implement real-time validation rules to catch errors at the point of data entry or ingestion.
Data Stewardship: Assign responsibility for ongoing quality monitoring and issue resolution to designated data stewards who understand both the technical and business context of the data.

Remediation Strategies

Cleansing Workflows: Establish automated processes to correct common errors, standardize formats, and enrich data with missing elements.
Root Cause Analysis: When quality issues arise, trace them to their source to prevent recurrence rather than simply fixing the symptoms.
Feedback Loops: Create mechanisms for users to report data quality issues they encounter, closing the loop between data consumers and producers.

Best Practices for Designing a Data Estate

Moving forward, to build a robust future-ready data estate, consider the following best practices:

Scalability

Design the data estate to handle growing data volumes and increased demand.
Implement scalable data processing tools for efficient management of large datasets.
Ensure infrastructure can grow alongside business requirements.

Flexibility

Choose storage solutions that support various data types and formats, such as structured, semi-structured, and unstructured.
Use flexible integration tools that connect to multiple sources.
Ensure systems can adapt to changing business requirements over time.

Cost-Effectiveness

Optimize storage costs by combining on-premises and cloud solutions.
Store frequently accessed data in the cloud and less critical data on-premises.
Implement data archiving and tiering strategies for better cost management. Organizations should allocate resources based on data importance and usage.

Data Governance

Establish governance policies to ensure quality, security, and compliance.
Clearly define data ownership, access controls, and stewardship roles.
Implement cataloging tools to maintain an inventory of data assets. Teams should track metadata to improve discoverability and understanding across the organization.

Security and Compliance

Protect sensitive information with robust security measures and protocols.
Utilize encryption, access controls, and regular security audits.
Ensure compliance with regulations like GDPR, HIPAA, and CCPA. Organizations must implement appropriate data protection practices across all departments.

Partnering with Collective Intelligence

Collective Intelligence is at the forefront of helping companies build future-ready data estates. By leveraging industry-leading tools like Microsoft Fabric and Databricks, we ensure that your data infrastructure is not only scalable but also adaptable to future technological advancements. Our holistic approach includes setting up modern data platforms, integrating data into OneLake, and establishing the Medallion Architecture using Data Warehouses and Lakehouses. This ensures seamless data management and real-time analytics, empowering your business to stay ahead in the competitive landscape.

Scalability and Adaptability: Build systems that grow with technological advancements and accommodate evolving business needs.

Modern Data Platforms: Implement innovative platforms for efficient data management and optimized workflows.

Integration with OneLake: Seamlessly connect data sources to OneLake for unified access across organizations.

Medallion Architecture: Utilize Data Warehouses and Lakehouses for comprehensive management of both structured and unstructured data.

Real-Time Analytics: Enable immediate data processing for timely insights and informed decision-making.

Real-World Examples

To illustrate these concepts, let’s examine some real-world examples:

Healthcare: Mayo Clinic

Mayo Clinic implemented a hybrid data estate to manage patient data from various sources, including electronic health records (EHRs), IoT devices, and research databases. This approach enabled them to integrate and analyze data more effectively, leading to improved patient outcomes and operational efficiency.

Finance: Goldman Sachs

Goldman Sachs built a scalable data estate using cloud storage and advanced analytics tools. This allowed them to process and analyze large volumes of financial data in real-time, enhancing their risk management and trading strategies.

Retail: Walmart

Walmart leveraged a data lake to store and analyze vast amounts of customer data from their online and physical stores. This enabled them to gain insights into customer behavior, optimize inventory management, and personalize marketing campaigns.

Conclusion

In today’s data-driven business landscape, a robust data estate is no longer optional—it is essential for competitive advantage and innovation. Building such an infrastructure requires careful attention to key components: diverse data sources, flexible storage solutions, and powerful processing tools, all supported by thoughtful governance and security practices.

The organizations that thrive in the digital economy are those that treat their data as a strategic asset, investing in scalable architecture that can evolve alongside changing business needs and technological advancements. As demonstrated by industry leaders like Mayo Clinic, Goldman Sachs, and Walmart, a well-designed data estate creates tangible business value through improved decision-making, operational efficiency, and customer insights.

Whether you are embarking on a comprehensive data transformation or looking to optimize your existing infrastructure, the principles outlined in this guide provide a foundation for creating a future-ready data estate. Remember that building a future-ready data estate is not a one-time project but an ongoing journey of continuous improvement and adaptation.

By embracing best practices in data management today, you position your organization to harness the full potential of your data assets tomorrow, turning information into actionable intelligence that drives business growth and innovation.

To learn more about our services and how we can assist you in building a future-ready data estate, please visit our page or schedule a virtual meeting.

The post Building a Future-Ready Data Estate appeared first on Collective Intelligence.

Data-Driven Strategies with Microsoft Fabric

Michelle Driscoll — Mon, 16 Dec 2024 16:19:26 +0000

Microsoft Fabric empowers organizations with advanced data integration, analytics, and visualization tools. These capabilities enable seamless data collection, insightful analysis, and impactful visualizations. Embracing data-driven decision-making is crucial for staying competitive. Just as a GPS uses real-time data to guide you to your destination efficiently, Microsoft Fabric helps organizations navigate their data landscape, providing clear directions for strategic decisions. By leveraging Microsoft Fabric, organizations can transform raw data into strategic assets, fostering growth and efficiency.

Collecting and Integrating Data

Data Sources and Connectors

Microsoft Fabric supports a wide range of data sources, including modern cloud services and legacy systems. Legacy systems, such as on-premises databases and older software applications, can be integrated using specific connectors. Examples of connectors include Azure SQL Database, Apache Kafka, and various SaaS applications like Salesforce and Dynamics 365. These connectors facilitate seamless data collection, ensuring all relevant data is integrated into a unified system for comprehensive analysis.

Best Practices for Data Integration

To maximize the effectiveness of data integration, follow these best practices:

Standardize Data Formats: Use common formats such as CSV, JSON, Parquet, and Avro. These formats ensure consistency and compatibility across different data sources.

Validate Data Quality: Implement validation processes to maintain high data quality and accuracy. Tools like Power Query can help automate data validation and cleansing.

Automate Data Cleansing: Use automated tools within Microsoft Fabric, such as Data Factory and Power Automate, to cleanse data, removing duplicates and correcting errors.

Ensure Data Security and Governance: Protect sensitive information through robust security measures and compliance with regulations. Data governance is crucial for maintaining data integrity and compliance. Microsoft Purview can be used to govern and protect data across the organization.

Monitor Integration Processes: Continuously monitor data integration processes to identify and resolve issues promptly. The Monitor hub in Microsoft Fabric provides a centralized location to track and manage integration activities.

Analyzing Data for Insights

Using Built-in Analytics Tools

Microsoft Fabric offers a suite of built-in analytics tools designed to provide comprehensive data analysis. Key tools include:

Power BI: Enables users to create interactive reports and dashboards with ease. It supports natural language queries and AI-driven insights.

Azure Synapse Analytics: Integrates big data and data warehousing, allowing for large-scale data processing and advanced analytics.

Azure Data Factory: Facilitates data integration and transformation, enabling seamless data movement and preparation.

Real-Time Intelligence: Provides real-time data processing and analytics, allowing for immediate insights and actions.

Creating Meaningful Visualizations

Microsoft Fabric’s visualization features are robust and user-friendly, enhancing the ability to communicate insights effectively:

Power BI Visualizations: Offers a wide range of customizable charts, graphs, and maps. Users can create interactive visualizations that allow for deep data exploration.

Notebook Visualization: In Fabric notebooks, users can visualize data using built-in commands like display, which transforms data frames into rich visual formats. This includes table views, chart views, and the ability to create multiple charts from different columns.

Embedded Visuals: Visualizations can be embedded directly into Microsoft 365 apps, making it easier to share insights across the organization.

AI-Enhanced Visuals: Copilot in Power BI uses AI to generate visualizations and insights based on natural language prompts, simplifying the process of data exploration and reporting.

These tools and features enable users to analyze data comprehensively and present findings in a clear, impactful manner, supporting data-driven decision-making across the organization.

Implementing Data-Driven Strategies

Data-Driven Transformation: Microsoft Fabric Implementation with Wipfli

Wipfli, a leading consulting firm, successfully utilized Microsoft Fabric to drive digital transformation for its clients. By leveraging Microsoft Fabric, Wipfli simplified, unified, and accelerated data management and analytics processes. Their strategic approach of “listen and learn before we advise” ensured tailored solutions that met specific client needs. This comprehensive data platform enhanced decision-making and operational efficiency, improving data accessibility and quality.

Despite facing challenges like siloed data and limited infrastructure support, Wipfli overcame these obstacles through strategic planning. They developed a centralized data hub and a strategic data foundation roadmap. Leveraging Microsoft Fabric accelerated delivery time by roughly 20% compared to previous implementations using Azure PaaS solutions. Additionally, the adoption of Power BI increased, leading to better insights and growth opportunities.

Steps to Develop and Execute a Data-Driven Strategy

To enhance the steps for developing and executing a data-driven strategy, consider the following detailed approach:

Assess Current Data Environment:

Conduct a comprehensive audit of existing data assets.

Identify data gaps and areas for improvement.

Evaluate the current data infrastructure and tools.

Define Objectives:

Set clear, measurable goals aligned with business objectives.

Prioritize objectives based on their potential impact and feasibility.

Develop a Plan:

Outline the steps required to achieve the defined objectives.

Allocate resources, including personnel, technology, and budget.

Establish a timeline with milestones and deliverables.

Implement Solutions:

Deploy the necessary tools and technologies, such as Microsoft Fabric’s data integration and analytics capabilities.

Train staff on new processes and tools to ensure smooth adoption.

Integrate data governance practices to maintain data quality and compliance.

Monitor and Adjust:

Continuously track progress using key performance indicators (KPIs).

Use tools like Power BI and Azure Monitor to visualize and analyze performance data.

Regularly review and refine strategies based on insights gained from data analysis.

By following these enhanced steps, organizations can effectively develop and execute data-driven strategies, leveraging Microsoft Fabric to drive innovation and achieve their strategic goals.

Measuring and Optimizing Performance

Continuous Improvement through Data Analysis

Key Performance Indicators (KPIs)
Common KPIs vary by industry but generally include metrics that measure performance, efficiency, and outcomes. Here are examples for different sectors:

County Government:

Citizen Satisfaction: Measures resident satisfaction with services.

Response Time: Tracks the time taken to respond to citizen inquiries or emergencies.

Budget Variance: Compares actual spending against the budget.

Insurance:

Claim Settlement Time: Average time to settle claims.

Loss Ratio: Ratio of claims paid to premiums earned.

Customer Retention Rate: Percentage of customers who renew policies.

Financial:

Return on Equity (ROE): Measures profitability relative to shareholders’ equity.

Net Profit Margin: Percentage of revenue that remains as profit after expenses.

Liquidity Ratio: Assesses the ability to cover short-term obligations.

Manufacturing:

Throughput: Number of units produced in a given time.

Cycle Time: Time taken to produce a product.

Defect Rate: Percentage of defective products produced.

Review Frequency
KPIs should be reviewed regularly to ensure they provide timely insights. A common practice is to review KPIs:

Monthly: For operational metrics that require frequent adjustments.

Quarterly: For strategic metrics that track long-term goals.

Annually: For high-level performance reviews and strategic planning.

Utilizing Data Analysis to Adapt Strategies and Identify Improvement Areas

To adapt strategies and identify improvement areas through data analysis:

Collect and Analyze Data: Use tools like Power BI to gather and visualize data.

Identify Trends and Patterns: Look for recurring trends or anomalies in the data.

Set Benchmarks: Compare performance against industry standards or historical data.

Develop Action Plans: Create specific, actionable plans to address identified issues.

Implement Changes: Apply the changes and monitor their impact.

Continuous Monitoring: Regularly review KPIs to assess the effectiveness of the changes and make further adjustments as needed.

By following these steps, organizations can continuously improve their performance and adapt their strategies based on data-driven insights.

Conclusion

Microsoft Fabric offers a comprehensive suite of tools for data integration, analytics, and visualization, empowering organizations to make data-driven decisions. By collecting and integrating data from diverse sources, including legacy systems, and following best practices, businesses can ensure data quality and consistency. Built-in analytics tools and powerful visualization features enable insightful analysis and clear communication of findings.

Implementing data-driven strategies with Microsoft Fabric can significantly enhance operational efficiency and customer experiences. Following a structured approach to developing and executing these strategies ensures alignment with business objectives and continuous improvement.

Partner with Collective Intelligence

To fully leverage the strategic advantages of Microsoft Fabric, consider partnering with Collective Intelligence. We offer comprehensive support at every stage of your journey with Fabric:

Setup: Initial deployment and configuration tailored to your organization’s needs.

Development: Building and customizing solutions to meet specific business requirements.

Optimization: Enhancing performance and efficiency through continuous monitoring and fine-tuning.

Governance: Ensuring data integrity, security, and compliance with robust governance practices.

Training: Providing courses to equip your team with the skills needed to maximize Fabric’s benefits.
- Register Here

By embracing Microsoft Fabric and partnering with experts like Collective Intelligence, your organization can transform data into a powerful strategic asset, driving growth and innovation.

References

[1] Data source management – Microsoft Fabric | Microsoft Learn

[2] Connector overview – Microsoft Fabric | Microsoft Learn

[3] Making Connections: Utilizing Microsoft Fabric’s Native Connectors

[4] Data formats supported by Real-Time Intelligence – learn.microsoft.com

[5] Automate deployment pipeline by using Fabric APIs – Microsoft Fabric

[6] Governance and compliance in Microsoft Fabric – Microsoft Fabric

[7] Use Microsoft Purview to govern Microsoft Fabric

[8] Use the Monitor hub – Microsoft Fabric | Microsoft Learn

[9] Fabric workload monitoring hub – Microsoft Fabric | Microsoft Learn

[10] What is Real-Time Intelligence – Microsoft Fabric

[11] Real-Time Intelligence documentation in Microsoft Fabric

[12] Notebook visualization – Microsoft Fabric | Microsoft Learn

[13] Product Features | Microsoft Fabric

[14] Data Analytics Platform | Microsoft Fabric

[15] Data-driven transformation: Successful Microsoft Fabric implementation with Wipfli

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Starting with Data Science Using Microsoft Fabric in Your Company

Michelle Driscoll — Tue, 18 Jun 2024 14:06:51 +0000

Data science has become an integral part of gaining insights and making informed decisions in the business world. Yet some companies are reluctant to begin the journey to gain the benefits of data science. Some feel it is a daunting task, while others feel it requires specialized tools and skills. While tools and skills can provide added benefits, there are tools found within Microsoft Fabric that can get you started quickly on the Data Science journey.

The keys to data science are data, and a question or need to answer. Let’s assume your company has plenty of both. Data science is just one means to uncover important info for your company.

Here’s a step-by-step guide to getting started with data science using Microsoft Fabric in your company.

Step 1: Understand the Microsoft Fabric Platform

Microsoft Fabric is a robust platform that brings together data science, data engineering, and data analysis. It facilitates end-to-end data science workflows, enabling data enrichment and business insights. Before diving in, familiarize yourself with the platform’s capabilities and how it can cater to your specific data science needs.

If you are already using Power BI, or other components of Fabric, you’ve already got a great start. Data science needs data, a need or question to get answers to, and a means to get that answer. If you have workspaces and data established in Fabric, you’ve already made the first step. If you have not, follow the next steps to get things started.

Step 2: Set Up Your Workspace

Begin by creating a workspace within Microsoft Fabric. This will be your central hub for managing data, notebooks, experiments, and models. Ensure you have the necessary permissions and access to the data sources that will be used for your data science projects.

The workspace is the centerpiece of the Data Science work, and houses most of the tools and items. Start small – one question or need, with a limited scope. You can always branch out to other areas, other workspaces, other needs.

Now that you have a home for your Data science needs, time for data.

Step 3: Data Ingestion and Cleansing

Data ingestion is the first practical step in your data science journey. Use Microsoft Fabric’s tools to connect to various data sources and ingest data into a centralized Lakehouse. Once ingested, cleanse and prepare your data using tools like Apache Spark and Python-based libraries to ensure quality and consistency.

This step is critical, and sounds easy to say, but will require the most effort. If you are working with your data that has already been prepped, Congratulations – as you’re in better shape than most. If not, decide what data you will need to help you answer that question or need, and begin to gather. Now is also a great time to plan with those who normally handle data within your area, as prepping for the future and sharing the data is part of the best practice in data governance. More on that in another blog!

Step 4: Exploratory Data Analysis (EDA)

Exploratory Data Analysis is crucial for understanding the underlying patterns and anomalies in your data. Utilize Microsoft Fabric’s notebooks to perform EDA, leveraging built-in experiences on Spark and Python-based tools like Data Wrangler and SemPy Library. Not a python or coding expert? No worries, Copilot within Fabric can help you get the code you need. Microsoft Fabric and notebooks can use a plethora of languages, so if you have in-house expertise in another coding language, use that.

Step 5: Model Training and Experimentation

With clean data in hand, it is time to train machine learning models. Before going further make sure you have stored that data in a Lakehouse or warehouse within Fabric for easy retrieval and collaboration. Microsoft Fabric provides seamless experience for creating experiments and tracking model training metrics with MLflow. You can also use Fabric notebooks to train models using various machine learning algorithms.

Training data is matter of sampling some of your data to use to “train” your model to look for the answers to your question(s) in mind. Remember to be focused and start small, no World Hunger type of questions.

The remaining data, not part of the training, is used for analysis.

Step 6: Model Scoring and Deployment

After training, score your models to evaluate their performance. Microsoft Fabric allows you to run batch predictions and save the results to the Lakehouse. You can then register and track trained models using MLflow and the Fabric UI. The nice part is that most of this happens seamlessly, without much intervention on your part. It is always good to know what is happening in each step, but scoring and modeling batches can help fine tune that model. Once you’ve happy with the model, time to get it ready for widespread use. This is matter of publishing the model into the Fabric workspace.

Step 7: Visualization and Insights

The decisive step is to visualize the predictions and insights generated by your models. Microsoft Fabric integrates with Power BI, enabling you to create interactive reports and dashboards that can be shared across your organization for data-driven decision-making.

You can also use the model in other apps or code to ingest new data and gain further insights on your questions and needs. The model is now ready for any new data, or revisit older data, looking for trends and analytic insights.

Conclusion

Implementing data science in your company using Microsoft Fabric requires a structured approach, starting from understanding the platform to visualizing the insights. By following these steps, you can leverage the power of data science to uncover valuable insights and drive your company’s success. The more you learn, the better your Data analytics get, and provide better insights for your company. But do not let the concept of data as a science keep you from exploring.

There are a lot of steps and considerations, but getting started in Data Science can be achieved with some focused interest and tools found in Microsoft Fabric. Once you have started, you’ll soon see the limitless possibilities.

Collection Intelligence has experts who can assist you in the journey, or work with you to plan and implement an enterprise-wide data solution. If you need us for large or small efforts, we are one contact away.

Remember, start small, but keep the end game in mind – plan for future use, sharing and collaboration.

The post Starting with Data Science Using Microsoft Fabric in Your Company appeared first on Collective Intelligence.

Why should you upgrade to Power BI Premium in 2023? We walk you through it

Collective Intelligence — Fri, 03 Mar 2023 15:41:14 +0000

Microsoft Power BI product line has a variety of licenses – It’s hard to choose the right fit. Today we explore the features and benefits of Power BI Premium.

From better governance, better pipelining, automatic insights from machine learning, better data availability, enterprise level security, better workspace flexibility, custom embedding, and custom development features.

Richard Krott, Dieter Neubauer and Charles Nordine from Collective Intelligence explore how Power BI Premium offers a range of features, including paginated reports, AI-powered insights, advanced data modeling, and improved collaboration and sharing capabilities. Power BI Premium has a lot of benefits, including more storage, memory, and deployment options, as well as access to custom embedding and development features. However, it’s important to assess your organization’s needs and only invest in what you actually need. Premium Per User is also a great option for organizations that only need premium features for a few users. With these features, organizations can get a more comprehensive view of their data, make data-driven decisions, and share insights with stakeholders in a more meaningful way.

Our team of experts can help you implement Power BI Premium and leverage its advanced features to drive growth and improve decision-making across your organization. Whether you need help with data modeling, report design, or collaboration and sharing, we can provide the guidance and support you need to succeed.

At Collective Intelligence, we specialize in helping organizations get the most out of their Power BI investment. With Collective Intelligence’s help, you can unlock the full potential of your data and gain a competitive advantage in your industry. Contact us today to learn more about how we can help you achieve your business intelligence goals with Power BI Premium.: Level Up Power BI – Collective Intelligence

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Did you know that AI is already a part of Power BI? Let CI introduce you to Cognitive Services

Collective Intelligence — Wed, 08 Feb 2023 12:07:49 +0000

AI is all the rage these days – did you know you can bring AI into Power BI right now?

Power BI Cognitive Services provides three pilars of AI support into the platform. Textual Analysis, Image Analysis, and the powerful AutoML. These tools can help you gain meaningful insights from data that previously might have needed a human to analyze.

Dieter Nuebauer and Charles Nordine explore the three main features of the Power BI Cognitive Data Services platform.

If you want to know more about how Collective Intelligence helps businesses acquire, deploy, govern, train, and succeed with Power BI and Power Platform look here: Level Up Power BI – Collective Intelligence

The post Did you know that AI is already a part of Power BI? Let CI introduce you to Cognitive Services appeared first on Collective Intelligence.

Power BI Data Marts

Charles Nordine — Tue, 17 Jan 2023 19:12:28 +0000

Dieter Neubauer explores Microsoft’s recently released a new feature for Power BI called Data Marts.

Data Marts allows users to generate subject area-driven data sets that are driven by user needs, making it easier for end users to access large volumes of data including web data and other information that is not part of the data warehouse. This feature also allows for easy management of the data as they are part of the Data Marts and Power BI.

At Collective Intelligence, we can help you understand how Data Marts can fit into your current environment and assist you in getting started with this powerful new feature. See how we help others take their Power BI systems to the next level: https://www.collectiveintelligence.com/level-up-power-bi/

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Power BI Audiences

Collective Intelligence — Mon, 09 Jan 2023 15:45:20 +0000

Dieter Neubauer and Charles Nordine breakdown common use cases for the Audience feature, and how it solves some common frustrations with Power BI.

Are you looking simpler ways to share Power BI reports and dashboards with the right people in your organization? Look no further than Audience, the new feature from Power BI. With Audience, you can create specific groups or audiences within the app and tailor the content that is shown to each group. This means that your executives will see the reports and dashboards that are most relevant to them, while your analysts will see the content that they need. This adds reusability to the reporting content sharing process and ensures that everyone in your organization has right security access to the insights they need.

Additionally, Audiences helps to prevent the duplication of data and reduces the amount of time spent on testing and maintenance.

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