<img src="https://secure.leadforensics.com/133892.png" alt="" style="display:none;">

6 key benefits of using Power BI for reports and data analytics

Chris Nichols
Apr 29, 2021
Chris Nichols
Home
  • EN-GB
  • 6 key benefits of using Power BI for ...

If you’re currently on the market for a self-service business intelligence, it’s likely you’ve already come across Microsoft Power BI. Power BI is a solution that allows organisations to visualise their data for analysis, spot trends and patterns while obtain actionable insights to make better business decisions.

In this blog, we take a look at the 6 key benefits of Power BI for reporting and data analytics:

  1. Affordable for any organisation
  2. Regular monthly updates
  3. Allows for unparalleled Excel integration
  4. Access to refreshing real-time data
  5. Easily build personalised dashboards
  6. Wide range of connectivity

1. Affordable for any organisation

Power BI is competitively priced compared to similar business intelligence and analytics tools. The platform offers different versions so you can choose the one best suited to your business.

For example, you can get Power BI Pro with all the functionality in the Azure cloud with for as little as £7.20 ($9.99) per user per month. There’s even Power BI Desktop, a free downloadable version that you can use to make reports and dashboards on your computer.

Alternatively, if you need something on an enterprise scale, there’s Power BI Premium.

Some of the key features of Power BI Premium include:

  • License hundreds of users to access reports
  • Access to insights with advanced AI
  • Unlock self-service prep for big data
  • Simplified data management 
  • Refresh data more than eight times day

While it’s more expensive, you won’t need as many Power BI licenses. Plus, you’ll get a dedicated node in Azure, helping you become more responsive to your business’ needs.

2. Regular monthly updates

Another standout feature of Power BI is that there are regular monthly updates to the platform. Microsoft frequently respond to suggestions from the Power BI community and new feature releases are based on user feedback.

Plus, any new proposed functionality is voted on before being implemented into the platform. This means users like yourself will always have access to the latest and advanced features, helping you make better, more informed business decisions.

3. Allows for unparalleled Excel integration

Many businesses still rely heavily on Excel for their analytics and reporting efforts. Power BI integrates seamlessly with Excel, allowing you to connect Excel queries, data models and reports to Power BI dashboards.

This helps you quicker gather and share Excel business data in new ways. Plus, create interactive visualisations without needing to learn a new application or language.

 

power bi reports

4. Access to refreshing real-time data

Learn what’s happening right now in your business, not just in the past. Gain access to real-time analytics to help your team solve issues quickly, identify opportunities and handle time sensitive situations more efficiently.

Refresh your data up to eight times a day with Power Pro and up to 48 times a day with Power BI Premium. As data is pushed in, your dashboards will update in real-time and display the new information. Power BI’s data cache allows for simple and speedy report deployment. Data can also be refreshed at set times that meet the business’ needs, all in one application.

5. Easily build personalised dashboards

Dashboards can be customised to fit your company’s requirements with intuitive and interactive visualisations. Plus, the drag-and-drop functionality makes it easy for your staff to generate custom Power BI reports quickly.

Simply select and open the information that’s most important to you by drilling down into data visualisations. This helps you gain a better understanding of what’s going on inside your organisation. Plus, avoid the need to learn complex query language with Power BI’s use of natural language queries.

6. Wide range of connectivity

Power BI allows you to import a wide range of data sources such as the sales data recorded in CRM or financial data from ERP.

The platform seamlessly integrates with all Microsoft products like Azure, Dynamics 365, Excel and so on. It also offers data connectivity with third-party solutions such as Google Analytics, Salesforce, and Spark.

Connect data files (like XML and JSON) and SQL server databases to help you create new and compelling datasets from multiple sources for your analysis and reports.

Revitalise your business processes with Microsoft Power Platform

Recognised by Gartner for the 14th consecutive year as a leader in the Gartner Magic Quadrant for Analytics and Business Intelligence Platforms, Power BI can help you make better informed decisions across your organisation.

In recent times where budgets have been squeezed by business leaders, companies are searching for ways to revitalise their processes without large capital investment. Tools like the Microsoft Power Platform can be just the right solution for this – but only if a) you know how to use it properly and b) your workforce is fully onboard with the change.

Our guide helps you understand the importance of driving user adoption within your organisation and how you can ensure your business remains on the path to success.

Click the button below to download your copy.

 

How to build a Power Platform Centre of Excellence
Topics
Chris Nichols

Chris Nichols

Experienced D365 Business Central Functional Consultant. Chris' role is to solve business problems with software and to show businesses how to realise the art of possibility through the better use of new technology. The technology of choice is Microsoft Dynamics NAV, Microsoft Dynamics 365 Business Central, ColumbusFood and the Power Platform.

Find me on:

Discuss this post

Recommended posts

How AI can solve challenges for food and beverage manufacturers

Columbus

If you organise your data and use AI strategically, you can make better decisions faster. You can for example improve your market understanding and forecasting, optimise your maintenance or reduce food waste. Choose what is most important for you!

Machine learning: Bridging gaps in demand forecasting

Santosh Kataram

Demand forecasters do the impossible — predict what products and services customers want in the future. Their forecasts inform decision-making about production and inventory levels, pricing, budgeting, hiring and more. "While crystal balls remain imaginary, machine learning (ML) methods can give global supply chain leaders the support they need in the real world to create more accurate forecasts." The goal is to produce exactly the amount of product to meet demand. No more. No less. Demand forecasting is used to anticipate the demand with enough time to manufacture the right stock to get as close to this reality as possible. The cost is high if you don’t get it right. Your customers will go to your competitors if you don’t have what they need. Unfortunately, capacity, demand and cost aren’t always known parameters. Variations in demand, supplies, transportation, lead times and more create uncertainties. Ultimately demand uncertainties greatly influence supply chain performance with widespread effects on production scheduling, inventory planning and transportation. On the heels of the global pandemic, supply chain disruptions and a pending economic downturn, many demand forecasters wish for a crystal ball. While crystal balls remain imaginary, machine learning (ML) methods can give global supply chain leaders the support they need in the real world to create more accurate forecasts.

How to select AI use cases to help your business succeed

Avinash Gundra

If you have identified possible AI use cases for your business, the next step will be to test if they are possible to implement and if they will create great value. While there is a lot of momentum and excitement about using AI to propel your business, the reality is only 54% of AI projects are deployed. How do you ensure you’re one of the businesses that does unlock the new opportunities AI promises? Your success with AI begins by discovering AI use cases that work for your business. In the first blog of our Columbus AI blog series, we shared five areas where organisations should focus their efforts to generate ideas for AI implementations based on our experience. After generating some ideas for AI use cases that could potentially benefit your company from the first step of the Columbus AI Innovation Lab, the next step is to test which AI use cases could be operationalised by evaluating them. Columbus AI Innovation Lab

Applying AI: How to define valuable AI use cases

Santosh Kataram

Only half of the companies starting an AI pilot project are actually executing it. The key is to choose an idea that will benefit your business. Read more about how! In 2022, 27% of chief information officers confirmed they deployed artificial intelligence (AI), according to a Gartner AI survey. Even though businesses across all industries are turning to AI and machine learning, prepare your organisation before jumping on the AI bandwagon by considering a few factors. Ask yourself: Is AI necessary for achieving the project requirements or is there another way? Does your team have the skills to support AI and machine learning? How will AI impact your current operations if you adopt it? How will you integrate AI with existing systems? What are the data, security and infrastructure requirements of AI and machine learning? The Gartner AI survey found only 54% of projects made it from the pilot phase to production. After significant investment in AI, why aren’t companies deploying it? We found the problem begins when companies define a use case. Too often, companies are not identifying AI use cases that benefit their businesses and end-users will adopt. The question is then, how should companies unlock the value and new opportunities AI promises? It starts with a systematic approach for each stage of the AI life cycle. We developed the Columbus AI Innovation Lab, a comprehensive method to address and account for all challenges when adding AI to your business operations and bring stakeholders into the process at the right time to help you operationalise AI.

Why your AI project won't succeed unless you transform your data

Naga Chaitanya

It doesn’t matter how good your artificial intelligence technology is. If you have bad data, you’ll get bad results. While AI is one of the most innovative and impactful innovations for businesses today, bad data can prevent AI projects from getting off the ground. Having high-quality data and following data preparation best practices are crucial for a successful AI project. In the Transform phase of the Columbus AI Innovation Lab, the one we detail here, data is collected and prepared for use by AI and machine learning (ML) algorithms. Major phases in Machine Learning Machine learning, frequently used synonymously with “artificial intelligence,” is about using predictive methods to simplify tasks. There are several essential steps to building ML models, and these steps can change based on the issue you are trying to solve and the kind of ML model you are building. However, in general, the steps involved in creating and implementing an ML model are as follows: Define: Different approaches for finding AI use cases Discover: Scope and prioritise ideas before developing an AI solution and implementation strategy Transform: Transforming the business case including data acquisition and preparation with AI/ML (current blog) Model and evaluate: Analyse and enhance the business use cases using AI/ML algorithms Deploy and support: Deployment of AI/ML models and support/monitoring to guarantee quality and effectiveness

Filter posts

Archive

Subscribe

Subscribe to the blog
right-arrow share search phone phone-filled menu filter envelope envelope-filled close checkmark caret-down arrow-up arrow-right arrow-left arrow-down