There is no one industry where testing of BI reporting is more important than others. All  businesses can benefit from BI testing, although some types of organizations recognize the value of testing more than others.

In our experience

Organizations with Pro Business Analytics understand the benefits of continuous integration and the value of testing and share the following qualities:

Medium to large companies that have  an established BICC or Business Analytics Center of Excellence and need to implement the

standards they develop across a large user base.

Small companies  with limited resources  and a small IT/BI/Cognos admin team. For these companies, pre-testing and notification can be a second set of eyes for them to get a leg up on the competition.

Companies with a testing culture 

 In other words, some organizations have well-developed processes for project management that require testing as an integral part of every project as defined by Project Management Office standards. These companies budget time and dollars for testing.

The manufacturing industry  has a long history of testing and understands its value. Going back 30 or 40 years now, they have developed tests for everything from raw materials to finished products.

Self-sufficient, do-it-yourself organizations 

 These companies, while not software development companies, have a history of creating their own software, integrating Cognos into custom portals, etc. They know and understand the software development life cycle and the importance of testing.

Any company working with Big Data 

 Typically, these companies are more mature on the business intelligence analytics maturity spectrum. Testing reports and managing the BI ecosystem can no longer be performed manually.

Any large-scale Congo's implementation with two or more servers in multiple environments : development, testing, performance, production, production disaster recovery. Note that there are two environments dedicated to testing and performance. This type of ecosystem can easily have 10 to 30 servers that need to be kept in sync.

Any organization considering a Congo's upgrade  needs to build regression testing into its upgrade plan. Before migrating to a new version of Congo's it is essential to determine if the BI content is working properly. With testing you can determine if the content works, if there is any drop in performance and if the products are valid.

Any organization with a distributed development team has  many developers in different locations around the world . Ensuring that developers follow corporate standards and best practices is a challenge. When reporting across 3 or 4 time zones collaborate on a project, coordination becomes more challenging. The test will be decisive.

Any well-run business intelligence must ensure that the numbers used to make decisions are accurate . Intelligent decisions are based on the analysis of accurate, reliable and timely data. The test checks the accuracy of the data. Automated testing ensures that this verification is timely. Any industry that is highly regulated, has government oversight, or is at risk of an audit should respect the evaluation aspect of the test.

Five Benefits of Sentiment Analysis Using Power BI

This allows brands to understand consumer behavior and react based on those findings. So let's focus on the benefits that Power BI brings to users who are doing sentiment analysis.

1. You Don't Need to Be a Data Scientist

Using Power BI for sentiment analysis is actually working better. Why? Microsoft's Sentiment Analysis API is found in Microsoft Cognitive Services. Power BI Desktop allows users to better integrate sentiment analysis with Power BI reporting than with an add-in (ie via Azure Machine Learning and Excel). With an API call through Microsoft Consulting Chicago you can get a 1,000 score (more than what you would get from using the add-in) indicating a positive, negative or neutral score. The Text Analytics service provides natural language processing. 

When given unstructured text, it can extract key phrases, analyze sentiment, and can identify well-known entities (ie brands). These features allow users to quickly know what customers are talking about and their feelings. Power BI can use data from many different sources such as SQL databases or Facebook. That being said, using the Key Phrase API requires users to include some field data for each document, including the text, ID, and language fields. The "out-of-the-box" Sentiment Analysis API allows users to nix complex, intimidating algorithms.

 Some field data needs to be included for each document, including the ID and Language fields. The "out-of-the-box" Sentiment Analysis API allows users to nix complex, intimidating algorithms. Some field data needs to be included for each document, including the ID and Language fields. The "out-of-the-box" Sentiment Analysis API allows users to nix complex, intimidating algorithms.

2. Visualize the Data

Once you have that data, it is important to have the ability to transfer that data to the team so that you can act on it appropriately. First be aware that the Sentiment Score sent by Microsoft Cognitive Services Text Analytics uses a value between 0 (a negative sentiment) and 1 (a positive sentiment). Once data is connected in Power BI, it can be joined with other data tables and analyzed with many different types of visualizations. 

Power BI users can use and customize these built-in visualizations. For example, Pulse charts can allow you to build a timeline of events around reactions and response types. Data visualization has a lot of power and is a way of conveying information to decision makers in a way that makes important information easier to understand. These reports can be interactive and are valuable in real-time thanks to the Social Media Sentiment Analysis section, which is constantly gathering new information.

3. Shaping and Streaming Data Sets

Again, Power BI makes it possible not to be a coding wizard. With just a few easy steps, users can build a sentiment analysis solution using Microsoft Flow and Power BI. You can create many different data sets from a variety of sources using Power BI Desktop, including Microsoft Excel, web, text/CSV, SQL Server, and more. Today, your data can come from a variety of data sources and Power BI doesn't limit that connectivity. 

A lot of businesses move their data to the cloud, so that's not a problem. Power BI can do a sentiment analysis with the data which can be in the cloud or on-premises. Once you determine where your data is coming from, Power BI Desktop allows you to shape and combine that data and customize it into a useful query on a personal dashboard.

4. Data Storytelling

Sentiment Analysis and Power BI reporting allow users to take advantage of the new data storytelling trend corpora. It humanizes the data and, as mentioned earlier, visualizes the data so the critical data that is being gathered and analyzed can be turned into actionable data. Many corporations need a way to inform and engage their team to improve their overall operations – and sentiment analysis using Power BI provides a valuable strategy and solution to meet those needs. 

Also Read About: The Wonders Of Business Intelligence Reporting For Modern Enterprises

Business intelligence platforms – such as Power BI – are meeting the needs of organizations by merging core business workflows, processes, and embedded analytics. Actionable analytics is accelerating the decision-making process for data-driven companies. For example, monitoring

5. Data Flexibility

Power BI is very flexible with its dataflow. Power BI's Dataflow allows users the ability to generate and generate data without having to program an Extract-Transform-Load (ETL) system - talk about a time saver! Users don't have to wait for experts to build and test the ETL pipeline, just define the dataflow, test it, and if it doesn't work, try again! The entire development process of a dataflow allows users to easily collaborate with their entire team. Power BI also has a Social Engagement Content Pack, where users can analyze an organization's engagement on social media with KPIs based on sentiment, location, author and tag.