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Business intelligence, dashboarding and machine learning

by Sponsored Content
23rd May 22 2:41 pm

With new computing technologies being introduced, many businesses have started adopting Artificial Intelligence (AI) and Machine Learning (ML) consulting services. This adoption has led to a growing need to integrate the results of AI and ML algorithms into data visualisation tools.

Machine learning is a data analysis method that automates analytical model building. It is a sub-division of AI-based on the idea that systems can learn from data, identify patterns and be able to make decisions with less human interference. It brings dramatic change to analysing business KPIs. Artificial Intelligence-related KPIs assist companies in measuring AI success by ultimately displaying a concrete return on investment (ROI).

This article will give a clearer picture to business owners and employees who are looking to understand how the use of machine learning and artificial intelligence transforms the business sector. We’ll take a look at the existing integrations used by BI vendors and try to highlight the solutions that can be given by each type of integration

Types of the integrations

Built-in models/AI features

AI pre-built models help businesses add intelligence to applications and flows without having to collect or train data, and then publishing the businesses’ own models. For instance, a business can use a pre-built model in Power Automate to analyse whether customer feedback was positive or negative.

Examples are:

  • PowerBI Forecasting
  • Tableau Forecasting feature
  • PowerBI Anomaly Detection

Access to raw Python/R execution environments

Results can be delivered straight to the dashboard where machine learning models are trained and executed using raw Python or R framework. This type of integration is very convenient if you want to visualise the models’ output straight away.Here are some examples of this type of integration:

PowerBI custom Python queries

Power BI allows you to analyse your organisational data and collaborate with other team members, regardless of your location. However, PowerBI python/R queries can only be done on the user’s desktop.

Tableau TabPy

TabPy framework allows Tableau to remotely execute Python code and save functions. This results in smooth execution of ML models.

Server Side Extensions in Qlik Sense

Qlik Sense can be extended to use analytic connections, often called Advanced Analytics Integration (AAI), that integrates external analysis. However, in moments where you can’t find quick solutions, it requires so much effort to develop the extension.

Full cycle ML/AI support

The PowerBI system has a feature that makes it possible to train new models and get the predictions part of the typical BI and built-in transformations dataflow. This can occur due to its close integration with the Azure platform.

Other 3rd party services integrations

This integration allows BI tools to provide you with access to 3rd party services that can show results of some pre-trained models.

Check out these examples:

PowerBI and Azure ML integration – Power BI will show all the Azure ML web services that you can have access to.

Qlik AWS SageMaker integration – it enables direct data exchange between Qlik’s Analytics Engine and Amazon SageMaker in order to create a set of predictive data and updated calculations in real-time.

Tableau and JIRA integration – allows organisations to draw out and migrate data from JIRA to Tableau for analysis. Accounting software development company helps organisations to achieve better operational efficiency.

NetSuite ERP – offers built-in Business Intelligence, reporting, real-time dashboards, and analysis across all the integrated processes within the suite.

Conclusion

Business analysts now make use of data visualisation techniques to explore data stored in structured databases. Using different tools they create dashboards to make information easily accessible as well as help to analyse and adapt future strategies to improve KPIs.

AutoML for PowerBI and BigQuery ML for the google platform are good options for a trade-off between flexibility, quality, and development time.

You can opt to give your data scientists the liberty to build the models, and make use of systems that allow them to execute random python/R code right from the platform, such as PowerBI desktop Python Queries, Tableau TabPy service, and Qlik Sense server-side extensions.

Another option is to choose a platform that integrates with cloud solutions like Azure ML  or AWS SageMaker.

As great as these solutions might appear, they work best when combined with ETL and business intelligence development services. Get in touch with us and we’ll help you get and implement the right BI tool.

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