Can Autoregressive Language Model In Power BI Enhance Efficiency In Data Analytics?


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Feb 09, 2023


This is a Guest Blog by Mr Piyush Goel, Founder & CEO of Beyond Key

In today’s world, where everything is centered around data manipulation and meeting customer demands precisely, using natural language to search through your data is the quickest approach to finding an answer from it. To utilize the potential of data, most programmers, data scientists, and data analysts have enthusiastically adopted MS Power BI. It transforms diverse data sources into comprehensive, visually interactive ones while deriving meaningful insights from it. Power BI currently has over three million active developers who harness the potential of data while uncovering profound insights.

Can Autoregressive Language Model In Power BI Enhance Efficiency In Data Analytics?

Using a sophisticated language-generating model, which has the ability to generate text using algorithms, is being used by Power BI to evaluate usage trends while providing useful insights and incorporating analytic perspectives into their applications. GPT 3 is an advanced natural language AI model released in 2022 to automate regular tasks and communication while making better interpretations from data. It uses deep learning to produce human-like text with high quality based on a prompt in natural language. The use of an autoregressive language generation model within Power BI has been extensively edified with built-in prevention controls to ensure that no potentially harmful outputs are produced. With 175 billion parameters, it holds the record for the largest neural network ever created.

DAX is the transcription language used by millions of Power BI developers today, to define computations that can vary from one line to hundreds of code lines. With the unification of an innovative language generation model and Power BI, the developers and programmers will be able to use natural language, providing them with a homely platform to explain what they are trying to achieve while having Power BI generate a DAX expression for them. Before this amalgamation, creating a refined business logic from the data available was to be done by a DAX expert but with the introduction of a natural language model in MS power BI, the analysts need not be an expert of DAX while refining the prior formulas for the generation of a logical and insightful output. The use of such natural language-generating models is surely going to advance the application of data analyst tools like MS Power BI when it comes to drawing meaningful insights from available data and using it for further innovations and formulations.

Such pioneering language generation models were trained using nearly all of the data available on the Internet and demonstrated exceptional performance in a variety of NLP (natural language processing) tasks such as translation and question answering. While Microsoft is planning to make data analytics accessible to everyone and to provide a no-code and low-code experience for all aspects of data analysis. The company is now using an advanced language generation model to translate verbal text into code. It’s important to note that, while this simplifies programming, Microsoft emphasizes that users must still understand the logic of the application they’re creating. Such advanced language generation models are most likely more advanced than any other Natural language query functions in tools such as Excel, PowerBI, and Google Sheets.

Nowadays, as you scroll through your social media news feed, it’s difficult to believe that the person who wrote this article is human. Terabytes of digital data were used to train this powerful but unnerving AI-based model. An advanced language generation model once integrated with MS power BI, non-technical employees who need not be an expert in this regard will be able to create applications without writing any code while using a conversational language. It can now understand semantics, generate texts, provide relative answers, generate code, and much more. The goal is to use AI to simplify how developers build analytics. Furthermore, advanced language-generating models can respond to a user’s query in a manner that is indistinguishable from a human response. Multiple tools have already been developed to take advantage of such a model’s writing abilities and consolidation of such an advanced language model with Power BI data analysis will improvise its analytical capacity of it.

With the integration of an advanced language generation model and MS power BI best formula options would be generated from varied user inputs while allowing power BI developers to establish complete control over the formulas that are used, by identifying the expression from a catalogue of derived possibilities. While this simplifies programming, it’s important to note that Microsoft emphasizes that users must still comprehend the logic behind the app they are developing. The overall features are intended to assist people in selecting the appropriate formulas to achieve the desired results.

The autoregressive language-generating models, which are machine learning models, have been trained to yield text in multiple formats with very little input text. These can garner remarkable text that can address a wide range of machine-generated content needs. It is the most recent cutting-edge text generation technology which combines massive amounts of data with computing to achieve spectacular levels of accuracy. So, if a competent neural network is embedded with Power BI, then that is going to be a sea-change in the world of data analytics while improving the efficiency and effectiveness of Power BI processes. In other words, enhancing the overall process of data processing and delivering many desirable outputs with an advanced natural language AI model.


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