Data analytics is the science of analyzing raw data to make conclusions about that information. It is the process of examining data sets in order to find trends and draw conclusions about the information they contain. In recent days data analytics is done with the aid of specialized systems and software like Tableau, Power BI, python, etc.
Artificial Intelligence is a set of technology that enables the system to perform a wide variety of functions such as translation, voice recognition, image recognition, cyber-security, etc.
In Data Analytics, Artificial Intelligence can be used for Prediction, forecasting, data cleaning and preparation, Data visualization, categorizing and retrieving the data and also for many other purposes.
Let us see a few AI-powered tools that are being used for Data Analytics in this blog.
With the help of Artificial Intelligence, Data visualization tools like Tableau and Power BI can create more impactful and interactive visualization. These tools can also help data analysts to identify patterns and trends in their data that may not be easily available in spreadsheets. With Tableau AI, everyone—even those who are new to data analytics—can easily understand how predictions and insights surfaced and why they’re relevant. The result is better decisions made right in the flow of analysis.
Connecting to data analytics is one thing, but actually being able to understand what the data actually means is another. That is where Tableau is leaning on Artificial Intelligence to make a difference. With the enhanced Data stories feature, artificial Intelligence is used to create an explanation that Tableau refers to as a story about what the data means.
Power BI helps us to get insights from our data, Now with access to a set of models from Azure cognitive services, we can easily enrich those insights with Artificial Intelligence. Some of the important features in Powerand BI are AI-Augumented Visuals, AI-Powered Visuals, such as smart narrative, key influencers, Q&A is a powerful yet easy to use tools.
Data Cleaning and Preparation
With Al-powered data cleaning tools, data analysts can save hours of manual labour and reduce errors in their data sets. Tools like Powerquery editor, Trifacta and DataRobot can automate the data preparation process. It allows the data analysts to focus on analyzing the data instead of cleaning it.
The first step in the data analytics process is to identify bad data. The second involves taking corrective action. An example of this corrective action is replacing bad data with good data from another sample of the dataset. Before the advent of artificial intelligence (AI) and its subset of machine learning (ML), data analytics companies had to use traditional data cleansing solutions to do the job. These methods don’t work at scale or when working with ’empty-calorie data’. The traditional methods simply can’t keep up with large inflows of new data, of varying degrees of usefulness. Machine learning and deep learning allow the analysis of the collected data, making estimates, to learn and changing as per the precision of the estimates. As more information is analyzed, so also the estimates progress.
Natural Language Processing
Natural Language Processing(NLP) powered tools like OpenAl's GPT-3 and Google Cloud's Natural Language API can help data analysts extract insights from unstructured text data.
Natural language processing is a subfield of artificial intelligence and linguistics that focuses on developing techniques for computers to process, understand, interpret and generate human languages. NLP algorithms typically use machine learning methods to identify patterns in a given set of text data.The primary goal of NLP is to enable machines to interact with humans using natural language. This can be done by analyzing the structure and meaning behind words, such as syntax and semantics, as well as context-based approaches like discourse analysis or sentiment analysis.
These tools can also help data analysts automate summarizing large volumes of text data, saving them hours of manual labor.
Al-powered predictive modelling tools like H2O.ai and DataRobot can help data analysts build more accurate models in a fraction of the time. The DataRobot AI Platform allows users to easily develop models that make highly accurate predictions. It streamlines the data science process so that users get high-quality predictions in a fraction of the time it took using traditional methods, allowing them to implement those predictions and see the impact on their bottom line. These tools can also provide insights into which variables are most important in predicting an outcome, helping data analysts refine their models and make better decisions.
Al-powered code generation tools like GitHub Copilot can help data analysts to write code more efficiently and accurately. These tools can suggest code completion, reducing errors and saving time. The GitHub Copilot can suggest lines of code and even complete your functions. The AI learns from billions of source codes available on open platforms and GitHub repositories to suggest you a code line or sometimes whole functions.
In this blog we have learned some of the tools that use Artificial Intelligence to make the jobs of data analyst easier. These tools helps analyst to streamline our data, reduce errors, and make more informed decisions.
Hope you enjoyed reading this blog.