What Is AI Analytics? Purpose, Use Cases, and Tools (AI-driven Data Analytics)

Table of content

1. Introduction

2. Purpose of using AI for Data Analytics

3. Use cases of Artificial Intelligence in Data Analytics

4. Top AI Analytics Tools

5. Conclusion

Introduction

The practice of transforming an unstructured dataset into useful information is called data analytics. With the advancement in technology, AI analytics is projected to rise to unprecedented heights that will automate the practice of data analysis.

Data Analytics falls under Data Science to emphasize the queries raised by Data Science. Data Analytics aims to reveal the specifics of extracted insights, whereas Data Science focuses on seeking correlations between several data sets.

Purpose of using AI for Data Analytics

Artificial Intelligence empowers Data Analytics to analyze and understand the data from various sources to identify patterns and predict future trends or outcomes. AI-powered machine learning analytics uses algorithms and predictive statistical techniques to fetch useful insights from the collected data to support decision-making more accurately in less time.

AI-driven data analytics can analyze data from multiple sources and serve as a prediction of what might be beneficial or detrimental to the organization. Data Analytics and Artificial Intelligence, working hand in hand, can predict multiple key decisive components such as product development, consumer preferences, marketing channels, etc. Organizations nowadays prefer to hire AI developers in India with skills in Data science technology with an understanding of Machine Learning algorithms.

Use cases of Artificial Intelligence in Data Analytics

1. Demand Forecasting with AI

AI-driven data analytics and machine learning can assist o predicting changes in consumer demand. These machine learning algorithms can recognize patterns, identify the effects of datasets over one another, and detect demand fluctuation.

2. Uncovering New Insights with AI

Ai-powered analytics can identify the elements that contributed to the result. The algorithm can evaluate the cause that led to the result and suggest the steps to be taken next.

3. Predicting Business Outcomes with AI

Artificial Intelligence can predict sales and conversions with the consideration of the current practice of a business. In marketing, AI can smartly target the audience and potential prospects to grow business. In other departments also, AI analytics can help the organization collect data and make decisions that are profitable for the organization.

4. Customer Data Unification with AI

The process of collecting the incomplete, conflicting, raw data from multiple sources and turning it into a set of unified customer profiles to rely on as a decisive factor is called Customer Data Unification with AI.

Top AI Analytics Tools

I. Google Analytics

Google Analytics is an intelligent tool offered by Google to assist users in analyzing the data that is important for their business. AI-driven features may help users quickly analyze useful insights without much effort.

II. Adobe Analytics

Adobe Sensei empowers Adobe Analytics with the Artificial Intelligence and Machine learning algorithms for enhancing creative expressions, speeding tasks, uncovering insights, and offering real-time decisions.

III. IBM Planning Analytics

In IBM Planning Analytics, automating planning, budgeting and forecasting are supported by using Artificial Intelligence to make the analytics easier and more accurate.

IV. Azure Stream Analytics

Azure Stream Analytics is a data analytics platform offering services over streaming data. It assists developers in combining data with past data to derive insights for the growth of the business.

Conclusion

The application of artificial intelligence in the domain of analytics is increasingly being adopted in industries. With Artificial Intelligence, it will be easy for data scientists to quickly implement the strategy by extracting meaning from the data sorted by AI. Make a note AI can only help you with your data; decision-making is an art of data science that generates the need to hire data scientists for organizations. Within a decade, the integration of Artificial intelligence with data analytics might increase by 100%.