Data analytics is nothing new and neither is Artificial Intelligence (AI). Over the next few years, the impact of data analytics on the world will ramp up remarkably. In fact, the global market for data analytics is expected to be valued at over USD 77.64 billion, expanding at a CAGR of 30.08% by 2023. This is primarily because of the increased data generation and the ability to use statistical algorithms and the latest machine learning approaches to deliver actionable insights.
Data analytics can be used at a business scale to drive revenue, provide solutions to emerging trends, optimize marketing, and improve overall efficiency to create a competitive advantage. But with so many emerging technologies, including machine learning, AI, and big data, it can be challenging to learn where the value is coming from and what third-party providers can offer.
Artificial Intelligence for Data Analytics
A data analytics platform leverages AI capabilities to derive meaning from the massive volume of data generated every day. While advanced analytics enables businesses to reveal insights on current processes and even generate predictions from previously fed data, AI acts as a driver on this data crunching by feeding ML capabilities into these data models.
The best data analytics software and AI algorithms leverage machine learning solutions into the big data platform. In such a way, they transform data into useful information to drive revenue and operational efficiencies.
Given below are some of the ways AI is propelling data analytics.
- Deriving Actionable Insights from Data Analytics
- Deploy Analytics to Predict Accurate Results
Deriving Actionable Insights from Data Analytics
AI works together with machine learning techniques to find hidden insights and patterns from large datasets that developers often miss. AI-based tools help find answers about your business operations, such as, which operations cycle generated the maximum revenue in a specific quarter.
Deploy Analytics to Predict Accurate Results
AI-based algorithms evaluate data from different sources, providing accurate predictions on a business’s next strategic move. Besides, it offers insights about your customers, informing you about their choices and preferences. The algorithms also select the marketing channels as per your business requirements.
In short, artificial intelligence combines data collected from multiple sources and platforms, ramping up advancements across data science, data engineering, and business analytics categories.
Top Trends Observed in AI-driven Data Analytics
Data Analytics Software
Imagine if business intelligence was derived from a data analytics software that recognizes patterns and forms data relationships. This builds the roadway for actionable insights, interactive dashboards, and intelligent data discovery, using a set of analytics software on a business-grade analytics platform.
Machine Learning and Predictive Analytics
A trained platform allows you to analyze structured and unstructured data stored in different sources and data management platforms. AI and data analytics platforms combine machine learning with predictive analytics and self-service analytics to achieve business and data intelligence.
Text Mining and Natural Language Processing (NLP)
Unstructured data consists of scattered information, sentiments, and opinions of your employees, customers, and investors. Text mining and NLP extracts meaningful information from legal documents, videos, images, web pages, emailers, and social media messages to reveal the hidden value in unstructured data.
AI-driven Sentiment Analysis
AI-driven data analytics helps a business understand and pinpoint the customers’ views and opinions on social networks and the internet about its products and services. Additionally, it directs companies for targeted customer servicing and customer engagement and retention.
The Bottom Line
In a nutshell, AI-driven data analytics aims to increase overall business efficiencies and productivity, thereby increasing its brand value, generating revenue, and eliminating the need for traditional manual data processing techniques. With personalized business insights that are relatable and applicable to the business’s most crucial objectives, data analytics is here to stay.