The information age has sped up the process of data transfer, challenging businesses to handle and keep up with these massive and high-velocity data sets. Exploring and analyzing these complex data sets for information and insights requires advanced processes and technology. With AI preface in data analytics, businesses can process a vast amount of data in a short time to derive valuable insight.
The AI-powered software can analyze and process data faster, better, and at any scale. By overcoming human limitations, AI-enabled data analytics improves business intelligence and solves various business problems by uncovering hidden patterns, trends, and insights.
DefineRight AI integrated solutions support businesses with automatic data collection, sorting, discovering new insights, and predicting business and marketing outcomes.
AI and Data Analytics
AI is a wide-ranging field of computer science that refers to any intelligence demonstrated by the machines, usually cognitive functions such as learning, problem-solving, reasoning, and representation.
By applying this AI technology, industries are transforming their process and outcomes. They follow some common problem-solving approaches with AI; statistical techniques, search optimization, and artificial neural works.
AI application includes but is not limited to analytics, gaming, fashion, retail, automotive, etc.
Managing sourced data and ensuring its accuracy, relevancy, and consistency is another complicated task for organizations. Since data-based solutions are crucial and fuel an organization’s strategic planning and growth, the accuracy and relevance of input data are of paramount importance.
The increase in new data sources, data security issues, and awareness of data privacy among customers is adding to the challenge of data management.
Relationship between AI and Big Data
AI and Big data share a synergistic relationship between them. AI requires a large scale of data to learn and improve the decision-making process, and big data use AI for in-depth and accurate data analysis. They are also dependent on each other in terms of research and technological innovation. AI helps leverage advanced analytical capabilities like predictive or augmented analytics to discover actionable insights from big data. A high-value data insight helps improve business performance through anticipating and capitalizing on emerging marketing trends and consumer behavior, intelligent decision support, etc.
AI in Data Analytics: Augmented Analytics
Augmented analytic/AI-driven analytics use analytics, Machine learning, and Natural language generation to automate the data management process and support other complex data processing parts like identifying hidden patterns, uncovering trends, and actionable insights.
AI-driven analytics enables businesses to make data-driven decision making and easy handling of data. AI, ML, and NLP tools used for text analysis can uncover revolutionary insights.
How Advances in AI handles data wrangling and analytics
Analytics is a time-consuming task and requires skilled and talented manpower.
With the advancement in AI, like ML, natural language generation, natural language query, and improved UI, analytics is becoming automated and more accessible.
These advances in AI support data wrangling and analytics in the following ways.
AI systems with machine learning algorithms can analyze data autonomously.
Automated action based on the analysis or insights highlighted helps decide the best course of action.
Complex ML-based triggers or notifications can get set up to automate actions or discover insights.
ML can use past experiences and instances to improve data and make the change.
Report Generation Automation
Natural language generation enables automated report generation. It converts the findings from machine learning data into an easy-to-decipher insight or human-readable format.
Accessibility of analytics
Using Natural language queries or Natural language user interaction, users can easily and intuitively extract answers and get an insight into the data.
The increase in accessibility of analytics increases data literacy even among non-technical users.
Analysis of Unstructured data
Advances in AI have expanded the scope of analytics by analyzing unstructured and semi-structured data.
NLP enables text analysis, transcription enables speech analytics, and computer vision can perform image and video analytics.
Analysis of anonymized data
The increase in protection of personally identifiable information and analytics has increased the demand for the analysis of anonymized data. With the help of synthetic PII copies, which get generated artificially using simple rules, statistical modeling, simulation, and other techniques, the real-data restrictions get removed.
Augmented Analytics Improving Business Intelligence
AI is slowly becoming a critical component of business intelligence.
The technology helps BI in improved decision-making and a better ROI. List of a few ways how augmented analytics transforms BI are
Accelerate data processing
Extraction and cleaning of data is a time-consuming process.
Augmented analytics automates the data extraction, transformation, and loading process for further analysis.
Thus, AI reduces the time spent by data analysts on data processing.
Explore the data
AI in the BI system automatically classifies columns, tags them, and joins the matching data across the sets.
Using NLI, the AI system helps in exploring the data, and provides users with suggested questions and answers.
Automated visualization helps in a deeper insight into the data.
Learning from the end-user
AI in BI systems customizes and improves over time through machine learning. It learns from the user’s behavior and questions. The more a user interacts with a BI tool, the better AI knows what a user wants in analysis in presentation.
Data Science Innovating Banking and Finance
Data science & analytics is helping banking and finance to detect frauds, risk modeling for investment banks, analyze credit risk, operation & liquidity risk, sales analysis, and others. Thus, the technology is supporting to establish a more stable system.
Empowers everyone to use analytics
The querying feature of augmented analytics helps in getting a rich data insight, enabling everyone in the organization to use analytics products. It has increased the user base of BI and analytic tools as it does not requires to be a data scientist any more.
Future of data analytics with AI
According to a study by Gartner, around 74 percent of businesses will operationalize AI by 2024. It will lead to a five times increase in data and analytics infrastructure. The growing influence of AI is driving organizations to use augmented analytics to remain competitive.
The companies embracing AI in BI solutions are already delivering today with improved efficiency, performance, and ROI.
Whether a large enterprise or a small business, the infusion of AI with BI helps them gain an edge over the competition. For example, Netflix, whose users make 80 percent of their decisions based on the company prediction. Also, data deficient small businesses can succeed using AI and NLG tools and make better business decisions.
In the coming future, BI solutions will begin delivering strategic BI knowledge by integrating NLI tools into its AI in BI products.
Another cutting-edge integration of AI in BI is social analytics. The companies track a product’s reputation by users with social analytics.
With the arrival of cloud computing, the problem of the massive amount of computing power and storage space will resolve. It is one of the biggest trends in AI by virtually creating supercomputer-powered apps and services. DefineRight AI-enabled analytics integrates NLP, ML, NLQ, cloud computing, and other advanced tools to improve the data wrangling process, deliver high-value data insight, and improve BI. Our AI integrated BI solutions help businesses in data governance, better business results, and gain a competitive edge.