AI & Data Analytics
Data analytics is a standard practice across enterprises. Organizations use Business Intelligence (BI) tools to highlight trends and make sense of raw data. Artificial Intelligence (AI) augments data analytics further, however. It utilizes intelligent applications to generate actionable insights for informed business decision-making.
Artificial intelligence and data analytics combine to leverage numerous technologies, including machine learning and natural language processing (NLP). These drive value from raw data, which comes from multiple sources in structured and unstructured formats. Previously, cleaning data in preparation for use was a time-consuming affair.
"By the end of 2024, 75% of organizations will shift from piloting to operationalizing artificial intelligence (AI), driving a 5 times increase in streaming data and analytics infrastructures." -Gartner
Gartner states - By the end of 2024, 75% of organizations will shift from piloting to operationalizing artificial intelligence (AI), driving a 5 times increase in streaming data and analytics infrastructures.
AI capabilities augment data analytics and empower organizations to create data-driven decisions. Data analysts and data scientists save time by not performing repetitive manual processes and spend more time on specific skill tasks. Organizations find valuable data more easily, saving time and resources.
How does AI Enhance Data Analytics?
AI plays a vital role in efficient business processes and applications by using automation and data analytics. Some of the ways AI augments analytics include:
Automating data analysis using machine learning algorithms which provide undiscovered trends, patterns, and insights to make better-informed decisions.
Automating and simplifying the report creation process with Natural Language Generation.
Utilizing Natural Language Query (NLQ) to help organizations find answers intuitively and efficiently without constant reliance on data scientists and engineers. Staff can query AI-driven BI applications using plain English.
Streamlining Business Intelligence (BI)
How do AI and Analytics work together?
Traditional BI used rule-based programs to provide static analytics reports from data. Augmented analytics like Machine Learning and Natural Language Generation. These techniques automate data analytic activities and provide data visualization in real-time.
Machine learning uses data to identify, establish, and learn the relationships between data points. In doing so, it generates patterns and trends. It improvises data when adapting to new changes by referring to precedents and previous experiences.
Natural Language Generation (NLG) converts findings from machine learning data into valuable insights using language. Machine Learning derives all data insights, and NLG transforms them into human-readable format.
Augmented analytics supports user queries in plain English. It presents answers in informative visuals and text. Non-technical users easily understand and interpret insights from the data.
How does Augmented Analytics help organizations today?
The primary advantage of Augmented Analytics is to improve business decision-making, productivity, and ROI from processing data. Augmented Analytics in AI accesses relevant data more easily and quickly with actionable insights.
Ways AI and Data Analytics help organizations today
Faster Data Preparation
Data preparation is a time-consuming activity. Augmented Analytics saves data analysts time by automating the extract, transform, and load (ETL) data process to get data in better shape before performing analytics and realizing valuable data.
Automating Insight Generation
Augmented analytics works on ready-to-be-processed data. Machine learning algorithms automate analysis and generate insights faster, and in real-time. Manual work normally takes several days of a Data Analyst’s time to generate insights.
Enabling Data Querying
Ask questions in plain English and get answers in real-time. Data querying used to be a cumbersome process and needed technical know-how. Augmented Analytics, on the other hand, makes data querying a breeze by asking questions in natural language. NLQ and NLG translate natural language into machine language so that data query is accessible to all.
Increased Analytics User Base
Non-technical data query means non-technical professionals can now use BI products and analytical tools. Generate insightful reports without relying on data scientists and engineers. An increased userbase for analytical products leads to increased productivity and faster decision-making across the organization.
Augmented Analytics uses automation to generate reports more quickly. Time and effort are saved by forgoing the wait for technical help. Users can customize reports through multiple drag-and-drop options.
Where do we use Augmented Analytics?
Augmented Analytics solves a multitude of business challenges by enriching data and delivering actionable insights faster. Some application areas include demand forecasting, fraud, anomaly detection, performance tracking, and deriving customer and market insights.
Banking and Financial Sectors - Generate personalized client portfolio analysis reports. Augmented analytics mine insights and recommendations to detect and prevent fraud or anomalies.
Retail and FMCG companies - Track sales and market insights to make data-driven decisions.
Media and Entertainment - Use Augmented Analytics-generated insights to provide tailored content to users.
Marketing and Sales - Extract data from external and internal sources to gain insights into sales, customer trends, and product performance.
In an infinite data world, using data efficiently is a competitive edge. As digital transformations expand, produced data is progressively complex and massive in scale. More enterprises adopt Artificial Intelligence to achieve quick insights and informed decision-making. All valuable insights are extracted and covered. Artificial Intelligence transforms data and makes it more valuable than ever before.
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