Augmented analytics: when AI improves data analytics
Augmented analytics: the combination of AI and analytics is the latest innovation in data analytics. For organizations, data analysis has evolved from hiring “unicorn” data scientists – to having smart applications that provide actionable insights for decision-making in just a few clicks, thanks to AI.
Augmenting by definition means making something greater in strength or value. Augmented analytics, also known as AI-driven analytics, helps in identifying hidden patterns in large data sets and uncovers trends and actionable insights. It leverages technologies such as Analytics, Machine Learning, and Natural Language Generation to automate data management processes and assist with the hard parts of analytics.
According to Gartner, by the end of 2024, 75% of enterprises will operationalize AI, driving a 5x increase in streaming data and analytics infrastructures. The capabilities of AI are poised to augment analytics activities and enable companies to internalize data-driven decision-making while enabling everyone in the organization to easily deal with data. This means AI helps in democratizing data across the enterprise and saves data analysts, data scientists, engineers, and other data professionals from spending time on repetitive manual processes.
How does AI improve analytics?
The latest advances in Artificial Intelligence play a significant role in making business processes more efficient and powerful with the help of automation. Analytics, too, is becoming more accessible and automated because of AI. Here are a few ways in which AI is contributing to analytics:
- With the help of machine learning algorithms, AI systems can automatically analyze data and uncover hidden trends, patterns, and insights that can be used by employees to make better-informed decisions.
- AI automates report generation and makes data easy-to-understand by using Natural Language Generation.
- Using Natural Language Query (NLQ), AI enables everyone in the organization to intuitively find answers and extract insights from data, thereby improving data literacy and freeing time for data scientists.
- AI helps in streamlining BI by automating data analytics and delivering insights and value faster.
So, how does it work?
While traditional BI used rule-based programs to deliver static analytics reports from data, augmented analytics leverages AI techniques such as Machine Learning and Natural Language Generation to automate data analysis and visualization.
- Machine Learning learns from data and identifies trends, patterns, and relationships between data points. It can use past instances and experiences to adapt to changes and improvise on the data.
- Natural Language Generation uses language to convert the findings from machine learning data into easy-to-decipher insights. Machine Learning derives all the insights, and NLG converts those insights into a human-readable format.
Augmented analytics can also take in queries from users and generate answers in the form of visuals and text. This entire process is of generating insights from data is automated and makes it easy for non-technical users to easily interpret data and identify insights.
Augmented analytics for enterprises
Business Intelligence can help in making improved business decisions and driving better ROI by gathering and processing data. A good BI tool collects important data from internal and external sources and provides actionable insights out of it. Augmented analytics simply improves business intelligence and helps enterprises in the following ways:
- Accelerates data preparation
Data analysts usually spend most of their time in extracting and cleaning their data. Augmented analytics takes away all the painstaking processes that data analysts need to do by automating the ETL (extract, transform and load) data process and providing valuable data that can be useful for analysis.
- Automates insight generation
Once the data is prepared and ready for processing, augmented analytics uses it to automatically derive insights. It uses machine learning algorithms to automate analyses and quickly generate insights, which would take days and months if done by data scientists and analysts.
- Allows querying of data
Augmented analytics makes it easy for users to ask questions and interact with data. With the help of NLQ and NLG, it takes in queries in the form of natural language, translates it into machine language, and then produces meaningful results and insights in the form of easy-to-understand language. This makes data analytics a two-way conversation wherein businesses can ask questions to their data and get answers in real-time.
- Empowers everyone to use analytics products
The feature of querying data makes it possible for professionals to delve deeper into their data and also enables everyone in the organization to use analytics products. Enterprises no longer require data scientists or professionals with technical expertise to use BI tools to analyze data. This has led to an increase in the user base of BI and analytics tools.
- Automates report generation and dissemination
With augmented analytics, insights can be generated from data at the speed of thought. These insights can further be used to automate report writing, saving a lot of manual efforts in report generation.
Augmented analytics in action
Augmented Analytics can be used to solve various business problems. Some use cases and applications of it include demand forecasting, fraud, and anomaly detection, deriving customer and market insights, performance tracking, and so on. Here are a few examples:
- Banking and financial institutions use augmented analytics to generate personalized portfolio analysis reports.
- Retail and FMCG companies use intelligence powered by augmented analytics to track market insights and make informed decisions.
- Companies in the financial services sector use recommendations and insights mined by augmented analytics to detect and prevent fraud or anomalies.
- Media and entertainment companies use insights generated from augmented analytics to provide tailored content to their users.
- Marketing and sales functions across businesses use augmented analytics to extract data from external and internal sources and gain insights into sales, customer trends, and product performance.
The complexity and scale of data being produced and used by businesses across sectors are more than humans alone can handle. Enterprises have started adopting the new AI wave in analytics to tackle data and improve their processes. Augmented analytics is the disruptor, and leveraging it with BI platforms can help businesses to analyze data faster, optimize their operations and make data teams more productive.
Author: Neerav Parekh