AI-Powered Data Automation represents the next wave in the data and analytics market thanks to the combination of two technologies: Analytics and Artificial Intelligence (AI). It includes embedding Artificial Intelligence, often in the form of Machine Learning (ML) and Natural Language Processing (NLP), into traditional analytics.
Differently from traditional Business Intelligence (BI), it relies on ML technologies to enable faster access to insights to both business and technical users. By automating many time-consuming and bias-prone tasks, the AI-Powered approach expands the capabilities of business analysts and application developers, allowing them to develop AI-Powered models to be embedded into business applications.
Recent years saw a huge increase in the adoption of Analytics & Business Intelligence (ABI) solutions, which led to a revolution in how customers access data. These tools offer some key capabilities.
Automated insights with Natural Language Generation (NLG)
NLG can provide companies with an efficient way to automate the “translation” process from a complex set of data to a descriptive text: a more clear and concise way to communicate with clients or other entities who may not be data experts.
NLQ allows business users to query the system by simply asking questions in ordinary language rather than writing code, further reducing the gap between the business and the insights generation.
Auto visualization of relevant patterns
In order to support business users in the identification of potentially relevant patterns, BI solutions can apply a list of standardised algorithms such as statistical evaluation or clusterisation algorithms.
In real-life scenarios, data is not static and evolves with time, so it’s important to monitor the accuracy of BI reports over time. ABI tools can monitor data in real-time and notify users in case of unexpected or dramatic changes.
The continuous and fast development of AI techniques has accelerated the rolling out of platforms that serve the whole AI ecosystem, addressing data science and ML needs.
The operational tools that allow users to get easy access to Data Science and Machine Learning processes are the so-called Data Science Machine Learning (DSML) platforms. These solutions provide a smooth data consumer journey, offer a blend of functionality to meet the needs of business and technical experts, and support the continuous and sustainable creation and consumption of insights. The organisation, coordination and management of Machine Learning models is often provided through a single visual interface that allows all the ML development phases (such as experiment management, automatic model creation, debugging and model drift detection) to be performed.
Reply strongly believes in the AI-Powered Data Automation approach: we are actively exploring, testing the tools available in the commercial landscape and experimenting them in order to better understand the advantages and disadvantages of this new trend based on Artificial Intelligence and Machine Learning.
Reply's experts help to choose the best platform or solution for each customer’s specific needs through surveys and assessment sessions, analysing the business context in order to define the adoption measures of the new AI-Powered Data Automation approach.