Actify Data Labs: Expediting Data Lake Creation
According to Forbes, while the sci-fi-sounding AI scenarios highlight the technology’s incredible computational power, the practical and effective applications begin with data. To make AI real, organizations face three practical challenges: 1) integrating data across multiple sources 2) resolving data quality issues 3) implementing machine learning models quickly and efficiently. Most organizations spend a lot of time, technology resources and money for data integration and encounter significant delays in implementing machine learning models. These factors reduce ROI and increases the time required to leverage machine intelligence.A data lake that stores data from various source systems in a native or raw format is often used as the most robust and versatile solution for integrating data into a single repository. The data lake stores structured, semi-structured and unstructured data in a single repository. Data from the lake needs to be organized into specific data marts for building machine learning solutions.
In this backdrop enter Actify Data Lab’s ADAPTify - a single, nocoding, end-to-end data platform that addresses the challenge of data integration, data lake creation, data mart creation, development of machine learning models and singleclick implementation of machine learning models as APIs.
Using ADAPTify, organizations could solve the entire data integration challenge and create a complete data lake up and running in 4 weeks – along with data being populated in the lake. The simple drag-and-drop components in ADAPTify could be used to wrangle the data in the lake to create data marts within 2 weeks.
Once the data marts are ready, the building blocks for creating machine intelligence are in place. Actify Data Labs uses its machine learning capabilities to build models leveraging both traditional and unstructured data. Actify’s enablement solution helps organizations internalize data science capabilities and build such models themselves using data from the data marts.
Using ADAPTify organizations across financial services and retail industries have addressed business problems like creating customer 360 view, automating credit underwriting, addressing churn, creating micro segmentation, enhance supplier effectiveness, enhance channel effectiveness etc.
In addition to the robust data platform, organizations also leverage Actify’s machine learning capabilities to build machine learning solutions in areas such as satellite and geospatial analytics, sensor data analytics, alternate data for credit underwriting and customer management solutions.
Moving ahead, the Actify team is planning to launch the next version of ADAPTify, which will include new features such as automated metadata management, configurable chat-bot to query data and enhancement of the ML library.
ADAPTify provides simple drag-and-drop components to build machine learning models and dashboards – thereby empowering citizen data scientists. The platform is equipped with high-end utilities to leverage unstructured text, scanned documents, images and streaming data.
Using ADAPTify, organizations could solve the entire data integration challenge and create a complete data lake up and running in 4 weeks – along with data being populated in the lake
Using ADAPTify organizations across financial services and retail industries have addressed business problems like creating customer 360 view, automating credit underwriting, addressing churn, creating micro segmentation, enhance supplier effectiveness, enhance channel effectiveness etc.
In addition to the robust data platform, organizations also leverage Actify’s machine learning capabilities to build machine learning solutions in areas such as satellite and geospatial analytics, sensor data analytics, alternate data for credit underwriting and customer management solutions.
Moving ahead, the Actify team is planning to launch the next version of ADAPTify, which will include new features such as automated metadata management, configurable chat-bot to query data and enhancement of the ML library.
