Alexa Actify Data Labs - Transforming decision making by Augmented Intelligence
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June Edition 2021

Actify Data Labs - Transforming decision making by Augmented Intelligence

Actify Data Labs - Transforming decision making by Augmented Intelligence

In recent times, several businesses and social applications are using the power of machine intelligence to solve complex day-to-day challenges. Leveraging Machine Learning and Artificial Intelligence for the best potential use, the solutions must address real-life business. Many machine learning and big data initiatives fail to address such issues as they are not correctly developed for handling varied situations. Augmented Intelligence, however, makes machine learning more useful and accessible to everyone by making it more practical. It can be seen as an alternative conceptualization of artificial intelligence that focuses on AI's assistive role, emphasizing enhancing human intelligence rather than replace it.

Actify Data Labs is an Augmented Intelligence company that delivers rapid business impact using its data science and data engineering capabilities. The company was founded in 2017 as a wholly-owned subsidiary of True North for transforming mid-sized companies. Its flagship platform ADAPTify accelerates the complete data-to-decisions life cycle and democratizes AI by empowering data scientists.

ADAPTify - Single, Smart, Simplified, Secure platform for all your data needs

ADAPTify is an end-to-end data and analytics platform developed by Actify Data Labs. It addresses all the problems that are necessary to convert data to dollars. The platform is well suited for the need of mid-sized companies in terms of time to deliver, ROI, and open source-driven architecture. ADAPTify addresses all four stages of the data life cycle – data consolidation, visualization, machine learning model development, and model implementation.

Some of the ADAPTify Features are:

  • Pre-built connectors: They connect to all common databases and enterprise applications. These connectors are used to consolidate data from multiple systems into a single data lake
  • Drag-and-drop: A set of extensive drag-and-drop data-wrangling components that business users can use to create mini-data-marts without even writing a single line of code
  • Visualization: Visualization and ability to create a "PPT-like" drag and drop dashboard
  • Common visualization tools: Connectors to common visualization tools (e.g., Tableau) if the need is to integrate the same with the platform
  • Building machine learning models: Ability to build machine learning models using drag-and-drop components
  • Implementing machine learning models: Ability to implement machine learning models from the platform as web services
  • SMS and Email campaigns: Ability to execute SMS and Email campaigns based on models or rules
  • Utilities: Industry-specific utilities (e.g., RFM micro-segments for retail, Text-based sentiment analysis, Credit bureau processing, etc.)
  • Data quality management: Data quality management and master data management utilities which can be used as drag-and-drop components

Risk Management

Quantifying, predicting, and managing risks is critical to ensure a healthy P&L and solvency for lending and insurance businesses. Regulators in these industries also mandate robust risk management practices and quantitative estimation of risk so that adequate provisions and capital are set aside to cover both expected and unexpected losses.

The company understands that data-driven strategies and machine learning are vital tools for building robust risk estimation and prediction tools. The development of cutting-edge risk analytics tools is predicated upon alternate data and effective machine learning methods. Fraud often consists of many instances or incidents involving repeated transgressions using the same method. Actify Data Labs leverages Machine Learning and Alternate Data to identify fraud risk. A deep learning methodology is used to identify triggers that affect fraud and utilize proprietary parameter tuning methodologies to fine-tune the models.

Marketing & Customer Analytics

Traditional marketing has been focussing on the volume of the customer base. Over time, firms have realized that every customer is not equally valuable for the firm. This has forced most companies to follow a segment-driven strategy. The focus is now shifting to creating a relationship with the right customer who can provide the maximum value to the firm. This has resulted in the adoption of analytical and data-driven strategies that facilitate a deeper understanding of customer's needs and the intrinsic value of each customer. Marketers across industries have started to adopt the customer life cycle approach. The focus is to effectively manage the entire customer life cycle and not looking at marketing processes in isolation.

Data has become a precious asset in driving a customer-centric life-cycle management strategy. Having more information about the customer is the deciding factor of data-driven marketing. The customer's completeness that is the 360 DEGREE view is the ideal goal that every organization strives to achieve. Data Science aided by Machine Learning models and in-depth business analysis are tools to leverage customer data to drive life-cycle marketing.

AI/ML Enablement Services

Almost every industry has witnessed an exponential increase in the volume of data facilitated by a tremendous increase in computation power. With globalization and increased competition, most companies have witnessed significant margin pressures. These phenomena have forced most companies to explore ways to generate insights and predictions from their internal and external data. The potency of using data for driving competitive advantage has become so well recognized that most companies have started viewing data as an information asset rather than as a by-product of business processes.

A significant investment goes into developing tools and techniques to generate insights, predictions, and strategies through data analysis. Some companies view data science as a business subject like marketing, finance, information technology, or operations. While others believe that data science is embedded in every function, every function needs to use analytics tools to deliver incremental business value. Irrespective of an organization's approach towards data science capability, it is critical to have the right people and resources to build the right tools. Actify Data Labs is continuously working with organizations to help build strong data science capabilities.

About the CEO

Hindol Basu is the CEO of Actify Data Labs. He is an Engineer, MBA, Data Scientist, author, and teacher. Basu has 16 years of experience in Analytics Consulting, advising clients in India, Asia Pacific, and North America in financial services, manufacturing, and retail. He has been the pioneer in establishing credit bureau analytics for the Indian market and developed the first credit bureau scores for the Indian credit bureau. Apart from this, he has also developed innovative solution frameworks in IoT, leveraging macro-economic data for risk estimation and building grey-box ML frameworks. He is co-author of the book titled "Business Analytics: Applications To Consumer Marketing," published by McGraw-Hill India.

"Our Vision is to be the most admired partner to transform decision-making by augmenting intelligence."

"Actify Data Labs aims to achieve the trifecta of superlative value creation for its customers, specializing in cutting edge data science, and building a culture that will make it the most aspiring place for a data scientist to work."


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