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The Remaking of AI-Based Insurance: Agile, Efficient & Personal

By Sachin Andhare

The year is 2025, and you are on vacation enjoying the sunset on the beach. Suddenly your smartwatch flashes an alert that an intruder is in your backyard and that your home is about to be invaded! You run to your room, grab your smartphone, open an app, and instantly see a surveillance drone flying over your home, streaming live data, and capturing the scene. You can hear the alarm blaring and see that the intruder is baffled and aborts the mission. You receive a call from your virtual insurance agent informing you that the situation has been assessed, and a claim has been filed automatically. The virtual agent has already shared the pictures and video with the insurance company, thanks to the data sent to the cloud by the drone. By the time you settle down for dinner, your virtual agent texts you that all the damage was assessed,…

What Is Predictive Analytics and How To Get Started

By Sachin Andhare

TL: DR: Predictive Analytics is using historical and real-time data to generate useful insights and predicting critical outcomes in the future. A large number of organizations are leveraging this AI-powered technique to reduce risks, improve operations, cut business costs, and increase the bottom line. What is Predictive Analytics? Gartner defines Predictive Analytics (PA) as a form of advanced analytics which examines data or content to answer the question “What is going to happen?” or more precisely, “What is likely to happen?”, and is characterized by techniques such as regression analysis, multivariate statistics, pattern matching, predictive modeling, and forecasting. Grandview research recently estimated that the global market for predictive analytics is growing at a CAGR of 23.2% and projected to grow to $23.9 Billion by 2025. Initially, the purview of a few visionary companies, predictive analytics is rapidly gathering momentum in the market.  Several industries such as banking, financial services, insurance,…

AI Automation To Enable Smart Manufacturing

By Sachin Andhare

Heavy industries such as Steelmaking are ramping up digital transformation initiatives to improve throughput, efficiency, safety, and reliability of their operations. Metals, mining, and machine tool building companies are embarking on multi-year journeys to digitize operations by adding connectivity, automation, and advanced analytics.  According to McKinsey, most heavy-industry sectors are at the middle stages of digital maturity (Digital 2.0) relying on rule-based automation and distributed control systems.  Some have made progress in digital maturity (Digital 3.0) and are using collaborative robots and advanced process control systems. However, a few digital pioneers are leveraging AI Automation and applying Machine Learning (ML) to operational data (Digital 4). These digital leaders offer powerful lessons that others can emulate and follow to successfully deploy ML at scale.  Drowning in Sensor Data  With the ubiquitous sensor network and pervasive connectivity in industrial manufacturing, data from production machines has continued to grow at an unprecedented scale. …

Removing Barriers to AI Adoption with dotData 2.0

By Sachin Andhare

A fundamental problem that prevents AI from reaching the broad market is the complexity of this technology, the challenges around its implementation, and usability. To enable more people to embrace AI, we must lower the barriers to AI adoption. Software vendors should build platforms that make AI simple to use.  Enterprises customers need the right set of tools for business, operations, or LOB users. Tools that make using AI as easy as a drag and drop operation. The majority of existing AI platforms are designed for the experienced data science professionals. But what about the non-data science community?  If you are a citizen data scientist such as BI developer or business analyst and would like to infuse AI in your applications, very limited options are available. That changes with dotData 2.0, a platform designed for BI and analytics professionals.So what’s new in dotData 2.0? In addition to significant UX upgrades,…

Top Industries Where AI Applications Are Creating Value (Part 2)

By Sachin Andhare

According to a recent Adweek survey, two-thirds of business executives say COVID-19 hasn’t slowed AI projects. Some 40% said that the pandemic even accelerated their efforts. While the economic activity and business sentiment has deteriorated over the past couple of months, the scope of AI has expanded, the biggest impetus being decreasing costs, improving performance, and increasing efficiencies. So which industry verticals are embracing AI and what are the top applications? In the previous post of this two-part blog series, we discussed how AI is transforming industries, enhancing performance across a wide range of applications in Banking, Fintech, Healthcare, and Industry 4.0. In this final part, we look at the remaining industry verticals along with top enterprise applications:Insurance: Mckinsey’s latest research report on the Insurance industry noted that in the wake of the global pandemic, the insurers should invest in digital and analytics capabilities to make them more customer-centric, simple,…

Top Industries Where AI Applications Are Creating Value (Part 1)

By Sachin Andhare

We often hear about AI in consumer applications such as Alexa voice service (Natural Language Processing), Netflix recommendation engine (Machine Learning), and Facebook Facial Recognition (Deep Learning). However, we don’t hear enough about enterprise AI applications. Mckinsey Global Institute had predicted in 2018 that AI will transform the enterprise world. Today AI is generating tremendous cost savings and improving business operations across several industries. AI’s significance and impact will get even more dramatic in the future. In this two-part blog series, we look at the top industries where enterprise AI applications are being deployed and how AI is adding value. Let’s start with the top four industries and enterprise applications where AI has moved from PoC to production at scale: Banking: Banks are constantly facing pressure from competitors, growing governance, and regulatory requirements.  The banking industry must manage financial and operational risk, prevent fraud, reduce customer defaults while keeping costs…

Demystifying Feature Engineering for Machine Learning

By Sachin Andhare

What is Feature Engineering FE is the process of applying domain knowledge to extract analytical representations from raw data, making it ready for machine learning. It involves the application of business knowledge, mathematics, and statistics to transform data into a format that can be directly consumed by machine learning models. It starts from many tables spread across disparate databases that are then joined, aggregated, and combined into a single flat table using statistical transformations and/or relational operations. Let’s say you are addressing a complex business problem such as predicting customer churn or forecasting product demand using applied machine learning. Assuming a team is in place and the business case identified, where do you start? The first step is to collect the relevant data to train the machine learning (ML) algorithms. This is usually followed by the selection of the appropriate algorithm or ensemble of algorithms. Choosing the right algorithm depends…

Are You Wasting Time Developing AI for Your BI Stack?

By Sachin Andhare

Harvard Business Review recently published interesting research findings from Accenture. The research shed light on how 1500 C-suite executives across 16 industries in 12 countries think about AI. The survey concluded that while most C-suite executives recognize the need to integrate AI capabilities, many fail to move beyond the PoC stage. According to the research, three out of four executives believe they risk going out of business entirely if they don’t scale AI. The authors espoused a radical idea of killing the PoC and jumping straight to scale. But how do you move beyond AI experiments? What tools do you need to successfully implement AI at scale? According to Accenture research, companies that are succeeding with AI are doing three critical things: 1)Pivoting from PoCs to pilots, 2) Committing to action, and 3) Ensuring the right team is in the right place from the very start. At dotData, we believe…

How to Evaluate and Select the Right AutoML Platform 

By Sachin Andhare

If you are in the market looking for automated machine learning  (AutoML) tools, there are plenty of choices. Forrester Research recently published a report highlighting nine Automation Focussed Machine Learning Solutions and named dotData a leader. The report underscores the importance of Feature Engineering and Explainability as key differentiating factors for leaders in the AutoML space. But if you are new to machine learning or are part of a BI and analytics team with a mandate to incorporate predictive analytics, how do you decide which AutoML tool is right for you? What are some of the factors that you should consider as you make your decision? The end-user & skill set Any data science project is going to start with identifying business use cases and requirements. The process is also heavily dependent on the available resources of the business as well as the skill-set of the primary intended users. In…

Shattering 5 Misconceptions about Automated Machine Learning

By Sachin Andhare

Ask data engineers about the most frustrating part of their job and the answer will most likely include “data preparation.”  Talk to a data scientist about the AI/ML workflow and what bogs them down, the answer invariably will be feature engineering. Analytics and data science leaders are well aware of the limitations of current AI/ML development platforms. They often lament about their team's ability to only manage a few projects per year. BI leaders, on the other hand, have been trying to embed predictive analytics in their dashboards but face the daunting task of learning how to build AI/ML models. Automated machine learning (AutoML) was built specifically to address some of the challenges of data science - the underlying practice at the heart of both problems. Like every new technology, there is a lot of confusion surrounding AutoML. Here are the top 5 misconceptions about AutoML: 1. AutoML means selecting the algorithms…

Are You Ready For Full-cycle AutoML on Python? – Part 2

By Sachin Andhare

conclusion from last week...Part 2 Beyond AutoML : Data Science Automation  While the rise of AutoML platforms has provided for faster execution of "test and learn" ML development, it has also brought about additional challenges. In most ML and data science projects, ML development is only one part of the process. The earlier stages of the process that require handling multiple raw tables and manipulating them based on in-depth domain knowledge to create flat, aggregated feature tables is a far more complicated and time-consuming challenge. The data and feature engineering process in enterprise data science has to deal with such different data as relational, transactional, temporal, geo-locational, and text data, which never starts from a single, flat, aggregated and cleansed table. Data science automation provides for a full-cycle automation process that includes data and feature engineering, in addition to standard AutoML. The ability to automatically generate features from massive and…