Enterprise organizations have large amounts of data which is utilized by multiple Machine Learning (ML) models over various software frameworks. These models provide trends and insights from the data that can help enterprises define business rules around their processes. However, if certain aspects of this data are removed from the datasets, it could influence the business rules and policies in place. When a user requests data to be removed, the model retraining may be required called Machine Unlearning (MU). Recent research works in the area of MU include different methods of retraining the machine learning models. It turns out that there is lack of work in removing certain aspects of data, and quantifying its impact on the models. This paper aspires to provide a novel methodology IDMU (Impact Driven Machine Unlearning) that performs quantification of the impact of data removal requests while performing MU. Our method provides recommendations for data removal requests, factoring in underlying features of data. The results from the industrial application and evaluation of our method on a financial services dataset are encouraging. The overall IDMU had a mean MAPE of 10.25% over a set of 120 data removal requests. It also saved ~1900 hours of model retraining time by factoring in urgency and impact of data removal requests over a period of three years.

Enterprise organizations have vast datasets that need comprehensive analysis on a frequent basis, in order to manage data and take business decisions based on it. However, we observe that there can be a lack of industry standards for definitions of key terms. Additionally, there is a lack of governance for maintaining business processes. This typically leads to disconnected siloed datasets generated from disintegrated systems. To address these challenges, we developed a novel, integrated methodology DaME (Data Mapping Engine) that performs data mapping using ensemble of NLP techniques.The results from the industrial application and evaluation of DaME on a financial services dataset are encouraging that it can help reduce manual effort by automating data mapping and reusing the learning. The accuracy from our dataset in the application is much higher at 69% compared to the existing state-of-the-art with an accuracy of 34%. It has also helped improve the productivity of the industry practitioners, by saving them 14,000 hours of time spent manually mapping vast data stores over a period of ten months.

Due to increased competition in the IT Services business, improving quality, reducing costs and shortening schedules has become extremely important. A key strategy being adopted for achieving these goals is the use of an asset-based approach to service delivery, where standard reusable components developed by domain experts are minimally modified for each customer instead of creating custom solutions. One example of this approach is the use of contract templates, one for each type of service offered. A compliance checking system that measures how well actual contracts adhere to standard templates is critical for ensuring the success of such an approach. This paper describes the use of document similarity measures - Cosine similarity and Latent Semantic Indexing - to identify the top candidate templates on which a more detailed (and expensive) compliance analysis can be performed. Comparison of results of using the different methods are presented.