Digital transformation is underway in practically every industry in the world. Companies, businesses and organizations throughout the world are leveraging their assets, big data and analytics for an edge over their competitors. In fact, data analytics and big data have gained popularity to the extent that data analysis for differentiation is considered advantageous by nearly 91% (click to learn more) of executives. What’s more is that information-based products are impacting
Data & Analytics have become main-stream. Executives and their boards are increasingly starting to question whether their organizations are truly realizing the full value of the insights. A study suggests that 58% of organizations have difficulties evaluating the quality of the data and its reliability, raising a big question to the stakeholders as to “can you trust your data?” On one hand these is this set of people who are worried about the authenticity of their organizatio
Digitization is disrupting every business and is spreading like a wild fire across every sector such as Banking, Financial Services, Insurance, Retail, and Manufacturing.
Digital Transformation does not happen overnight. It is a continuous process. That is why it is very hard to plan too far ahead in a digital transformation program. The technology is evolving so rapidly that your plans will certainly change.
How do you measure return on digital transformation in order to m
In this post, you discovered how to train a final machine learning model for operational use. You have overcome obstacles to finalizing your model, such as: Understanding the goal of resampling procedures such as train-test splits and k-fold cross validation. Model finalization as training a new model on all available data. Separating the concern of estimating performance from finalizing the model. The machine learning model that we use to make predictions on new data is call