The ability to use AI to enhance decision making, reinvent business models and ecosystems, and remake the customer experience will drive the payoff for digital initiatives through 2025.
The AI (artificial intelligence) ecosystem contains a broad number of technologies and approaches, including machine learning, with new methodologies entering the market constantly. Because AI initiatives are complex and require specific advanced skillsets, GreenPages helps clients determine which areas within their organizations are the best candidates for AI initiatives to deliver the highest business value.
Any successful AI or data science initiative first starts with a comprehensive data investigation. This process involves planning, cleaning and grouping data, collecting data sets, mining data for patterns, and refining algorithms. Before any organization can solve a problem or use the data to drive revenue for the business, the data must be accurate and it must be organized.
Data Scientists often spend 90% of their time in the data preparation phase before modeling.
GreenPages helps clients through the data investigation process from strategy to data collection so that clear objectives are set and parameters are observed, and all activities are closely tied to the specific business goal the data investigation is designed for.
Any intelligent system begins with raw data which, on its own, has little business value. Someone must prepare and transform raw data into features that represent the business case so that algorithms can extract meaning. GreenPages helps clients with feature engineering initiatives—including eliminating irrelevant features that could derail the process—to ensure accurate modeling.
Feature visualization is a crucial approach that helps identify and recognize patterns inside of neural networks. Neurons are multifaceted and sense abstractions, edges, colors, textures, and patterns. The process of feature visualization involves neurons interacting with each other to form a larger, more complex understanding of data sets. By implementing feature visualization, GreenPages helps organizations build intelligent, intuitive systems capable of extreme efficiencies and predictive analytics.
GreenPages helps clients eliminate data redundancies so that data can be more easily and accurately stored and used by information systems and databases. Whether data sets are structured (text files that have been categorized and ordered) or unstructured (emails, spreadsheets, digital images, word processing files, etc.), modern organizations can use data modeling to unite large and disparate data structures into a cohesive, inseparable whole.
Pre-processing & Data Augmentation
When it comes to machine learning and building neural networks, robust data sets are crucial to ingest the huge number of parameters necessary to achieve positive results. But what if you want to implement machine learning in your organization but your data set is small?
Data augmentation is an approach GreenPages uses to help clients add “noise” by virtually increasing the number of data sets you have based on your existing data. By pre-processing the data, for example, darkening the color of a particular image, you achieve two images for your data set instead of one; although it’s technically the same image, its variations (or noise) add complexity and robustness to your data set, enhancing your machine learning initiatives.
With machine learning, defining the experiment is crucial; basing business decisions on data patterns or assumptions that don’t fit your goals will yield unusable results. You must determine the business objectives, the parameters, and ultimately the success criteria.
GreenPages helps clients apply different algorithms or input features to run multiple experiment trials that are backed by evidence and not pre-existing expectations. This ensures a final experiment that has undergone rigorous testing under against multiple variables—including a baseline (control)—to achieve the highest accuracy.
GreenPages helps clients use predictive analytics to gain competitive advantage by anticipating business spikes, customer behavior patterns, and market trends. By going beyond descriptive reporting, predictive analytics approaches allow modern businesses to generate new information, recognize patterns, and predict outcomes and probabilities.
Every digital-era company can benefit from predictive analytics to drive new services and innovation, as well as enhance organizational efficiencies and minimize risk by averting possible future system or operational crises.