Turning disparate data sources into actionable insights
The Data Journey
Step 1: Data Collection
Proprietary identification methodologies are utilized to establish and integrate the most relevant data from thousands of external sources. This data is collected via third-party API integrations, internal data sources and scraping from publicly accessible sites.
Step 2: Data Preparation
The data collected by Skai arrives in unstructured formats from across multiple data sources. This data is normalized, validated and cleansed using machine learning engines before being converted into structured formats. Automated quality control continually flags any changes to the structure of source data.
Step 3: Data Contextualization
Once the data has been structured and prepared, the next step is to extract context and ensure relevance. Skai’s patented natural language processing (NLP) algorithm is designed to do this with a high level of accuracy. The platform is unique in its ability to understand the context of specific sentences — not only what is said, but what is meant – and map this back to taxonomy values (e.g. flavors, benefits, ingredients) for insights specific to your market or category.
Step 4: Data Access
Support the data needs of your entire enterprise with 100+ pre-built dashboards that provide unprecedented accuracy and the ability to integrate into native business intelligence environments.
Step 5: Data Customizations
Don’t see a data source? Want to add a new geography? Looking for additional dashboards? No problem! Skai is fully customizable for insights that meet your specific business needs.