As our world becomes more and more digitized, organizations need to leverage behavior data to provide a better customer experience and detect potential problems before they arise. This can be achieved by analyzing behavior patterns from digital platforms customers operate.
Contextual search can be labeled as a search capability that focuses on the context of the user-generated query including the original intent of the user to show the most relevant set of results. It is quite different than traditional search technologies which focus only on keyword matching.
Advanced analytical techniques help manufacturers reduce inventory levels of parts required in manufacturing activities while maintaining confidence that they will not run out of parts. Mosaic was tapped to help a manufacturing company in the semiconductor industry solve the complex problem of optimizing their inventory for both future orders and historical demand rates
Mosaic helped an energy company optimize its hydrocarbon inventory management process by working with them to develop a centralized system to collect and control inventory data quality, improve accuracy, and improve the bottom line. Why Intelligent Inventory Management Matters in the Oil & Gas Sector The oil and gas market Read more…
In this blog post, Mosaic examines how to identify & measure culture during a digital transformation.
Text data presents a tremendous opportunity to benefit all stakeholders of an organization – investors, employees, processes, and the all-important customer – if the organization can find a way to sift through this data in an automated way to extract key information and solve specific challenges. In that case, they could learn about their firm and start optimizing the way they operate.
Mosaic sees Digital Transformation differently; our view is that while the technology is a critical part of any Digital Transformation, it’s only a part of a greater whole that includes people, process, and culture change that all combine to enable effective use of the technology.
For the past several years, ML has exploded in popularity, while the excitement for MO has mostly plateaued. Why this has occurred is very much up for debate. One might surmise that ML is simply a better tool than MO, and therefore it replaced it in terms of popularity. This, however, is wrong-headed. ML and MO are typically used to solve very different problems. One might also think that problems MO has historically solved no longer exist.