Businesses are flooded with constantly changing thresholds brought on by seasonality, special promotions and changes in consumer habits. Manual monitoring with static thresholds can’t account for events that do not occur in a regularly timed pattern. That’s why historical context of influencing events is critical in preventing false positives, wasted resources and disappointed customers.
As enterprises seek to accelerate the process of getting insights from their data, they face numerous sources of friction. Data sprawl across silos, diverse formats, the explosion of data volumes, and the fact that data is spread across many data centers and clouds and processed by many disparate tools, all act to slow the progress.
In digital transformation projects, it’s easy to imagine the benefits of cloud, hybrid, artificial intelligence (AI), and machine learning (ML) models. The hard part is to turn aspiration into reality by creating an organization that is truly data-driven.
The manufacturing industry, like any other industry, is not immune to data challenges. Sourcing data, wrangling it and ensuring it’s being used in a governed, standardized way are not uncommon problems. Particularly in manufacturing, issues surface with inventory management, within the supply chain and with logistics.
