Data is necessary to feed algorithms and to produce analytics, but avoid falling into the trap of simply collecting and storing more data. You need to be able to transform any data you collect into useful information, otherwise it is more likely to just waste resources and add even more complexity. IT Web brought this topic to our attention in their article, “AI needs the right information architecture.”
This is where artificial intelligence (AI) can be of assistance. Analytical challenges have become overwhelmingly complex for many organizations. Data often travels a complicated journey before reaching a place of purpose.
Machine-learning can identify patterns in data and uses statistical algorithms to build a model based on those algorithms. With machine-learning, companies can tap into the rich vein of data in their historical system of records. This results in predictive analytics – based on data from the past, there is the capability of predicting the future.
Melody K. Smith
Sponsored by Access Innovations, the world leader in taxonomies, metadata, and semantic enrichment to make your content findable.
