By Neil Radan, contributing writer for Diginomica.com
In my last piece, I asked: do data scientists really spend 80% of their time wrangling data? Now it’s time for the follow-up: can machine learning make a difference in data management? Can it alter that 80/20 data cleansing ratio?
By Ajay Khanna, contributing writer for Dataversity.net
With 2022 well underway, many businesses are deploying new data-driven strategies and models to promote growth, accelerate digital transformation, and increase operational efficiency. However, to maximize success, enterprise leaders must incorporate decision intelligence and data-driven insights into their decision-making processes.
By Morgan Rehnberg, contributing writer for Phys.org
A common problem in the geosciences is the need to deduce unseen physical structure based on limited observations. For instance, a ground-penetrating radar observation attempts to infer underground structure without any in situ measurements. This class of problems is called inversion, in which an assumed physical model is repeatedly adjusted until it is consistent with observations.
By Thomas C. Redman, contributing writer for HBR.org
Regular people, those without “data” in their title, are central to all data-related work. Without buy-in and contributions from your company’s rank and file, even the cleverest AI-derived model will sit idle and “data-driven decision-making” will just go around in circles. Conversely, costs go down and products get better when people help improve data quality, use small amounts of data to improve their team’s processes, make better decisions, and contribute to larger data science and data monetization initiatives. Yet, recent research confirms that these people are missing from too many data programs, limiting the scale and impact of these efforts.
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