AtScale has been helping bridge Enterprise BI and Data Science for years, recently announcing AtScale AI-Link to simplify access to our semantic layer platform with a Python library designed for data scientists. We clearly see an explosion of interest around data science and enterprise AI and often get involved in conversations on how AtScale can help.
We consistently hear three basic challenges to scaling enterprise data science programs. First, there is a clear shortage of data science skills relative to demand that will be the case for years to come. Second, keeping data scientists productive is complicated by the amount of time they typically spend wrangling data (vs. applying their knowledge of sophisticated model development). Surveys of data scientists show 40-60% of time is spent working with raw data. Third, organizations are challenged to demonstrate return on data science investments. Data science programs deliver hard value when AI/ML-generated insights get in front of decision makers at the time of decision. These challenges are most significant as organizations scale.
AtScale’s semantic layer platform can be a key enabler by addressing some of these fundamental challenges head on. Here are 10 important ways an AtScale semantic layer can help.
