Operationalizing Large Language Models (LLMs) is the next big opportunity in AI. Any organization…
DataOps Report Breakdown: Data Collaboration & Machine Learning
Welcome to the final segment of our 2017 Definitive Data Operations Report Mini Series!
Teamwork Makes the Dream Work
Data collaboration is quickly becoming the norm for companies in almost every industry, from finance to food service. We asked 311 data professionals if their company currently or has plans to ingest data. An astounding 91% of respondents said “Yes,” while only a minuscule 9% said they never plan to do so. It is interesting that 61% of respondents currently ingests data, while 31% have plans to. More significantly, 28% of those have plans to begin ingesting data within the next 6 months.
Ingesting data from third parties was also found to go hand in hand with sharing data out. The survey found that 70% of companies either have plans to share or are already sharing raw data or data reports with third party partners. It is crucial to highlight that 37% of respondents who are sharing or have plans to share data are planning to share raw data. Raw data can come in many shapes and forms, making it very crucial for
Machine Learning Takeover
What exactly are these companies doing with all of this data that is being sent and received? Though for now it is still the tech industry that takes the lead in working with data overall, it is significant that from over 40+ industries, 70% of respondents are currently working for companies who have dedicated teams for either machine learning or artificial intelligence.
On top of this, 64% of respondents state their company plans to do more machine learning. Only 15% reported they will do the same amount, and a surprising 9% stated they will be doing less. Of those who stated they do not currently have teams working on ML or AI, 34% said that there are future plans to do so.
Data continues to grow at unprecedented rates, and it’s imperative for us to tackle the biggest problems of Data Operations before it’s too late. To maximize value from these investments in ML and AI, companies need to get smart about Data Operations.
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