AI is driving many companies to change their business models, but a new report from Databricks reveals that a lack of team unity is hampering success.
Some call it the "AI dilemma:" Companies recognize the importance of incorporating artificial intelligence into their business models, but only one-third of their projects are successful. The issue? Data.
Some 96% of organizations face data-related problems including silos and inconsistent datasets, according to a Tuesday report from Databricks. The data issue can also lead to interpersonal conflict in the workplace, with 80% of the 200 IT executives surveyed citing that there was friction or lack of collaboration between data scientists and data engineers.
Some 90% of respondents noted that unifying the data scientists and data engineers could help solve the AI dilemma, according to the report.
SEE: IT leader's guide to the future of artificial intelligence (Tech Pro Research)
These data-related challenges are driven by new machine learning tools, and technology and organizational silos, the release noted. Unified analytics, which combine data processing and AI technologies to make AI adoption easier for enterprises, can potentially help organizations overcome implementation barriers.
Having too many organizational tools can be a challenge to enterprise AI implementation. According to the report, 87% of organizations invest in, on average, seven different machine learning tools, which creates more organizational complexity.
Similarly, the process of implementing AI is a slow one. According to the release, it takes businesses more than six months to move from concept to production. Some 98% reported that the preparation and aggregation of large datasets in a timely fashion is a major challenge.
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The big takeaways for tech leaders:
- 96% of organizations face data-related problems including silos and inconsistent datasets when it comes to AI implementation. -- Databricks, 2018
- Some 90% of companies are working on AI projects, but they are only successful one-third of the time. -- Databricks, 2018
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- Why it's your fault your data scientists keep quitting (TechRepublic)