Reaching data and AI maturity: the key to unlocking business value

While many companies across a range of industries have placed artificial intelligence (AI) and machine learning (ML) at the heart of their growth strategy, most do not feel they are in a position to successfully harness its power. The major reason for this is because many Big Data projects lack a mature approach to getting the best out of AI and ML deployments. 

According to a 2021 Databricks and MIT Technology Review Insights survey, companies’ most important business objectives for their enterprise data strategy over the next two years are expanding sales and service channels ( cited by 45 percent of respondents), better operational efficiency (43 percent) and improving innovation and reducing time to market (42 percent). It’s great to have these objectives, but are businesses equipped to execute them? According to Gartner, 85 percent of big data projects fail, and according to the MIT Report only 13 percent of companies excel at implementing their data strategy with measurable results. When asking “low-achievers” (organizations having difficulties with their data strategy initiatives) what their main barriers are, the feedback highlighted limited scalability of their data management platform, difficulties in facilitating collaboration and slow processing of large data volumes. It’s clear that many organizations face challenges in scale, speed and collaboration in all areas of data exploitation.

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