CIO OPINION
Like a lot of technology-enabled domains , success isn ’ t guaranteed .
Why data projects fail – and what to do about it
Data Army Director Michael Ogilvie says the root cause of project issues often runs much deeper than technology selection .
Data projects today come with high expectations of success . The progression from business intelligence to big data , to data science and now the fast-tracking and mainstreaming of AI into a broad range of operational contexts , confirms that the future of business is data driven .
But , like a lot of technology-enabled domains , success isn ’ t guaranteed and does not always come as anticipated . This is confirmed by the decades of collective experiences of Data Army consultants operating in this space .
Sometimes , data programs expose foundational shortcomings around data definitions , structure , formatting and cleanliness that require significant work to remediate . Additionally , a broad range of skills are required to achieve success . These include clear top-down direction and strategic leadership , data engineering and science talent to build out technical capabilities and data literacy among intended users .
Importantly , there ’ s often no single reason why a data project is not where it needs to be . Projects may suffer from one or more challenges that individually would not derail the project , but that collectively can become a strain on achieving key deliverables .
These challenges can affect the unlikeliest of candidates : organisations that are broadly on-board with data ; that have an actively engaged staff who want to be more data-driven and who are constantly bringing new ideas or approaches to the table ; and have successfully secured internal funding off the back of a strong data-driven business case .
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