Data Science projects are failing at an alarming rate. Depending on who you ask, most industry survey’s will site that nearly 9 out of 10 data science projects fail, and we can attribute this failure to three specific reasons.

The first factor revolves around your data.. Having your data in silos prevents employees across the organization from accessing a set or source of data, while inaccurate data can lead to inaccurate decision making and eventually a loss in revenue. Furthermore, if the speed at which your data is digested and made available to you is slow, real time analytics will never be an option. Therefore, successful data science initiatives rely on aligning data quality, master data management, and data governance to ensure all three are integrated and fully working together to prevent inaccurate, siloed, and slow data.

The second factor is a lack of business readiness. There is often a lack of an honest understanding of requirements and capabilities needed to take on data science initiatives. We will be tackling the people and process side of business readiness, by touching on how to set up your data science team within your organization and how other teams should be interacting with data scientists. Then well take a deep dive into defining achievable use cases that can be easy wins for you and your team.

The last factor attributing to why data science projects fail is centered around operationalization being unreachable. In order to set your team up for success, your company should be investing in business modernization, specifically around making sure the technology stack is up to date, and that data pipelines and processes are scalable. There should also be a clear distinction between roles on the team, where data scientists and data engineers are working together to create and push models into production.

I will step through each of these in greater detail, giving you solutions to prevent these common pitfalls.

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