Why 85% of Data Science Projects fail

Because of a number of reasons, both technical and people-related, it is hard to accomplish Big Data projects. Here the main ones

Poor Integration

Poor integration is one of the major technical and technological problems behind the failure. Actually, integrating siloed data from heterogeneous sources to get the outcomes that organizations want, linking multiple data and building connections to siloed legacy systems is easier said than done.

Technology Gap

Companies often try to merge old data silos with new sources, without success. This is because with different architectures data processing needs to be done newly: use the current tools for an on-premises data warehouse and integrate it with a big data project, which will become too expensive to process new data. It is necessary to learn new languages and adopt an agile approach.

Abandon the Legacy

Legacy architectures create more silos and struggle to process big data with the speed and consistency needed. At present, legacy data architectures are bending beneath the weight of these data-centric challenges — volume, variety, and velocity. The only way to survive is to get out of these systems rigidity and find modern tools for new complex projects.

Machine Learning

Taking the data scientists’ work from prototype to production stage is a common problem faced by organizations all around the world. Machine Learning workflow which includes training, building, and deploying models is still a long process with many barricades on the road. New technologies and approaches need to be employed to solve the heterogeneity and infrastructure challenges

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