There will always be a plentiful supply of data scientists on-hand to perform hand-cut custom data science. For what most businesses requirements, the typical data scientist is over-skilled. Only other data scientists can understand their work and, importantly, only other data scientists can check their work.
What businesses require for most tasks are people with the data-engineering skills of data scientists and not necessarily their statistical skills or their understanding of a scientific-method of analysis.
Data engineering on a big scale is fraught with challenges. While Excel and Google Sheets can handle relatively large (~1mn row) datasets there is not really a similar software solution that allows easy visualization and manipulation of larger data sets. NoSQL / SQL-databases are required for super-scale data engineering, but this requires skills of the super-user. As 'data-is-the-new-oil' mantra makes its way into businesses, people will become exposed to a growing number datasets that are beyond the realm of the software available to them and, potentially, their skill sets.
At Knowledge Leaps we are building a platform solution for this future audience and these future use-cases.The core of the platform are two important features: Visual Data Engineering pipelines and Code-Free Data Science.
The applications of these features are endless; from building a customer data lake, or building a custom-data-pipeline for report generation or even creating simple-to-evaluate predictive models.