Rajendra

I write columns on news related to bots, specially in the categories of Artificial Intelligence, bot startup, bot funding.I am also interested in recent developments in the fields of data science, machine learning and natural language processing ...

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I write columns on news related to bots, specially in the categories of Artificial Intelligence, bot startup, bot funding.I am also interested in recent developments in the fields of data science, machine learning and natural language processing

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Opinion Data engineers will be more important than data scientists

By Rajendra |Email | Nov 15, 2017 | 10914 Views

Does it seem like the ability to find, hire and retain data scientists is a losing battle? Is spending $500.000-plus per year for a data scientist worth it? What is a data scientist anyway?

Those a real questions and are the markers that how you are supporting your insight strategies might be at odds with reality.

Data science is a high value endeavor. It is one of the defining factors that will make or break a company in the age of insight and AI. However, without data, data science is a mute point.

What makes data scientists unique and costly is that they are expected to sit across two roles - statistics and computer science. This is where we go wrong. We are trying to find someone that is both competent at analytics and data. Yet, where data scientists don't help out is activating the data and the analytics into our business processes, applications and systems. That is for someone else.

So, let's look at how insight driven businesses are overcoming these issues. They take back ownership of data engineering and the computer science side of data architecture, management and governance.

Data engineers instrument data and analytics. They harness the strategy and investment plans of data architects. They enable analytics and data science. They adopt and activate data governance policies. They ensure data and analytic investment is getting its full return vertically and horizontally.

  • Want to accelerate data science - create a data engineering workbench.
  • Want to ensure data lake adoption - create a data engineering workbench.
  • Want to activate data and analytics in systems and processes - create a data engineering workbench.
  • Want to create consistency and reduce data risk - create a data engineering workbench.

Source: IM