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What Is Enterprise Data Science?


Enterprise data science combines data scientists, data engineers, software engineering, information architects, IT teams, and more to generate value out of big data. 

In this page, you will learn:

There’s a lot of overlap between enterprise data science and general data science, and very often the terms get used interchangeably. 

Still, there are a few key features that set enterprise data science apart. 

  1. Enterprise data science involves cloud computing, because laptops and on-prem servers can’t process the huge datasets that are needed and end up generating bottlenecks. Serverless data pipelines remove the headache of managing infrastructure.
  2. Data science for enterprise applies data insights and predictions to the entire organization to provoke strategic, holistic change, instead of using data analytics on a case-by-case basis.
  3. Enterprise data science uses ML and cloud computing to drive change across the organization. Data science that’s carried out in isolation often results in abstruse models that don’t serve a practical business purpose. 

Why do you need enterprise data science?

Businesses want to tap into big data to improve strategic decision-making, but big data is larger than we realize. Enterprises have massive amounts of uncategorized, unindexed, inaccessible data, and they need data science pipelines to convert it into insights and predictions that add value to the business. 

Even when data is indexed, it’s often shut away in siloes where many teams can’t access it. When they can, they still frequently struggle to unlock its value. 

Business leaders need enterprise data automation to gather datasets, bring it to life in data dashboards, and make it accessible in centralized data repositories. Data science pipelines streamline the process of making data actionable. 

Who uses enterprise data science?

Enterprise data automation involves citizen data scientists, ML experts, software engineers, data scientists, analysts, information architects, and more. To succeed, you’ll also need input from domain experts like C-suite executives who can help identify the most relevant datasets. 

An enterprise data science platform is particularly relevant when corporations have a large amount of semi-structured, structured, and unstructured data that they want to combine to generate predictions that guide business decision-making. It brings benefits to enterprises across industries and verticals. 

When do you use enterprise data science?

There are many use cases for a data science platform:

  • When you have too much data coming from too many disparate sources and you need to combine them without slowing down your analytics machines
  • When data is incomplete, imprecise, inaccurate, or varies too much between datasets.
  • When you need to accelerate knowledge discovery across the organization
  • When you need to flatten the silos that obstruct departments from accessing the data insights that power better decisions

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What are the benefits of enterprise data science?

Speed up time to valuable insights
An enterprise data science platform provides a fast, effective way for businesses to process and analyze piles of data, even when the data types vary and the data comes from disparate sources. The right platform displays better data visualization to assist departments in mining actionable insights from their data. 
Mitigate risk and fraud
Data pipeline automation helps enterprises to mine data in real time so that they can prevent fraud. By spotting risks long in advance, business leaders can develop ways to mitigate, prevent, and soften their impact to preserve the enterprise safely. 
Improve product-market fit
With serverless data pipelines, enterprises can explore the pain points and needs of their target market, forecast demand, and apply data-driven predictions to create a better, more relevant product. 
Cut the right costs 
With the help of data pipeline automation, enterprises can analyze their organizational costs and expenditure to find the best places to make efficiencies and reduce costs. These insights guide you to allocate resources in the right way so that you can cut costs without undermining productivity or performance.

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