Data Analysis

Nov. 20, 2019 -- Posted by : Jalen.Seguin

In a manufacturing setting, data analysts generate information and communicate insightss (analytics & predictions) that enable organizations to optimize their operations to a higher degree than previously thought possible. 

Insights harvested from data improve manufacturing responsiveness, deepen and sharpen management's understanding of true costs, and enable better decisions in a more timely manner.

More specifically, analytics are frequently used to:

Optimize use of production capacity
Predict and mitigate equipment failures
Manage supply chain risk
Optimize pricing
Dramatically improve product quality
Reduce waste and variability 
Streamline inventory management
optimize use of factory floor space
Target energy-inefficient components

Data Science has become an increasingly hot topic over the past few years as more industries have realized that they have access to a largely untapped resource in "Big Data" — the data they themselves generate, as well as the data generated by their suppliers and customers.  For many years, big tech companies have generated massive competitive advantages from their skilled use of Big Data, and now the same opportunities are emerging for other industries.  For adept forward-thinking business leaders, the question then arises:

How can I know more about the processes and ecosystem of my business in order to make better decisions in a more timely fashion?

Data analysts support a company’s profitability and efficiency by collecting, organizing, managing and analyzing large amounts of data (internal and external) in order to:

Identify trends, patterns, and relationships within data
Use data-driven techniques to solving business problems
Convert data into compelling visualizations
Create predictive models to forecast relevant events/outcomes
Communicating the results to business leaders

There is clearly a vast potential for innovative forward-thinking companies to generate an industry-disrupting advantage by capitalizing on their access to Big Data. This fact doesn’t warrant blind and wholesale investment in data science initiatives, but it does make a strong case for prudent and calculated exploration and experimentation.

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