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80-20 Principle – A Key Metric to apply in Manufacturing Data Analytics

Author: Pranita Patil
by Pranita Patil
Posted: Jan 15, 2022

The 80-20 principle is such a metric that can be applied at every stage of data science and manufacturing data analytics projects – right from data source evaluation to cleansing techniques, IT infrastructure / tool selection, modeling methods to deployment.

What is 80-20 and how can it be applied to Data Science and Analytics projects?

80-20 rule is a metric to be applied in analyzing the efforts / investment /approach / weightage during different stages of a Data Science or manufacturing data analytics implementation projects. It could be as simple as asking the 80-20 questions and taking right approach/decisions. It can be broadly classified in four main categories: '

  1. Data Infrastructure

  1. Analytics Needs

  1. Analytics Environment

  1. Scale-up Strategy

Read detail here:

Each of the above categories will have multiple activities and 80-20 principle can be applied for each of the activity.

1. Data infrastructure:

Data Source – Which data source to select for data analytics – DCS/ SCADA / Historian? Often times companies believe that the Scada/ DCS systems can be used to perform manufacturing data analytics and ignore the challenges with the data set and its limitations for analytics.

2. Analytics Needs and Solution required:

In order to take a decision on selection of right analytics environment / technology / tools, you need to ask following questions

What percentage of your analytics end objective fall into following categories?

  • 80-20 (% of use cases): Monitoring the current state of the system – Process Monitoring

  • 80-20 (% of use cases): Learning from the past and deriving Process Understanding, identifying critical process parameters through RCA, its limits and monitoring the same. Do you have enough volume of data?

  • 80-20 (% of use cases): Monitoring the future state of the system – Predictive/Prescriptive analytics. How much % of time will go in coding vs. analytics. Which ML models are prominent for the use cases you are handling, etc.

3. Analytics Solution Evaluation: You need to have analytics solutions appropriate for your use cases. One of the Industry leading Process data analytics solution Seeq gives a good evaluation matrix to analyze the analytics needs as follows:

  • 80-20 (which data)

  • 80-20 (% of experts time)

  • 80-20 (% of use cases solved)

  • 80-20 (Data handling time)

  • 80-20 (ease of use)

4. Analytics Scale-up / Roll-out Strategy: It is observed that the manufacturing or process data analytics initiative always starts with a small group, which evaluates the potential, prototype analytics, and try to scale it up. There are many important factors that play a significant role in scaling data analytics across the board. You need to analyze the percentage weightage of these factors to devise a scalable strategy. You can apply 80-20 principle here as well as follows:

  • 80-20 (level of Commitment)

  • 80-20 (Analytics Maturity %)

  • 80-20 (Roadmap)

For any query regarding data analytics ask here:

About the Author

Parth Sinha: Sr. Data Scientist, who holds a degree of Masters in chem. engg with a deep know-how on the application part of data analytics in the process and manufacturing industry.

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Author: Pranita Patil

Pranita Patil

Member since: Dec 19, 2021
Published articles: 3

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