The opportunity landscape of Data Analytics is increasing day-by-day in every industry. Many organizations have done huge investments in building and aggregating a data layer, whether it is in MES, Historian or a data lake. The value of data is being unlocked and leveraged for getting better insights. The innovative organizations are exploring the complexity of the problems (for e.g. deterministic statistics to Predictive/ Prescriptive Analytics) that can be solved using statistical/ML/AI methods. The value of analytics / ROI ranges from few hundred thousand dollars to millions of dollars. In a nut shell, you can’t avoid this now, to remain competitive; it will be inevitable for every organization to look into it in near future if not today!
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.
Read detail here: https://dataanalytics.tridiagonal.com/80-20-principle-a-key-metric-to-apply-in-manufacturing-data-analytics/
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:
- Data Infrastructure
- Analytics Needs
- Analytics Environment
- Scale-up Strategy
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)
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