I recently pursued an international certificate in product management, and throughout the learning process, I focused largely on providing answers to questions such,

• How is an analytics product defined as a mainstream product?

• How does the value development of an analytical product make sense?

• Why an analytical product?

• What ideas and actions resulted in the creation of the analytic product?

  The list is extensive.

Today, I'll be looking at a use case for asset management in the field of production and manufacturing. There are many excellent opportunities to develop AI/ML solutions with the asset management apps when we deal with asset management in a particular industry. Industry assets have numerous pipelined systems and sub-systems that are connected in very deep and reliable ways. We have several metrics set for the functional requirements to run each system and produce the appropriate output, and each system is capturing a lot of data.

How ML or Data science is enhancing the strength and viability of asset management by reducing loss in terms of money, human life, and risk. It is very much in play to proactively mitigate any weakness in any nook or cranny of the system and sub-system by forecasting and predicting ML model. This action has now been classified as a "Analytics Product for industrial revolution".

Basically, with the advent of any system application it has been observed that for integrating analytics in the solution pipeline, first understanding the current solution and reversing it to the problem statement is the place to start, and then finding room for analytics fit to check if this can be one of the USPs for the current application and can be graded as the mega feature going to be released in the next wave. The major criteria when creating analytics products is not hitting the data stream at first place. It may be claimed that this is the MVP launch for an analytics solution.

The place where the ML/Data Science team begins exploring the data and discovering the problem statement without the involvement of SMEs is how any application is capturing data between system and sub-system, and that would be a good journey as it will put you to take a lot of wrong turns and keep your curious side of the brain on fire for finding the right path for discovering. For a while, I would urge my team to do this.

Analytics Product is a manifestation of sustainable growth and innovation in the modern day. It has always been regarded as the deciding criterion for any SaaS application, regardless of the sector, job function, or business strategy.

How does a product lifecycle for analytics look?

A product funnel for analytics

Diagram by Author

The use of analytics in asset management functionality can significantly reduce risk and expense. It contributes significantly to root cause analysis and plays a key part in defining the solution for industrial alert systems, performance, system health, raising alerting data, functional failure/threshold point, etc.

Conclusion:

I've just given you a bird's-eye view of how analytics products are being used and providing value across a variety of industries, including large-scale and hard-core manufacturing units, the oil and gas industry, and many more. Future time will see continued elaboration on numerous products.....

My sharing and insight may need some improvement, so any comments would be very appreciated :)