“ice cream for breakfast?”

By Inspectivity

Data Alchemy

Since 2017 Ben and Jerry’s have been producing a range of ice cream products that target the breakfast market.  The idea of ice cream for breakfast is somewhat strange and given the massive investment cost to produce a new frozen product line, it would seem like a crazy decision. Or so you would think. In fact, working with Unilever, Ben & Jerrys discovered massive popularity for breakfast desserts. This included discovering 50 popular songs with lyrics referring to “ice cream for breakfast”.

To discover the data to support this investment they mined unstructured real-time data (from a wide variety of sources) to get metaphors and identify emerging trends.  This type of data science is fast becoming the norm for forward-thinking businesses.

This process of using AI algorithms combined with raw unanalysed data from broad sources is called Data Alchemy. Turning data of lesser value into something of greater value that empowers business decision-makers.

Boston Consulting Group’s strategy think tank, the BCG Henderson Institute prepared this article which explains the process well.  It compares traditional methods of data mining, data gold mining and enhanced AI data gold mining with that used for data alchemy.

In another example of data alchemy, Canada’s BlueDot was among the first in the world to identify the emergence of COVID-19 in Wuhan. BlueDot continually aggregates data from refreshed and broadly gathered data sources (news media, reports on animal and plant diseases, blog entries, airline-ticketing statistics, and social media) and processes all this data through machine-learning algorithms. In late 2019 they detected unusual patterns of activity in Wuhan, China, and their system automatically brought the observations to the attention of the company’s epidemiologists – a week before the World Health Organisation’s first report.

Inspection Data Alchemy

At Inspectivity we have a focus on helping operators, engineering providers and service companies to improve the value of inspection data, turning it into much higher quality engineering data.  Traditionally, however, engineering inspection disciplines “describe” the condition of infrastructure using subjective and wordy results. In fact, there is a premium placed on verbose and wordy reports. Major third party inspection service providers have a propensity to deliver detailed executive summaries instead of a “strongly typed” summary of facts. Quantity over the quality of data.

It is an inefficient and outdated approach.  The outputs produced are subjective and the facts are often hidden amongst the “war and peace” style of reporting.  The data capture process is slow, involving too much time bringing together notes, photographs, annotations, video and voice recordings. The outputs are poor and mostly non-parseable. You end up with siloed information that cannot be shared and the true value of available data is never achieved.

Downstream engineering teams rely on facts in order to perform analysis.  The quality and effectiveness of their outputs generally suffer following this traditional approach. The measurements, data points, findings, media etc. are not easily located by stakeholders and systems (eg. risk-based inspection platforms) which means decision making about life expectancy (for critical assets) and predicting failure rates is often no better than a “gamble”.

Applying a digital approach to how inspection data is captured and “fed” to systems and stakeholders is an important part of solving these inefficiencies.  Not only does it provide direct and substantiated cost savings on inspection resources but it sets the foundation for improved data quality.   

In the years to come, the level of uncertainty and unpredictability in the business environment is unlikely to decrease.  Organisations should act fast to adopt strategies such as Data Alchemy.

“Data alchemy will not be a short-lived trend. Because it enables faster, more granular, and more accurate decisions, it is bound to lead to permanent changes in decision-making mechanisms—both for incumbent companies and for digital natives.”

With Inspectivity’s integration into your business intelligence suites, you are able to leverage proven machine learning (ML) and natural language capabilities to help you gain deeper insights from your data. There are powerful, out-of-the-box features with tools like AWS Quicksight that make it easy for anyone to discover hidden trends and outliers, identify key business drivers, and perform powerful what-if analysis and forecasting with no technical expertise or ML experience needed.

Are you interested in supercharging your inspection management and laying high-quality data foundations?  

Contact Inspectivity today.


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