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The year and a half after the COVID crash has left consumer confidence adrift on a never-before-seen kind of bubble. This one is different from what the Baby Boomers rode in the late 90s leading up to the tech bubble burst; or from the pre-2008 bacchanalia that got people believing they can take out multiple mortgages because of their own “worth”, when they were but a means to pumping up mortgage-backed securities. Credit bureaus have started to sound the alarm on delinquency trends, but it’s difficult to fathom what’s to come because income estimation has gotten more difficult (despite the tons more data available on every person who has any online activity). While consumer confidence metrics seem green based on how much people are spending, consumer psyche is further and further habituated to negative financial events and consequences. Six factors contribute to this conditioning:

If we want to win, we need to learn quickly at speed. This applies to business as well. Winning enterprises have a data ecosystem that exposes all data, makes it trusted, applies machine learning, and eventually lets it self-learn and evolve – the genesis of a data-driven enterprise. The enterprise that learns and executes the fastest always wins. To accomplish this, enterprises need an Enterprise Intelligence Hub (EIH).

Organizations across industries understand the importance of Customer Experience and are making large investments, and rightly so, to offer an enhanced experience to their customers. Companies that lead in Customer Experience outperformed those farther behind by almost 80%. (The Total Economic Impact of Qualtrics CustomerXM, 2019) However, it’s hard to improve the customer experience without a clear understanding of their actual experience. Mapping out the customer journey is the first essential step a business must take to improve its overall customer experience.

Technology spend has been shifting to the business for some time and now more than 60% of businesses allow non-IT functions to lead technology decisions (KPMG & Harvey Nash, 2019). Further, most technology workers now reside outside the traditional IT function (TechRepublic, 2020). Together, these occurrences offer evidence that the business is becoming increasingly dominant, not just when it comes to making technology choices, but also when it comes to determining technology strategy.

Antifragility is a strong criterion that says that if we experience shocks or unexpected variances in the inputs, the model performance (by whatever measure of performance you choose to use) will actually improve. Most machine learning models can be made to be reasonably robust, but I have never seen an example of a “classic” machine learning model that is antifragile. Yes, machine learning benefits from exposure to more data and more diversity (when that variability is related to the underlying process) but, obviously, not all shocks lead to improved performance in such systems.

In the movie “The Core” [spoiler alert!] there is a great example (completely made-up, of course) of a theoretical element called (with tongue in cheek) Unobtanium. It has the unique property that it becomes stronger (gains structural integrity) as it is exposed to increasing levels of heat and pressure. So, it turns out to be the ideal material to build the fantastic ship they use to journey to the center of the Earth. Unobtanium strikes me as an idealized example of an antifragile physical material: https://www.youtube.com/watch?v=b_HhiU1mOwU

A client recently asked if our entity matching algorithms are “antifragile”. This got me thinking. It is a really interesting question. Bear with me as I take you on a mind trip to explore this question and its implications. First, let’s start with a common understanding of “antifragile”. We’ve all read the google definition: When a system gains from stressors, shocks, volatility, noise, disorder, mistakes, faults, attacks, or failures, it is termed “antifragile”.

Leveraging Oracle Sales & Service Cloud, Field Service, Commerce, CPQ, CDM, and OIC

An industry-leading automation solutions provider to many of the world's most successful companies used their extensive knowledge base and global capabilities in custom automation, repeat automation, automation products, and value-added services, including pre-automation and after-sales services to address the sophisticated manufacturing automation systems and service needs of multinational customers in markets such as life sciences, chemicals, consumer products, electronics, food, beverage, transportation, energy, and oil and gas.

The ultimate innovation in electric vehicles is extending battery life without sacrificing the driving experience. In a highly competitive market, now more than ever, convincing consumers to convert to electric cars boils down to competing in battery life, where consumers count every extra second between charging that they are promised. Even though driving styles and conditions can affect battery life, the ultimate scorecard is still about battery performance itself. Mastech InfoTrellis recently helped an electric car company design an easy-to-read heads-up dashboard (HUD) with predictive maintenance capabilities so that drivers can adapt their driving styles while the car beams up driving data to an Intelligence Hub and continually receives dynamic battery life forecasts to inform the driver.

Have you ever seen science fiction action films like Minority Report, Avatar, or one of those Jason Bourne’s series? There are scenes of tracking and tracing people through computer that knows everything, including where they are, what they look like, and even their temperature at that moment. These are examples of digital twin of people, and they are no longer science fiction. The Enterprise Intelligence Hub offers cutting-edge technology tools to implement digital twins for your business.