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).
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:
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”.
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.
In today’s rapidly expanding Electric Vehicle (EV) market, an EV charging company wanted to capture a more significant market share. With recent studies predicting a 10% uptick in EV sales by 2025, rising to 58% in 2040, it’s no wonder that EV charging stations are focused on their strategy now. As the world “goes green”, electric vehicle companies are hard at work besting each other on improved battery life. This is because conversion rates for first-time EV buyers depends so much on alleviation of buyers’ range anxiety - that feeling of being caught short on dead batteries miles from a charging point. This made it imperative for EV charging companies to figure out EV charging stations' best placements in popular driving destinations and routes.
The first wave of Master Data Management (MDM) started in the early 2000s. Early adopters from a few industries which included Banking, Insurance, CPG, and Retail started implementing MDM for customers, products, and a few other master data domains. Over time, MDM became mainstream as companies from all industries realized the need for MDM as part of their data strategy to resolve data inconsistencies across multiple systems and establish a single version of the truth. Over the years, there have been many successes but plenty of big data failures as well, due to the lack of skills and a proper understanding and positioning of MDM solution versus other systems.
Our clients frequently complain that their understanding of their business is hampered by the fact that much of their vital data resides as ‘dark data’ in silos that don’t reside on traditional IT infrastructure (system data).
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