Face it, you are paying attention to robotics. Maybe it’s as a curiosity, maybe it’s out of due diligence, or perhaps your interest stems from a realization that robotic technology has become useful in ways well beyond the expected use. Regardless of your interest in robots, the fact is, robotic technology is quickly expanding beyond the realm of industrial automation and has steadily been making its way into new industries and use cases. As this technology expands into new areas, it is important for companies developing business applications, IoT and analytics platforms, and systems integration to pay attention and look for opportunities to capitalize on a new and growing market.
In October 2018, a Reuters article informed the world that Amazon had scrapped an AI–based recruitment application that turned out to be biased against women. Most headlines about this story highlighted the company’s failure in developing an actionable and fair solution for one of the most important processes of the HR team.
However, what this and similar examples of today’s AI “failures” neglect to acknowledge is the complexity of end-to-end process automation based on AI technology. This complexity stems not only from current technical limitations but also from the immaturity of corporate policies, government regulations, and legal systems to deal with machines that automatically analyze, decide, and act.
No one questions that companies should do more to get a better Return on Investment in Data (ROID) – but…
With PTC’s $70M acquisition of generative design software vendor Frustum announced today, and the continued focus on expanding generative design capabilities by Autodesk, Dassault Systèmes, and Siemens, manufacturers have multiple options for AI and machine learning-infused CAD and CAE (i.e. simulation).
More than 3 years ago, I wrote a IDC Community post, “Using Robots to Curb Labor Shortage in Chinese Manufacturing” highlighting a factory in China that replaced 90% of the people in the factory with automation and robots. In that case the workforce was reduced from 650 employees down to only 60 people, those remaining were doing drastically different work than those jobs that were replaced. The jobs shifted from manual labor to oversight, maintenance, and support of the automation and robotics systems.
The proliferation of data types and quantities should be a major advantage for enterprise organizations. More data and more types of data should offer complex insights into challenges and opportunities in how the business runs and should lead to better decisions and business outcomes. However, ask any data analyst, and they’ll share this reality: data analysts spend a bulk of their time on search, data preparation, management, and governance activities, and not on data analytics where the true value lies.
There are two primary ways to buy or trade data in the Data as a Service (DaaS) market – direct sales from a data provider to end users, or via a data marketplace. While large, established information services businesses continue to make direct sales to their customers, many are also participating in data marketplaces. For smaller and emerging providers of DaaS, the rise in data marketplaces has made it simpler for them to package and sell their offerings, and for potential customers to find them. Marketplaces simplify the searching process, providing a variety of sources and types of data, along with a ready group of potential buyers.
It’s been said that all businesses are technology companies in the age of digital transformation. It’s also true that many are becoming information businesses as the amount and value of data they produce and consume continues to increase. In fact, business leaders and CIOs will find themselves not only missing opportunities but also at a competitive disadvantage if they don’t leverage data assets before markets are crowded with competitors.
Data governance is no longer optional for enterprise organizations. Aside from complying with new regulations, such as the General Data Protection Regulation (GDPR), organizations are finally realizing the value of data as an asset that needs to be protected, managed and maintained to increase asset value. But just because businesses understand the value of data governance, doesn’t mean that enterprises are confident in their abilities to execute on it.