After years of screaming headlines about data breaches, we all know the drill. A major company announces it has been hacked, a brief public outcry ensues, and then… not much happens. Down the road you might read about a government inquiry or a class-action suit being settled. People have become numb to these announcements. We assume our personal information has been compromised in some way, take reasonable precautions like canceling a credit card or instituting credit monitoring, and move on with our lives.
The Equifax breach is different. It isn’t the largest (that would be the 2016 Yahoo breach, with over one billion accounts affected), the most embarrassing (Ashley Madison, the affair website), or the one that raises the most national security concerns (North Korea’s hack of Sony). But a couple of key facts give Equifax its own watershed moment in the sordid history of data breaches: (1) Many of
The online economy — from search to email to social media — is built in large part on the fact that consumers are willing to give away their data in exchange for products that are free and easy to use. The assumption behind this trade-off is that without giving up all that data, those products either couldn’t be so good or would have to come at a cost.
But a new working paper, released this week by Lesley Chiou of Occidental College and Catherine Tucker of MIT, suggests that the trade-off may not always be necessary. By studying the effects of privacy regulations in the EU, they attempted to measure whether the anonymization and de-identification of search data hurts the quality of search results.
Most search engines capture user data, including IP addresses and other data that can identify a user across multiple visits. This data then allows
Even for the experts, the recent data breach at Equifax was staggering. The data that undergirds the credit records of 143 million consumers was compromised. Social Security numbers, dates of birth, and drivers’ license records are used to authenticate identity. It is not difficult to change a credit card number, but changing Social Security numbers and birth dates is a whole different matter. Data breaches are on the rise in the United States. It’s time for Congress to act. Why does this require action by Congress? There are at least five major reasons that the private sector cannot handle this issue on its own:
Still, few managers have hard evidence or any real appreciation for the impact of bad data on their teams and departments. They are thus unable to give data quality its due. To address this issue, in our teaching in executive programs in Ireland, we ask participants — executives that come from a wide range of companies and government agencies, and departments such as customer service, product development, and human resources — to develop such evidence using the Friday Afternoon Measurement (FAM) method.
The method is widely applicable and relatively simple: We instruct managers to assemble 10-15 critical data attributes for the last 100 units of work completed by their
The buzz over artificial intelligence (AI) has grown loud enough to penetrate the C-suites of organizations around the world, and for good reason. Investment in AI is growing and is increasingly coming from organizations outside the tech space. And AI success stories are becoming more numerous and diverse, from Amazon reaping operational efficiencies using its AI-powered Kiva warehouse robots, to GE keeping its industrial equipment running by leveraging AI for predictive maintenance.
One of the key enablers is the analysis of email traffic and calendar metadata. This tells us a lot about who is talking to whom, in what departments, what meetings are happening, about what, and for how long. These sorts of analyses are helping EY, where some of us work, by working with Microsoft Workplace Analytics to help clients to predict the likelihood of retaining key talent following an acquisition and to develop strategies to maximize retention. Using email and calendar data, we can identify patterns around who is engaging with whom, which parts
There is no argument about whether artificial intelligence (AI) is coming. It is here, in automobiles, smartphones, aircraft, and much else. Not least in the online search abilities, speech and translation features, and image recognition technology of my employer, Alphabet.
The question now moves to how broadly AI will be employed in industry and society, and by what means. Many other companies, including Microsoft and Amazon, also already offer AI tools which, like Google Cloud, where I work, will be sold online as cloud computing services. There are numerous other AI products available to business, like IBM’s Watson, or software from emerging vendors.
Whatever hype businesspeople read around AI — and there is a great deal — the intentions and actions of so many players should alert them to the fundamental importance of this new technology.
This is no simple matter, as AI is both familiar and strange. At heart, the algorithms and computation are dedicated