Machine learning is programmatic at heart. It typically starts with a large amount of data that’s been organized by people. This might be tagged photos, for example. The program analyzes the data closely and sniffs out patterns that correlate with human-made patterns. After it does this, it tests those patterns on different data sets. Most people’s interactions with machine learning so far are with things like speech and facial recognition, like when Facebook suggests who you can tag in a photo.

Machine learning powers a surprising amount of today’s world.

But machine learning is becoming an increasingly important part of the digital economy together with service assurance. A Huffington Post article points out that as machine learning grows, so does the reliance on the IT infrastructure. Service disruption simply can’t be tolerated when it comes to work and personal activities. Businesses and their leaders must manage this digital transformation so as to reduce risk while enabling innovation. It is essential to note that algorithms and data aren’t created equal when it comes to direct impact on people’s personal and work lives.

Algorithms Already Replace People in Some Sectors

We ask ourselves whether algorithms and data should replace people, but in many cases, they already do. For example, Computerized Numerical Control (CNC) machines have been around a long time, with software combining with robotics to drive drills, cutters, and other tools. In fact, algorithms and data have had an enormous impact on manufacturing in recent decades, with millions of jobs having been lost to automation over the last 30 years.

At the same time, however, manufacturing output has risen steadily. In the US, it’s more than doubled during that same 30-year period. Today, however, algorithms have potential to automate more than just machine-based decision-making. They’re being harnessed to analyze data in fields like law and medicine, and many questions have arisen on what effect they may have in terms of the role of the human lawyer or doctor, and on costs of services.

What if Fallibility or Bias Are Baked into Algorithms?

The fear, naturally, when it comes to algorithms powering legal processes or medical diagnoses, is whether or not the people who create those algorithms are inadvertently building their own biases or fallibilities into them. The prospect can be scary in some instances. On the one hand, justice is supposed to be blind, and what is more “blind” than a program designed to analyze data and deliver an answer? The problem is, the legal minds behind the programmers creating the algorithms certainly aren’t blind, and while they may strive to be unbiased, it’s hard to engineer out human tendencies entirely.

Visibility into automated processes is essential.

Machine learning is likeliest to power predictable tasks involved in human-driven professions. In other words, it’s far more likely that tasks done by the legal clerks and lab technicians will be (and in some cases already have been) automated, rather than the work done by, say, judges or physicians.

The Cost of Digital Failure

The risk of depending on algorithms and data to accomplish what used to require a brain and a pair of hands is what happens when something goes wrong. Production snags can be one consequence, but so can lost revenue and damaged brand image. In 2015, the average cost per hour of application failure was $500,000 to $1 million. And inefficiently fixing these problems as Forrester Research pointed out can cost a company $11 million per year. So while yes, algorithms and data can save massively on production costs, application and service performance disruptions can be remarkably expensive too.

The IT infrastructure is increasing in complexity due to greater expectations from it (such as UC, IoT, cloud, and security) and other factors like deploying new software dozens to thousands of times per day!

Visibility Key to Success

Visibility is of utmost importance in an increasingly algorithm-driven world. Insights gained through machine learning impacts our work and personal lives. Just as important, when it comes to the IT infrastructure, traffic data and complementary sources like synthetic transactions and NetFlow enable real-time actionable insight to assure service delivery. If IT visibility restrictions are removed, through smarter data and superior analytics, then it is possible to accelerate digital transformation. And in this new digital world, where disruptions are very costly, businesses are mandating exceptional service performance by harnessing IP intelligence.

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