Technology “Machine learning” — one of the hottest buzzwords sweeping the C-suite — isn’t new; however, its latest iteration is an extension of a relatively recent trend wherein data- and analytics-driven organizations used machine learning as a competitive advantage.
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In a 2006 article for the Harvard Business Review , Thomas Davenport wrote, “At a time when firms in many industries offer similar products and use comparable technologies, business processes are among the last remaining points of differentiation. And analytics competitors wring every last drop of value from those processes.” That was 13 years ago, and since then, machine learning has matured.
The latest trends it is driving — especially within the financial services industry — are:
– Deeper, more complex models
– Application to a broadening set of data types (such as unstructured text, images, video)
– An ever-accelerating prediction velocity, with real-time predictions becoming standard
Around the world, business leaders are under intense pressure to establish machine learning and data science teams to accelerate processes, increase performance and quickly provide value. So, how should you tackle this challenge?
Start with the Basics
Fundamental to the process is fully understanding your existing business processes, the problem you are trying to solve and what data you have to work with. This information allows you to formulate the best approach, which must then be measured against the resources available to you.
Models are fairly simple to set up; the challenging part is the real-time execution and monitoring of production.
Manage Expectations and Deliverables
Be aware that, along with adding complexity and cost, haphazardly implementing machine learning can also increase risk. For example, if you are seeking to better manage payment flows, start with batch testing.
It is much easier to catch and prevent negative consequences in testing than in a live scenario. And this illustrates only the technical risk; machine learning technology typically touches many areas of the organization, and coordination across all stakeholders can be complex, slowing the project down.
The lack of data and/or data quality is one of the biggest factors in data science and machine learning talent dissatisfaction.
Pick your battles carefully, and build a simple, yet reliable machine learning solution that consistently demonstrates business value. From there, it will be easier to get internal buy-in for more ambitious plans that target larger problems.
Prioritize the Infrastructure
Successful machine learning efforts depend critically on large amounts of quality data. According to The Financial Times, the lack of data and/or data quality is one of the biggest factors in data science and machine learning talent dissatisfaction.[…]
read more – copyright by www.brinknews.com
Technology “Machine learning” — one of the hottest buzzwords sweeping the C-suite — isn’t new; however, its latest iteration is an extension of a relatively recent trend wherein data- and analytics-driven organizations used machine learning as a competitive advantage.
copyright by www.brinknews.com
The latest trends it is driving — especially within the financial services industry — are:
– Deeper, more complex models
– Application to a broadening set of data types (such as unstructured text, images, video)
– An ever-accelerating prediction velocity, with real-time predictions becoming standard
Around the world, business leaders are under intense pressure to establish machine learning and data science teams to accelerate processes, increase performance and quickly provide value. So, how should you tackle this challenge?
Start with the Basics
Fundamental to the process is fully understanding your existing business processes, the problem you are trying to solve and what data you have to work with. This information allows you to formulate the best approach, which must then be measured against the resources available to you.
Models are fairly simple to set up; the challenging part is the real-time execution and monitoring of production.
Manage Expectations and Deliverables
Be aware that, along with adding complexity and cost, haphazardly implementing machine learning can also increase risk. For example, if you are seeking to better manage payment flows, start with batch testing.
It is much easier to catch and prevent negative consequences in testing than in a live scenario. And this illustrates only the technical risk; machine learning technology typically touches many areas of the organization, and coordination across all stakeholders can be complex, slowing the project down.
The lack of data and/or data quality is one of the biggest factors in data science and machine learning talent dissatisfaction.
Pick your battles carefully, and build a simple, yet reliable machine learning solution that consistently demonstrates business value. From there, it will be easier to get internal buy-in for more ambitious plans that target larger problems.
Prioritize the Infrastructure
Successful machine learning efforts depend critically on large amounts of quality data. According to The Financial Times, the lack of data and/or data quality is one of the biggest factors in data science and machine learning talent dissatisfaction.[…]
read more – copyright by www.brinknews.com
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