How to Learn to Love Machine Learning
Harry Stahl, Director, Strategy and Solutions, FIS
October 15, 2018
As automation moves to robotics, and robotics to machine learning – and eventually artificial intelligence (AI), the pace of change is challenging firms on multiple levels. Leadership is wondering which technologies will have the biggest impact; managers are rethinking how to organize their teams; and individuals are worried about whether these changes are a threat to their careers.
But emerging technologies such as machine learning can create immense opportunities for people as much as the company itself – if you approach it in the right way.
Business impact: Fundamentals first
If the goal is to maximize the overall impact on the business rather than solving a particular problem, don’t jump to robotics and machine learning first. Start with operational fundamentals. Invest in a clean organizational design, sound processes, and good data management. This not only improves the overall organization but allows you to see more clearly where emerging technologies will have the most impact.
For example, if your objective is to reduce total cost of ownership, you want to streamline organization and processes first and then add automation and machine learning to amplify those efficiencies.
If your focus is on the customer experience, then begin with data management and a clear digital strategy, and add sophistication and dynamism to that environment.
Our research in the FIS™ Readiness Report bears this out. Firms that modernize their operations capabilities by focusing on the operational fundamentals first and then investing in emerging technologies grow almost twice as fast as the industry overall.
Once the operational foundation is in place or underway, evaluate potential tools with a clear set of business cases in mind. You may see multiple opportunities, but remember that every technology project is an investment. Assess where the return will be worth the effort.
The next step is to select the right tool to solve the problem. Machine learning, for example, can be descriptive, predictive or prescriptive:
- Descriptive machine learning helps understand what has already happened, e.g., historical analysis
- Predictive machine learning suggests what will happen – market or consumer behavior, for example
- Prescription machine learning helps systems achieve a particular set of goals, such as operational efficiency, portfolio performance or an efficient trading strategy
In addition, there are different types of machine learning that employ very different mathematical and programming technologies to solve problems.
Encourage your organization to understand the choices and trade-offs before making any investment. Just as the one who only has a hammer sees only nails, the team that has only Logistic Regression tools may only see outputs classified in a binary way. While this may be useful for classifying whether a borrower will default, it’s not necessarily the best way to optimize trading strategies.
Build an innovation organization
As a manager in an organization that embraces emerging technologies, your priority is to put your organization and your people in charge of the change by shifting from running the status quo toward continuous innovation. This can mean embedding technologists into current operations, cross-training operations in new technologies, and/or creating cross-functional, tech-savvy SWAT teams to brainstorm and prototype new approaches to take advantage of emerging technologies.
And while machine learning is increasingly good at solving problems with a particular system or meeting a specific set of goals, it is people who understand, set and evolve those goals. They’re the ones who know how processes flow across multiple systems, and they can envision new ways to deliver services or organize execution. So use machine learning to multiply the impact of your people’s innovations and ideas. Build an organization that enables and empowers your team to generate and implement fresh ideas for efficiency, customer value and growth.
As an individual employee in this environment, you can get ahead the curve too. Educate yourself, seek cross-training and discover how new technologies can multiply the impact of your own experience, ideas and insights and enable you to provide more value to your organization.
Old dogs can learn new tricks, and there are new ones every day. But in the innovative business, it’s not the trick but where and how you use it that makes you valuable – and that makes it fun for you to boot.
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Tags: Technology, Digital Innovation