What Treasury and Finance professionals need to know about data science to be competitive in the Fourth Industrial Revolution of AI, machine learning, robotic process automation (RPA), and emerging tech.
We have a need for smarter and faster decision making. It stands to reason, then, that more data would create more informed decisions. Big data and machine learning make this possible by analyzing a larger data-set across a longer time period, giving a better view of trends and other important analysis. Together, they are the what (data) and how (machine learning) of solving complex problems faster and smarter.
Wait, what about AI? While the term is certainly in market, most current technology solutions are machine learning, a subset of AI. While machine learning may not sound nearly as sexy, it holds an estimated $5.8 trillion for the global economy — value that will come primarily from solving existing business challenges. Value that can be gained now while we are still on the road to full cognitive AI solutions.
“Machine learning is a specific task-targeted subset of AI. It will make a big difference in the short term,” explains Brian McClendon, technical advisor to C2FO, former VP of Google, and co-creator of Google Earth and Google Maps. “We are going to see five to ten years of machine learning just solving interesting problems more efficiently than humans have in the past, even solving some problems that had never been solved before because the signal is hidden in the noise, and machine learning managed to find it.”
If incorporated correctly into existing processes, the technology can help teams increase efficiency and complement the existing workforce. It’s a far more realistic view than a future of humans being replaced with machines. In fact, the right human capital is more important than ever for this brave new world.
In addition, your data must be centralized, and accessible across your entire business, supporting the needs of multiple functions. To attain your portion of the trillions in value, you will need to start organizing your people, processes and data now.
Here’s how to prepare.
Cross-functional collaboration to build better data
For a data science initiative to be effective — regardless of whether it is AI, machine learning, or RPA — cross functional business teams must connect. For example, all the historical data and forecasting data to predict future cash flow won’t result in an accurate forecast if the critical inputs from M&A are missing. Think beyond Treasury to the goals of your organization. Ask yourself, “how can we bring it back to the business for holistic performance improvement?”
Identify the problems you want to solve
To select and deploy the most efficient and appropriate technology for your organization, it is important to identify your needs. The latest generation of technology offers powerful tools to solve problems and make better informed decisions. You still need to decide what the problems are and how they should be solved.
For treasury, these problems may be understanding cash positions, risk mitigation, and cost reduction. All three of these ladder up to supporting larger business needs, so teams should focus on identifying the data set and technology that enables better cash forecasting and better utilization of cash – whether that is from making better returns on cash and short-term assets, or by minimizing risk in investment strategies.
In forward-thinking organizations, evaluating what use cases and technology will be a fit for your business strategy and offer actual ROI is a must. This human intelligence is the most important facet of any effort and cannot be replaced by emerging technologies.
Human capital: invest in the skill sets of the future
Emerging tech is less about replacing people, than it is helping your team focus on the right challenges that move your business forward. Robotic process automation, machine learning, and even true AI, eventually, are not replacements for humans, they are for automating tasks and crunching volumes of data to free up your brain and team to think through complex strategies for tax reform challenges, global cash management, and macroeconomic impacts from events such as Brexit and tariffs.
Equip your team with individuals who can label data and “train” the algorithm to find the answers you need. Ask yourself, do you have a set of data engineers at your disposal who can readily digest your information, ascertain the right approach, and create a system that manages inflows and outflows at scale with logic? Or, do you look outside for a technology partner who can fulfill your needs and navigate all your caveats and wild cards?
Build your strategy
Once you have identified your problems and how you can solve them with the help of data science, it is time to build your strategy.
Define your scope, delineate explicitly what information is needed that would lead to a smart solution, and determine when and where human intervention is needed to make a decision. Once you have these down, then you should start looking at how you can bring your data strategy to life.
Ready to build your intelligence?
Join us for our webinar to find out what you need to leverage the next generation of technology.
AI Eats Big Data for Breakfast
Presented by C2FO’s Chief Data Officer John Young, Managing Director Jordan Novak, and Microsoft’s Principal Program Manager Guru Kirthigavasan, the webinar will cover real-life applications for treasury management leaders looking to take their organization to the next level smartly, efficiently and safely.
Join this live webinar to:
- Understand how to simplify your solution to create a compelling repository of information that can be used across your entire business
- Learn how to incorporate machine learning into existing processes to increase efficiency and compliment your existing workforce; not replace it with a machine
- Discover what you should be focused on now to prepare for the wave of big data, machine learning, and AI
- Learn the differences between AI, machine learning, and RPA and how these are used in real-life use cases