Our article was published in Barron's on January 10, 2019
Surveys show many advisors believe they need business intelligence (BI) and artificial intelligence (AI) tools to be competitive and successful. What the surveys don’t show is how few of those advisors can, in layman’s term, explain BI, AI, big data, predictive analysis and machine learning.
Theoretical explanation – in English
BI and AI tools help advisors make sound business decisions. BI is known for analyzing trends and AI for predicting the future. Both applications provide advisors with (intelligent) information derived from thousands, or millions, of data points (“big data”). The information enables advisors to understand what is happening in their business and why. Practical BI/AI supported applications are:
- Client behavior trend and profitability forecast
- Risk profile and investment analysis for optimal portfolio structure
- Operations statistics for productivity gains
- Marketing expense and effectiveness
You should be concerned with what vendors don’t tell you about their BI/AI applications. Do you know these tools can also deliver misleading information that leads to erroneous decisions? Advisors need to step away from the results and take a closer look at a couple of BI/AI success factors in the decision-making process:
Identify areas of focus. Years ago, I supported interest-rate risk management for a company using asset-liability management software. Our group modeled various hypothetical scenarios on interest-rate and yield-curve movements and their impact on the company’s net income. Management used the results to determine if, and how, to hedge interest-rate risk. Our success was due in part to defining the situation before focusing on technology.
Many providers sell BI and AI solutions without discussing your goals. You should control the process and begin by identifying the business areas to analyze and determine how to proceed. Are you looking to alter your business model based on client trends and needs? Are you thinking about your operations and whether re-organizing to a centralized ops pool makes more sense?
Then understand what’s inside the technology. Too many advisors focus on the results, not how the technologies derived them. While the technology looks complex, you don’t need expertise in algorithms or programming languages to appreciate the information displayed.
Useful information results from accurate data inputs, valid assumptions and correct calculations. GIGO (garbage in, garbage out) plays a key role here. Your CRM may provide BI reports based on data fields, but if the fields are not populated or incorrect for some clients, the results will be meaningless. Vendors should lay out assumptions used in the calculations, and both should make sense to your firm.
The data pool’s characteristics are also important to understand. For example, your service provider delivers information comparing your firm with your peers in areas such as management fees or software products used. Before analyzing the results, ask how peers are defined. Look for firms similar in AUM, firm size, business model, and investment strategies. Comparing your investment management firm that focuses on individual securities to a pool of financial planning firms or firms investing in all mutual funds, may result in information that could lead to the wrong decision for your firm.
What you know about BI and AI can lead to good decisions. It’s what you don’t know that can hurt your business.
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