Machine Learning Will Change What We Value


Barry Libert

December 20, 2016

This piece was coauthored with Megan Beck, Chief Insights Officer at OpenMatters.

When we examine and value companies, we use a lens that is more than five hundred years old. Generally Accepted Accounting Principles (GAAP), which dates back to a Venetian Friar who lived in 1500 AD, has long been the determinant of what we how society measures value. According to this now global standard, things and money are valuable assets. People and ideas, and their development, are expenses. This means that investments such as education, healthcare, training, and research actually eat away at value creation. Our systems for valuing and measuring have driven countries and markets in a particular direction (towards physical assets), but machine learning offers an opportunity for us to shift what and how we value.

Each of us knows that human beings and the exciting new ideas we create drive the real value creation by organizations. But people and concepts are much harder to measure than inventory and bank accounts. Machine learning and big data will help us bridge this gap, assuming we can overcome this historical bias. By gathering and processing new types of data—data about people, what we want, what we do, what we know, and who we know—entirely new insights can be gleaned about what actually creates value, and why. And we are confident that these new insights will reveal the importance of intangible assets.

Indeed it appears that intangibles make up the bulk of corporate value today, despite the poor job we do at actually tracking and investing in intangible assets like skills, ideas, and networks. Many would say that the data we need to understand these assets is not readily available, but we disagree. The bigger problem is that GAAP accounting and the myriad value systems that surround organizations and entities have long deprioritized intangibles. Most companies have access to enormous amounts of data; there just hasn’t been much incentive to dive into the intangibles. But times are changing. More and more organizations are figuring out how to measure and understand what really matters today—people like you and me!

We have long researched the value of intangible assets and the impact they have on an organizations short- and long-term trajectory. We use analytics, text mining, and machine learning to correct historical biases and prove that it’s thoughts, insights, data, and networks that are today’s biggest drivers of value. And we have found that we can get a real insight into a company’s intangible assets just by digging into commonly reported financial metrics. The data is there; it just takes a new machine learning enabled perspective to see it.

Three Steps To Use Machine Learning To Understand Your Intangible Assets

If you are a leader, whether of country, company, or division, it’s time to start using today’s technologies to start understanding and getting full value from your undermanaged, underutilized intangible assets. To do that, we recommend that you follow the steps below.

1. Redefine what is an asset: The five most valuable companies in the world—Amazon, Apple, Alphabet (Google), Facebook and Microsoft—are all based on intangible assets: people skills and innovative ideas. We must expand the definition of assets to include intangibles like these, rather than focus on the physical assets of yesterday. In your organization, start small and take an inventory of all your intangibles, particularly data, insights, and networks. Shift these items to your mental list of “assets,” and manage them appropriately.

2. Take a fresh look at the data you have: Most companies have a wealth of data that is stored and rarely, if ever, studied. Take a look at your newly uncovered intangible assets, and consider how much you really know about each. In many cases, the data you need to learn more will be at your fingertips, or easily accessible. Consider what would be possible if you more actively managed these intangible assets. Increased customer value? New business models? More optimized sales channels? The possibilities are endless.

3. Use data science to uncover the facts, and dispel the fiction: Start building out your data science capability in order to fully leverage the data you have, and to fully understand and value your intangible assets. This will likely require an investment in new talent for the hard skills, but remember, the direction must be yours. Determine what analysis would be purposeful and useful, and send your team off in search of new insights.

Every leader in every organization must begin to access this new tool for managing organizations, and particularly the undervalued intangible assets that seem to be driving value for the biggest companies in the world. And simply talking about it, hiring a data science team or ‘moving to the cloud’, doesn’t count. Real change means actively questioning and interrogating every aspect of value and perceived value within an organization, and asking where is the Airbnb and Uber opportunity. This starts with the leadership team.

Seize The Moment

Even at its most efficient and unbiased, big data and machine technology will not solve all our country’s problems and will not be the silver bullet to shift how companies view value, if we don’t begin to change GAAP and Federal Regulations. We are hopeful, however, that technology will help us measure what was previously unmeasurable, and shift our business institutions.

There is every reason to believe that 21st-century technologies can be used to rebalance how we allocate both our personal and global capital in an age of digital platforms and network businesses. We no longer have the excuse that we lack the means to understand these precious resources. It’s time we seize this opportunity to redefine what is valuable, use machine learning and data science as evidence where companies and countries can have the most impact, and measure what matters.


This article was written by Barry Libert from Forbes and was legally licensed through the NewsCred publisher network.

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