Bill Schmarzo, Chief Innovation Officer at Hitachi Vantara sits down with Jeff Frick for a Digital CUBE Conversation. #theCUBE #HitachiVantara #DeanOfBigData https://siliconangle.com/2020/08/05/more-than-an-asset-hitachi-vantaras-bill-schmarzo-challenges-traditional-views-on-the-value-of-data-cubeconversations/ More than an asset: Hitachi Vantara’s Bill Schmarzo challenges traditional views on value of data VIDEO EXCLUSIVE BY MARK ALBERTSON A company’s assets, liabilities and shareholder value are reported on the balance sheet. What about the value of its data? As data volume grows along with its importance to enterprises, a case could be made that it should be treated as an asset and its value reported accordingly. Nothing could be more wrong, according to the “Dean of Big Data,” who has published a paper on the topic. “Data has no value,” said Bill Schmarzo (pictured), chief innovation officer at Hitachi Vantara LLC. “It’s how you use it to derive customer, product and operational insights. If you think about data as an asset, it never depletes, it never wears out. And the same dataset can be used across an unlimited number of use cases at a marginal cost equal to zero. Value is determined not by how much someone is willing to pay you for it, but the value you can derive by using it.” Schmarzo spoke with Jeff Frick, host of theCUBE, SiliconANGLE Media’s livestreaming studio. They discussed a view of value in economic rather than accounting terms, the critical relationship between use and data, examples of how large tech companies have applied use-case principles and the organizational value of design thinking. An economics conversation The proposition for data to be viewed and treated differently than other balance-sheet items is carefully outlined by Schmarzo in a paper — “Applying Economic Concepts to Big Data to Determine the Financial Value…” It’s co-authored with Dr. Mouwafac Sidaoui, dean of the School of Business at Menlo College. Schmarzo had been seeking to apply centuries-old principles to the value of data, based largely on the work of 18th-century economists, such as Adam Smith, who based the value of an asset on what someone was willing to pay for it. “I’d been thinking about this in an accounting conversation,” Schmarzo said. “But Adam Smith also talked about another valuation methodology, valuation in use, which is an economics conversation, not an accounting conversation.” A current example of valuation in use can be found in the Tesla car, according to Schmarzo. Tesla vehicles are guided by computerized systems and artificial intelligence that continually gather and analyze data. Tesla Inc. Chief Executive Elon Musk has declared that, unlike traditional cars, Tesla’s vehicles will appreciate in value, not because they are rare, but because use of the data will continually improve the autonomous technology over time. “What he meant is that he is building an autonomous asset that the more it’s used, the more valuable it’s getting,” Schmarzo said. “The more that they are used, the smarter they get. That’s why it’s important to build analytics that can be used over again.” The relationship between use and data highlights a key element in the value of information. Large technology companies became successful in part because they can monetize data through use. “Google is using their data to dominate search. Netflix is using it to be the leader in on-demand videos,” Schmarzo noted. “They’re great at monetizing data because they are not selling it; they’re using it. Most organizations really struggle with this. They struggle with this data-in-use concept.” Use case-first One of the hallmarks of Amazon.com Inc. is a process it employs called “working backwards.” Before any new product or project is discussed or funded, it must start with the writing of an internal press release that captures precisely how a new product would be used by customers and blow away existing solutions. Amazon’s emphasis on “use case-first” as part of its product development culture is instructive for how other organizations might build datasets in the future. Rather than try to focus on massive amounts of information, a different approach could be based on one use case at a time. “I don’t need to stuff 25 data sources into my data lake and hope that some of them are valuable,” Schmarzo explained. “How do you know in a dataset what’s signal and what’s noise? The use case will tell you.” ... “Design thinking and data science are different sides of the same coin,” Schmarzo said. “When you use design thinking to bring subject matter experts together with data scientists, all kinds of magic stuff happens. It’s unbelievable how well it works. Tesla gets it, Google gets it, Apple gets it, Facebook gets it, but most other organizations in the world don’t think like that.”