This type of system prevents individuals from unnecessarily creating a personal data footprint on the digital ecosystem. Based on OPAL's model, businesses should look to create a trusted data aggregator network consortium that will value providing insights versus collecting data. Many of these platforms will be built on MIT's concept of open algorithms (OPAL). Platforms built not only on utilizing data but also preserving privacy in a commercially practical way can help businesses share insights while avoiding exposed raw data. So, what is the best way to gain optimal commercial value from personal data while avoiding unnecessary risks and costs? Utilizing what I would like to coin as trustworthy insights platforms, all three sets of stakeholders can gain in the process of sharing knowledge under unprecedented privacy and security protections. Looking To The Future: Fully Protected Insightsįinding innovative and cost-effective ways to derive usable knowledge from organic data sets will be key to market success in the 21st Century. Their concerns reflect the imperative of consumers gaining more control over the terms of access to their personal data. Third, individual data subjects have had enough of company practices that fail to address their legitimate privacy concerns. By one estimate, each and every data breach on average creates direct and indirect costs for companies per incident of over $7 million. Regulators have been amping up efforts to stop this with expensive compliance mandates and hefty fines. Cloud-based storage and processing sites are particularly vulnerable to breaches, hacks and leaks. state laws like the California Consumer Privacy Act (CCPA) have already impacted global organizations reliant on raw data. The EU’s General Data Protection Regulations (GDPR) and U.S. A growing raft of national and state laws now protects the data subject’s privacy and security. Second, for both data providers and consumers, the build-up of potential and incoming regulations is significant. The quality and origin of data cannot always be verified. Moreover, current data-based ecosystems are highly fragmented and inefficient. Sharing raw data with third parties is typically prohibited by law and risky to commercial reputation. All three sets of stakeholders have the potential to be harmed.įirst, for data consumers, data today is retained in siloes within an entity’s boundaries. One could even say that data shows a more ominous side than even the oil industry it is compared to and is a highly toxic extractive resource. When culled from end users, and then spread across corporate landscapes via vulnerable cloud server farms, raw data becomes highly problematic. The DaaS data maximalist business model is in serious jeopardy with the liabilities increasingly exceeding the economic returns. Unfortunately, this one-sided data maximalist approach of grabbing and holding as much sensitive data as possible simply ignores the legal, commercial and ethical downsides. Under the current DaaS business model, companies utilize the so-called SEAMs paradigm: surveilling people, extracting and storing their personal data via distant cloud servers and analyzing it to manipulate their behaviors. The challenge comes with how one arrives at those higher-level insights. The premise is that while a lot of data exists in the world, it takes effort to glean useful insights from it. Rather, the profit is in the insights gleaned.ĭata scientists are familiar with the DIKW pyramid, which moves from the broad foundations of raw data to information, knowledge and finally the slender apex of wisdom. Crucially, however, that immense and growing value-economic and otherwise-comes not from the raw data itself. To tap into this DaaS monetization supply chain makes good business sense. The data governance challenge is balancing mutual benefits between and among these stakeholders. Data Subjects: Individuals whose personal data gets extracted, stored and shared (as well as misused and hacked) between providers and consumers.Data Consumers: Institutions purchasing data for their business needs.Data Providers: Collectors, aggregators, processors and sellers of data. The DaaS new world order inadvertently has created three sets of stakeholders:
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