Disruption dominates our lives, globally. Depending on the context, this condition can be viewed as a positive or negative development.
But what if disruption is not temporary? What if disruption becomes the defining dynamic of the next decade as the data revolution gains momentum? Among other things, it will hold significant implications for how we approach predictive analytics and the data upon which it is based. It will also hold profound implications for how regulatory systems function.
Welcome to our Disruption and Data blog. Here we will analyze regularly the tensions and contradictions that constant change creates for the newly emerging data analytics discipline and predictive analytics. This week, we introduce the top disruptors and the implications they hold for data analytics. Going forward, the blog will periodically assess the opportunities and challenges at the nexus of disruption, data, and policy.
Disruption is Everywhere
Let’s start with clarity on terms. Dictionary.com defines “disruption” as:
1. a forcible separation or division into parts; 2. a disrupted condition; 3. Business. a radical change in an industry, business strategy, etc., especially involving the introduction of a new product or service that creates a new market.
Clay Christensen, who first used the term to describe business innovation, recently refined the definition in 2015 specifically to apply to a narrow situation: “‘Disruption’ describes a process whereby a smaller company with fewer resources is able to successfully challenge established incumbent businesses.” It encompasses “new market” disruption (where true innovation creates an entirely new supply and demand ecosystem) and “low market” disruption (which is just about using technology to deliver an existing product or service more efficiently).
Disruption is far from new. From the 1960’s protest movement (“challenge authority!”) to Schumpeter’s concept of “creative destruction,” different forms of disruption have periodically popped up. Disruption, by definition, delivers an uncomfortable break from the past. It is of course particularly unsettling to incumbents from companies to politicians.
Culturally, we seem to be welcoming disruptors. The recent financial crisis (a profound disruptor in its own right) seems to have provided a tipping point. But not all disruptors are viewed in a positive light. Consider:
- Technology: This is the most well-known disruptor, and it is gaining momentum as the digital and data revolutions take root. From enhanced cognition and IoT, from big data analytics to data protection, from FinTech to machine learning, from smart phones to smart objects large (autos, refrigerators, container ships) and small (coffee makers, security systems), every aspect of daily life and business is being re-defined by the application of advanced computing processes. Lower-skilled and lower educated workers are quite adversely affected by this disruption. Others additionally view increased reliance on technology with concern regarding the amount of personal data being shared by companies for commercial reasons and by governments for national security reasons.
- Culture: Following the wild success of upstart technology companies, people in the industry who defiantly refuse to follow fashion convention are labelled “disruptors”….in a good way. But not everyone views in a positive light the sartorial choices made by some in the technology industry, as this CNN Money story indicates. Then there is the question of originality….if everyone is taking meetings wearing black jeans and a t-shirt, isn’t this kind of conformity to a uniform the antithesis of disruption? And if one is female, one blogger has suggested being disruptive involves rejecting the disruptive dress code altogether.
- Economics: Central banks are re-writing economics textbooks by pursuing “unconventional monetary policy” (which involves central bank purchases of sovereign and occasionally corporate debt securities as well as other direct injections of central bank liquidity into the financial system) and “macroprudential policy” (which involves using regulation to promote financial stability in the aggregate). These policies are widely credited for saving economies in Japan, Europe and the United States from more severe recessions (if not full depressions) during and after 2008. However, critics argue that the approach creates inappropriate market distortions and amplifies uneven distributions of wealth in society.
- Trade: Companies, voters, and sovereigns are all pulling away from support of international trade for different reasons. As Gillian Tett recently observed in the Financial Times, “…even before Mr Trump arrived in office, the C-suite was losing its blind faith in globalisation. For better or worse, we face a more localised world. And that trend owes as much to robots and digital technologies as any political firebrand — and will probably outlast any president, too.” Localization movements from agriculture (“locally grown”) to data governance (see the EU’s General Data Protection Regulation) to central clearing (see the Brexit drama) to manufacturing (3-D printing) to infrastructure development (China’s Belt & Road Initiative) and FDI continue to crop up, creating challenges for traditional trade relationships and decreasing grass-roots political support for classical trade.
- Politics: At nearly every juncture since the autumn of 2008, voters especially in advanced economies have consistently elected political outsiders promising to shake up the system domestically. Political disruptors have come from the left (President Obama, President Macron, Prime Minister Trudeau, Prime Minister Renzi, Prime Minister Tsipras) as well as from the right (President Trump, President Orban, President Erdogan). Political polarization and extremism has, relatedly, been on a steady upward trajectory ever since violent protests disrupted the World Trade Organization talks in Seattle in 1999.
- Geopolitics: The Foreign Affairs quarterly journal recently devoted an entire edition to this theme. Russia and Europe (for different reasons and in very different ways) seek a return to their pre-World War II status. Ironically, the United States under President Trump also seeks a return to the pre-World War II era by rejecting the international organizations, norms, and collective decision-making structures created after World War II. Japan, by seeking to amend its constitution in order to eliminate the commitment to pacifism, also seeks a return to the pre-World War II era. China, in contrast, seeks to resurrect its 15th century global role.: The balance of power that existed after World War II is famously under siege.
Disruption is everywhere, and it is gaining momentum.
The Data Revolution 1.0
Appreciate the ironic symmetry on display. While the world worships disruption, the data “revolution” seeks to impose order.
The data revolution is a consequence of digitization. Every interaction online throws off mammoth amounts of data regarding behavior patterns. The most popular example is the autonomous vehicle which generates and collects vast amounts of data on the vehicle’s operation, the personal preferences of the user/passenger, and the road conditions.
The data revolution at its core consists of assembling vast amounts of data and then hoping that systematic categorization and analysis will generate new insights regarding behavior. It assumes that order exists at some meta-level which is discoverable through advanced computing capacity. It will change behavior in the future.
The amount of data generation increases exponentially as new elements of life become digitized , particularly as the words we use to communicate (“unstructured data”) are transformed into quantifiable components. The data revolution grafts math onto language, then assesses quantitatively how the verbal puzzle pieces fit together.
Here is how the Harvard Business Review describes the role of data science:
“…what data scientists do is make discoveries while swimming in data…they are able to bring structure to large quantities of formless data and make analysis possible. They identify rich data sources, join them with other, potentially incomplete data sources, and clean the resulting set…data scientists help decision makers shift from ad hoc analysis to an ongoing conversation with data.”
The impulse to impose order from apparent chaos is understandable, particularly during periods of great change. The optimism that new tools and technology can deliver previously unattainable insights is palpable. The excitement associated with seeing the world from a different perspective is contagious. But it also raises a number of questions:
- What happens when the data set involves behavior from a period before the great disruption?
- What happens if the data is collected amid great disruption?
- How can the data have predictive power under these conditions?
- How will this approach change everything we know about decision-making at the personal, policy, and corporate levels?
- How will this change financial regulation, especially in the context of FinTech developments?
We will start examining these questions in the next blog post and throughout this blog.
An earlier version of this article first appeared on www.bcmstrategy2.com. BCM International Regulatory Analytics LLC is a boutique consultancy focused on policy quantification and trend projection analysis. The company is developing a proprietary technology platform that quantifies policy risk and anticipates outcomes using its proven, patented process. During the construction phase, quantification and trend projection is available to non-clients in the financial technology sector through the FinTech RegTrends Report, available at: www.fintechreg.biz.