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Data Research Briefings
Classic Topics

Data

In a digital economy, data—and the information it produces—is one of a company’s most important assets.
Data research at MIT CISR explores a variety of emergent, contemporary themes:

Data Monetization Strategies and Capabilities

MIT CISR research has identified that companies need five enterprise capabilities—a data asset, a data platform, data science, acceptable data use, and customer understanding—to execute data monetization strategies. Building these capabilities, however, is not easy; our research found that the capabilities companies have in place today are … average. In this research stream, we explain why you need to be persistent and purposed for your data monetization capabilities to pay off.

Data Monetization Capabilities

In this diagram, data monetization capabilities are represented by the grey segments and practices by the colored bands. Companies evolve data monetization capabilities, with more advanced practices building on foundational predecessors.

Data-Driven Transformation

A data-driven company pervasively creates, integrates, and liberates analytics knowledge to help people and processes continuously work smarter. Becoming data driven requires much more than hiring data scientists and rolling out dashboards and visualization tools; it requires building an enterprise capability that can regularly generate innovative new analytics-based work practices and scale best practices across the company. Companies with a data-driven capability are more likely to maximize returns from data—and to produce unique knowledge that creates competitive advantage.

How to Build a Data-Driven Capability

Building a data-driven capability involves a multifaceted approach that requires time.

Valued Data Wrapping

MIT CISR defines data monetization as the conversion of data (directly or indirectly) into financial capital. Companies can monetize their data in three ways: by (1) selling information solutions, (2)  improving business processes and decisions, and (3) wrapping products.

What makes data wrapping unique?

  • Data wrapping converts data indirectly into financial capital. Data wrapping generates value by influencing a lift in sales of a core product.
  • Product owners control the wrap. Data wrapping decision rights reside not with IT but with a product role or unit that manages data wrapping as a component of the product’s overall feature and experience portfolio.
  • Data wrapping is highly coupled with a core offering. Data wrapping must be delivered at service levels that meet quality standards similar to those for the underlying offering so as not to negatively impact the offering. Further, the costs and benefits of data wrapping must be evaluated within the context of the offering’s profit formula to ensure wrapping activities are lucrative.

Typical economic metrics for Data Wrapping

Data wrapping is a data monetization approach whereby a data-based feature or experience enhances the value proposition of an underlying core offering.

Lucrative Information Solutions

In a digitized world, there is no question that data is an important firm asset. Operationally, data is required to understand and hone decisions and business processes across the organization. Strategically, data serves as a critical component of the firm’s operational backbone for customer engagement and digitized solutions, both of which are foundational for digital strategy.

In some organizations, however, the role of data is shifting—from serving as a secondary asset that supports decisions, processes, and digital strategy to being a primary asset that businesses can productize and sell. MIT CISR refers to this new role as data monetization: the act of exchanging information-based offerings for legal tender or something of perceived equivalent value. Companies that engage in data monetization are in effect running information businesses.

Since 2013, MIT CISR researchers have been investigating information businesses to understand what it takes to generate competitive revenue streams from data monetization. The researchers have discovered that the capabilities and business models for information businesses are unique. Understanding the distinct requirements of an information business sheds light on how organizations should approach data monetization to ensure positive bottom-line results from their efforts.

Successful Artificial Intelligence

Artificial intelligence (AI) is a set of technologies that seeks to mimic human ability to understand data, find patterns, make predictions and find recommended actions without explicit human instructions. What distinguishes AI technology from traditional predictive and prescriptive analytics is (1) its ability to self-learn and (2) its ability to process natural language (source: Gartner).

The problem: Only 20 percent of AI-aware companies are currently using one or more of its technologies in a core business process or at scale (source: McKinsey Global Institute).

By collaborating with data leaders and senior executives, this research stream seeks to better understand how organizations are laying foundations that support AI adoption and consumption:

  • What are the top impediments to AI adoption/consumption ?
  • What are helpful practices that enables pervasive use of AI in business processes or products?
  • What new approaches/capabilities are required so that AI can scale ?
  • What are examples in which building explanations for AI has been useful for AI adoption/consumption?
2019 MIT CIO Symposium: Crafting Data Strategies that Pay off
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Sloan School of Management
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