The data economy: 3 paths to value

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Transforming data into revenue? That is the burning question in today’s boardrooms across industries. While there are many enablers for turning data into business insights or predictions, the question remains:

Let’s take a closer look at the data economy.

Data and value

What makes data so valuable? In fact, it’s the insights that the data reveals. Incorporate environments, Forrester estimates that between 60-73% of enterprise data goes unused for analytics.

Consider Forrester’s view of the economic impact of insights-driven businesses. Their revenue model forecasts that insights-driven businesses could see skyrocketing compound annual growth rates ranging from 27% to 40%, from a collective baseline of $400 billion in 2016.

Think about the Airbnb example. Launched with data scientists on board, and equipped with the ability to adapt quickly based on what the data revealed, the company experienced 430x growth in only five years.

Today, new business models around data and data insights — often termed data-as-a-service business (DaaS) — are emerging. Core elements of this business model include:

  • Gathering new raw data sets (e.g., to train models, for instance in predictive maintenance)
  • Cleaning, refining and enriching existing datasets
  • Developing new insights and the ability to act based on those insights

Think, for instance, of ScaleAI. This company recently raised $100 million (valuing it at $1 billion) for further development of its platform, which provides highly relevant training data for AI developers. The platform doesn’t build AI systems but supplies a key ingredient for those who do (such as self-driving car companies).

In this evolving, more data-intensive business climate, no one organization can do it all alone. Formulating the right partnerships is an important key for understanding the data economy and staking a claim ahead of the competition.

This is one of the main features of the Schneider Electric Exchange business platform, which can connect data specialists with organizations (such as commercial building owners or industrial enterprises) that want to optimize efficiency with granular data analysis).

Senseye, for example, specializes in applying AI models to conditioned-based monitoring for predictive analytics services.

Path #1 – Creating new business opportunities

According to a BCG Henderson Institute study, digitally enabled trade will be worth between $800 billion to $1.5 trillion this year.

The study also estimated that up to 70% of all global trade flows could eventually be meaningfully affected by digitization.

Digitization initially enabled the cost-efficient matching of buyer and seller based in different countries.

Now, it’s paving the way to new digital offerings that either substitute or complement existing goods and offline services or create entirely new digital ecosystems altogether, such as consumer-targeted energy services (informed by usage data) in Oil & Gas.

Path #2 – Transforming business models

There’s a widespread shift from selling products to selling value. For example, data-based services extend an organization’s existing product and service portfolio by creating completely new services that use both data and associated analytics.

A good example of this is a machine builder (e.g., Berto Coffee Roaster) who, in addition to selling a manufacturer a shop floor machine, can now offer the ability the remotely monitor that machine’s behavior in order to optimize performance and minimize downtime.

Path #3 – Discovering untapped efficiencies

For a long time, data was considered a business asset…but a very intangible one that was mostly untapped and largely unvalued.

Now, with new digital capabilities, rapid advances in ways to seize data’s business value are becoming both omnipresent and numerous.

In fact, data is turning into a foundation for making leaps forward in efficiency gains. One of the most radical examples of this is the digital twin/digital thread framework.

This approach utilizes data to accelerate business outcomes by capturing IoT’s business value while developing efficiency opportunities, in real time, across CapEx to OpEx cycles.

Those 3 value paths remind us that the tooling of industrial-scale, data-centric applications is just starting to be built, and that new technologies — for instance based on blockchain or crypto — are opening vast new possibilities (think, for instance, of the potential for technologies such as zero knowledge proof or homomorphic encryption for sensitive datasets).

To learn more about how Schneider Electric can help support your efforts to leverage data’s value in the emerging data economy, download our new e-guide “Digital twin: Bring your data to life for better performance.”

Link to Digital Twin e-guide

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