Turning data into data capital: Here's how to make the most of this huge hidden asset

In economics, capital is generally considered a good that is produced that can then be turned into an input to produce yet even more goods and services. The traditional sense of the term often denotes financial capital, such as assets that can be traded in a market, or human capital, with hours spent by people to create a product or service. Intellectual or instructional capital is the notion of the transfer of knowledge used to produce goods, encompassing human and social capital. Natural resources can be spent as capital, as can organizational infrastructure like computers or servers.

In the era of hyperscale information, a new kind of capital has emerged that enterprises would be wise to understand and use to their advantage:

Data capital.

In a white paper, MIT Technology Review spelled out what it means for data to be a form of capital:

To call data a kind of capital isn’t metaphorical. It’s literal. In economics, capital is a produced good, as opposed to a natural resource, that is necessary for the production of another good or service. Data capital is the recorded information necessary to produce a good or service. And it can have long term value just as physical assets, such as buildings and equipment, do.

“With data capital, if you know something about your customer or production process, it might be something that yields value over the years,” Erik Brynjolfsson, professor at Stanford and author of books such as “The Second Machine Age,” said in the white paper.

The idea is simple: modern enterprises are awash in data. Instead of thinking about it as simply an object that needs to be managed and endured through technology, data can be activated to make better services, improve operations, and create value throughout the organization. Transforming data from a burdensome cost center to a capital asset can be the difference between an organization that stagnates and one that thrives.

“Data is fueling our new economy to the tune of 2.5 quintillion bytes of data created each day across humans, edge devices, IoT devices, and machines,” Jimmy Priestas, the global managing director of data and AI at Accenture, said during a presentation at Google Cloud Next ‘22. “The volume of data and the pace of its accumulation will only accelerate in the future. Companies who harness data to derive value through data-driven decision making are differentiating themselves. They're driving new business outcomes, more efficient and effective operations, understanding their customers more deeply, and transforming the businesses that serve them.”

Datafying is the new digitizing 

Now many companies may believe they’ve been making these data-driven connections for some time, as they have built up their digital businesses, implemented analytics, and deployed dashboards. The reality is, as the author’s of MIT’s white paper argue, most companies have simply been “digitizing” their information and workflows, not “datafying” them:

Digitizing activities means involving sensors or mobile apps in the activities in some way. Datafying activities means expanding the observations you capture about them.

From paper records, to on-premises data centers, to the cloud, digitization has been the focus for for decades, particularly at traditional enterprises. Just get it online and connected, the thinking went, and the analysis can follow. It’s the last step — to the cloud — that finally enables the analysis, that enables the datafying of assets to the point where they can be used in tangible and meaningful ways to enhance a business and its operations in ways previously not possible. The cloud has created a new enterprise computing architecture where data becomes capital.

The key phrase in datafying assets is “expanding the observations you capture about them.” In the last decade, the ability to measure and understand data has grown by leaps and bounds through the use of artificial intelligence and machine learning. What used to take a team of quants working to clean and gleam nuggets of wisdom from data is now a matter of working with company engineers, cloud providers, and integration partners. 

For instance, an organization working with Google Cloud can organize data from a variety of sources into BigQuery, then build machine learning tools to extract observations about the data through Vertex AI. Just look at recent work by telecom leaders Vodafone and BT, and how they were able to turn their multi-million-user, continent-spanning networks into fields of data-rich insights.

Once the observations from the data are discovered, data can then be used as capital to create value. 

Vertex AI Forecasting, for example, can help companies make predictions about their data such as supply and demand for the upcoming year. Or Recommendations AI can allow companies to use data to give their customers recommendations on products they may be interested in. These are examples of turning customer interactions — data that may have otherwise gone unused — into new revenue-generating tools. 

Et voila, data was spent as capital to create something new and useful.

Spending data capital to improve core business

According to a survey of 190 enterprise executives by Accenture, only 28% of organizations have implemented an enterprise-wide data strategy. Furthermore, only 32% of of companies reported being able to realize tangible and measurable value from data.

“This strategy could not be more critical and crucial to your business in a post-digital era and should be ingrained in all business strategies going forward,” Priestas said. 

Accenture cited three reasons why enterprises have trouble realizing benefits of their data capital: 1) lack of talent, resources, and strategy; 2) poor data quality and; 3) data stuck inside corporate silos.

The lack of strategy often rises from the lack of talent to understand how data can be organized and put to work. Why? Because most companies are not technology companies. They are retailers or manufacturers or financial services companies and so forth. Successful companies are typically good at doing one thing very well, and IT is often not central to that core competency.

For instance, L'Oréal is the largest and among the most prestigious makeup retailers in the world. The company wants to understand its data and spend its data capital in service of that mission. But L'Oréal is distinctly not an IT company. Thus, L’Oréal partners with companies like Google Cloud and uses tools like BigQuery to aggregate its data and make it usable to its decision makers.

“Why BigQuery? It is for one very simple reason,” Antoine Castex, enterprise architect at L'Oréal said at Next ‘22. “We don't want to manage the infrastructure not at all. But why? Because we are not an IT company. What we prefer to do instead is focusing on the data that we put on BigQuery. The added value provided by this data is clearly more important for the L'Oréal people.”

L'Oréal solves another one of the problems that Accenture noted above by using BigQuery Omni, a fully managed, multicloud analytics solution that allows you to cost-effectively and securely analyze data across clouds. Omni allows L'Oréal to take its data out of silos and make it actionable across the enterprise. The results for L'Oréal are faster time to market, better management of a diverse data ecosystem, and the reduction in complexity while increasing security and trust in the data.

“And at the end, having another view of everything in one single pane of glass was also a very, very big benefit,” Castex said. 

Ultimately, the effective deployment of data capital is proving to be as essential to the success of an enterprise as any other kind of capital — just look at who is leading in which industries, who is transforming, and who is falling behind. Understanding and utilizing data capital to produce new goods and services is one lever that companies can use to create a competitive advantage in a competitive marketplace.