From Business Data to Business With Data

After an initial phase where companies discovered the hidden "treasure" of their systems, today any business that wants to sustain itself successfully cannot do without the most important asset: data. What is meant by "monetizing" data?

We have moved from looking at the past for trends in decision making to data-modeled businesses. To proactively manage information assets means clean, quality, easy to find, reusable data — in short, data which reduces the complexity of ML and AI analysis processes. For this, data governance is a facilitator and the central element. Because data by itself has no value, it acquires value when it is processed. Information is like a metric or indicator, but it needs a context. And knowledge is based on channeling and using it in order to achieve our objectives.

The real value is to monetize the investment of knowledge, information and data: all this is Data Management. Responsibility for data management must be shared between business (functional) and information technology functions, and people from both areas must be able to collaborate to ensure that an organization has high-quality data that meets its strategic needs. But what is meant by monetizing data? If we look at Wikipedia's definition, it is pretty clear about what we are talking about: "Data monetization, a form of monetization, can refer to the act of generating measurable economic benefits from available data sources. Less frequently, it can also refer to the act of monetizing data services.

When we talk about monetizing data we have essentially two recognizable typologies:

Internal Data Monetization — An organization's data is used internally, resulting in an economic benefit. This often occurs in organizations that use analysis to uncover insights, resulting in improved benefits, cost savings or risk avoidance. Internal data monetization is currently the most common form of monetization, requiring far less security, intellectual property, and legal precautions than other types. The potential economic benefits of this type of data monetization are limited by an organization's internal structure and situation.

External Data Monetization — An individual or organization makes data they hold available to external parties for a fee, or acts as an intermediary for the data. This type of monetization is less common and requires various methods to distribute the data to potential buyers and consumers. However, the economic benefit resulting from data collection, packaging and distribution can be quite large. Think of an airline that can exchange its data with the host. It can be a pure exchange or it can be a win-win operation where both companies benefit from the cross information. The limit is always "compliance" as the companies to retain the data have to inform the users about its use beforehand. But the power of data goes beyond the nominal data, anagrafic and all, and the aggregated data is a great resource, often wasted.  

Although recent, monetization is already more prevalent in certain industries: according to a McKinsey study, more than half of respondents in the basic materials and energy, financial services and high-tech sectors say their companies have begun to monetize data. What's more, these efforts are also proving to be a source of differentiation. Most notably, data monetization appears to be correlated with industry-leading performance. The industry leading companies are already monetizing data in many more ways, including adding new services to existing offerings, developing entirely new business models, and partnering with other companies in related industries to create shared data sets. According to this study, respondents report that their monetization efforts contributed more than 20% to company revenue.

Value Is Not Monetization

The value of the data goes beyond its monetization; it is possible to calculate and measure the value: What damage would it be to lose it? What does poor-quality data mean to us? Imagine a company like Booking that loses all its assets overnight. This company has only one asset, its data. Imagine a company that has to make postal deliveries with wrong shipping addresses, wrong zip codes, etc. It is necessary to know how to differentiate the value of the data from its potential added value due to its monetization. But how can we monetize the data?

Data as a Service

This is the simplest and most direct method of data monetization. The data is sold directly to customers or intermediaries. The data is raw, aggregated or anonymized and buyers extract the data for information. Buyers do not benefit from receiving information, they get it for themselves. Nor do they benefit from advanced analyses.

Insight as a Service

This involves the combination of internal and external data sources and the application of analysis to provide information. Knowledge can be sold directly or provided in formats such as analysis-enabled applications that provide up-to-date data driven insight. Insights are limited to specific data sets or contexts that the buyer has acquired.

Analysis as a Service

This is a more flexible type of data monetization that provides much more value to customers. The analytics and BI platform is installed and implemented to provide customers with highly versatile and scalable real-time data analysis.  It is about selling knowledge.

Integrated Analytics

It may be the most advanced and exciting way to monetize the data that provides the most value to customers. Simply put, integrated analytics means adding features normally associated with BI software — such as dashboard reporting, data visualization, and analysis tools — to existing applications. Product teams can build and scale custom, actionable analytics applications and seamlessly integrate them into other applications, opening up new revenue streams and providing a powerful competitive advantage. An example is Google Analytics, where the entire process from data collection, analysis and delivery goes through one ecosystem. 

Understanding the Role and Value of Data in Your Business

Good data management is about making sure you have the right data to support your business and improve performance. Using data wisely also helps manage risk and provides assurance that the company is complying with laws and regulations. But you can only serve this purpose effectively if you know where your data resides, how relevant it is, and how valuable it can be. Often companies do not accurately value their data because it is not strictly accounted for as an asset, even though it has real value in external markets. You need to govern this data, you need to work proactively with the metadata, you need a culture change within companies, and herein lies the real problem.

The Importance of Metadata

Many organizations lack metadata, that is, data about the data, such as the quality of the data, where it is stored and what it means. In fact, many companies are more likely to have a more detailed inventory of their office furniture than their own data. Before thinking about monetizing data, companies need to discover what kind of data they have about their partners, customers, products, assets or transactions, and what publicly available data can be used to increase the value of their proprietary data. They also need to find out whether that data has value internally to reduce costs, streamline operations, or improve sales processes, or as an external revenue source, such as customer intelligence as a service, or both. Again, all of this is directly linked to Data Governance.

First Step: Make a Data Strategic Plan

Sometimes the business strategy is not supported by related data management initiatives and vice versa. Managers must assess their key business objectives and strategic initiatives and understand how data can support them. Once the quality of the data is understood and linked to the business strategy, only then can the right strategies be established to monetize the data. This often involves creating a multidisciplinary and multifunctional team.

The potential for data to deliver value to many parts of the business is enormous and growing. Still, it is sometimes difficult for companies to imagine what the opportunities might be, because they are so accustomed to pursuing growth through traditional strategies and revenue streams. That's why all businesses should be open to learning from other businesses and partnering in ways that make sense from a data standpoint. There is a need to move from a concept of "competition" to one of "cooperation" and, again, this is a problem of business culture.

Communicating the Value of Data

Data monetization remains a relatively new experience for many organizations and, even when successful initiatives exist, they are not always known to the company as a whole. As data becomes more important, companies will need to communicate and educate internal and external stakeholders to fully understand the value that data can bring.

DAMA: The Data Management Reference Framework

The Data Management Association (DAMA) defines Data Management as a set of 11 knowledge areas. Thankfully, the 700-page Data Management Book of Knowledge 2 —DMBoK 2 reference[1] [2]  — provides a framework of excellence and good practices, and it is available worldwide. An entire chapter titled "Big Data & Data Science" guides Data Management professionals, explaining the difference between structured and unstructured data and introducing the new concepts of pattern analysis for creating predictions. From DMBok 2 they warn that due to the great variety of data formats, an even more rigid discipline is needed than that of traditional relational data models. The text mainly emphasizes the concept of SMART DATA as a true source for decision making, this clashes a bit with the "bad habit" of filling DataLake with unstructured and non-contextualized information.

Why DAMA? If we speak the same language, if we apply the same framework, it will be much easier for companies to understand each other when sharing resources and data assets, but it must always be done from an ethical perspective, with clients at the center, understanding that they are the owners of their data.