Data Monetization: Transform Your Cost Centers into Profit Centers by Leveraging Data Democratization
The first in a series on data monetization and the concepts, myths and approaches to realizing economic benefit from your data
By Sridher Arumugham
Enterprise data technology organizations have traditionally been treated as cost centers. They are part of shared services funded by enterprise business functions across the organization. Organizations have implemented ways to democratize enterprise data. This has been done by involving business units in areas of key decision-making such as priorities, funding, etc. In turn, business units participate in a cost-sharing model (such as for capital and ongoing operations) that aligns with one of the following categories:
- Central cost
- Show back
The cost of enterprise data is positioned centrally with no formal distribution. It is a default practice in budding small organizations as a starting point to enable fast-track decision-making, promote agility and grow credibility.
The cost continues to remain central across the enterprise, but clear visibility is provided on the cost and the value to the business functions. This is a common practice in growing mid-size organizations as they move up the maturity curve to deliver value to business units, thus moving towards chargeback.
Enterprise data functions facilitate democratization efforts like prioritization and decision-making between business units. In return, the enterprise data functions charge them back for their respective split of cost (infrastructure, software, delivery, operations, etc.). Business units also transfer funds to the respective enterprise data organizations. Established, large organizations adopt this practice.
All these models treat enterprise data organization as a cost center since the enterprise data spend did not directly tie to the business drivers, needs, use cases and value.
Chief data officers (CDOs) and other senior data leaders are assuming the responsibility of transforming enterprise data organizations from cost centers to profit centers using the concept of data monetization. This means that in addition to sharing capital investment and operational costs with business units, they also want to demonstrate the value of data and analytics in enterprise business functions. This is an emerging concept with growing importance as it has transformed chargeback models into data monetization, which directly contributes to an organization’s growth.
CDOs are seeking financial relevance beyond simply reallocating costs and are looking for ways to impact the company's top-line growth directly through data monetization.
In this first of a series of blogs on this topic, I will discuss data monetization concepts, myths, benefits and challenges. I will also explore how Informatica can help you in your monetization journey.
What Is Data Monetization?
Data monetization is the process of using enterprise data to increase revenue and economic benefit for the organization. The types of monetization include:
- Internal – Economic benefits are realized by a company using internal data and insights
- External – An organization's data is made available on a for-fee basis to external parties, or as a broker
- Indirect – Managing a technology debt (such as retiring an outdated, poorly performing asset) also provides an opportunity for economic benefit
Some examples include:
- Insights and reports published by industry analysts using value realized through subscriptions
- Facilitated data exchanges of third-party data for business entities
- A hosted data marketplace using crowdsourced data
- The role of customer attributes in a 360-degree view of a customer, which in turn you can use to improve the customer experience
Now let us examine some common myths in the industry and how leading practitioners view them.
Common Data Monetization Myths
Data monetization begins with informed decision-making powered by data. It also involves the selling of data and myriad other areas, including value gained from insights, metrics, algorithms, predictions and prescriptions derived from data. Practical examples of how data is monetized include:
- Increasing sales activities like reselling and cross-selling
- Reducing the total cost of ownership
- Streamlining business processes
- Gaining insights derived from external data
Data monetization entails any quantifiable economic value that can be measured and communicated. It is not only about understanding the validation, prescription and prediction you can achieve from data; it is also about the value of the missed opportunity without it.
Now let us understand why monetization is important.
Why Monetize Data?
Now that we have reviewed some of the benefits of data monetization, let’s explore the components of enterprise capabilities and how to bring monetization to life.
Enterprise Data Capabilities
Enterprise data capabilities consist of the following components:
Traditionally, we have taken a linear approach to build data analytics. We move from left to right, phase by phase, which has caused many challenges in the prioritization process. One of the challenges is articulating the value proposition of the capabilities. The other is the long time spent implementing each component before realizing the value of results.
The value proposition is better understood from a business use case perspective outside the enterprise data and analytics initiatives.
More recently, the industry approach has become from right to left as follows:
- Begin with use cases that support business strategy
- Outline the underlying business processes and outcomes, drivers and KPIs
- Build a stable and trusted data domain foundation to support the business use case
- Build a strong technology foundation with data governance capabilities to support the data domain
Tangible value can be quantified only from a use case perspective, which needs to factor the cost associated with data domains and the technology foundation capability. With this approach, every data initiative effort is tied to a business use case. These are the building blocks involved in transforming organizations from cost centers to profit centers.
Now let’s look at some practical challenges encountered with data monetization.
Challenges with Data Monetization
Data quality – Providing quality data is important to create trust in the outcome. It should be blended into ongoing business-as-usual processes.
Context – Relating data to the relevance and context of business process and scale is a complex task.
Technology barriers – Assessing the value and cost of complex technology involved with multiple data assets pose difficulty.
Data literacy – Establishing a common understanding of the outcome is a significant undertaking and needs to be aligned with data literacy and change management.
Now that we’ve reviewed the definition of data monetization and explored common myths, benefits, components of enterprise capabilities and challenges, my hope is that you can relate this information to data monetization efforts in your own organization.
Please stay tuned for the next installment of my data monetization blog series, which will focus on a data strategy framework, ownership and storytelling. I will then follow up with industry-specific examples involving healthcare plans and providers.
In the meantime, if you’re interested in further exploring topics that are top of mind for CDOs, I encourage you to check out our CDO hub.
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