Data Monetization: Data is Crude
Updated: Jan 11
This article originally appeared on cdomagazine.tech.
Article co-authored by Thiag Loganathan, Founder, Goldfinch Data AI Solutions and Dennis Kettler, Global Head of Data Science & Data Products at Worldpay.
By now we have all heard the ubiquitous and somewhat misleading new phrase “Data is the new oil”. Oil derives its value based upon the many useful and critical products created through its refinement. However, in our experience, it is not universally true that data is being leveraged effectively to create value for companies. Thus, data is the “new oil” only for select companies that have been able to appropriately define, refine and monetize. For those that aspire to drive value on their data, how and where should one begin?
To continue the analogy, similar to oil, data can be refined in many ways to deliver meaningful insights, decisions and, ultimately, value to a business’ top and bottom line. This process involves science, art, and experience to successfully monetize data. In this series, we demystify and layout a framework to understand and extract value from your data asset.
What is Data Monetization?
Data Monetization is a fancy way of saying, “making money from your data.” Whether your organization is a data producer, data aggregator or data consumer, you have the potential to generate new revenue streams through data monetization. This value can be achieved across a broad spectrum of analytical activities from descriptive through preemptive. Additionally, data can be leveraged across disparate disciplines: new data-driven products and services, client lifecycle management, internal cost optimization, sales funnel optimization and cross-sell/next best product to name a few. The most successful companies will monetize data in a great range and variety of activities.
Why do it?
Demand for additional data to understand customer behavior is at all time high across various industries.
Historically accumulated data is like a massive reserve of Oil which is not turning into revenue unless mined and refined. Failure to tap could lead to significant opportunity misses.
New revenue generated from monetizing the data can be invested to reduce overall expenses or into adding resources to the company to improve insights and revenue.
Market pressure…differentiation…market disruption…value chain expansion…industry protection (in saturated markets or at-risk verticals)
How? Define, Refine & Market
1. Leader, Strategy & Goals:
Companies that succeed in data monetization will have multiple programs using different business models/stakeholders with varying degrees of success. It takes the right leader to manage the successes and learn from the failures.
Identify the right leader who possesses strong data skills, business understanding and proven monetization experience.
Create a data strategy & roadmap that aligns and supports the strategic initiatives of the business.
Define goals on a timeline and make sure the roadmap has incremental goals.
2. People, Process & Technology:
We have heard this a lot, “I have old tech, it’s not all in one place, it’s not clean”. Yes, it is a challenge, and it is almost always the case when you start. But right people & process will allow you to get started while dealing with the tech and data quality constraints. So, invest in the right people with light processes before implementing hard processes & technology. But don’t forget:
A data monetization team is multi-disciplinary, will include data scientists, functional analysts, engineers, designers, and developers.
Processes will continually evolve as solutions progress from innovation to deployed at Scale.
Use modular and inter-operable architecture that will allow for technology to mature and scale over a period. Cloud technologies make it easier.
3. Risk, Consent, Privacy & Security:
Adherence to ethical, legal and compliance policies, Data protection laws, PII, PCI, HIPAA, and other regulatory compliances, is quite possibly more challenging than the technology skills required to build data apps. We will dive deeper into Risk, Consent, Privacy & Security in our next article but a few important points of note include:
Using and sharing data for analysis or monetization will have constraints that need to be overcome.
Establish a business & risk council that will continuously balance constraints, risk, and potential value.
4. Business Value and Monetization (Extracting Value): Understanding your data, as well as its gaps, is a necessity in order to overlay possible value streams with restrictions & constraints to create a business plan that extracts value leveraging your data assets. Other areas to consider include:
Layout a plan that incrementally proves the value hypothesis
Use MVPs and small scale pilots to prove out business models & adoption before a large scale roll-out that can become an ongoing value stream for the company
As you make progress, continuously reevaluate value to address market and business changes.
Build out a backlog of use cases and prioritize them to have simple/descriptive use cases early on to complex/preemptive use cases that require increased maturity.
5. Implementation Roadmap & Getting Started
This is the most difficult step; it is important, while getting started, to focus on value instead of technology. The roadmap should have incremental business and technology milestones that are achievable while setting you up for long-term success.
What's next in our series? With data privacy and security being a key factor especially with the newer consumer privacy policies such as GDPR and CCPA in place, leaders need to be cognizant of various processes and controls required to ensure compliance. In the next article we will dive into the details of the risk, consent, privacy, and security as they relate to Data Monetization.
About the Authors:
Global Head of Data Sciences, FIS
Dennis is currently the Global Head of Data Sciences, Governance and Business Development for FIS. In his nine years at FIS, he has established Data Science as a core competency enabling transformative capabilities such as advanced data visualization, predictive analytics, and ML/AI. Ultimately, Dennis has played a key leadership role in activating data-driven decisions that have established competitive advantages in market for both FIS and Clients alike.
In his current role, Dennis continues to lead data sciences and data product development. He also is responsible for driving governance strategy, capital investment, and business development as a senior leader of the FIS Data Solution Group and the Ethos ecosystem of data solutions. He brings a wealth of experience supporting many of the world’s largest retailers, corporations and payments brands for more than 10 years.
Dennis and his team are focused on driving disruption and innovation on multiple fronts, including the payment lifecycle through intelligent real-time decisions (fraud, authorization, cost and dispute), advanced consumer analytics and operational analytics.
Dennis currently holds four patents related to payments, attribution and consumer consent. The four patents held by Dennis are:
#10706393 - Systems and methods for routing electronic transactions using predicted authorization approval
#10621599 - Systems and methods for computer analytics of associations between online and offline purchase events
#10528926 - System and method for payment tender steering
#10776802 - Systems and methods for capturing account holder consent for transaction data collection
Founder, Goldfinch Data AI Solutions
A serial entrepreneur, Thiag Loganathan utilizes his deep expertise in enterprise data assets to monetize, drive measurable value and differentiate to improve business outcomes. He has experience in developing and rolling out end-to-end data driven platforms and business solutions including pricing optimization & elasticity, financial modeling, portfolio insights, loyalty programs, customer analytics and segmentation.
Both of Thiag’s current ventures focus on leveraging data through purpose-built AI and ML models to deliver better insights leading to data-driven decision making and improved outcomes.
Cardinality.ai is the first true Health and Human Services as a Software company (HHSaaS). Thiag works with agency leaders and caseworkers to create modern applications to streamline workflows, integrate AI to assist caseworkers with decision making and create better outcomes for vulnerable citizens.
Goldfinch Group is a data cloud platform company that consumerizes data through products and consulting for businesses ready to streamline data transformation. Thiag leads strategy efforts working with clients who need assistance with employee well-being using our Wellness AI solution and finalizing Velox, a new data app solution launching soon.
Prior to his current roles, Thiag led DMI’s Big Data Insights Division. In 2007, Thiag started Kalvin Consulting Inc., a business intelligence solution provider, and an SAP Partner, which was acquired by DMI in May 2013. During his time at Kalvin, he was named the Executive of the Year for 2011 by the Ohio North East Chamber of Commerce, for his long-term commitment to bettering the community.