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?
Irrespective of a company’s level of data and analytical maturity or the maturity of their broader industry, there is opportunity to drive immeditiate value through data monetization. In early stages, this may come via new product offerings, product features or data-driven targeted marketing or, in later stages, via compression circumvention strategies, automation and cost mitigation strategies or disruptive services leading to new business. More specifically, we believe:
- Demand for additional data to understand customer behavior and deliver personalized messaging is at all time high across various industries.
- Data is being generated and accumulated at a rapid pace with storage and computation more efficient and cost-effective than ever. Failure to monetize this reserve of information could lead to significant opportunity misses.
- Value generated from monetizing your data can reduce overall expense, slow compression, lead to new revenue streams or simply be reinvested into additional resources and technology allowing for greater agility and scale in your monetization journey.
How to? A Framework to Define, Refine & Monetize
1. Leader, Strategy and Goals:
- Identify the right leader with strong data skills, business understanding and proven monetization experience.
- Create a data strategy and 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, the data is not all in one place, the data is not clean”. Yes, it is a challenge, and it is almost always the case when you start. But the right people and process will allow you to get started quickly while dealing with the tech and data quality constraints. So, invest in the right people with light processes before implementing hard processes and technology.
- A data monetization team is multi-disciplinary, will include data scientists, functional analysts, engineers, designers, and developers.
- Processes will continuously 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 [future article]:
Adherence to ethical, legal and compliance policies - Data protection laws, PII, PCI, HIPAA, and other regulatory compliances. In the next article, we will address this in more detail.
- Using and sharing data for analysis or monetization will have constraints that need to be understood and addressed
- Establish a business and risk council that will continuously balance constraints, risk, and potential value.
4. Extracting Value [future article]:
Understand your data and gaps, overlay possible value streams with restrictions and constraints to create a business plan that extracts value leveraging your data assets.
- Before getting started, layout a plan that incrementally proves the value hypothesis
- 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 requires increased maturity.
5. Getting Started and Roadmap [future article]:
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.
6. Business models and monetization [future article]:
Companies that succeed in data monetization will have multiple programs using different business models, stakeholders with varying degree of success. It is important to use MVPs and small scale pilots to prove out business models and adoption before a large scale roll-out that can become an ongoing value stream for the company.
What is next?
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.
THIAG LOGANATHAN BIO
A serial entrepreneur and expert in data/AI solutions, Thiag Loganathan uses data-driven frameworks to capture, measure and improve abstract socio-business problems to deliver incremental results fast. He is a proven leader in leveraging technology to improve business outcomes, and a trusted advisor with a career of building culture and environment for high performing teams that deliver solutions using Digital, Cloud, Data and AI with a focus on CRM & IoT.
Thiag helps government workers and U.S. constituents with modern experiences using Cardinality.ai for health and human services agencies. He also leads strategy and products for Goldfinch, a data cloud platform company, focusing on supporting businesses to use data and tech to execute updated strategies and make granular data-driven decisions in the post COVID-19 reality.
Prior to his current roles, he led DMI’s Big Data Insights Division, helping organizations turn data into profit through mobility, big data and data science, with a focus on Customer Experience and IoT.
In 2007, Thiag started Kalvin Consulting Inc., a business intelligence solution provider, and an SAP Partner, which got 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. Thiag holds a bachelor’s degree in electrical engineering. He lives with his wife and three children in Potomac, Maryland. Whenever possible, he enjoys playing golf and tennis to keep some social engagement going.
He brings deep expertise in utilizing “enterprise data asset” to monetize, drive measurable value and differentiate. He is an accomplished leader with knowhow of “data and analytics” solution lifecycle and leveraging it to improving business outcomes.
Demonstrated 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, marketing mix, customer analytics and segmentation.
DENNIS KETTLER BIO
Dennis is currently the Global Head of Data Science & Data Products at Worldpay. His specialties includes Big Data Analytics, Pricing, Business Intelligence, Data Science, Data Strategy, Customer and Retail Insights, Payments, Segmentation and Creative Problem Solving.