The Data-Boom: The Opportunities And Challenges Businesses Face As Data Becomes More Prevalent
By Devesh Ginodia, CIO Advisory, Enterprise Data Strategy & Governance at EPAM Systems, Inc.
In 2021, about half of the global population (4.6 billion) was using the internet, think Facebook, Instagram and Netflix, and about 46 billion IoT devices, think the Apple Watch, generated around 60 zettabytes of data. International Data Corporations (IDC) estimates that the data generated annually will surpass 175 zettabytes by 2025.
Wait… what? What’s a zettabyte? Well, it’s not a sorority. A zettabyte is a storage capacity measurement, and if you were to store all 60 plus zettabytes of data on blue ray disks, you’d have enough to lay a path from planet earth to the moon about 23 times.
This data deluge, along with advancements in computing and storage capabilities, has created new opportunities and challenges for individuals and organizations of all sizes, public or private. Individually, people can now use the data meaningfully to make informed decisions and take actions that improve quality of life (like personalized ways of getting out of debt or tailored diets and medications for a healthy lifestyle). Businesses can generate insights from operations that add value their customers or suppliers and package in the form of data products for new revenue streams or hyper-personalized customer experiences that, in turn, produce additional demand for the business’s core products and services.
On the flip side, the increase in the volume of data also magnified the intensity of data-related challenges, such as cybersecurity threats and data breaches, the difficulty of data sharing and ownership, the spread of misinformation, and privacy governance.
Business opportunities
One of the opportunities businesses are capitalizing on from the colossal influx of information is monetizing data assets. Specifically, organizations can create new revenue streams based on direct or indirect monetization of their data assets. Various online marketplaces and consumer credit reporting companies directly monetize their data through hyper-target advertisements and marketing campaigns. As an example of in-direct monetization, a major Fortune pharmaceutical distribution and health information technology provider leveraged the data-as-a-service (DaaS) model to build relationships with new customers, and used these relationships to pull-through demand for their distribution business. Likewise, companies can indirectly monetize data to reduce costs, increase asset utilization and productivity and instill preventive ethical and regulatory compliance measures.
The key here is to understand how your business can use its data assets to add value to the existing customers or identify new markets within its ecosystem. If you manage data between buyers and suppliers, your suppliers may be interested in benchmarking information across suppliers. They could also be interested in buyer’s purchasing behaviors. These insights could add tremendous value to the supplier’s business and potentially open up new markets for your business to explore. Your business can grow brand imminence, add value to core products and offer metered services with Insights-as-a-Service to improve your bottom line. Lean on your organization’s domain experts to identify the use cases to monetize your data. You can connect with EPAM’s data monetization practitioners for guidance.
Case in Point: Financial Service Providers and the Power of Data
Various financial service providers, rich in data, have already started to monetize their information. One such organization, a multi-national fintech payment services provider, understood that their prime data assets could help identify fraud and identity thefts; fraud accounted for $56 billion in losses in 2021 (based on Identity Fraud study). This fintech analyzed transaction history and flagged transactions with a high probability of fraud, enabling mid-tiers banks, credit unions and insurance agencies to further investigate transactions before issuing credit, saving them from incurring huge losses from fraud.
Another large financial services provider – also a back-end credit card processor – reaped insights from their transactions (value, time, locations) to determine money velocity and give actionable insights to businesses. Powered by this insight, the businesses could launch demand-driven products/services, determine new branch locations, optimize inventory management, and make competitive personalized offers – all through leveraging data. Their insights were invaluable, especially for mid-tier organizations such as credit unions that, again, don’t have their own infrastructure or data to help make such informed executive decisions.
The Challenges
While the opportunists that have emerged from the increase in data have benefited consumers and businesses alike, no technological advancement is without its share of challenges. Currently, there is a significant lack of talented and qualified professionals in the data management field. Few businesses have the skills and capabilities to properly manage information as an asset and possess a limited understanding of use cases to monetize data externally. Many organizations understand generating operational and analytical benefits from their data however, struggle with how or where to get started with data monetization. Moreover, most efforts for an organization-wide data monetization strategy are hampered by turf wars and poorly administered standards, reducing the likelihood of information sharing. Meanwhile, its likely that their competitors have already found various ways to monetize their data.
An Ideal Approach to Data Monetization Strategy
EPAM’s proprietary data monetization framework works well for businesses that aim to start small, showcase the value and scale up their success without committing a great deal of investment upfront.
Phase 1: Market Analysis and Pilot
- Explore market opportunities
- Develop Pilot data product roadmap
- Build the business case
- Curate data insights
- Plan and execute on Pilot
- Synthesize lessons learned
Phase 2: Prioritize and Productize
- Assess industry and segment focus based on Pilot results
- Prioritize and redefine follow-on targets
- Create data product & monetization strategy
- Prototype with targeting using model from Market Analysis
Phase 3: Data Monetization Factory
- Define target operating model for sales or internal implementation
- Create and implement an repeatable model for data product solution factory and data product marketplace publishing that allows for internal / external monetization
Phase 4: Enhance capabilities & Scale
- Upgrade data product solution factory capabilities to embed data quality controls & governance, advanced analytics, ML/AI models to generate improved and high quality Insights-as-a-Service products
- Enrich data products by ingesting new data sources
- Enable advanced orchestration platform features to promote self-service
- Drive intake and onboarding of new business units and operational functions
Conclusion
To capitalize on these new opportunities, organizations will need to treat data as a product regardless if they are using it to generate internal efficiencies or taking new data products to the market to add value to their customers.
Understanding the path to data monetization is multi-dimensional. It requires organizations to consider new and existing industries and sectors for productization. They must then align the value proposition of the product where they will compete. Winning will require sound product strategies. Similarly, data assets must be devolved and monetized without violating privacy or any other compliance regulations.
Operationalizing the path to data monetization efforts is cross-functional. It takes the combination of mature functions like legal, strategy, product, IT, analytics, sales and marketing, etc., that can all work collaboratively towards a common goal. Ensuring the greatest value will necessitate that these functions remain agile and nimble. Likewise, it is useful for businesses to reach out to a partner to assist with end-to-end data monetization efforts, be that data valuation, risk and insurance, pricing models, GTM strategy, or deriving specific business outcomes.
Devesh Ginodia is an award-winning enterprise data strategy advisor and has 15+ years driving value for Fortune 100 private and public organizations.
He is a passionate technologist and serves as a subject matter expert in driving transformation initiatives in areas of digital enablement, citizen analytics, enterprise data management & governance, and data asset monetization.