In-Depth Guide to Data Commercialization in 2024

This comprehensive guide examines the emerging practice of data commercialization – how savvy companies are turning their data into new revenue streams and strategic assets. With the exponential growth of data from IoT, AI, blockchain and other technologies, organizations have unprecedented opportunities to monetize and capitalize on their data. Read on for an in-depth exploration of the components, strategies, challenges and best practices around successful data commercialization in the year ahead.

What is Data Commercialization and Why Now?

Data commercialization involves taking data generated through business operations and identifying ways to profit from it. This could mean directly selling data or using data to develop new products and services.

According to Forrester, 47% of firms are already dabbling in data commercialization. However, even more companies are expected to embrace data commercialization in 2024 as enabling technologies mature and data‘s value becomes apparent. The global big data market alone is projected to grow to over $234 billion by 2027 according to Valuates Reports. Clearly, data is becoming essential to competitive advantage. Organizations that successfully commercialize their data will reap significant rewards.

The Explosion of Data Volume and Sources

The universe of potentially monetizable data is rapidly expanding. By 2025, the global datasphere is expected to grow to 175 zettabytes according to IDC. Data is being generated from several key sources:

  • Internet of Things (IoT) data – With over 30 billion connected IoT devices predicted by 2025, IoT data represents a tidal wave of opportunity. IoT allows collection of rich telemetry data from equipment, products and environments that can reveal insights.
  • Customer data – Customer transactions, engagement, demographics, preferences and other data are prime targets for commercialization. Customer data can uncover needs and inform highly personalized offerings.
  • Internal business data – Operational, sales, financial and other internal data can help optimize business performance. This data can also be packaged into valuable analytics offerings.
  • Public/syndicated data – Combining internal data with purchased third-party data sets enables even deeper insights. For example, blending a company‘s sales data with syndicated demographic data may reveal growth opportunities.

Developing a Data Commercialization Strategy

Effective data commercialization requires careful planning and a well-defined strategy:

  • Start with high-value data – Prioritize data sets, either customer or operational, that can deliver the greatest business impact. Quick wins build momentum.
  • Identify commercialization opportunities – Brainstorm specific ways high-value data could be monetized or enhance operations. Compare opportunities to focus efforts on most viable options.
  • Assess required technology capabilities – Determine what technology is needed to collect, store, process, analyze and share data for commercialization goals. IoT, blockchain, AI and cloud platforms are common enablers.
  • Define data governance – Establish proper oversight, security, policies and controls around commercially exploited data. Rules for internal data use may require adjusting.
  • Construct data supply chain – Map out how data will flow across the organization and to external parties. Cleaning, integrating and preparing data for consumption is key.

Critical Roles for Data Commercialization Teams

Data commercialization requires some dedicated roles and teams:

  • Data analysts – Perform deep analysis to derive insights from data that can form the basis of valuable offerings. Requires data science and analytics skills.
  • Data engineers – Responsible for building and managing systems to collect, store, integrate and prepare data for use in commercialization.
  • Data governance specialists – Define policies and controls for how data is used and accessed internally and externally. Manage regulatory compliance.
  • Product managers – Drive development of monetizable products and services powered by commercialized data.
  • Marketing specialists – Generate interest in and communicate the value of commercialized data offerings to customers.

Navigating Organizational Change

Introducing data commercialization can spur significant organizational change:

  • Communicate change – Educate staff on what data commercialization is, why the organization is pursuing it, and how roles may evolve. Clear messaging prevents confusion.
  • Involve cross-functional teams – Get perspectives from sales, marketing, product, legal etc. to build buy-in. Data commercialization touches many parts of an organization.
  • Plan transition of data governance – Existing data policies and standards may need adjusting to enable commercialization. Changes should happen incrementally.
  • Rethink data culture – Look for ways to incentivize data sharing and appropriate data usage as assets to monetize rather than only as costs.

Key Technologies Enabling Data Commercialization

Several major technology trends are accelerating data commercialization opportunities:

Internet of Things – The IoT allows collection of vast amounts of data from network-connected physical objects. This opens new possibilities for monetization.

Business ValueExample
Optimize operations using IoT data for predictive maintenance, quality control, etc.Boeing uses IoT data to predict commercial jet maintenance needs
Develop new usage-based or outcome-based service offeringsKaeser Compressors sells compressed air as a service based on IoT monitoring of equipment.
Offer customers analytics dashboards and reporting based on IoT dataRockwell Automation‘s FactoryTalk Analytics provides industrial analytics services for manufacturers using IoT data.

Artificial Intelligence – AI techniques like machine learning extract insights from data that can inform commercialized offerings.

Business ValueExample
Recommend complementary products to customers based on past purchase historyAmazon uses AI recommendation engines to suggest additional purchases
Create personalized shopping experiences by predicting customer needs and preferencesStarbucks serves AI-optimized, individually tailored content and offers to app users based on stored customer data
Automate data analysis to quickly surface insights without manual effortMorgan Stanley employs AI to extract insights from data to inform financial recommendations for clients

Blockchain – Blockchain establishes secure, tamper-proof data sharing between parties, enabling commercialization.

Business ValueExample
Safely share data with ecosystem partners to deliver joint offerings or analyticsInsurers leverage blockchain to securely share selected customer data with healthcare providers
Provide assurance of data integrity and compliance for commercially sold data setsWalmart tracks food supply chain with blockchain to provide visibility into sourcing for consumers
Establish data marketplaces where participants can monetize data while maintaining control of accessOcean Protocol allows data owners to control access to data traded on blockchain-based marketplace

Key Challenges and How to Overcome Them

Despite its potential, data commercialization comes with challenges that organizations must plan for:

Data quality issues – Inaccurate or incomplete data lacks value. Methodical data governance and vetting processes are essential. Master data management tools can help tackle quality issues.

Identifying the "right" data – Just because data is abundant doesn‘t mean it can be commercialized. Evaluating data‘s relevance and potential business value is critical before investing in commercializing it.

Navigating data silos – Data often resides scattered across multiple legacy systems. Establishing common data schemas and carefully planned ETL processes helps overcome integration hurdles.

Negotiating data rights – Companies must be careful not to violate customer privacy or existing vendor agreements as they seek to commercialize data. Clear data usage rights and transparency are imperative.

Pricing data offerings – Identifying optimal pricing requires balancing production costs, perceived value, and market rates. Price testing and segmentation helps pinpoint pricing models that work.

Best Practices for Data Commercialization Success

Follow these proven guidelines to maximize the likelihood of effective data commercialization:

  • Start with high-ROI data domains – Prioritize customer, sales or operational data that can deliver tangible business value.
  • Implement methodical data governance – Carefully control access, security, policies and compliance for monetized data. Assign dedicated data stewards.
  • Test commercial viability before scaling – Pilot data offerings with small customer sets to refine approaches and build the business case before full rollout.
  • Leverage partners to expand reach – Ecosystem partners can help extend the visibility and distribution of commercialized data offerings.
  • Evaluate both licensing and new services – Consider directly licensing data as well as using data to develop premium SaaS offerings or analytics services.
  • Invest in internal data literacy – Ensure staff have the skills to properly handle and extract value from data targeted for commercialization.

Conclusion

Data commercialization presents a prime opportunity for forward-thinking companies to derive new revenue streams and strategic advantages from data assets. By harnessing emerging technologies like IoT, AI and blockchain, organizations can turn their data into game-changing products and services. However, careful strategy and planning is required to overcome challenges on the road to data commercialization success. Organizations that follow the best practices and commitment needed to effectively execute data commercialization will gain a significant competitive edge.

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