5 key attributes of effective data monetization strategy

In cognitive computing era, new revenue generation stream has emerged with data at center of the modern digital business model. One of the key capabilities cognitive computing enables for an organization is the ability to generate additional revenue streams by using data effectively.

In the big data world we call it data monetization. The internal data monetization has already done amazing job at transforming business in all verticals by improving customer experience, enabling more personalized marketing and sales, deterring fraud and so on.

The emergence of big data has shown to transform professions and industries. We are seeing big data doing wonders with cost optimization and enhancing customer experience. We are increasingly seeing a growing trend among our customers to create new revenue streams with big data. Customers ranging from banks, telecommunication providers, energy and utilities companies and retailers have potential to earn new revenues from the vast amount of data they hold. Each of these businesses are experimenting with different ways to monetize the value of the data they gather during their normal operations. Each are expecting to make considerable revenues based upon the difference between the cost of collecting and storing the data, and what the insights and outcomes can be sold for.

As per the McKinsey Global Institute report on “Big data: The next frontier for innovation, competition, and productivity,” big data can create as much as $700 billion in value to consumer and business end users. Capturing this value will require the right enablers, including sufficient investment in technology, infrastructure and personnel as well as appropriate government action.

1. Identifying your target customers' needs, requirements and aspirations

Before you embark on journey to make money out of your data. It is important you profile your target customers, verticals and their parameters for success.

Case in point is telcos targeting retailers and mall operators with insights about anonymous movement of people throughout the property and surrounding. Delivering store or business catchment analysis based on real behavior, not just proximity to your location.

2. Identifying data assets—raw and refined, internal and external

Data monetization is much more than just storing and selling the data. Data monetization is about making revenue out of data enablers like insights, outcomes and partnerships. Companies can benefit from a centralized Data Science team that partners with the business and potential customers by identifying data that differentiates, exploring use cases to solve, and helping to jumpstart business teams.

One of the customer engaged with us is a retail company who is selling real time supply chain report to merchant wholesalers. The company is using the data from their Hadoop and Spark cluster to generate revenue-driving reports for wholesalers. The key parameter here is blending of purchase data from POS with transaction data from banks. With Apache Spark and Kafka, they run these reports in just hours, and with the scalability models in place they expect to grow this business to 25% of overall revenue. The analytics from these reports help merchants with customer segmentation, cross-sell analytics, and more.

How you share your data is about balancing needs to innovate against the risk of using your data. Strike that balance with clear responsibilities and pragmatic access, enforce compliance to data security, privacy and retention policies and processes to ensure continued trust by consumers and meet regulatory and legal requirements. Company privacy policies must be clear and well-understood by overall business and technical team. Access should be determined by the use case requirements and priorities.

4. Data as a service and business model

Operationalizing your data monetization strategy calls for having the right business model, the right strategic alliances and the right partner.

The companies are working on driving sophisticated big data as a service business models based on both volumes and values. The win-win business model will be highly influenced by the number of insights business can provide to customers and value those insights can generate for their customers.

5. Defining the technology strategy—Hadoop, Spark and IBM Watson Data Platform

The emergence of open source technologies gives tremendous power to organization in this new emerging data monetization space to break even more swiftly. Data provides maximum value when it is fresh. Technologies like Apache Spark and Kafka give real time analysis capabilities to business at lightning speed. This technology has a wholly different approach to data and data management than what we had before. It is the key enabler to the far reaching transformation that is really “big data.”

In short, these changes all lead back to the simplest of facts in the underlying technology—the agility of data.

A big data environment that supports collaboration powered by open standards is ideal. IBM Watson Data Platform provides the power of machine learning and cognitive computing based on open source “Apache Spark” to enterprises. Data platforms such as this will form solid foundation for a data monetization strategy and will enable organizations to quickly and easily monetize data.

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