By Anne Buff, SAS Best Practices
More can be achieved by making money from data than just selling data and making money. Sales are certainly a way to make money from data, but there are myriad ways companies across industries have generated significant business value through data.
Data monetization requires extensive rigor in business practices, creating five often unknown, non-monetary benefits that directly inform, support and drive data strategy. These added benefits have such value that I have come to refer to them as Data Monetization High Five.
1. Postponement of data and technology requirements
Data revenue generation requires platforms and architectures that meet aggressive technical requirements, including advanced data integration, high performance processing, extensibility and scalability. To maximize value in monetized solutions, companies must design architectures that support extensive reuse and the ability to reformulate and redistribute as needed.
When considering revenue generation data requirements, be aware that the data value is measured based on a number of attributes, including – but not limited to – volume, availability, rate of intake, accuracy, depth, diversity and completeness. It is key for companies to always assess these value factors in the context of intended use, specifically in terms of what will be needed from data to effectively and effectively solve the problem or take a specific action.
Companies should use the technical requirements and data value assessments defined in the development of an information-based offering or service to evaluate their current capabilities. Participating in the company’s ability to deliver data and technology capabilities will be easily exposed and will therefore define the technical components of a strong data strategy moving forward.
2. Requires cross-functional skills and abilities
Strategic revenue generation conversations are not about how business and IT departments need to be aligned. Instead, they are a shift to a full integration and commitment of cross-functional resources to solve problems and drive action through data. From data management professionals and technical engineers to business professionals who are intimately familiar with industry nuances, companies should assume that specialized skills are needed to solve defined problems. Data strategies informed of the need for monetization or business value generation are performed by cross-functional teams assembled and dissolved as business needs dictate. The organizational mindset is not to divide (IT and business) and conquer. The process is to unite and deliver.
3. Definition of results-based measurements and objectives
Different from concepts such as value chains or life cycles, data policy monetization has an inverse focus, starting with data usability in mind. Value is determined by measurements defined by the user’s intention. This means that all measurements and measurements must lead to the desired results. They are not measurements over historical time periods, correlations with past performance or comparisons with industry norms.
This does not imply that performance metrics need to be scaled. What this means is data strategies driven by output-based metrics will be less concerned with pinching needles forward on dashboards and more concerned with delivering specific, defined results measured in terms of real value, often expressed in monetary terms. A data strategy with metrics expressed in money or saved money earns executive support.
4. Encouraging innovation
The development of a data strategy is often guided by the question of “What can we do with our data?” or “How can we use data to run our business?” While these are good questions to ask, they should not be the questions that drive data strategy. The leading question should be “What do we want to achieve (the problem we want to solve) and how can data help us do that?”
Companies that make money from data already understand that data policy activation focuses on the usability of data in the context of solving problems or driving decisions. They return to needs and requirements. Creativity and ingenuity kick in when identifying gaps. Team members gain energy from problem solving. As data revenue processes are normalized, their innovative capabilities also include problem prevention. And in the end, the real industry disruptors are looking to detect problems and challenge the old adage of “If it’s not broken, don’t fix it.” They use data to redefine “bread” and consistently raise the bar.
5. Increased relevance and confidence
There are two critical factors in establishing and maintaining value for data use: relevance and trust. And when it comes to launching a data strategy, these are two of the biggest challenges companies face. Companies that develop information-based products and services recognize their success depends on a well-built foundation of relevance and trust. Consistency and clarity are table efforts. Transparency cannot be negotiated. And relevance is the most important goal in the value proposition for data consumers. These core components are baked into their business models for monetization, and they are critical elements of data strategy.
The promise of monetizing data
Companies that commit to make money (internal or external) transform their business through data practices and behavior there are inherent sources of value. Data revenue generation is far more than exchanging data products or services for monetary value. It is a strategic approach to creating and maintaining business value in the context of usage. Data strategy informed of the practices and behaviors required for data policy monetization will equip the company with a measurable plan to generate internal and external value and achieve defined business goals – the right definition of being fully data driven.