How Do You Turn Data into Profits?
The need for monetization lies at the core of every business strategy. A contemporary business is driven to create value from its data and to focus its efforts accordingly.
The need for monetization lies at the core of every business strategy. A contemporary business is driven to create value from its data and to focus its efforts accordingly. It is axiomatic that data and insights are invaluable tools to drive business growth in a highly demanding and competitive market. However, in an insights-driven business, data still needs to revolve around a revenue model that can sustain and justify its long-term expenses.
The new success model is a data-driven company that takes insights and converts them into meaningful sales growth. Technology and process investments that support this will be rewarded. Reports show that businesses driven with high-quality insights were 8.5 times more likely to report revenue growth of at least 20% in 2021.
With that said, only 25% of organizations today are classified as data leading. It is one thing to achieve data literacy and take steps to optimize your data infrastructure; it is an entirely different matter to achieve data efficiencies that have the potential to transform into revenue.
Certain classes of businesses have managed nicely to achieve an above-average return on their data. Market research firms such as Forrester and Gartner are notable examples. These businesses are data-rich and copious amounts of immediately usable, actionable insights are ready to be ingested by information-seeking entities. Most of us are familiar with their insightful, neatly compiled reports, research articles, and white papers.
The research analyst’s monetization model comprises secondary researchers and businesses who download treatises and articles for a fee. This manner of provisioning processed high-quality data for businesses via a direct sale is termed the “direct monetization model.”
Analyst and research firms further expand and monetize their data through membership or subscriptions and impose limitations on freely accessible content. These include, and are not limited to, the following:
Many fixed data literature items are available each month for free access.
Reports and other research-based content are available to download for a price.
Limited insights can be obtained for free access, and deeper interpretation and industry-influencing inferences are available upon subscription.
Generic data is available for free downloads but charged extra to reveal specific insights and information.
Each market research firm’s strategies evolve and align to their data type and objectives, such as market access and interests, primary or secondary research, deliverable creation from that data, and sales and monetization.
Quite a few businesses across industries happen to own a lot of data that can be monetized after removing company identifiers. A good example would be user pattern analysis or geo-location data that can be applied to tasks such as traffic forecasts on street maps.
By removing indicators that could lead to potential user privacy infringement, many big-name companies happily generate revenue from their data, enriched with raw information regarding consumer behavior and habits. (Spotify and YouTube are notable examples of this monetization model).
Generalized or anonymous data is usually unstructured and consequently difficult to process. Extracting monetary value— pitching it to businesses—is suspect unless coupled with tools to structure or compile it into usable forms.
The resulting data-driven culture has the following attributes:
Enhance data curiosity and explorative ventures.
Encourage data reuse and promote interoperability, collaboration, and transparency.
Embed internal systems with tools to derive value from data.
The indirect monetization model can save costs and add value to data collected internally. This practice is predominantly evident in services that serve data as a product, for example, business intelligence deployed on the enterprise intranet. Companies can make more informed decisions on resource planning and allocation based on utilization insights and return on investment when they gather, analyze, and process internal data.
Apps planted strategically across enterprise systems generate contextual data that is processed through hyper-intelligent systems to gain insights on process efficiencies and, in many cases, predictive and prescriptive insights. Modern technology such as artificial intelligence and machine learning lead a company’s indirect data monetization model. Multifaceted automated tasking enables cost savings and adds value to the end products of a business. Key business processes, as follows, are also streamlined:
Sales, marketing, and customer experience
Predictive, cognitive, conversational, diagnostic, descriptive, augmentative, and prescriptive analytics
Data exploration and sale to third parties
Many of today’s digital-era necessities require businesses to improvise and be more agile. This trend drives the data exodus from legacy, hardwired systems into the Cloud—another indirect data monetization strategy. Cloud functionality assists companies to catalog unstructured data with semantic data layers, metadata, data lineage, and other organizing methods, which are available on a pay-as-you-go basis.
We knew already that data holds power. However, as the world evolves around technology, information is being increasingly commoditized. Are you positioned correctly to profit from your data? If not, now is the time.
Manoj K Chandra, Chief Technology Officer, PreludeSys