Anshuman Singh, VP of analytics & digital transformation at Polestar Solutions, breaks down why companies should consider monetizing data and how they can approach it in an ever-increasing competitive landscape.
Though this concept is not entirely new, the advantages of data monetization are being realized now more than ever. Businesses are always trying to identify new sources of revenue, bring in more cost-cutting measures, or expand their portfolio. One thing they must keep in mind – in addition to the market conditions and research – is to think about the data they already have. Â
Many organizations focus on external factors and information when they can tackle most of their issues with a goldmine they are sitting on — internal data. By leveraging techniques of data monetization on the internal data, wonders can be created. Data monetization is using data to obtain quantifiable economic benefits. It can be classified as direct — selling data to third parties or indirect — making business performance improvements with data.Â
In case you want to know about Indirect and Direct methods or methods based on internal or external stakeholders, read on to find out.Â
Why Companies Should Consider Monetizing Data
Simply stated, it is because of the business benefits it provides. Â
Companies with various levels of analytics maturity or technological adoption levels would receive different benefits from the data.Â
If you are thinking about data monetization or how effectively data is being leveraged in your organization, then you are in the right place. To get you started, here are some questions. Â
- Are you looking for cost-reduction opportunities?Â
- Want to improve any of the existing products or services capabilities?Â
- Do you have a clear understanding of the revenue systems? Â
- At what level of operational productivity are you? Do you want to improve it?Â
- Is there a possibility of adding new features to generate more revenue?Â
- What is the level of understanding about your customers, resources & stakeholders?Â
If there was even one question that made you think, this is something that I want to know more about, or this is something that we should do — then you’ve started thinking about Data Monetization. Â
How To Approach Data Monetization
Now, you might want to get the best out of your data and make the processes live up to their potential. But the question boils down to where we can start. The answer would be the same: Data.Â
What is the data being collected — is it right for your question, the data quality, the data quantity, and the basic architecture? The strength of the foundation calculates the strength of a building. Similarly, the finesse of data monetization lies in data management. Only with effective data management can monetization be achieved.Â
Once the data management or architecture layer is sorted, you can take the next step with data strategy. Every business unit, every function, and every organization has a different goal with the same data. C-suite executives want overviews, managers want updates, and executives want trends. Therefore, a strong, coherent, and consolidated data strategy with guidelines about usage is essential. Â
Let’s continue with building an example for a while; if the foundation is data management, the beams and pillars supporting them would be the strategy, and finally, the walls would be the data analytics layer deciding what room can be created with the data, i.e., what analytics techniques and use cases can be created. Â
In short, data management, data strategy, and data analytics form the three core pillars of data monetization. In addition to these, it is also essential to identify the areas that would suit your business according to the industry and levels of analytics. Here’s an example of how business practices and industries can be affected by data.
See More: Rethinking Data Management in the Data Economy Era
How To Make Data Monetization a Differentiator for Your Business
Even though the basic pillars and the levels of data utilization can be established, the implementation and the “stickiness†of the implementation fall back on market conditions. According to a survey by McKinseyOpens a new window , more than 70% of the respondents believe that applications of data analytics have more than a moderate change like the competition, of which 26% believe that it has changed significantly. Â
Some of the top applications that have brought changes to the nature of competition include:Â
- New entrants launching propositions that undermine traditional value propositionsÂ
- Traditional competitors improve their operations by improving their core businesses with analyticsÂ
- Competitors launching new products and servicesÂ
- Formation of data-related partnerships along the value chain, etc. Â
Therefore, it is important to analyze the current as-is and create strategies based on internal requirements and study the underlying industry trends and competitors’ value propositions to create the best possible solution for your business. Â
Examples of Data Monetization
Every company can monetize its data differently based on what data they have. But let’s take a few examples of how data can be utilized. One of the recent examples that came into notice was of Tesla offering insurance based solely on their “observable†real-time driving behaviorOpens a new window . With this average, drivers could save 20% to 40% on their premiums; these offerings can entice more customers for their products. Â
Examples of data monetization with mainstream supply chain data can be for the manufacturing industry. With data models of plant information, you can predict machine failures and schedule maintenance, i.e., predictive maintenance. One can have more precise planning based on the geolocation of products and the demand, combine this with digital twin manufacturing, and you can have continuous quality control and potential realization.Â
By monetizing your data, you can have access to scalable, agile, and flexible data analytics, which will, in turn, help your organization to adapt to new requirements and drive shorter times-to-insights. Â
How can data monetization help your organization to adapt to new requirements and drive shorter times-to-insights? Share your thoughts with us on FacebookOpens a new window , TwitterOpens a new window , and LinkedInOpens a new window .
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