Technical Debt Management: How To Avoid Technical Debt?
Technical debt is the practice of intentionally introducing faulty code because it appears to have short-term benefits (faster development time etc.)
Data is similar to iron ore: it's pretty useless until you process it properly. And everyone can benefit from data monetization. Even if you don't have any data, you can still monetize data sets that are publicly available.
Whether you are an individual or a big organization, this article will show you available options when it comes to data monetization. And, based on your specific situation, you will learn strategies that best apply to you.
Data can help companies either benefit directly from selling their data/insights or improve their internal efficiency by using the insights derived from the data. Whatever the case may be, the result is a real, measurable impact on those companies.
Data monetization business models generally fall under two types: direct or external (you sell data or insights to other entities), indirect or internal (you use the data insights to improve your own efficiency/profits).
We can also split the data itself based on its origin: internally generated (data you collected yourself or gathered from your users) and externally generated (data purchased from other entities or open data that is publicly available).
Direct or external data monetization is the most obvious approach where you directly sell your data or insights to other entities or individuals. But you still have to convince others of your data quality and usefulness.
Direct data monetization has several drawbacks:
And in general, it is better to sell data insights than raw data. Because it significantly reduces the chance for your competitors to get your data relatively cheap and potentially exploit it to become more competitive. And they can do that using indirect data monetization.
So maybe you earned an extra couple of bucks in the short term, but your overall market position is weakened by this data "leak". In plain English: you won the battle, but you're losing the war.
Indirect or internal data monetization focuses on turning available data into insights and then using those insights to improve a company's performance. It requires creativity and business thinking, but the payoff can be immense.
Whatever business you're in, you generate a lot of usable data, from big corporations to single-person operations. Even if you're just a small blogger, you can still use a small amount of data to make substantial improvements.
If you know what your most visited pages are, you can invest a relatively small amount of effort to improve the content on those pages. Usually, a small number of pages will have the bulk of the entire site's traffic. And this is a great way to efficiently improve your website.
Another approach is to use data to identify which blog posts are on page two of search results. Improving those pages could mean moving to page one on search results. And that is a huge difference.
But in all these cases, you can see that data can help you identify critical points in your business. And once identified, you can do a very small set of targeted changes that have a significant overall effect. Also, there is no downside to leveraging your data this way, so give it a try.
This is everything your business, website, or project generates during its regular operation:
Note: when it comes to employee data, be very careful when measuring the performance of customer service employees. Because "resolved" issues per hour can backfire like crazy.
And in general, when dealing with data and statistics, make sure you apply common sense and do not follow the numbers blindly.
This is any data that is not directly generated by your business. But you identified it as useful and acquired it through marketplaces like RapidAPI or by getting publicly available data on sites like Data.gov etc.
Externally generated data is the key to your company's growth because limiting yourself only to your own data is… limiting. And companies that use the full potential of the data are the ones that are going to win.
We already spoke about high-level concepts like selling insights (processed data) rather than just selling the raw data. And this makes sense in any industry. For example, raw lumber is much cheaper than a finished piece of furniture. And the same is with your data.
If you provide additional value by processing the data and "package" it nicely, you can sell it more efficiently and for a higher price. Just like everyone wants a nice comfy sofa, almost no one wants you to leave a couple of heavy wooden planks at their doorstep.
Specific data monetization examples are:
Regardless of your chosen path, you will need a place to store your data. And it's best if that place is:
And one such solution that satisfies all the requirements is the 8base platform. It has a graphical interface that allows you to build a database in just a couple of clicks. And on top of that, all the APIs are automatically generated for you. So if there ever was a plug-and-play database, this platform comes as close as possible to that ideal.
After you create your database and APIs, you just connect them to the API marketplace. This is all explained in this article about API monetization strategy. Simply follow the steps, and you will monetize your data in no time.