More Valuable Database = More Valuable Hive

When we break things down to the basics, we find something very simple.

Boring deep into Hive it becomes obvious what we are dealing with. When people ask "what is Hive", there is a fundamental answer:

Hive is a decentralized text database.

This is not the sexist answer in the world. It is also not one to get a ton of capital flowing in. We are not going to see green candles and price mooning with a response like this.

However, we are dealing with something very powerful. It is also rather unique.

If we apply to the same notion to Bitcoin, we get the answer, Bitcoin is a decentralized financial database. I know there are some changes with ordinals but, for the most part, financial transactions are still the majority of the activity.

So what is the difference? The fact that Hive is a text database natively is an element we do not see in other places, especially if we apply the standard of decentralization.

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The World Runs On Databases

Many claim data is the new oil.

The validity of that statement is certainly up for debate. However, it is interesting to see, with allthe discussion about the value of data, why is little said about databases? After all, if data is the new oil, aren't containers required?

We also have the fact that data, in raw form, is not very valuable. It only becomes valuable once it is structured in some way. This comes from labeling, linking, and in a form that can be analyzed.

Some of the largest companies in the world are tied directly to data. They might operate in other ways but, at their core, is data. They are the masters of the database.

If we look at the cloud, we find nothing more than the housing, sorting, and providing access to data. Amazon is known as a retail company yet most of their profits come from AWS. Netflix presents data in the form of video, as does YouTube. Social media companies are huge data warehouses, as users keep adding to the databases on a daily basis.

Here is a list of the top public companies, by marke capitalization. Of the top 10, how many drive a large portion of their value via databases? I would say at least 6, with one moving to make it 7. This means that the majority of the top companies are overtly build upon data.

In many instances, this is essentially their product.

Hive: Decentralized Text Database

If we want Hive to be worth billions of dollars, that means building a database that reflects that. In other words, it is developing a system expansion in data collection that is valued by the market.

Of course, this is easy to state, very hard to do. As they say, if creating a multi-billion dollar database was easy, everyone would do it. The reality is we are dealing with a difficult proposition.

It also brings up the topic of raw versus formatted data.

Anything posted to Hive is raw data. Whatever is posted little correlation to anything else. It could be social media activity, financial transactions, or custom JSONs providing validation of some off-chain event.

The point is that, without filtering, structuring, or analysis, we simply have a mess.

That does not mean, however, all is lost. What we have here is of value since it is data that is accessible. Someone can take the raw data and process it. The key is no permission required.

While this does have value, it appears the barrier is the volume of data necessary. The wall to a network effect based upon data is high. This is not to say it cannot be scaled. Simply put, we are going to need a ton of it. This is where those like Facebook excel.

The other option is to provide some form of structured data. This is often done by applications. A great example of this is the recommend feature. Netflix and YouTube both take the data, i.e. what people watch, process it, and present results to the user. The network effect grows as more is watched since the algorithms has more to analyze.

Once again, this takes a lot of data to feed the algorithms. The consumer can quickly find value in this since it is presented in a form he or she understands.

It becomes more convoluted when we realize that data is being generated at an insane pace, one that keeps growing. Companies are finding it easy to collect massive amounts of data. Basically the oil is becoming abundant.

What Separates Hive

With these challenges, what makes Hive different?

To me, one of the keys is the fact that Hive's data is open to everyone. Since we are dealing with a permissionless database, people are free not only to write to it but also scrape it. The data is free to use.

When we look at what other companies are doing, their data is proprietary. They are not sharing it without payment. We already saw this with companies taking measures to reduce the data one can process through the APIs.

If we revert back to the abundance-scarcity model, what is scarce when it comes to data? We can see how data itself is becoming abundant. Yet, is accessible data following the same pattern? This is something worthy of examination.

Also, the question of data control is important. This is tied to accessibility as we know what happens when a company cuts it off Many are starting to talk about what happens if Wikipedia suddenly shut down their servers. This is a fundamental question to much of what we see online.

The fact that almost everything that appears on our screen is the result of a centralized server. We are accessing a database controlled by some company. That means if the entity disappears, so does the data.

Is that the end of the world? In many instances, no. However, there are use cases that serve millions which could go dark immediately.

This is the nature of what we are dealing with. Hive can change this in many ways. Here is where the opportunity to build a more valuable database enters. Since open access is inherent in the system, we are providing a service that is not commonly offered.

We often discuss the idea of network effects as it pertains to social media and digital networks. If you really want to see Hive explode in value, instigate the network effects tied to data. This appears to be a harder proposition than even with the social media but will have enormous impact.

To pull this off requires a number of things:

• a lot more data
• the building of features that sort, label, and assemble data in useful ways
• an ability of users to search, filter, and find the data they are looking for

In short, we need to build utility to this decentralized database.

If that happens, we will end up with something that can help to push the value of Hive a lot higher. Databases can be worth a fortune. However, we also have to keep in mind that most are worthless.

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What is Hive

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