Unifying data is one the quickest routes we have to change the world for the better — and in this article, we’ll explore the ways that data unification will impact real-world organizations, including enterprises.

In the previous two articles of the “Why Unified Data is Inevitable” series, we explored the problems being caused by a lack of unified data, and how blockchain technology is allowing brand-new solutions to emerge.

At a basic level, the fragmentation inherent within current database management models — in which data is stored in widely varied formats, with different markers across data sets — makes it difficult for the world’s data to be correlated efficiently.

Simply put, data sets don’t speak the same language, and thus cannot communicate without significant translation efforts by humans.

For the first time in human history, the recent evolution of blockchain technology has paved the way for a truly unified data landscape. Blockchain enables seamless standardization between data sets to emerge as a truly viable solution, through its cryptographically protected data exchange mechanisms and self-executing Smart Contracts.

This new possibility of truly unified data, united around a single standard, is already shifting our understanding about what is possible in both business and tech.

Perhaps most excitingly, the proliferation of enterprise use cases of this newly unified data provide many powerful examples of what can happen in the short- to medium-term when technological innovation is harnessed at global scale.

Allowing the machine to play against itself

Unifying data around a single standard changes the way data interoperates. By making data speak a common language, it is easy for data sets to be compared with one another, and for potential overlaps to be found.

One of the biggest benefits of a unified data landscape is that it creates a dramatically increased ability for computers to self-discover patterns invisible to the naked human eye.

At the moment, because there are such a wide variety of markers used to categorize data points, checking for possible correlations within data sets requires you to submit to an onerous process: Select the points you wish to compare; manually render the raw data into similar formats; submit queries for each correlation you suspect might exist in the data; rinse and repeat for any other potential correlations you wish to measure.

In this situation, correlations require human intervention to be found. Similar correlations can only be reliably discovered by a machine working independently if they occur within a single data set, in which everything is already formatted according to a unified standard that the machine can process on its own.

If, however, you wish to find patterns between diverse data sets, you are still required to manually render certain markers into a standardized format, before you can start asking the machines to accurately verify whether or not correlations exist.

The limits of the human imagination

The problem with the existing model is that it creates a ceiling on the types of correlations that can be discovered between the world’s data sets, by making discoveries dependent on human to format and query diverse data sets.

As long as data is fragmented and stored in different formats across different management systems, discoveries within that data are inherently limited by the human imagination. Each potential correlation is the result of a hypothesis, thought of by a human and then manually tested in a piecemeal fashion.

In practice, this means a human-generated command is required for to get the machine to correlate data. For example, something like: “Check GPS data from males ages 30–55 from App A against rates of heart disease for males aged 30–55 in App B.”

But when data is unified to a single, standard format, the machine can essentially “play against itself,” processing massive amounts of data to display correlations that human may have never thought to query. The result is that the computers processing the data sets are able to find correlations we may not have thought of.

For example, the machine might run App A’s data against App B’s data and find that there is an unusually high correlation between the GPS coordinates collected by App A, and calcium levels recorded in App B — even if no human ever thought to actually ask the machine to examine these two data points against each other.

Removing the need for human intervention in the data correlating process means that we can use data far more efficiently than ever before. This allows us to accelerate data processing exponentially, creating major breakthroughs for enterprises, artificial intelligence, and scientific research, just to name a few.

Creating massive growth opportunities for enterprises

Like most successful technological revolutions, the process of adoption of unified data relies first on the strength of its business applications. When a new innovation makes sound financial sense for enterprises, it makes it far more likely to succeed in both the short and long term.

When it comes to enterprises, we can see clearly that this new form of unified data, which can be seamlessly processed and cross-referenced for correlative trends, is a highly precious resource that carries major upside to businesses’ bottom lines.

Unified data allows enterprises to unlock a series of benefits heretofore inaccessible in the fragmented data system:

Enterprises are able to easily access and readily incorporate data sets collected by other businesses, finding correlations between diverse databases that were previously difficult to uncover.

Accessing a better map of data correlations allows businesses to produce more robust functionality within their products, informing their internal algorithms so it can make better predictions.

A single data standard allows enterprises to seamlessly exchange data amongst themselves, without needing a sales team to buy and sell data, or engineers on hand to reformat purchased data sets. This increases monetization opportunities for enterprises, as well as reduces the development costs of acquiring data.

Enabling a full-cycle data revolution

Within an enterprise context, it is a common mistake to think of data exchange in silos. The fallacy occurs when we view companies are either data producers (sellers) or data purchasers, missing the inherently recursive nature of data exchange.

The truth is that, in a healthy data ecosystem, each enterprise is both a seller of the data they are generating, and a buyer of new data that helps them improve the efficacy of their product.

To actually conduct this type of open data exchange between enterprises has, until now, been a resource-heavy endeavor that only well equipped organizations can engage in sustainably.

Unified data unlocks the potential of a true full-cycle data exchange, in which both parties have instant and immediate access to both the buying and selling of their data assets.

In practice, within Unification’s ecosystem, this is taking the shape of a unilateral data marketplace hosted on our BABEL interface. Within BABEL, data that complies with our unified data standards can be instantly and immutably exchanged between parties via smart contract with just the click of a button.

Removing inefficiencies from the data exchange process

Up until this newly emerging era of unified data, the process of finding these correlations has highly cumbersome and time consuming for enterprises.

To understand the scope of the current inefficiencies, it’s necessary to understand the processes by which businesses are currently forced to use when exchanging data.

Pre-Unification Data Acquisition Process:

Create correlation hypotheses: Employees brainstorm what types of data is most relevant, and create hypotheses about which data is likely to produce correlations. Approach potential sellers: A sales team goes out and approaches other businesses who may have said data, so that they can test these hypotheses. Price and other factors are negotiated on a case-by-case basis. Engage a third party: When data is available and terms have been agreed upon, companies engage a third-party, escrow-type service to conduct the transactions fairly and ensure they receive the data they have been promised. Convert data into usable form: Once data is acquired, a team of engineers manually renders said data into formats that can be properly interpreted by the business’s internal algorithm. Test hypotheses for accuracy: The business’s machines parse the data searching for correlations to put to use within the algorithm. If the team’s correlation hypotheses prove to be incorrect, the time and effort acquiring new data has gone to waste. In order to discover which hypotheses are relevant, it is once again the responsibility of people on the team to imagine which correlations might be more relevant, sending them back to step one.

Similarly, the current data sales process also presents similar significant challenges, requiring labor-heavy, inefficient systems to sell data to other organizations.

Pre-Unification Sales Process:

Create purchase hypotheses: An enterprise generates data about the usage of their app and the activities of their users. They know this data might be of interest to other parties, such as real estate agents, but they don’t have a clear path to alert potential buyers to the fact that the data is available. They want to use their data as an income source to support their business, so they brainstorm potential parties who might be interested in purchasing it. Approach potential buyers: The business hires a team to go out and approach potential data buyers on a piecemeal basis, asking them if they would like to purchase data and negotiating price on a case-by-case basis Navigate purchaser demand: The business encounters friction when approaching potential buyers, who frequently complain that the format of their data doesn’t play nicely with the purchasers own databases Engage a third party: Data exchange terms are negotiated and agreed upon, and then a third-party escrow-type service is engaged to ensure that the terms of the agreement are carried out in good faith.

In this current model, inefficiencies abound, slowing down an organization’s progress and cutting into their profit margin.

Inefficiencies of Current Enterprise Data Exchange Models:

Potential of fallible logic: They rely on hypotheses created by humans, who are limited by the bounds of human imagination.

They rely on hypotheses created by humans, who are limited by the bounds of human imagination. Labor intensive: They require human beings to initiate and conduct transactions, requiring the hiring, training, and maintenance of staff.

They require human beings to initiate and conduct transactions, requiring the hiring, training, and maintenance of staff. Lack of a marketplace: There is no easy place for interested parties to find each other, or to alert each other to what types of data may be available.

There is no easy place for interested parties to find each other, or to alert each other to what types of data may be available. Lack of automation: Transactions are conducted on a piecemeal, case-by-case basis, without automated mechanisms to cut back on manpower.

Transactions are conducted on a piecemeal, case-by-case basis, without automated mechanisms to cut back on manpower. Lack of market rates: There is no obvious fair market rate for any one piece of data, especially in cases of information asymmetry about said data.

There is no obvious fair market rate for any one piece of data, especially in cases of information asymmetry about said data. Requires third parties: Third-party escrow-type services are needed to guarantee that both parties will honor the terms of the agreement.

Third-party escrow-type services are needed to guarantee that both parties will honor the terms of the agreement. Data fragmentation: Each party holds data in different formats, and exchanged data must often be re-formatted before it can actually be used by the purchaser.

Each party holds data in different formats, and exchanged data must often be re-formatted before it can actually be used by the purchaser. Difficult to scale: The process is time consuming, which means it is hard to scale. Acquiring data from thousands of sources at a time is challenging to do. Thus, correlations are often limited to just a few data sets at a time.

Enabling a more efficient data exchange process

With unified data, however, many of these inefficiencies can be reduced significantly, allowing enterprises to exchange data using far fewer resources than in the pre-existing model. This improvement in efficiency supports organizations’ bottom lines by reducing costs, while also unlocking tremendous value in terms of potential improvements to app functionality.

Unification’s Data Exchange Process:

Integrate with Unification: Chief decision makers decide to integrate with Unification’s HAIKU Smart Contract suite and CAPSULE (our proprietary SDK). CAPSULE is installed client-side and server-side, instantly enabling HAIKU smart contracts deployments (verified by MOTHER) with data standardization and encryption/decryption through on & off-chain channels enable the, as well as control further sharing of data access permissions.

Chief decision makers decide to integrate with Unification’s HAIKU Smart Contract suite and CAPSULE (our proprietary SDK). CAPSULE is installed client-side and server-side, instantly enabling HAIKU smart contracts deployments (verified by MOTHER) with data standardization and encryption/decryption through on & off-chain channels enable the, as well as control further sharing of data access permissions. Standardize data through smart contract: Businesses engage Unification’s smart contract suite to instantly verify whether or not their data sets conform with MOTHER, Unification’s master smart contract. Once verified, the data sets can be listed for sale.

Businesses engage Unification’s smart contract suite to instantly verify whether or not their data sets conform with MOTHER, Unification’s master smart contract. Once verified, the data sets can be listed for sale. Access the data marketplace: The business creates an enterprise account on BABEL, Unification’s Data exchange user interface, to see find available partners and construct data combinations useful to their business. Enterprises can search by specific or broad parameters, engage existing users, or find new users. They can also request data from app companies, advertisers, existing data packages, or users directly.

The business creates an enterprise account on BABEL, Unification’s Data exchange user interface, to see find available partners and construct data combinations useful to their business. Enterprises can search by specific or broad parameters, engage existing users, or find new users. They can also request data from app companies, advertisers, existing data packages, or users directly. Buy and/or sell data through smart contract: Once buyer and seller have agreed to an exchange, they engage Unification’s smart contract suite to instantly transfer data set. They are compensated in UND tokens, with a portion of the sale price feeding back to the Unification system, as well as the user themselves.

Once buyer and seller have agreed to an exchange, they engage Unification’s smart contract suite to instantly transfer data set. They are compensated in UND tokens, with a portion of the sale price feeding back to the Unification system, as well as the user themselves. Seamlessly integrate purchased data: Because the data acquired via BABEL is already standardized according to the same standards as the enterprise’s own database, the data can be instantly read and correlated on a number of vectors.

The simplification of this process, and the introduction of blockchain-backed smart contracts, affords enterprises significant advantages compared to older models.

Benefits of Unification’s Data Exchange Models for Enterprise: