As data has become more robust and accessible, businesses are increasingly interested in deeper, advanced analytics. Currently, businesses use scattered data that takes weeks or months to clean and merge together. Human error can easily affect the integrity of the data, discounting the end analysis. The data then usually lives in one centralized location with high risk of being compromised. Data centers can be tampered with and released to the public without notice. We all want and need data, but it’s a huge responsibility to ensure data accuracy and security.

Data science requires a solid, functional dataset to properly perform statistical analyses and predictive modeling. Decentralized blockchain technology strengthens our ability to handle data and set a solid infrastructure. Here are the reasons why data science still has a long way to reach its potential and how the blockchain can solve for its current disadvantages.

1. Fragmented and Siloed Data

It’s very rare for companies to have all of their data in one place. Not many tools are robust enough to capture everything. The limitations require companies to compromise the data quality. This, in turn, becomes a nightmare for data scientists as they spend a chunk of their time cleaning and aggregating the data when they could be using their advanced skills in real analysis to drive the business.

Even when it’s finally time to build a predictive model, the data is not complete. As we all know, the real world is quite complex. How can you properly predict a real-life scenario when you can’t account for all of the variables that could affect the results? On top of that, more variables will be left out because they were not foreseen to ensure complete tracking. As you can see, many issues arise from having siloed, incomplete data.

Blockchain Solution: Relational, Robust Capabilities

Blockchain relieves us from these issues because all of your data and everybody else’s data can live within the same network. The unique capability of the blockchain network is not to just be a form of currency, but also to track all kinds of real-life interactions like supply chain logistics and IoT sensors. This requires a level of customization for each business need so that you can still maintain your business’ unique data logic.

If all businesses’ data remained on the same network, the data will have a common unifier to easily merge together for more robust analysis if needed. For example, if a clothing manufacturer carries data for the entire supply chain and the retailer has perspective on purchasing and customer data, the entire cycle from supply to purchase can be understood if both datasets lived in the same network. Data is technically separated in the blockchain, but can be consolidated all within the network based on common, relational attributes.

2. Disproportionate Data Collection

The completeness of data also heavily depends on how the data is collected. With different methodologies across scattered data, it’s difficult to maintain consistency across all datasets. The logic must be uniform while being void of any technology and development issues. Many times, the programming logic breaks and it’s too late to fix the data retroactively. The data essentially becomes widely caveated and just causes more confusion for the actual analysis.

Blockchain Solution: Less Human Tampering Effect

Data collection needs to have a uniform logic as data rolls into the system. Each blockchain network has one centralized logic that admits new data, which is programmed by the blockchain team. It’s ideal to create a direct communication path from machine-to-machine through the use of IoT (Internet of Things) to prevent any further human effect. Then, the initial blockchain logic would be the most human effect on the data in the entire collection process. Data only gets added to the network if it adheres to the pre-existing data logic, so tampering is very low risk.

3. Inaccurate, Centralized Validation

With pre-existing centralized data logic, businesses usually have a small team of experts that go through the quality assurance process. This leaves a high risk of human error if data issues are not identified early on. In turn, the rest of the data manipulation and analysis gets heavily impacted. Along with the special administrative access controlled by an internal team, the data can easily be compromised from anywhere. The security doors can easily be identified, especially cloud-based databases. Quality assurance performed manually can be quite subjective and can easily affect the end dataset without notice. This leads to misinformed analysis and modeling and delays any new changes into the business logic.

Blockchain Solution: Decentralized Validation

With decentralization, there is obviously no centralized location to have sensitive gaps. All of the data is distributed across the network in fragments with multiple private and public keys. Each new data chunk gets validated by the pre-existing data, lessening the risk for human and technology error. The decentralization prevents highly dangerous hacks from occurring since the probability of shutting down the entire network is low before validation kicks in. The network can only add in new information, and only if it matches the current logic in the system. This secures consistency and reliability in the blockchain data where many datasets today have many issues.

Data has become the focus of many businesses to be more results-oriented and streamlined. Data science is highly sought after to increase the usage of advanced analytics. However, businesses forego the foundational requirements to successfully apply data science. It requires a complete dataset that’s uniform and accurate to predict models more effectively. The less human effect on the data collection process, the better will be the end result of analysis as the foundation remains solid.

Think of data collection like a supply chain. If one thing goes wrong in the process, everything else is taken down with it. With the decentralization, automated validation of the blockchain, we are getting closer to reaching data science’s full potential.