Blockchain & Data Management

| Rachel Abrams | 31 October 2018

Effects of Blockchain Implementation

As today’s data needs only increase – in size, scope, and depth, companies begin to turn to new methods and systems for managing these vast quantities of data. And blockchain is being discussed as a possible solution for these storage and management issues. This article will go into a brief overview of the role blockchain can play in data management and the positives and negative effects of its implementation.

Currently, blockchain as a tool for data management has strong potential for the future as the technology matures, but it is hard to tell if blockchain will disrupt traditional data management and integration because there are little to no actual implementations of this process, so many of the claims discussed below are primarily speculation. In comparison to a traditional database, blockchains can handle larger quantities of data and have a greater scope of use. Traditional SQL queries and relational database systems are designed for structured data with focused applications in limited domains, and blockchain can provide a more innovative method to store data and revolutionize how organizations can realize the true value of their data. In the following section, the positive and negative effects of blockchain on data management will be discussed. One of the overall positives of integrating blockchain and data management is that it would reduce the costs of creating data and analytics infrastructure, as it decentralizes and democratizes the technology and reduces the cost of predictive analytics.                                                                      

Data Quality and Reference Data

Regarding data quality, the integration of blockchain would increase security and trust in the data system, as well as validate the data. The lack of a centralized database negates the risk of data tampering and requires all users to be validated to view or access specific portions of data – seeing nothing more or nothing less than permissioned. Additionally, the predictive analytics inherent to blockchain systems can detect fraud in real-time, helping the system become more effective. However, a drawback is that, despite all these measures that secure data, there are no methods of checking data accuracy written into the system, therefore, the data must manually be checked to ensure inaccurate data doesn’t enter the immutable blockchain. 

Blockchain can benefit reference data by facilitating collaboration between cooperating parties (ex: a financial company and its regulatory agency) by maintain contractual data, reduce organizational costs and operational risks. In addition to managing reference data, blockchains can also be analyzed to determine behaviors – for example, examining transaction data and databases in order to understand trading behaviors.

Master Data and Data Warehousing

Many of the attributes of the blockchain system– its security, transparency, and shared network – can benefit master data management. Building master data management on the blockchain provides traditional benefits while taking advantage of new paradigms for flexibility, consensus, and embedded analytics. Consensus-building is a huge aspect in managing accurate data, and sophisticated algorithms can perform all data matching and linking while avoiding human error. However, regarding master data, implementing blockchain can be more trouble than it’s worth, as integrating the blockchain can complicate the process. Blockchains need more data silos and are spread across multiple networks, therefore creating a greater need to properly manage master data. Additionally, the individual blocks within the blockchain, the key components that make the system unique, could pose greater problems to master data management. Since each block on the blockchain corresponds to a specific entity, each blockchain could have a different data scheme and require different software to consolidate each unique block. Overall, master data technology and processes may be required to manage blockchain, not the other way around.

Another aspect of data management where blockchain could provide more problems than solutions is in data warehousing, as the transactional nature of blockchain means it cannot store historical data, which is a requirement of building a data warehouse. Similarly to master data, the implementation of blockchain would create a larger need for data warehouses in order to track the various parties, data schematics, and other aspects of blockchain transactions.

In conclusion, it's difficult to predict the impact that blockchain will have on data management since we have limited real-world implementations to gauge success. As it often is in the case of blockchain, it seems that we are waiting for our technology needs to catch up with blockchain's capabilities. 


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