Blockchain is quickly becoming a key business focus for companies within the United States and senior business executives are very excited about the technology. Crypotocoins News points to a Deloitte survey that polled 308 senior executives from companies with $500 million or more in annual revenue in the United States, who are aware of blockchain.
The survey produced some interesting results, for example, reporting that 61 percent of respondents claimed to have knowledge on blockchain ranging from ‘broad’ to ‘expert.’ In addition, one-third of those knowledgeable about blockchain plan to make it a top priority for 2017.
Blockchain is an exciting technology for many reasons. One of them is the ability to create secure transactions without the need for trusted oversight or a middleman. This eliminates the need to use financial institutions as a trusted third party to validate the authenticity and accuracy of transactions. This means blockchain can theoretically be used to secure and verify any type of transaction, from simple goods-for-cash exchanges to complex transaction management of multi-party trades and settlements. Large enterprises can use blockchain pretty much anywhere records are stored digitally and with any type of transaction that currently needs to be verified by a trusted third party.
Although blockchain has a very exciting future that does not eliminate potential challenges. The major challenge to blockchain is data quality. Blockchain’s anonymity makes it impossible to know the data’s owner, and provides no standards or regulations around the quality of the data. That means without the proper solution there’s nothing to prevent bad data from infiltrating blockchain.
With the major risk around blockchain being data quality, a combination of a flexible data integrity solution integrated with a dynamic data governance framework can ensure data quality and data provenance. A data integrity suite can automatically monitor an organization’s data flow as it continuously moves throughout an enterprise. The platform can identify data quality issues and immediately flag it so it doesn’t corrupt other internal information. Automated data controls can help monitor, validate and capture metrics around the data. Only a robust data integrity suite can create quality control systems to prevent the proliferation of bad data.
A data governance framework that integrates tightly with your data integrity solution can help ensure data provenance so organizations know where the data originated. What is needed is to simply identify data provenance for the data entering to make it more genuine, trustworthy and useable. Data metrics captured by automated controls can be stored in a meta-metrics data repository along with the metadata of the digital asset, and the blockchain ID that is associated with the data asset. This can be done through a data governance framework that captures the data definitions to ensure that all information is working on the same level and meaning.
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