Building a Trusted Data Framework
Enables insights from encrypted data without collecting, holding, or revealing the underlying data. It also enables the assessment of protected datasets by prospective buyers while protecting the interest of sellers.
Data Customers expect Data Teams to deliver data they can trust. These expectations are shaped by five determinants, which can be addressed through a variety of strategies.
In an age where data is increasingly becoming the oil of modern society, a new framework needs to be built that is fit for purpose. This new framework must allow efficient real-time data sharing while preserving data privacy. It also must be resilient to data-related threats and attacks, and protect the integrity of the shared information.
This framework should include a set of core properties – verifiable identity, integrity, transparency and availability. It should also provide automated reasoning systems that can reduce uncertainty and vagueness in the web environment and help identify malicious behaviour. It should also decentralize content and take power away from the web monopolies.
Trusted data solutions are built with a rigorous process that includes requirements gathering, iterative wireframing, testing and implementation. This is different from ad-hoc reports, as they include quality validations across all stages of data processing. Those solutions can be found in the PROD database schemas prefixed with EDM or SPECIFIC, depending on whether they model the Enterprise Dimensional Model or Specific application data.
In data-driven enterprises, accurate insights are key to making effective business decisions. When data is inaccurate, it may lead to erroneous conclusions or prevent an enterprise from reaching its goals. This can happen in many ways, from financial institutions making investment decisions based on flawed market data to healthcare providers using inaccurate medical records.
To increase data reliability, organizations can implement data governance policies and validation procedures. They can also work with reputable sources that have experience collecting and verifying data. These practices help to minimize inconsistencies, errors, and biases in data and improve overall data quality.
Another way to enhance data reliability is to communicate transparently with stakeholders. This includes being honest about limitations and biases in data. Additionally, data repositories should have the technological infrastructure and storage capabilities to keep data safe and secure. They should also adhere to ethical standards to build trust. In addition, they should be able to detect and resolve data incidents promptly.
Integrity is a key component of any trusted data framework. It can include rules for human-error checks, restricted raw data access, cybersecurity countermeasures, and frequent data back-ups. It can also include business-friendly dashboards to visualize overall test coverage and success rates. This helps to identify potential problems early in the process, so that they can be addressed before they become serious issues.
Repository managers are the primary audience for these types of certification mechanisms. However, they may also provide benefits to users and funders of datasets. These benefits can be in the form of new insights or predictions that motivate data sharing. In addition, a data trust can offer legal agreements and a technology platform for sharing data.
The emergence of comprehensive data pipeline automation platforms has brought the necessary rigor to operationalize this type of framework at scale. Claravine can help your organization achieve this through a seamless network of technology connections that permeate data standards across your entire enterprise.
In this era of massive data breaches, it is important to protect your organization’s private information. To do so, you must map out how confidential information flows throughout your business. You also need to identify your weakest link and make sure that it is protected. This can be a difficult task since most businesses share data with partners who are responsible for protecting this information. The June 2019 breach of 12 million Quest Diagnostics patient records occurred because the company’s billing vendor was hacked.
The MIT Connect Science and Engineering book “Trusted Data” describes a revolutionary framework and architecture to build an Internet of Trusted Data. This new framework allows efficient real-time data and insights sharing while preserving privacy. This approach is especially beneficial for companies that use complex algorithms in their decision making. In addition, the architecture will help protect against bias and unfairness in algorithmic decisions. To achieve this, the framework must include legal agreements and a technology platform to collect, aggregate, protect, and manage the data.