数据共享是加速数字业务的必需品

Ahead of the COVID-19 pandemic, 10 large pharmaceutical companies — including Johnson & Johnson, AstraZeneca and GSK — undertook collaborative efforts to train their drug discovery, machine learning (ML) algorithms on each other’s data to promote drug discovery. The goal? To accelerate and reduce the cost of the discovery of drugs. They used digital trust technologies, including blockchain, to share data without compromising confidential or commercial secrets. 

This rare example shows that organizations can deliver more value when they collaborate in sharing data externally — even with competitors — yielding comparatively increased value through efficiency and cost savings for each organization. 

“There should be more collaborative data sharing unless there is a vetted reason not to, as not sharing data often frustrates business outcomes and can be detrimental,” says Lydia Clougherty Jones, Senior Director Analyst, Gartner.

Data and analytics leaders who promote both internal and external data sharing are more successful in demonstrating superior team and organizational performance. In fact, Gartner predicts that by 2023, organizations that promote data sharing will outperform their peers on most business value metrics.

Yet, at the same time Gartner predicts that through 2022, less than 5% of data-sharing programs will correctly identify trusted data and locate trusted data sources.

Many organizations inhibit access to data, preserving data silos and discouraging data sharing. This unnecessarily undermines efforts to maximize business and social value from data and analytics — at a time when COVID-19 is driving demand for data and analytics to unprecedented levels. The traditional “don’t share data unless” mindset has outlived its original purpose.

Read more: How to Achieve Smart Data Sharing

This default must be reversed to an approach of “must share data unless.” By recasting data sharing as a business necessity, data and analytics leaders will have access to the right data at the right time, enabling more robust data and analytics strategies that deliver business benefit and digital transformation. 

While it’s not easy to change the status quo, data and analytics leaders must ask themselves what two areas to prioritize now to foster a data sharing mindset. The answer: Establishing trust-based mechanisms and preparing a data-sharing environment. 

Establish trust-based mechanisms

If you do not introduce trust throughout your data-sharing process, you cannot achieve business value from the data you collect. Gartner predicts that through 2023, organizations that can instill digital trust will be able to participate in 50% more ecosystems, expanding revenue-generation opportunities. 

Develop trust-based mechanisms that establish high levels of trust in the data source and separately in the trustworthiness of the data. This allows you to align appropriate data use with your business goals, both within and outside your organization.

It’s important to trust the quality of the data you collect, use and share to match your business context and requirements. Separately, organizations must trust their data sources so that they can rely on (and pass on to others) appropriate and enforceable rights to use, reuse, share and reshare data.

Adopt digital trust technologies such as blockchain smart contracts, which enable a trusted data collection method, while also enabling the efficient transfer and sharing of any asset of monetary or nonmonetary value.

Overall, use data-quality metrics and augmented data catalogs to compile your data and data source trustworthiness evaluations. By 2021, organizations that offer users access to a curated catalog of internally and externally prepared data will realize 100% more business value from analytics investments than those that do not.

Preparing a data-sharing environment

To establish a data-sharing environment, work with your business leaders across business units to create a data-sharing mindset. Foster a data-sharing culture — not a data “ownership” culture — by identifying the emotional impacts and inherent biases that hamper data sharing.

Within your IT department, distinguish your data management strategy between data warehouses, data lakes and data hubs. Gartner predicts that through 2020, organizations that adopt data hub strategies will achieve outcomes dependent on shared and governed data with at least 60% lower cost. 

Create new and flexible data management practices that adapt to uncertain and changing environments. And, drive organizational enablement of data sharing, prioritizing use cases in which increased data sharing will yield maximum alignment with business outcomes — including increased costs savings, net new revenue or nonmonetary value creation, or improved risk mitigation decision making.