Optimize Data Sharing to Drive Better Business Outcomes

What is data sharing?

Data sharing is all about making data resources accessible to various parties—between multiple business units of an organization, a company's third-party partners, and in some cases the market as a whole.

When a data sharing policy is in place, data flows freely through the enterprise as needed, instead of being isolated within the data infrastructure of a single department or team and rendered virtually inaccessible.

Data sharing: A response to the dangers of data siloing

There are plenty of reasons why data sharing has become so important to enterprise-scale organizations. The simplest, perhaps, is that it helps ensure all business units can rely on one another to serve the best interests of the enterprise. Cross-departmental communication and collaboration are faster and so much easier with data sharing.

But another key reason for the recent hyper-focus on data sharing is that it's a direct response to the difficulties and data collection inefficiencies caused by data silos. Although silos aren't formed with any sort of malicious intent, the considerable dangers of siloing have become well-established in recent years.

Top dangers of data silos

• Restricting accessibility: Siloing restricts databases and other sensitive data resources to specific business units or otherwise inaccessible locations. This makes it difficult and sometimes even impossible for anyone outside of those units to access and use the siloed data for any reason.
• Limited collaboration: Departments like marketing, which rely on data from many different areas of the business to develop effective campaigns and generate sales leads, can't deliver on key objectives in organizations prone to siloing.
• Lack of holistic perspective: Running efficient and effective analytics projects is all but pointless if siloing is common among departments, as data teams can't easily develop a comprehensive picture of the organization's data ecosystem.
• Data integrity difficulties: Siloing is detrimental to data integrity across the organization. It can be a major contributor to problems including incomplete data sets, inconsistencies in reporting, space-wasting redundancies, and other inefficient uses of valuable resources.

These are just some of the challenges data siloing can lead to. As well-known as those negative factors are, silos are still not uncommon across enterprises. In fact, some organizations actively encourage siloing, perhaps not fully understanding the value that data sharing can bring. In the years to come, such approaches will, most likely, become increasingly untenable.

Sharing data for better business outcomes

A recent Gartner survey found that regardless of how reluctant certain enterprise departments may be to share data, the organizations that do make the choice to start data sharing will realize undeniably positive results.

Chief data officers (CDOs) who successfully increased data sharing activities among their data and analytics (D&A) teams were "1.7 times more effective at showing demonstrable, verifiable value to D&A stakeholders." Other survey results revealed that deconstructing data silos and promoting data sharing didn't just help data teams influence decision-makers, but also projected that enterprises practicing this trend would outperform those that didn't emphasize data sharing "on most business value metrics" by 2023.

Lydia Cloughtery Jones, a senior director analyst for Gartner with more than two decades of experience in the data industry, noted, “Data sharing is the way to optimize higher-relevant data, generating more robust data and analytics to solve business challenges and meet enterprise goals. D&A leaders who promote data sharing have more stakeholder engagement and influence than those who do not.”

Major benefits of data sharing

Using leading-edge data management technologies and best practices will allow business units to share data efficiently and effectively with other departments that need it—whenever they need it.

• Enterprise-wide analytics initiatives benefit from data sharing because analysts can get all the data they need to comprehensively assess the organization's performance.
• So will cross-functional collaborative efforts, such as joint marketing and creative campaigns, or logistics teams having open access to purchasing and inventory data.
• Additional advantages that data sharing can help enterprises realize include increased cost savings, improved risk mitigation, better decision-making, and even new net revenue creation.

A recent study found concrete evidence of data sharing's value to organizations that embraced this practice over the course of two to three years, including the following key performance indicators (KPIs):

• 14% increase in workflow efficiency
• 15% improvement in customer satisfaction
• 11% reduction in costs

The research also predicted that effective users of data sharing strategies could see financial benefits reaching as high as 9% of their annual revenue in the next five years.

Data sharing not only promotes cooperation between different teams in the most literal sense but can also help foster a company culture of openness and togetherness. It's part of what Gartner refers to as "digital trust." Organizations that instill an atmosphere of digital trust and open data will be able to leverage greater value from their data sharing ecosystems. In terms of organizational philosophy, sharing data may also discourage siloed mindsets in department leaders, making them less likely to run their teams overly autonomously and "hoard" their valuable data.

Notable use cases of data sharing

General examples only go so far toward illustrating the usefulness of data sharing. It's important to look at how specific enterprises and other organizations put it into practice.

• AstraZeneca, GSK, Johnson & Johnson, and seven other pharmaceutical companies had implemented a data sharing project driven by machine learning (ML) algorithms prior to the COVID-19 pandemic. This allowed them to benefit from one another's discovery data in a manner that may have contributed to the speed of COVID-19 vaccine development.
• The state government of Rajasthan, India implemented a data sharing and integration process to break down silos and leverage data more effectively in departments ranging from social welfare to public health.
• Video game companies that capture valuable customer data during gameplay share this data with marketing teams and other relevant departments—within the bounds of applicable data privacy laws—to create more personalized experiences and subscription regulations.
• Casual dining restaurant conglomerate Brinker International used a data integration and sharing strategy supported by a hybrid multi-cloud analytics platform to speed up reporting and analytics across its cloud and on-premises data repository tools.

Avoiding potential challenges of enterprise data sharing

Any data sharing ecosystem that is established too quickly or haphazardly is likely to cause problems.

For example, if a financial institution wants to improve its fraud detection processes, but the data pipeline starts with batch processing instead of real-time analytics, that simply won't work. Transactions won't be processed fast enough to find fraud in a timely fashion and intervene before it's too late. It's important to know and implement the right method of data distribution for each data sharing use case.

A slapdash data sharing strategy may also lead to mistakes that violate the legal requirements of certain data security or sovereignty standards. The EU's General Data Protection Regulation (GDPR) and California Consumer Privacy Act are the best-known of these, but they aren't alone. Compliance with data protection laws for personal data is always important, but it's especially critical for enterprises aiming to monetize their data by sharing it with third-party partners.

Security is another potential challenge. It's true that data sharing often has restrictions, encryptions, and other caveats in place to stop unauthorized access—for example, preventing a data set from being simultaneously changed by two users. But external data sharing may put information at further risk of being compromised because the data isn't fully under the sender's control. A thorough risk assessment before external sharing begins will help data teams understand any dangers that may be present—e.g., vulnerabilities in a partner's system—and take steps to mitigate them.

Optimize data sharing with leading tech

It's worth pointing out that implementing a data sharing process isn't one-size-fits-all. Enterprises will vary in their approaches to ensure the practice meets their specific needs, and some will need to establish a sharing ecosystem more gradually than others. This is perhaps part of the basis for Gartner's expectation that throughout 2022, only 5% of data sharing programs will be capable of always correctly identifying trusted data and locating its various sources.

Choosing cloud-centric (not cloud-only) data sharing

Using the cloud as a primary arena for your data management provides you with elastic resources that allow for the creation of a truly efficient data sharing ecosystem.

• Data lake repositories can be easily put together using cloud-based object storage.
• Using a versatile data analytics platform like Teradata Vantage in conjunction with the data lake and other data stores allows teams to achieve simplified integration and easily share data-based insights.
• Deploying these tools within the context of a hybrid multi-cloud deployment means you can protect sensitive data with dedicated, security-heavy clouds or store it on-premises while ensuring it remains accessible and shareable for those with proper access permissions.
• New data architecture concepts like data fabric—which project to play major roles in the future of data analytics—also promote simpler data sharing by automating integration and forming clear roadmaps through the data ecosystem.

Learn more about how Teradata can support data sharing with Vantage and through QueryGrid, our high-speed parallel data fabric system.

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