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5 steps to becoming a data-sharing master

Goutham Belliappa Vice President of Data and AI Engineering, Capgemini

As people increasingly live their lives online, the significance of data, and the rate at which companies collect and analyze it, has grown exponentially since the mid-2000s. Although businesses are accustomed to following their consumers' data trails online, many fall short of taking full advantage of the wealth of information at their disposal. Organizations throughout the world are neglecting vast quantities of consumer data. 

The future of data-driven business will no longer rely on simple collection and analysis. Enterprises will have to go above and beyond and strategically share data to gain improved visibility and meet consumer demands.

Specifically, organizations must learn how to distribute their data in broad, cross-sector data ecosystems—networks of multiple organizations that agree to share and manage data. These mutually beneficial ecosystems serve each member, ultimately improving revenue, productivity, and customer satisfaction through widespread data-sharing methods.

In fact, according to a new study from the Capgemini Research Institute, participating in a data ecosystem can increase an organization's annual revenue by nearly 10% over the next five years alone. And when these partnerships expand to an ecosystem of more than seven organizations, each member can possibly double its market capitalization.

Despite these findings, over 80% of data-driven enterprises currently do not engage in these highly collaborative networks. But this competitive lag won’t last. In the next three years, the Capgemini study said, 84% of all organizations will launch new data-sharing initiatives, and one in four enterprises will invest upwards of $50 million in data ecosystems.

So, given this accelerated push, where do you start on your journey to becoming a data-sharing master? Here are five steps to ensure preparedness.

1. Formulate a data ecosystem strategy

Before orchestrating a data ecosystem, it is essential to create a strong data-sharing strategy that outlines goals and expectations at the onset. This begins with clearly articulating the purpose of engaging in the data ecosystem as well as identifying data-sharing use cases that create value.

Once the purpose is clearly established, the enterprise must assign a leader entrusted with the overall responsibility of managing the organization's data ecosystem initiatives. Capgemini's research found that roughly half of enterprises currently in data ecosystems do not have assigned leaders to ensure that there is clear ownership of, oversight for, and support for organization-wide data-sharing initiatives.

In the future, expect CIOs and chief data officers to fill this gap, leading teams of support staff to focus wholly on data-sharing activities.

2. Make key design decisions

Enterprises also must consider key data ecosystem design decisions before entering a network of data-sharing partners.

For instance, organizations must weigh their data-sharing choices to determine what data they should share and what data they should source from their partners, allowing them to align both internal and external data management systems.

Additionally, enterprises will have to carefully consider said partners—not only noting who can be trusted within the ecosystem, but also identifying partners that can truly add value. With this partnership knowledge, organizations can then determine the exact collaboration model that will work best for all participants within the data ecosystem.

3. Build the framework to establish rules with ecosystem partners

Like all great teams, data ecosystems rely on open communication. When leveraging a network of data-sharing partners, it is important to collectively establish a rules-based framework.

By clearly defining the terms of the engagement, this framework will provide the two key ingredients for growth: transparency and trust. Moreover, the predetermined rules will proactively and transparently address privacy concerns, ethics, trust issues, cybersecurity threats, and regulatory requirements—all of which can be handled in a way that is detrimental to the ecosystem and its partners if not carefully considered.

4. Kick off implementation efforts

Once the data ecosystem is organized and all participating enterprises are in agreement, the network must jointly run small-scale pilots. These soft launches will flag any remaining pain points for the data ecosystem to address, such as aligning required capabilities, analyzing processes that need to be transformed, and defining new ways of working for the ecosystem.

This phase will also allow the ecosystem to test suitable data-sharing technology platforms to determine which one controls access rights in the most efficient manner.

5. Sustain the advantage

After completing extensive design phases and launching the data ecosystem, it is important for organizations not to sit back and relax. Throughout the entire engagement, enterprises must continuously strive for progression—both individually and collectively as a data ecosystem.

By scaling up data sharing use cases to their full potential, measuring and monitoring for ROI, and moving up the data and insights value chain, data ecosystems will evolve and improve over time.

Don't delay

Although data ecosystems are not new—61% of organizations already engage in such data-sharing networks—only 14% of all organizations truly take advantage of the more collaborative, extensive models described above.

Late adopters of widespread, well-designed data ecosystems face serious risk of disruption from better-equipped competitors that are leagues ahead, gaining data-driven insights from their partners across research and development, supply chain management, and sales and marketing, among other areas.

Over the next five years, it will be essential for organizations throughout the world to enter into data ecosystems and become data-sharing masters. With notable financial gains and industry-wide insights, these data-sharing masters will lead the field into the future of analytics and innovation.

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