Presented by Capgemini
Data collaboration is a massive growth opportunity. Organizations that make extensive use of external data enjoy a financial performance premium. The analysis from our recent Capgemini Research Institute report, Data-sharing masters, found that organizations that use more than seven data sources have nearly 14x the fixed-asset turnover and twice the market capitalization than organizations that do not use any external data for decision making.
Moreover, there is a clear trend within organizations to accelerate data ecosystem engagements: 84% of organizations plan to launch new data ecosystem initiatives within the next three years. One in four (25%) organizations will invest upwards of $50m in data ecosystems in the next two to three years.
Data-sharing masters, and how they outperform
These data-sharing masters are in fact a group of organizations that significantly outperform others. They are set apart by the way they can leverage external data to enhance their own data-driven insights and decision making. They look beyond traditional sources of data and make use of data aggregators and data disruptors — such as hyperscalers — turning volumes of multi-source data into value, accelerated by their ability to share and collaborate.
How do they manage this? The answer lies within the concept of data-sharing ecosystems, driving business outcomes across domains, industries, and value chains. This high-performing cohort is able to fully exploit the data collaboration business opportunity, go beyond expected or usual outcomes, and create new intelligent experiences, products, and services, or business models.
If we look at Starbucks, besides customer purchase behavior analysis or enhanced personalization, using external data helped them to position themselves in niche areas of business. Another recent example, is the Future4Care initiative in which Capgemini was involved alongside Sanofi, Generali, and Orange. Together, they created a unique health-focused open-innovation ecosystem in Europe to stimulate the development of ehealth solutions and their go-to-market plan, for the benefit of both patients and health professionals.
Adding 10% financial advantage
It can be perceived as counter-intuitive to say that the more you share, the more you gain.
Organizations involved in collaborative data ecosystems have the potential to drive an additional ten percentage points of financial advantage (including new revenue, higher productivity, and lower costs) in the next three years.
And the more you invest, the more you sustain. Investments made by organizations vary across sectors and countries — 55% of telecom firms will be investing over $50 million, while 43% of banking-sector companies will do so. This includes investments made towards the acquisition of technology infrastructure, tools, talent and skills, and process re-engineering, among others.
New, powerful approaches to data architecture
If you’ve already modernized your data infrastructure to be cloud-centric, new approaches such as data mesh architectures can link distributed data lakes into a coherent data mesh, enabling consumer data to be protected and stored locally, but remain accessible globally.
A data mesh connects the various data lakes that you need into a coherent infrastructure, one where all data is accessible as long as you have the right authority to access it. This doesn’t however mean there is “one great big virtual database;” the laws of physics mean that large, disparate data sets can’t just be joined together over huge distances with any degree of performance.
This is where new approaches such as federated analytics come in, enabling you to deploy analytics to multiple remote data sets and have the analytics run there and then and collaborate on the results. When looking to provide external access to data, technologies such as homomorphic encryption enable you to provide secure access to external organizations without either their algorithms or your data being directly accessible.
Differential privacy enables you to store the data in its raw form and provide high-quality access, but then adds noise to the results which protects privacy or IP. These new technologies, and others such as data marketplaces, will build upon a well-built and well-managed data infrastructure; they won’t, however, solve problems for organizations with unmanaged, disjointed data silos and misaligned data governance.
So, the good news for those that have already undertaken the transformation of their current data landscape is that data ecosystems are an obvious evolution that requires new technologies, but not the re-engineering of solid foundations. However, for those that have not made that transformation, their business disadvantage will continue to grow.
Compliance: A mandatory act
Ethics continues to be a big topic for any organization working with data and we’ve all seen what happens when data is mistreated. Data masters understand that you must put the security and protection of your data assets above all else, or risk losing everything. As such, organizations investing in data ecosystems must first lay the foundation for trust, ethics, and compliance.
In order to create a sustained competitive advantage, organizations have to build a clear roadmap from the beginning and answer some key questions at each stage, including why to engage in an ecosystem, which use case to tackle, which data can be shared, which data platform to use, and how to measure and monitor results. Above all, proactively addressing privacy, ethics, trust, and regulatory requirements across your data value chain is mandatory.
Even if one ecosystem partner’s ethical guidelines and charter are well defined, it can be discussed and adapted as a shared set of policies for all parties involved. More so, by having this shared set of principles, organizations will have a bedrock of ecosystem partnership and trust.
The era of data commerce
Every organization should catch the wave now and ask themselves: “Do I want to keep my data in my four walls and take the risk of being left behind by my competitors — or do I take the opportunity to multiply my business by engaging in data sharing?” Whatever question you’re starting with, remember you can start small and learn from your on-going initiatives and progressively infuse the data-sharing culture within your teams. Learning by doing is again the best approach.
Anne-Laure Thieullent is Artificial Intelligence and Analytics Group Offer Leader at Capgemini; Steve Jones is Chief Data Architect at Capgemini.
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