Building data ecosystems starts by looking outside your walls

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Joining the ranks of data masters is not easy, and it’s not a straight path. But there are well-defined methods for getting started.

The impact of new technologies on business goes beyond trends like the shift to the cloud or the surge in data. More fundamentally, this evolution means leaders have to rethink how they optimize their companies.  

Gone are the days when executives can just look internally to refine their operations. That inward focus is far too limiting today because it relies only on their own data leaving them with too many blind spots. Instead, to successfully drive their business, they must look externally to understand the reality and the context in which they operate.  

The key to this new external view is collaboration, a concept that’s easy to state and hard to achieve. Fortunately, new technologies along with new mindsets are unleashing a host of new possibilities for those ready to seize them. 

Mastering this new sharing environment enables leaders to make the leap from optimizing processes to optimizing decisions. The result is a clear advantage in an increasingly competitive world. 

“We’ve spent a lot of time optimizing what is within out four walls based on the context of what is within them. What we’re able to do now is collaborate externally with organizations to get additional data, which gives me that external view of my business reality.”

The tools propelling this change are well documented: cloud, artificial intelligence, dynamic compute. Underlying all of these is a swell of data that executives know has value but often struggle to fully leverage.  

A recent report by Capgemini Research Institute (“Data Sharing Masters: How smart organizations use data ecosystems to gain an unbeatable competitive edge) reinforced the power of external collaboration through data sharing and data ecosystems. Only 39% of organizations, however, have been able to use that data to gain the kind of insights needed to achieve a clear competitive advantage. Those who do manage to wrangle this data, the so-called “data masters,” clearly outperform competitors across a range of metrics. 

Joining the ranks of data masters is not easy, and it’s not a straight path. But there are well-defined methods for getting started. People don’t like sharing data. It’s the Gollum phenomenon: “It’s my precious and it’s all mine”. That is a cultural problem that people still have, this idea of data hoarding. That just doesn’t work in this world.

A good way to begin is by taking stock of the company’s internal data dynamics. One of the reasons so many have failed to harness their own data is because it remains siloed internally. Historically, IT departments and the business compete to create different data architectures and systems for different departments. That often left different managers pulling in different directions.

Addressing this division can be a great way to test the data sharing waters. Finding ways to collaborate and create an internal ecosystem that can help create a kind of template for asking the right questions, determining the needs of different stakeholders, and understanding how to overcome them. That includes revolving technical impediments, but also mindsets. 

“How can you ensure collaboration between finance and marketing, between sales and supply chain marketing? What do they care about from a data sharing perspective? This is your first collaboration challenge.”

This alignment can provide some early wins that build support and momentum for more ambitious collaborations with external sources. For instance, when a company finds it can refine a marketing plan based on its supply chain data that reveals the inventory it will have in a store. Or when finance data is able to feed better sales estimates. 

These successes can also lead to a natural progression. They provide confidence, but they can also reveal shortcomings. For instance, a company improves its supply chain metrics, but then realizes it could do more if it had richer information beyond its own data. Realizing it doesn’t have all the data to drive efficiencies can create a curiosity about the possibilities of exploring external data. 

Data masters are led by problems, challenges and opportunities. They understand that logistics providers and other specialists they work with have more information than they do about current contracts.

Getting access to the right external data is the next step, and it also poses big challenges. Increasingly, Capgemini sees data masters moving beyond the existing sources of big data troves and working with giants such as Google or Amazon for a boost. Yet this approach has its own limitations. Such relationships can often be lopsided and more favorable to the big data aggregator than to the those joining these ecosystems. 

As an alternative, Capgemini is seeing the emergence of data ecosystems that don’t have a middleman but are rather a neutral playing field of equals. These collaborators understand that they have data to improve their operations, but they don’t have the power on their own to optimize a larger system, such as a supply chain.

“All of them have a reason for the system to improve, that’s why they want to collaborate. So, you start in areas where you can easily identify the value of participating and collaborating because the whole point about these collaborations is that they drive up the value of the system and the network not just for one participant but for all participants.”

The role of Capgemini is often to help a company identify the financial benefits of engaging within an ecosystem to enable that data to start to come out from those different corners. As collaborators come to together, they begin to understand that the ecosystem only holds together as long as everyone feels they are benefitting. In turn, that gives each participant the power to shape how products are built on top of this ecosystem.

This all may sound daunting, but rapidly evolving new technologies such as homomorphic encryption, confidential computing, differential privacy, and federated analytics are giving companies even richer toolsets for granular control over sharing data while maintaining security. As an example, imagine a retailer who is selling beer. That retailer isn’t going to publish its analytics because it contains sensitive data from different brewers. But those brewers would love to have access, so they have a better view into local markets.  

Technologies like differential privacy and homomorphic encryption could let brewers run their analytics inside the retailer’s system without seeing the data of other brewers. With this insight, those brewers could tune their deliveries and shipments to stores to optimize their sales. 

Such ecosystems are becoming increasingly easy to launch from a technology viewpoint. Using platforms like 890 By Capgemini or the firm’s partnership with Snowflake, which eases the burden of sharing data across different systems, the barriers are lowering as control and security increase.  

These and other technologies are making obsolete the idea that all of a company’s data should sit in a central database. Taking the leap into the era of data ecosystems represents will eventually have the kind of transformative impact on business seen with other major shifts in computing. 

The analogy I’ve been using recently is these conversations about data collaboration are like the conversations I had with people in 1996 when I was describing the internet and e-commerce, or in 2006 when I was talking about the cloud. Everybody is going to get there, but some people are struggling for their first step on that journey.

Interesting read?

Have a look at our POV  to learn how to make the most of data collaboration business opportunities, gauge your organization’s digital maturity and determine where you should start your data ecosystems transformation journey. 

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