Hans-Christian Brockmann is CEO of SEMANTiCS 2019 Premium Sponsor eccenca, advisor to the president of the APICS Supply Chain Council and the APICS Digitalization Task Force as well as head of the APICS Readiness Task Force. With eccenca he has built a leading German company in the field of enterprise knowledge graph technology. eccenca supports companies with the automated integration of heterogeneous big data from disparate sources. In this interview, Chris talks about the main business areas of eccenca, recent innovations as well as remarkable use cases of the company which is listed as a Gartner Cool Vendor in Intelligent Supply Chain Execution Technologies as well as Top 10 GDPR Solution Provider by CIOApplications.
You are named as one of the Top 10 GDPR Solution Providers by CIO Applications. What do solutions have to provide to address the imminent challenges of GDPR in enterprises?
We believe Data Protection Officers (DPO) and Data Compliance Officers need to balance the extremes. They need to ensure compliance, automate lean GDPR processes while at the same time reducing barriers for digital transformation and big data projects. Classic concepts of data management alone – like relational databases – can’t meet the requirements of our modern times. They generally create data silos which strongly restrict data visibility and usage. What we generally experience at our clients is a highly dynamic yet disparate and sometimes contradictory data landscape. But companies must be able to integrate, curate and work with data even across entire business eco-systems. It is highly ineffective, if you have to deep-dive into every data silo separately to bring data together. Of course, we don’t want to demolish the existing IT infrastructure. But we believe that the typical data management approaches need to be enhanced by knowledge graphs to fully use the potential of digitalization and big data. And this applies to GDPR just as much as it does to ePrivacy, smart supply chain, Industry of Things or multi-cloud. eccenca provides knowledge graph technology that creates a lingua franca for a company’s data. The main advantage is that it’s completely independent of the number of systems a company uses, their data formats and processing purposes. In terms of GDPR this allows DPOs to create a single source of truth for all personal data in the company. This is possible because every system and its stored data are referenced and linked in the knowledge graph without duplicating the data. The management is done purely on metadata level. This allows a DPO, for example, to (automatically) check in which systems data of a requesting person is stored, what data is stored, for which processing purposes it is used and on which legal grounds the data is available (or if legitimate reasons exist in the first place).
And eccenca does this without proposing the next proprietary silo or disrupting the entire legacy IT.
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You also address another business field that has to work out their data landscape: supply chain management. Gartner even named you Cool Vendor in Intelligent Supply Chain Execution Technologies. How can semantic knowledge graphs help enterprises to realize the vision of a smart supply chain?
No matter if we are talking about privacy or data transparency, it really all boils down to what we call digital maturity. The questions are: How mature is an organization in managing data and information across its corporate functions? How mature is it in its ability to put data to use?
In your question about our GDPR solution I described that eccenca is creating a centralized map of data sources and data types, and combines this map with background information on privacy and data protection rules. In essence, our solution allows an organization to enforce a governance across a multitude of silos that was previously only known on a “per silo” level.
This same type of “governance” on meaning and rules that apply to data is also necessary for supply chains to function coherently.
The smart supply chain takes the challenges of GDPR several steps further. Because here enterprises need to integrate internal data sources as well as external data sources. This can be suppliers, distributors or things like a weather forecast database. Also, smart supply chain management is strongly process driven and dependent on domain expert knowledge from the company’s people. So there are four dimensions to be considered: product, process, partners and people. Today all too often data definitions for those “4 P” throughout the supply chain are inconsistent. We are helping our clients to discover these inconsistencies both within their organizations and throughout their eco-systems.
But we do not only discover the inconsistencies. We also provide a way of mending the broken pieces together by way of adding knowledge graph technology to the supply chain. We call this the concept of a business digital twin. This concept establishes a knowledge graph based digital representation of a company’s “products”, “processes”, “partners” and “people”. The aim is to visualize and link interdependent data sources to create a “single source of truth” – or at least a central place that defines the common understanding of the truth, even if locally these truths may vary. Being able to bridge multiple “versions” of the truth/data is the only way to achieve seamless digital collaboration across the supply-chain.
Our solutions allow organizations and its partners to continue working with existing systems while at the same time leveraging the knowledge graph to bridge the inconsistency gaps. This is key to process automation, improved S&OP planning, inventory balancing, shorter lead times and higher customer satisfaction through quality, timeliness and accuracy of delivery.
I think Gartner named eccenca a Cool Vendor, because we have found a way to address all these little data inconsistencies in supply chains.
Rolling out these technologies you certainly have worked on challenging and exciting cases. What have been the biggest challenges for companies you worked with, and what were the most valuable takeaways from mastering them?
We have worked and continue partnerships with Nokia, Volkswagen, Daimler, RFS, Siemens and Bosch to name a few. Our clients’ use cases range from GDPR and legal compliance challenges to software delivery, data as a service, smart supply chain and product data management.
At one of our clients the DPO was initially looking to reduce discovery time for personal data requested by a GDPR Subject Access Request. Mind you, the client ran hundreds of systems. This meant that employees had to access all of them to find out, if they were actually storing personal data of a particular person. So we created a GDPR metadata and data subject index. Within weeks the workload of the service center staff was reduced by an order of magnitude. Our solution would provide automated insights into what systems were related to the request and what types of information about the person was stored in the system.
In a supply chain scenario, we created worldwide visibility and shared definitions of product specifications. This immediately resulted in better S&OP planning data, better customer service and better capacity balancing. For that same client we also looked at how data could be used to enhance the product. We found that IoT data can be utilized to provide “proof of quality” of a product. The end customer would even pay higher prices for getting this additional data.
Another one of our clients is dealing with highly complex hardware solutions whose functionalities are driven by software specifications. We created digital twins of the products, processes, functions, features and specifications. This allowed for the automation of their software supply chain and delivery model. While the roll out of new features previously took several months, the new eccenca solution enabled a real-time roll out. This consequently created new business value propositions including a “pay per use” of features.
In the end, what we have learned is that it’s not just about some use case oriented data project. Product data, process data, ecosystem data and the domain expert knowledge are all linked and highly interdependent. That’s why we developed the aforementioned concept of the business digital twin. And that is why we believe that knowledge graph technology is going to play a key role in almost every company’s journey to digital transformation.
Speaking of CIOs and their challenge of digital transformation: Please name 5 important steps for SMEs and Enterprises as they steer their ships to digital maturity?
Digital Transformation is about empowering the organization in its use of data. So it is about relying on and using data without the need to think about applications, systems or silos etc.
So my five suggestions would be:
What really worked for all of our clients is a maturity journey that starts with establishing a shared understanding of product, then process, then partners and people. You will be able to show real-world results in 90 sprints, if you discipline yourself to a lean and focused approach.
You are a speaker at SEMANTiCS 2019: What will be the topic of your talk?
The talk will outline the business needs of the supply chain industry that were discovered during the past three years working with the APICS Supply Chain Council as well as a wider group of its members including companies such as Nokia, Ericsson, Infineon, GE, Boeing, and JD.com. It will explain how Knowledge Graphs and standardized industry ontologies can be successfully deployed in the industry. Last but not least, the presentation will walk the audience through a use case based 12-month digital maturity journey with a focus on business needs and outcomes.
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