�鶹ԭ�� Innovation Center Network Archives | �鶹ԭ�� News Center

Self-Sovereign Identity Gives Users Control Over Their Personal Data

Jeanette Rohr — Thu, 28 Dec 2023 13:15:00 +0000

Self-sovereign identity (SSI) is the first technology to give individuals maximum control over their digital persona – and it’s as simple to use as a social login. Businesses will benefit too.

At the moment, we identify ourselves online in one of two ways.

Typically, we sign into an online service – a Web site or app – using an e-mail address or username and password, and in doing so share some of our personal information. With this method, whenever we switch to another service we have to repeat the log-in process with a different password. This leaves fragments of our data behind on each service we use and forces us to create and remember a different password for each one, which is annoying but essential for security reasons.

The more convenient option for initial log-ins is to use a “social log-in,” such as your Facebook ID, which allows you to use the same username and password to access a variety of different services. With this one “federated identity,” you can complete your initial log-in to a participating app or service by simply selecting “Sign in with….”

The downside of this convenience is that your personal data – and that of millions of other users – is controlled by a single company, creating honeypots of data that hackers have often successfully targeted in the past. There are many examples of sensitive information being stolen, including address data, medical data, credit card details, and more. Aside from not being ideal from a privacy perspective, most people would probably not feel comfortable using a social log-in to access a sensitive service like their bank account.

But there is a third option. Self-sovereign identity (SSI) – also known as decentralized or portable identity – allows users to identify themselves on the Web using credentials stored in a digital wallet on their smartphone. It offers the same convenience as a social log-in, but the user is in full control of the data.

“This technology is not owned by a company,” says Mehran Shakeri, development team lead at . “It’s the first completely open standard for digital identity on the Web, and it gives individuals maximum control of their data when deployed with a proper registry.”

User-Friendly ID Technology – For Businesses Too

Companies and organizations can also use SSI technology. After all, they also have an identity – usually in the form of a public entry in a commercial register. For example, when logging into a business network, a supplier must share a range of data, including its tax number, IBAN, and postal address.

Currently, this data cannot be automatically verified. Entities created in internal systems must be verified using a relatively complex and costly third-party service. Records can be inconsistent and difficult to keep up-to-date. Data is often transferred from one system to another without being automatically verified.

This major issue can be solved if a company has a self-sovereign identity. Its digital wallet then serves as a single, vetted source of truth and there is no need for an external third party to verify its credentials.

This opportunity for a “golden record” of master data extends beyond company boundaries. “At �鶹ԭ�� Innovation Center locations, we have projects in progress with DATEV, the Dutch government, and a host of other customers to validate use cases with SSI,” says Alexander Schaefer, head of �鶹ԭ�� Innovation Center California. “We see tremendous potential in integrating the SSI standard into �鶹ԭ�� software.”

This technology would eliminate many of the identification processes currently in use, some of which are highly complex. Anyone who has opened an account with an online bank knows just how many steps it takes to prove your identity. With SSI, all users have to do is scan a QR code with their smartphone to share the relevant, verified credentials stored in their digital wallet. This digital wallet can be on their phone or laptop, for example, as a browser plug-in. The wallet is secured by common authentication mechanisms such as FaceID or TouchID, which have proven to be very secure.

Trusted Authorities Can Issue Credentials

For SSI to work, individuals need a digital wallet containing credentials that have been issued by a trusted third party – such as their driver’s license, MBA certificate, or tax ID. An empty wallet is useless. Trusted authorities that hold this type of data can issue the relevant credentials, which is why it is so essential that these entities adopt SSI.

Among the many companies that can issue these credentials, German IT service provider was one of the first to embrace the concept. “Together with DATEV, we have demonstrated how self-sovereign identity enables seamless business processes across ecosystems. Transactions from all parties are digitally notarized and tied to their cryptographically verifiable credentials,” says Schaefer. With decentralized identity, master data of organizations becomes trustworthy, which can ensure that companies communicate with verified and trusted partners in the ecosystem.

Before we can all be issued with these credentials, however, SSI technology needs to be widely adopted. Shakeri is confident that mass adoption will come. “There is hardly an industry that would not benefit from SSI,” he says.

SSI as a Driver of Transparency on Sustainable Business

To enable apps to use SSI for business-to-business communication, �鶹ԭ�� Innovation Center Network has developed a multi-tenant service built on (�鶹ԭ�� BTP) called Decentralized Identity Management. With this service, customers can issue and manage verifiable credentials – and verify the credentials themselves – creating the foundation for a decentralized business network where multiple parties can collaborate and share data.

Shakeri’s team is currently working on a use case for sharing ESG (environmental, social, and governance) certificates. “These certificates certify that a supplier operates sustainably, does not use child labor, does not exploit its suppliers, and so on,” he says.

At present, it can be time-consuming to achieve transparency in the supply chain, particularly in terms of how well individual suppliers focus on sustainability in their businesses. With SSI technology, it will be possible to check whether suppliers operate sustainably by requesting their ESG certificates from their digital wallet before deciding whether to work with them.

SSI could even make it possible to trace the origins of a product’s individual components across supply chain parties that work with different solutions – theoretically all the way back to the raw materials. While there is still work to be done to bring the full vision of SSI to life, customers can already benefit from the technology today in one of the many use cases that are likely to be significantly impacted by this technology. This can include supplier onboarding, master data management, and supply chain collaboration in the areas of sustainability and human rights.

Questions for the Experts

Q: Will SSI be widely adopted?

Shakeri: The outlook is very good at the moment, with many large companies actively investigating SSI and its potential. Government projects are also underway, including eIDAS (electronic IDentification, Authentication, and trust Services) in Europe, which aims to provide European Union citizens with a digital ID based on SSI. One of �鶹ԭ��’s cooperation projects with the Dutch government is exploring the impact of SSI in the public sector. If a European digital ID were to be introduced, it would drive adoption enormously.

Is SSI a blockchain-based technology?

SSI is not inherently tied to blockchain technology. However, blockchain is often associated with SSI because it provides a decentralized and secure way to implement some of the key principles of SSI. Blockchain can be used to create decentralized identity systems where individuals have control over their identity information and can selectively share it with others without the need for a central authority.

In the context of SSI, blockchain can be employed to record and verify identity-related transactions, ensuring transparency, security, and immutability. Some SSI implementations use blockchain or distributed ledger technology to anchor identity-related data, but SSI itself is a broader concept and not all SSI systems rely on blockchain.

Is SSI interoperable by design?

There needs to be a standard format for identity data that all industries and governments can agree on. SSI is still in the prototype phase, so we aren’t there yet. But given that it is in everyone’s interests, it’s realistic to assume that a standard format will be agreed on.

Sharing Data Without Sharing It: Secure Computation with Bosch

Mathias Kohler — Thu, 07 Dec 2023 13:15:00 +0000

Data is among the most valuable assets of �鶹ԭ�� customers and partners, with the power to enhance strategic decision-making and ensure competitive advantage. However, privacy and security concerns immediately arise when collecting and processing sensitive data.

This is why �鶹ԭ�� and Bosch have joined forces to harness the power of secure multi-party computation (MPC) and help enable secure and privacy-preserving data analysis across different organizations and industries.

Bringing Secure Computation to the Industry Level

MPC is an advanced cryptographic technique that can offer significant benefits to �鶹ԭ�� customers and partners that often deal with sensitive data from various sources and stakeholders. MPC allows multiple parties to jointly perform a computation without revealing any sensitive information that may be contained in their input data.

That’s a great achievement because companies often fear that the risk of sharing their data openly is greater than the potential value of the sharing itself. One example is the supply chain, where multiple parties are involved. Object-level tracking allows companies to collect large amounts of data, such as time, location, or handling of the goods they produce. Combining the data collected by the different companies involved can have significant benefits. However, two companies may only be willing to share information about common items that they have both handled along the supply chain. MPC can help solve this dilemma with secure and private computation.

As a result, organizations can perform complex data analysis and processing without compromising confidentiality or compliance. predicts that by 2025, 60% of large organizations will use at least one privacy-enhancing computation technique in analytics, business intelligence, or cloud computing.

“Companies can thrive the most when collaborating in business networks, and sharing data is a key component of these ecosystems. MPC can help protect sensitive data from unauthorized access and misuse while still enabling valuable insights and analytics,” says Volkmar Lotz, head of �鶹ԭ�� Security Research at . Powering secure benchmarking, fraud detection, supply chain optimization, or personalized services, MPC facilitates data sharing and collaboration across different organizations and sectors, creating new opportunities for innovation and growth.

Lifting MPC into the Cloud

In response to the demand for greater data privacy, Bosch Research has initiated the open-source project , which makes MPC available for a cloud environment. This way, confidentiality and privacy are maintained when data is processed by cloud services.

“Carbyne Stack is a kind of cloud-native operating system for MPC workloads, managing resources to make them run as efficiently as possible in multi-cloud deployments,” explains Sven Trieflinger, senior project manager and group lead at Bosch Research. “From a business perspective, it’s the seed for an upcoming open ecosystem of technology building blocks that will accelerate the development and adoption of MPC technology across multiple industries.”

�鶹ԭ�� has recently joined Carbyne Stack as a contributor. Building on both partners’ leadership in data security, cloud computing, and business applications, the collaboration will explore the potential of MPC for various use cases and industries currently constrained by security and privacy concerns. One of the first topics for �鶹ԭ�� will be to make the Carbyne Stack storage and processing services easily consumable from within the browser and to add support for deploying Carbyne Stack on Amazon Web Services (AWS). These changes will help �鶹ԭ�� work towards its vision of providing services for privacy-preserving data operations across different organizations and sectors, creating new opportunities for innovative business cases.

“By combining the strengths of �鶹ԭ�� and Bosch, we aim to advance the state of the art in MPC and enable new business cases for our customers and partners,” says Lotz.

�鶹ԭ�� targets use cases in industries such as automotive, manufacturing, healthcare, and finance. The exploration of MPC’s potential holds the opportunity to revolutionize those industries by solving critical data privacy and security challenges without compromising collaboration and innovation.

To find out more about secure multi-party computation, get in touch with us at icn@sap.com.

Mathias Kohler is a research manager for �鶹ԭ�� Security Research.

Empowering the City of Antibes to Redefine Digital Contract Management

Corinna Schmidt — Thu, 30 Nov 2023 12:15:00 +0000

In the era of digital transformation, multi-company processes governed by legal contracts are becoming increasingly common. With the untapped potential of decentralized processes, there is a growing prospect for digitizing and automating collaborative contract management, streamlining operations, and reducing risks and costs.

Recognizing this potential, the City of Antibes and have joined forces to redefine digital contract management with a focus on public procurement processes. Patrick Duverger, chief technology officer of the City of Antibes, describes its previous procurement operations as follows: “Although we achieved a high level of operational proficiency, our business processes lacked full traceability. This resulted in process opacity when it comes to recurring delays, delivery errors, or administrative sanctions. By gaining a deeper understanding of our processes, we can optimize them, reduce costs, and minimize business risks.”

Transparent and Traceable Collaboration

The lack of visibility and traceability in these processes hindered automation, cross-organizational data and process mining, and intelligence. This directly affected how disputes were resolved and impacted organizational budgets.

To address this challenge, �鶹ԭ�� Innovation Center Network developed a prototype based on the service, combined with a blockchain-based shared ledger on �鶹ԭ�� HANA Cloud. “This prototype aims to replace manual practices with automated, cross-company orchestrations. By using an �鶹ԭ�� HANA Cloud shared ledger, organizations like the City of Antibes can benefit from a permissioned, publicly verifiable, and immutable audit trail of cross-organizational interactions,” says Benjamin Stoeckhert, innovation product manager at �鶹ԭ�� Innovation Center Network, illustrating the potential of this innovation.

“Leveraging the full potential of an �鶹ԭ�� HANA Cloud shared ledger allows us to connect digital platforms that unite businesses and government to track the compliance of services and product deliveries according to contractual terms and conditions,” explains Duverger. “We can leverage data from different parties, such as logistics companies and quality certification bodies, and move beyond the era of cumbersome paperwork and manual processes. For example, this pattern is applicable to procurement processes where multiple parties verify the compliance of suppliers and their deliveries.”

Complete Control of Unencrypted Data

However, opening up collaborative processes directly conflicts with customer privacy concerns. This dilemma poses a significant challenge for next-generation contract management. How can businesses ensure transparency without compromising the privacy of private business information and processes? Duverger emphasizes the importance of protecting sensitive public procurement secrets: “Confidentiality, including negotiated prices and deal sizes, is paramount for us and our suppliers.”

In response to this pressing need, the �鶹ԭ�� Security Research team, in collaboration with the �鶹ԭ�� Innovation Center Network blockchain team, has extended �鶹ԭ�� Cross-Company Workflow Collaboration with a hardware-based privacy-enhancing technology (PET) innovation. Dr. Laurent Gomez, who led this innovation with Antibes, explains: “Before committing a transaction to the shared ledger, organizations encrypt it along with its associated sensitive information, using their own managed cryptographic material. Encrypted data and processes are only processed within secure and trusted hardware modules, with the explicit consent of each stakeholder.”

This innovation can unlock significant business value by helping to automate cross-company operations and protect the privacy of customer data while still allowing for analysis. The key contribution of this approach is that it gives customers complete control and ownership of their encrypted material. “�鶹ԭ�� can never access unencrypted data; the only data that leaves our information system is encrypted by us,” says Duverger. “�鶹ԭ�� Business Technology Platform (�鶹ԭ�� BTP) allows us to hold our own encryption keys, which is different from competing solutions.”

5% Less Late Payment Penalty Costs

The co-innovation has revealed the bottlenecks that slow down the procurement process at the City of Antibes and lead to late payment penalties for suppliers and delivery delays. “By adopting this secure distributed ledger approach, we have improved these key performance indicators without changing the process itself. We can now reliably manage all our procurement transactions,” says Duverger. “A key achievement was that we reduced our delinquency rate by 5%, which significantly minimized the impact of late payment penalty costs on our global spend budget.”

The project was recently recognized at the highest national level with the Territoria Gold Award by the French Ministries of the Interior, Ecological Transition, and Territorial Cohesion.

© Niclas Fagot Studio9

To find out more about �鶹ԭ�� Cross-Company Workflow, get in touch with us at icn@sap.com.

Corinna Schmidt is part of NVT Communication at �鶹ԭ��.

From Sentiments to Solutions: Decoding Business Correspondence with Gen AI

Thomas Kongats — Thu, 12 Oct 2023 13:15:00 +0000

Whoever said “practice makes perfect” has never spent their days replying to client e-mails or sending invoices. After a while, finding the appropriate way to say “hello” can even get tiring.

The reality is that most business processes these days require extensive correspondence with stakeholders – whether it is for customer support, hiring processes, sending and chasing invoices, or scheduling deliveries and meetings via e-mail, service tickets, or phone calls.

As of today, understanding, contextualizing, and prioritizing this correspondence is still a highly manual task that is repetitive and time-consuming. This also makes the chances of error or delay notably higher, leading to increased costs or even incompliance. A new exploration from offers help.

The Bridge Between LLMs and Line of Business Applications

Communication intelligence is a service on (�鶹ԭ�� BTP) that can analyze, categorize, and correspond to written communication by leveraging artificial intelligence (AI) and pre-trained models like large language models (LLM). It scans information found in e-mails or Microsoft Teams, including their attachments, before categorizing key elements such as their sentiment – positive or negative – degree of urgency, and mentioned stakeholders into a quick and easy-to-read summary.

With generative AI capabilities, the service can proactively suggest responses, schedule meetings, or correct invoices. It allows adjustments in tone, length, and style for an immediate and personalized response with just a few clicks, even during the busiest parts of the day.

“Communication Intelligence serves as the bridging layer between line of business applications, such as account receivables management or HR, and LLMs,” explains Greg Harrelson, product manager for Communication Intelligence at �鶹ԭ�� Innovation Center Network. “This allows developers to focus on domain-specific use cases without having to fully understand the complexities of LLMs.”

For instance, for account receivables, the text analysis enabled by communication intelligence can be used to prioritize time-sensitive or critical requests, trigger system activities like creating dispute cases, and leverage suggested responses. In HR, the service can help gauge employee sentiment and engagement through feedback surveys, communications, and social media. Communication intelligence is suitable for a wide range of use cases by helping to reduce errors and improve response times for business correspondence.

Ensuring Flexibility

Communication intelligence allows customers to choose and configure interactions from a wide range of LLM providers without being locked into a specific vendor. “We understand the need for flexibility and adaptability in today’s rapidly evolving technology landscape,” says Harrelson. “This flexibility not only helps ensure the ability to combine solutions but also future proof the applications against potential changes in the LLM offering.”

Communication intelligence incorporates advanced, built-in anonymization and re-animation techniques, working to ensure that personal data remains secure throughout the analysis process. The team is currently exploring opportunities to plug the solution into �鶹ԭ�� Build Process Automation to deliver the same benefits as part of �鶹ԭ��’s low-code/no-code tooling in the future.

To find out more about communication intelligence, get in touch with us at icn@sap.com.

Thomas Kongats is part of NVT Communications at �鶹ԭ��.

The Immediate Impacts of Video Data Stories

Jeanette Rohr — Fri, 16 Jun 2023 11:15:44 +0000

When it comes to offering audiences engaging and easy-to-consume content, platforms like Instagram and TikTok lead the way. In the world of business, too, storytelling-on-the-go has huge potential. �鶹ԭ�� Innovation Center Network is creating a solution that helps address this opportunity.

“We want to enable companies to share business insights fast and in a fresh and immediate way,” says Michael Wittmann, senior developer at , on the rationale behind the data storytelling solution he and his team are working on.

The purpose is to provide business users with short, animated “video data stories” about the key activities relevant to their individual roles.

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Eye-catching videos that are 10 to 15 seconds long will mean that users won’t have to refer to dashboards or wade through reports as they do today. Built-in templates for the most common business processes will enable anyone to create their own video data story and link it to their data sources. No design or programming experience is required.

The solution’s video data stories apply the same user experience principles as other content-sharing platforms. New videos are displayed in chronological order, and viewers can navigate easily through the different parts of a story or skip entire stories that are not relevant to them.

By incorporating animated visualizations, users can show changes in data over a specific time period dynamically. If more details are needed, they can point the audience to the right people and include links to the data sources and applications.

“One potential use case is sales reporting,” says Wittmann. Here, the solution has a predefined template that can be used to generate a personalized story for each sales accountant, showing how sales figures have changed over a defined period and including highlights and historical comparisons.

“Other information, such as headcount growth, quarterly earnings, and fact sheets for communications and sales, can also be shared through video storytelling in a far more compelling way than in PDF files or PowerPoint,” continues Wittmann.

The same applies to many of the e-mail notifications that land in our inboxes. Though not every notification needs a video data story. “It’s about finding the right balance,” notes Wittmann. “Instead of separate alerts for each event, you might receive a daily summary of the most important updates on a particular topic.”

In principle, video data stories can translate their insights into animated videos like those on Instagram. Users can decide for which events and applications video data stories are generated. “For some scenarios, daily or weekly updates are best. For others, it might be when a particular threshold is exceeded or when other kinds of exceptions occur,” explains Wittmann.

It is hard to imagine any department that would not benefit from this type of storytelling. Yet what might at first seem highly generic is in fact very individual.

“Right now, business software is clearly moving toward hyper-personalization,” says Wittmann. “That is why we want our video data stories to be used not just to share generic content but also to be tailorable to the individual viewer.”

Examples of personalized content could be team members’ birthdays and reports indicating how many of a manager’s direct reports have completed the latest compliance training.

“At the moment, we’re still defining the product,” says Wittmann. “We are working on a first version of an service that can generate videos from different applications. We have also teamed up with colleagues from to create an initial integration function for exporting video data stories directly from an analytics story.”

At �鶹ԭ��, Wittmann and his team have been working mainly with the Customer, Competitive, and Market Insights (CMI) team. In an early prototype, stories were generated weekly for a specific solution area to see which topics were seeing the most customer demand.

In another pilot project, the team is testing personalized video data stories for the sales organization. Here, account executives receive stories with updates on the specific customers they serve.

“We are really keen to trial the solution with customers to learn which use cases they might have, how they want to tell those stories, and where we can best integrate this capability,” says Wittmann.

To find out more about video data stories, get in touch with us at icn@sap.com.

Jeanette Rohr is a brand journalist at �鶹ԭ��.

Knowledge Graphs: The Dream of a Knowledge Network

Stefanie Glenk — Mon, 24 Apr 2023 12:15:24 +0000

In 2019, Gartner placed knowledge graphs alongside quantum computing in its . The reaction from the research community was one of bemusement: knowledge graphs, which are semantics used to search data across multiple sources and forge connections between them, were essentially nothing new.

Even as far back as the 1950s, computer scientists were already experimenting with this concept of data modeling. In the 1980s, knowledge graphs were a much-discussed topic in the context of expert systems, and in the 2000s they once again came to the attention of the scientific community, as they took on a fundamental role in Semantic Web. Finally, in 2012, it was a search engine that gave knowledge graphs their big appearance among a wider audience: Google announced that its users would be able to search for “things not strings.” Rather than just trying to match keywords from a query, the search engine would also place them in the right context – all with the aim of delivering better, more intelligent results.

“Put very simply, knowledge graphs are a technology that seeks to turn data into machine-interpretable knowledge,” says Michael Burwig, innovation engineer at Potsdam. Their key focus is to model the relationships between objects, which are shown as a network of interconnected points.

“Knowledge graphs allow us to map how we humans understand the world – how we go through life, accumulate knowledge, and how we contextualize that information in our minds,” Burwig explains. This ability allows humans to draw conclusions that produce fascinating “aha” moments. Ultimately, this is also the goal of business software: to connect knowledge and find solutions, ideally in an automated way. There are many potential use cases of knowledge graphs, such as knowledge and data management and chatbots. Detecting insurance fraud is one example, but, in short, they can be used in any scenario in which patterns are examined to identify exceptions and anomalies.

The Potential of Hybrid AI

Burwig believes, however, that automated insights like these have not yet become part of everyday practice. “Hybrid AI is a major step in this direction. It combines semantic technology, in other words knowledge graphs, and static machine learning, which is now being used for a host of scenarios of this kind,” he says.

Dr. Jan Portisch, lead architect at Value Accelerator Delivery, describes the difference between classical machine learning concepts and hybrid AI machine learning as follows: “Classical machine learning methods use extracts of existing databases. These methods draw on such data sets, which are effectively snapshots without any context, to create machine learning content. With knowledge graphs, on the other hand, new data can be added at any time so that they keep on ‘learning’ and, unlike static methods, stay current.”

Even though knowledge graphs are not new, it took the massive increase in computing power that we have seen since the 2000s to unleash their full potential, Portisch explains. And since this technology is not yet widely taught at universities, few developers know much about it. “Another difficulty is that designing graphs is highly complex, with developers having to think beyond their own application. The modeling behind the graphs has to be complete and semantically accurate. Nonetheless, their potential is huge,” he says.

As an architect, Portisch is involved in creating a central graph for process knowledge at �鶹ԭ��. The �鶹ԭ�� Signavio Value Accelerator Delivery team collates and models the process knowledge �鶹ԭ�� has built up over the past 50 years to give customers an integrated view. In the future, the accelerator will offer a semantic search where users will be able to formulate a business problem they’d like to solve and the system will display a list of processes and data that are impacted by this problem, Portisch explains. Further, this capability could be a helpful transition tool in migration projects or could provide presales teams with the visibility they need when tailoring solutions to customers’ specific situations.

Felix Sasaki, expert in Knowledge Graphs & Semantic Technologies in the �鶹ԭ�� AI unit, explains additional benefits: “Standards-based knowledge graph technologies facilitate the modeling of business scenarios. So-called constraints complement the existing logic-based modeling. Since constraints can easily capture the knowledge of business experts, modeling becomes easier. In addition, knowledge graph-based vocabularies like schema.org have found widespread adoption and thus help to find a more ubiquitous language.”

Decoupling Business Expertise and Application

For �鶹ԭ��, the power of this technology lies in how graphs can be combined in different ways and, therefore, in how data models – and ultimately the applications themselves – can be integrated and composed.

Rendering of �鶹ԭ�� Signavio Value Accelerator Delivery team knowledge graphs. Click to enlarge.

Portisch explains how it’s important to recognize that RDF – Resource Description Framework, a syntax used to model metadata for Internet resources – is a publicly acknowledged standard and that knowledge graphs are already widely used outside the corporate world. One of the best-known knowledge graphs is ; another example of a large-scale graph is . “These graphs are public resources that can also be used commercially,” Portisch says.

That creates interesting use cases. For example, private corporate data could be combined with public data. A supplier system running on �鶹ԭ�� software could automatically import metadata from a graph about companies it interacts with, such as the type of business, company logo, and who their managing directors are.

“Semantic Glue” – Integration by Design

Burwig sees another advantage of these graphs in their “semantic glue.” Because of their flexible structure that can be enhanced in real time, individual graphs can be “glued” together to link up data silos. Unlike graphs, the table structure of a relational database has to be defined at the beginning – and changing that structure later requires a lot of work. But graphs can store and link metadata in a semantic data layer that is separate from the data stored in an application’s tables. This makes it possible to consolidate data across different products and data silos.

Graphs offer huge advantages over relational databases in certain scenarios, such as calculating the shortest route or associating a specific material with a customer material. “If such a scenario is based on a relational database, the query would have to touch applications from across the entire �鶹ԭ�� world. But with a graph, it would be a simple problem to solve,” Portisch says. While relational databases usually offer greater performance within their applications, graphs always have an advantage when it comes to creating bridges, known as joins, between data structures and recognizing the correct context.

Because it is possible to extend knowledge graphs, they represent a perfect data model for businesses to start small with and then, as an iterative process, to roll out to more locations and build on. For example, a company could begin with just one use case or department and then gradually roll the model out to the rest of the organization. Any additional data models are “glued” to the graph as new nodes and edges.

The Situation Knowledge Graph: One of the First Knowledge Graphs at �鶹ԭ��

One of the most advanced knowledge graphs to date at �鶹ԭ�� serves as the basis for the Explore Related Situations app, which is part of the Intelligent Situation Automation service on for situation handling in �鶹ԭ�� S/4HANA.

The business background: around 4% of the automated business processes within a business require manual intervention because of an unforeseen event, such as a late payment, a delayed delivery, or a transport issue.

The situation knowledge graph links these exceptional events to their business entities, enabling the user to better understand the situation in a business context and therefore helping solve the problem and optimize business processes. Further analyses of these situations often reveal relations of issues to certain materials or partners. “Today, this kind of knowledge is often hard-coded,” says Dr. Torsten Leidig, an architect in �鶹ԭ��’s Situation Handling team. The knowledge graph that underpins the Intelligent Situation Automation service enables a business expert or a key user to model processes and understand them within a comprehensive business context. A problem that has been detected can be resolved automatically based on simple rules and without additional programming.

Screenshot of situation handling graph. Click to enlarge.

Dr. Knut Manske, engineering lead for Situation Handling and Responsibility Management at �鶹ԭ��, describes the graph as a layer that stretches across various �鶹ԭ�� applications. To summarize the capabilities of situation handling, he explains: “The knowledge that is embedded in �鶹ԭ�� applications is extracted and made usable for algorithms. Situation handling runs alongside the application, analyzing data and reacting to information about data changes or events. The aim is to show solutions that span various applications or business areas.” Customers and partners can define situations themselves without ever touching the application. A selected group of customers is currently validating the solution.

Looking Ahead

Currently just a prototype, the �鶹ԭ�� “Business Decision Simulator” innovation project simulates the effects of internal and external factors on a company and presents the user with potential future scenarios and recommended responses. “One possible question could be: what does a bushfire in Australia mean for my global pharmaceutical company’s value chain and thus the achievement of our targets in Europe?” Burwig explains. “On the one hand, knowledge graph technology can help express the complex relationships between real-world events and business-related processes. Additionally, an extensive knowledge base makes it easier for the user to choose the best possible response to opportunities and risks.”

When asked about the possibilities of graph technology, Burwig says: “The potential of knowledge graphs – at least that’s the dream – is that someday there will be extensive graphs modeled on deep knowledge and experience that can be accessed by programs. This means that with every change in technology, we’d only have to recode the application and not the knowledge stored inside it.”

While the decoupling of data from applications and the accompanying scalability of solutions and programs are important aspects for an IT company, Burwig also sees a vision that goes beyond the �鶹ԭ�� context: symbolic AI, artificial intelligence that is linked to a large number of interdisciplinary knowledge networks, could have the potential to generate new knowledge that has not been explicitly modeled. Linking scientific content could have the power to accelerate the discovery of new drugs or forge innovations between different science disciplines that don’t interact on a large scale today.

In the history of humanity, things have often been – and still are today – “invented” multiple times, at least in part from lack of knowledge about other relevant findings. Connecting global research projects within one given discipline has increased sharply within the past years, thanks to the availability of extensive digital content. Uniting scientific knowledge across the individual disciplines – for example, mathematics, biology, medicine, and chemistry – could uncover new insights and lead to exciting interdisciplinary inventions, Burwig concludes.

The Virtuous Circle Between Knowledge Graphs, Machine Learning, and Natural Language Processing

Academic communities of symbolic AI and machine learning in the past had few touchpoints in terms of research methods and researchers. This is changing – among others leading to so-called hybrid AI. This results in various virtuous cycles in which knowledge graphs, machine learning, and natural language processing (NLP) cross-pollinate each other. Johannes Hoffart, head of the Chief Technology Office in the AI Unit at �鶹ԭ��, explains the relation between machine learning and knowledge graphs: “Knowledge graphs enable data scientists to work with complex and heterogeneous data sources. Their flexible schema can be easily extended and contains powerful data validation capabilities. At the same time, knowledge graphs facilitate access to and exploration of data, as they represent data and its schema such that it’s easier for humans, and also large language models, to understand.”

Christian Lieske from �鶹ԭ��’s Language Experience Lab adds about the relation to natural language processing: “Knowledge graphs can feed NLP and can be fed by NLP. Take the detection of new business entities as an example: a knowledge graph can inform NLP about known entities and NLP can add additional entities to a knowledge graph.”

To get further insight around knowledge graph technology, read this study co-authored by Portisch: .

�鶹ԭ�� and Sartorius Are Reinventing Collaboration and Manual Processes

Corinna Schmidt — Tue, 04 Oct 2022 11:15:02 +0000

Working across teams can be a challenge, but working across companies is even harder. Not only do process participants have to manage different expectations, work processes, tools, and dynamics, they’re also navigating in entirely different companies. In an economy that is increasingly becoming more globalized while supply chains are getting disrupted, enterprises and public institutions must enhance collaboration with partner enterprises to develop high-quality solutions at low cost and with a quick response to market demands. Cross-company automation and compliance are critical features to achieve these goals.

Consequently, it’s no surprise that during its ‘InnoDay,’ , a globally leading manufacturer of equipment for the development and production of drugs, identified manual and cross-company processes as the most promising area for digital innovation projects.

“InnoDay provides an open platform for creating digital innovations. Innovation is part of our corporate culture and supported by an environment and leadership that encourages creativity. Scalable innovation requires integration into existing processes and systems. Therefore, �鶹ԭ�� Innovation Center Network is our prime partner to validate and realize this potential,” said Thomas Lata, manager of IT Order to Cash at Sartorius.

Typically, processes within patient care and the healthcare system require companies to work with one another to achieve the same goal. This is especially apparent when it comes to removing harmful microorganisms on medical equipment, where a single error would lead to drug contamination. Sterilization processes are directed and executed between medical equipment manufacturers and sterilization service providers and overseen by regulatory organizations and pharmaceutical companies. In most instances, these processes are manual and paper-based, making them inefficient, unclear, and susceptible to having crucial details fall through the cracks.

With the lack of automation, transparency, and speed, sterilization processes and all the teams and companies involved needed a solution – which is when �鶹ԭ�� and Sartorius began working together. Based on the service, the two organizations developed a prototype that aims to replace manual practices with automated cross-company orchestrations. �鶹ԭ�� Cross-Company Workflow Collaboration is a blockchain-based innovation extending �鶹ԭ��’s no-code workflow capabilities on .

“While many internal processes can be digitized and automated with powerful tools and platforms, cross-company processes are often still very manual and lack transparency,” said Benjamin Stoeckhert, senior business development manager of Blockchain at �鶹ԭ��. “With the cross-company workflow collaboration service, we aim to solve this issue and replace manual, error-prone practices with real-time automation, boosting efficiency and compliance.”

�鶹ԭ�� Cross-Company Workflow Collaboration can be used to orchestrate and automate sterilization processes between Sartorius and other sterilization service providers. In addition to being more dynamic, the service also operates within an immutable shared blockchain ledger, enabling other market participants like pharmaceutical companies and regulators to verify the process compliance via a digital audit trail. This eliminates the need for paper documents.

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In an �鶹ԭ�� Sapphire session with Sartorius, Philippe Sanner and Alban Zacharia, in-house IT consultants with Sartorius, and Stoeckhert from �鶹ԭ�� explained that using �鶹ԭ�� Cross-Company Workflow Collaboration has eliminated repetitive tasks, which in turn has increased security and efficiency.

Sanner said, “The immediate benefits we can think of are permitted by the automatization and collaboration capabilities. This saves a lot of time, and the business process experts can focus on more value-add activities. In addition, reducing paper wherever possible is also very important for Sartorius, as sustainability is one of our core values”.

�鶹ԭ�� Business Technology Platform brings many capabilities in one, unified environment, which allows to seamlessly combine �鶹ԭ�� Cross-Company Workflow Collaboration with �鶹ԭ�� Analytics Cloud. “We have been pretty impressed with the data analytics capabilities. All information is available at the same place, and it is easy to implement personalized, user-friendly KPIs. This helps us detect risks and measure collaboration performance in real time,“ Sanner added.

Zacharia emphasized the importance of openness for cross-company automation: “Another key feature of the service is that it can connect participants with �鶹ԭ�� and non-�鶹ԭ�� systems. This allows scaling to all actors in the process.”

“With this prototype, we can demonstrate what a blockchain-based solution could really look like and we can offer real-time sterilization transparency for our customers,” Zacharia said.

To learn more about �鶹ԭ�� Cross-Company Workflow Collaboration, e-mail cross-company@sap.com.

Corinna Schmidt works in Communications for �鶹ԭ�� New Ventures and Technologies.

The Future of Analytics: How to Make Sense of Data and Mitigate Business-Critical Risks

Martin Heinig — Fri, 26 Aug 2022 11:15:45 +0000

By using new forms of databases and machine learning algorithms, real-time data processing capabilities, and the development of self-service analytics and data marketplaces, �鶹ԭ�� enables customers to base decisions on intelligent data-driven insights.

Sight, hearing, smell, taste, and touch: the classic five senses of humans allow us to experience the world around us, with our nervous system receiving and processing huge amounts of information and relaying signals to the brain in order to react with the world. It is almost impossible to say how much data the human brain receives and processes on a daily basis. At best, one can specify a lower limit of perhaps around 1,000 gigabytes.

Humans not only process but also generate data in abundance. How many Instagram Stories did you click through last night before falling asleep in bed? How much time did you spend on different news portals? Just one more episode of “Stranger Things” on Netflix? The list goes on and on.

Navigating the Data Jungle

Companies struggle with this too and collect and analyze more data than ever before. Not without reason, data is referred to as the new gold. The way to increased profitability and competitive advantage by means of intelligent data usage seems to be a rocky one. Using the sheer amount of data to its full advantage remains a challenge.

At �鶹ԭ��, we want to help our customers navigate the data jungle, regardless of infrastructure and where the data is saved. For 50 years, �鶹ԭ�� has been storing, processing, and analyzing innumerable amounts of customer data. Building on these strong data management capabilities, we want customers to be able to base all their decisions on intelligent, data-driven insights with easily accessible business data — when and where they matter the most.

Our ambition is a clear shift from the current state of analytics to the future, which means that we see the future of analytics as autonomous, hence always learning and monitoring; proactive, in order to provide solutions and alternatives to problems that haven’t even been discovered yet; and personalized, meaning that insights are contextualized and can be provided per desired medium or experience preference.

Bringing Future Technologies to Customers Today

While we envision a true system of intelligence in the future, �鶹ԭ�� is already demonstrating value for customers today. �鶹ԭ�� Innovation Center Network is exploring multiple key technologies to help customers capitalize more on data and boost productivity to stay competitive in the long run.

Businesses are constantly in motion, and data continues to grow in size, complexity, and velocity. “Laura,” a risk manager at a company that evaluates the potential risks of a business and strategizes preventive measures, needs to be able to monitor standard KPIs via dashboards and constantly adapt them to catch trends and patterns or anomalies — at least it should be that way.

Today, however, autonomous, proactive, and personalized tools are needed to automate business monitoring and proactively identify and report relevant signals in data.

To address this need, we built a prototype of a self-learning, easy-to-integrate cloud service that continuously monitors business data for anomalies and proactively delivers actionable insights to risk managers like Laura in real time. We call this , and by integrating it into her day-to-day operations, Laura can spend less time exploring data and can detect unknown yet critical signals without manual intervention. She can also filter anomalies by relevance to receive critical signals, ultimately helping to get insights faster and react accordingly — before the business is impacted.

With all information at hand, the system can autonomously propose a response in real time, such as, “Hey Laura, something is going wrong, and here are a few proposals on what you can do.” This is made possible by what-if scenarios enabled by the . It helps her autonomously consider multiple scenarios, simulate their outcomes, and offer recommendations for the next steps. For example, instead of relying on the usual measures, she might be shown alternative approaches that she had not thought of before but that would solve the problem even faster.

By providing holistic impact analysis and autonomous identification of what-if scenarios, Laura can better plan ahead and react quicker to uncertainties with data-driven insights. Optimized recommended actions that are aligned with company priorities and constraints allow her to recognize and mitigate risks early on — and decide on actions with confidence.

Like most of us, Laura is a visual person who processes images much faster than text. No wonder visualizing data in an easy-to-understand form has also become a trend in analytics. Thanks to their length of just a few seconds, short videos have evolved into the dominant format for sharing and receiving updates and notifications. Consuming information like you would consume content in social apps helps professionals like Laura capture vast chunks of complex data and become the one-stop shop for business insights in the future.

�鶹ԭ�� is bringing such short-form videos to the enterprise. With automatically generated personalized content, concise stories deliver key insights about the business in a short, animated, and convenient format. Video data stories provide customers with a one-stop channel for timely notifications, digests, and alerts about business-critical events. It automatically translates real-time business insights into self-explaining audio-visual data stories, making it easier for Laura to make decisions.

Effective business processes depend on intelligent decisions with insights from data in the organization and beyond its networks. �鶹ԭ��’s vision is to infuse these data-driven insights into every decision-making process. This would be possible with AI-powered, lightweight, self-service analytics that seamlessly integrate into the day-to-day work routines of business users, blending human ingenuity and data-driven insights into context-rich, fact-based decision-making processes.

Automation and data contextualization are key trends shaping the future of analytics, requiring an evolution from the current state of analytics to the future of analytics being autonomous, proactive, personalized, and reinventing business decisions as a service. �鶹ԭ�� is a reliable partner to anticipate that change, drive innovation, and help our customers prepare for the future.

Data is the new gold of every enterprise. If you also want to base your decisions on intelligent data-driven insights in the future, you can become part of �鶹ԭ��’s innovation journey and get in touch with us. Customer feedback is key to solving real pain points in the data and analytics space and we would be delighted to co-innovate and solve your data challenges together.

Martin Heinig is head of New Ventures and Technologies at �鶹ԭ��.
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TUMO Labs Students Put Their Skills to Work in Innovation Challenges

Igor Belousov — Tue, 02 Aug 2022 11:15:19 +0000

A human digital twin that can anticipate a user’s intentions, wants, and needs using contextual information to provide personalized recommendations for next steps? Five students experimented with this concept and came up with a chatbot prototype during a four-month program from �鶹ԭ�� and Armenian educational initiative TUMO Labs. It aims to support university students to understand, utilize, and shape digital technologies.

With TUMO, provided 22 innovation challenges to explore emerging technologies and concepts, each associated with : augmented extended planning and analysis (xP&A), augmented access control, future of self-service, and future of asynchronous work. �鶹ԭ�� Innovation Center Network focuses on exploring new technologies through use cases to solve future problems. Innovation is an ongoing process that needs not only curiosity and determination but also inventive ideas and out-of-the-box thinking. It was precisely this fresh thinking and unusual approaches that made working with the TUMO students so interesting for both sides.

“For me, it was a great experience working with artificial intelligence (AI) and natural language processing, with the help of which we were able to find interesting insights from user conversations,” said Sasun Tadevosyan, one of the five students developing the chatbot. “Further research and improvements will help to create a virtual assistant to automate routine work.”

Photo courtesy of Igor Belousov

Sasun and her team built the chatbot as part of the challenge. Here, the students were asked to explore how user contextual data and the concept of human digital twins can be leveraged in a privacy-preserving, predictive manner to improve access control and authorization experiences. A digital twin could eventually help humans anticipate these access requirements and gather the necessary information to facilitate access, increasing productivity and enabling quicker business decisions.

For their chatbot, Sasun and team evaluated chat messages between two users who want to organize a meeting and extracted insights about meeting details using a rule engine. The chatbot helps set up the call and automatically creates authorizations and access to the meeting as well as relevant documents that will be shared.

This is a great example of how digital assistants can make life easier by automating daily routines, like in this case scheduling a meeting and making sure everyone has the necessary access rights to collaborate more easily.

During the four months, experts from �鶹ԭ�� Innovation Center Network monitored the progress of all challenges and were in constant exchange with the participants to support if needed. Apart from solving the challenge, working with TUMO Labs experts helped strengthen the students’ soft skills, and support from �鶹ԭ�� University Alliances provided all students with access to �鶹ԭ�� learning resources.

“We learned to work as a team and under pressure. We got problem-solving, creative and critical thinking, and public speaking skills,” recapped the team around Ani Galstyan that worked on the future of asynchronous work.

This group of five students was challenged to examine how digital avatars are used in various fields today. They created a Web site to showcase different possibilities to implement digital avatars in different fields such as business, the gaming industry, and the military. Plus, they added features to help users create their personal avatar. During their research, Ani and team learned how digital avatars can be a valuable addition in healthcare, as they could eventually lead to a more holistic understanding of the human body to predict and prevent possible side effects before working with real human beings.

Photo courtesy of Igor Belousov

After working on their projects, the students presented their outcomes at a in April in front of �鶹ԭ�� and TUMO experts as well as �鶹ԭ�� customer ACBA Bank and partners.

Bahareh Fatemi, Head of TUMO Labs, said in : “We’re super excited with working with the �鶹ԭ�� Innovation Center Network! Having these sort of collaborations internationally for us is very important because it will allow us to understand where we can improve and where we can push our students to adapt and get more skills.”

Plans are already underway to continue working with �鶹ԭ�� Innovation Center Network on additional innovation challenges, further explore emerging topics, and provide opportunities for more students to participate.

“That was a great experience for us to explore AI avatars in different fields,” concluded Ani after participating in the project. “We are looking forward to seeing new developments in the future as we are going through technological improvements nowadays.”

Lukasz Ostrowski is a development manager for �鶹ԭ�� Innovation Center Network.
Igor Belousov is global content delivery director and country manager of Italy, Turkey, and CIS for �鶹ԭ�� University Alliances.

How �鶹ԭ�� Builds Bridges Between Research and Development

Jeanette Rohr — Mon, 18 Jul 2022 10:15:55 +0000

Through academic fellowships within the �鶹ԭ�� Innovation Center Network, �鶹ԭ�� is breaking new ground when it comes to getting academia and business to work together on the problems of the future.

Prof. Mathias Weske, head of Business Process Technology at the Hasso Plattner Institute at the University of Potsdam, provides insights into his work with �鶹ԭ��.

“My specialty is processes,” shares Weske, who worked as an academic fellow at �鶹ԭ�� during a sabbatical over the last six months. “And when it comes to processes, �鶹ԭ�� software — with its extensive features and broad coverage — is simply the ideal environment.”

“To put it bluntly, scientists often create their own problems and then develop solutions for them,” he says. “Research projects only get specific when they address questions from real life. That’s an important reason for me why industry and academia should work more closely together.”

Businesses also have major interest in collaborating with experts from the research field.

“Technical innovation moves so quickly that we at �鶹ԭ�� need to work closely together with academic experts, to evaluate early on whether we should invest time and resources in a budding topic,” explains Dr. Matthias Uflacker, head of the �鶹ԭ�� Innovation Center in Munich.

from the �鶹ԭ�� Innovation Center Network were conceived with this very thing in mind, he continues: “Instead of starting a five-year project on a technological trend and not knowing how it will develop, or whether it will even be relevant for �鶹ԭ�� in two or three years, we can utilize external expertise very well in the framework of six- to 12-month academic fellowships.”

Weske, a computer scientist, first got involved with the technical modeling of processes after being accepted as a postdoc at the University of Muenster. After his first professorship at the Eindhoven University of Technology, where he concentrated on business process management (BPM) within service-oriented software architectures, Weske was appointed to the Hasso Plattner Institute of the University of Potsdam in 2001.

“I’ve been there for over 20 years now,” says Weske. “We deal with software systems that support especially knowledge-intensive, flexible business processes. Knowledge workers are the focus of these systems: they have major leeway in process execution and can even decide to deviate from a predefined process.”

Under the central theme of addressing real-world BPM problems with formal approaches and engineering useful prototypes, Weske and his group solicits specific questions from their collaboration with industry partners. Instead of conducting purely theoretical research, the group actually builds software itself.

“We have spawned a number of spinoffs, including ,” says Weske.

Inspired by Wikipedia, Professor Weske and his group began working on web modeling for processes around 2004. “It’s a kind of online reference work for processes,” says Weske about the idea. “It was intended for use by anyone, without requiring installation. We were fortunate that browser technology was so advanced back then that it could execute programs as well.” In 2009, the solution was spun off from the Hasso Plattner Institute under the name Signavio, which was acquired by �鶹ԭ�� in 2021.

This acquisition has had no small impact on process intelligence at �鶹ԭ��. Uflacker confirms: “We have to set the course now if we want to defend our thought leadership in this area and help shape new trends. That’s why it was an easy choice to work with Weske as an academic fellow in this area.”

Due to the traditionally close cooperation between Hasso Plattner Institute and the �鶹ԭ�� Innovation Center Network, Weske was already known to many of the �鶹ԭ�� employees whom he worked with during his academic fellowship. “My academic fellowship at �鶹ԭ�� taught me once again how important networks are. After all, the topic of processes has many points of reference throughout �鶹ԭ��.”

Uflacker underscores this: “From products like Signavio to a variety of innovation projects in the process mining and analytics area, this topic is spreading throughout our company. We highly benefit from Weske’s knowledge and experience when it comes to launching research-related projects at �鶹ԭ�� and supporting new ideas.”

In one example, the topic of composable enterprise is being researched in depth with the New Ventures & Technologies area. It involves the ability to compose processes dynamically as needed, like exchangeable modules. Among his other activities, Weske contributed to a research project here with a horizon of five to 10 years, which the �鶹ԭ�� Innovation Center Potsdam is pursuing.

“Of course, it’s still too soon to tackle specific technical implementations,” says Weske. “But I hope that I was able to help with understanding the fundamentals and with the system design.”

Weske was also involved in Signavio-related activities. “I’m proud to see that Signavio is being deployed, integrated, and enhanced in so many places at �鶹ԭ��,” he says. “During my academic fellowship, one of our focuses was exploring how data gained from process mining can be used for process analysis.” In the area of event log generation, Weske and Signavio conducted a survey among �鶹ԭ�� employees to identify the most important questions involving this topic at �鶹ԭ��.

The fellowship has opened up new possibilities, encouraging the collaboration to continue. In ideal research, a finished project is always the start of something new, says Weske: “The white paper on the event log survey raised a number of questions that can be pursued as part of doctoral or master’s theses from my group.”

This, too, is an important incentive for academics like Weske to work together with �鶹ԭ�� and other companies. “When we as a research institution point out what companies we work with; that our doctoral candidates, master’s candidates, and postdocs don’t stay confined to ivory towers, but that what they do has the potential to be implemented and used by thousands of people, then we can attract outstanding talents.

Uflacker hopes to expand the academic fellowship program in future: “We at �鶹ԭ�� want to acquire targeted expertise for strategically relevant projects. We’re looking for international experts for this program.

“�鶹ԭ�� is an outstanding partner for academic cooperation, not least due to the wealth of features in its software,” says Weske. “This type of fellowship benefits both sides.”

Continuous Intelligence: Definition, Benefits, and Examples

Matthias Uflacker — Mon, 04 Jul 2022 12:15:39 +0000

When Kate Middleton and Prince William announced their engagement in 2010, sapphire blue dresses like hers were sold out within 24 hours and the “Kate effect” was born. Yet unexpected spikes in sales, a surge in new user signups, or high rates of abandoned shopping carts continue to baffle retailers today.

With the growing volume, complexity, and velocity of data, it’s no surprise that businesses often consider only small subsets of what’s available for analytics. Research suggests that several of today’s best-of-class application operators are using of their collected data.

Continuously monitoring business data for anomalies helps identify the root cause and respond faster to such hidden events and changing trends. This approach to analytics provides continuous intelligence that helps brands make smart, real-time business decisions.

What Is Continuous Intelligence?

Traditionally, dashboards are checked periodically to monitor the most relevant KPIs and manually explore data along pre-defined paths. However, considering only pre-defined indicators and performance metrics leads to a limited perspective and misses information about unknown signals and trends in other parts of the data or at lower granularity.

Keeping track of what is relevant in this very moment is therefore a challenge for businesses across industries. Enter continuous intelligence.

According to Gartner, continuous intelligence “is a design pattern in which real-time analytics are integrated within a business operation, processing current and historical data to prescribe actions in response to events. It provides decision automation or decision support.”

The approach uses a variety of technologies such as machine learning, event stream processing, optimization, and business rule management.

Uncover the Unknown with AI

While humans cannot inspect every possible characteristic and combination in the flood of incoming data, machines can.

Complementing analytics that provide precise answers to questions users know to ask, a machine can continuously monitor data in the background to detect unknown correlations and trends that deviate from what would have been expected by the system based on previous observations.

This way, companies can identify hidden, but potentially relevant signals in the data. Gartner predicts that by 2022, more than half of major new business systems will incorporate continuous intelligence capabilities.

By integrating artificial intelligence (AI)-based continuous intelligence into their day-to-day operations, companies can:

Boost efficiency by spending less time sifting through data from a variety of disparate sources
Focus on what really matters for their business
Speed time to action

By automatically inspecting critical business health indicators such as revenue, Web page views, active users, or transaction volume in real time, businesses can accelerate their time to insight and action and better respond to situations before business is impacted.

Putting Continuous Intelligence to Work

is exploring the business potential and requirements of continuous intelligence and developed a prototype of a self-learning cloud service.

This service autonomously discovers patterns in historic data to detect trends in new data that deviate from the expected. Insights are ranked by relevance and proactively provided to the user as comprehensive data stories with full context.

Especially in industries with fast-moving assets such as retail, the service could be critical for quickly adapting to changing buying behavior, trends, and market conditions. It could alert retailers to surging sales of Kate Middleton look-alike dresses, food trends such as avocados and oat milk, or demand for umbrellas due to bad weather.

But more than retailers could benefit. Service ticketing is another area of application that the �鶹ԭ�� Innovation Center Network team is investigating.

With continuous intelligence, service personnel could, for instance, identify hot topics of interests, indicated by an unusually high number of open tickets. Then, they could respond with tailored material for additional information.

Moving at Market Speed

Relying solely on default indicators and monitoring standard KPIs is no longer sufficient when it comes to understanding and responding to the dynamics of complex systems or businesses.

Looking ahead, continuous intelligence is expected to become the norm, as the speed at which businesses must react to rapidly changing market conditions will only increase. It will be a critical component for companies that want to point users to relevant data and trigger actions when it matters most.

Is your business ready to become an intelligent enterprise?

Matthias Uflacker is part of �鶹ԭ�� Innovation Center Network.
This article was originally published on .