Investment Bank: Economic Architecture

We worked with a large investment bank who are embarking on a series of projects to further automate their back office. One of their first tasks was to understand in greater detail what all the 5,000 people in the back office were doing. They built an “Economic  Architecture” that was essentially the equivalent of a continually running Activity Based  Costing project.  

They asked managers to estimate the percentage of time each of their reports spent on a standard list of activities. However the activity list was not stabilizing, and many managers  had difficulty deciding which of the many activities they should use. As this was slated to  eventually become part of the reporting and perhaps eventually the charge back to the  front office for the activities performed to settle some of these very complex instruments. 

We were called in to create a rational basis for the activity taxonomy. We ended up  decomposing the working list of 800 or so activities into a set of orthogonal facets. What was fascinating was that the facets were far simpler than the long complex list of activities.  Once someone knew the facets (such as financial product, market, as well as a simple set of verbs and modifiers), they would know what all the activities were, as they were just concatenations of the facets.  

More interestingly we discovered as we performed this that the facets provided a level of categorization that it would be possible to instrument in the workflow and source systems.  The list of 800 activities was too arbitrary to allow for automation, but the facets were closely aligned with primitive concepts found in most systems. 

We completed the redefinition and got agreement on the new activities. The new activities are in production, and they are looking at applying this concept beyond the back-office operations.

Washington State: SOA Design and Ontology 

In our initial engagement, we did a rapid but detailed review of 200 applications, interfaces,  current initiatives, long-range plan, and a new system being proposed. We found several  areas where they could leverage work in progress to speed up their new project initiative,  and several areas where, with a slight change in scope and priority, the new initiatives  would actually reduce the amount of redundancy and inconsistency.  

We helped them build a high-fidelity depiction of their current “as-is” state. The content from an existing, unread 400-page report was rendered, and massively updated, to a very large graphic of the as-is condition. We then worked with them to define their long-term  SOA architecture with shared services.

Amgen: Data Centric Architecture

Amgen is a large biotechnology company committed to unlocking the potential of  biology for patients suffering from serious illnesses by discovering, developing,  manufacturing, and delivering innovative human therapeutics. Amgen, CEO Bob Bradway focuses on innovation to set the cultural direction. According to Bradway: “Push the  boundaries of biotechnology and knowledge to be part of the process of changing the practice  of medicine.”  

Amgen’s goal is to provide life-changing value to patients with expediency.  Democratized access to enterprise data speeds the process from drug discovery to drug delivery. One element Amgen’s strategic data leadership agreed upon is that a common language expedites product development by removing ambiguities that slow business processes.  

Data capture comes from a multitude of information systems, each using their own data model and unique vocabularies. Different systems use different terminology to refer to the same concept. An organization steeped in data silos no longer works. The challenge is to provide a common intuitive model for all systems and people to use. Once such a model is in place, it is no longer laborious and expensive for enterprise consumers to benefit from the data. A decision to establish a semantic layer for building an enterprise data fabric emerged.  

Amgen developed a vision of a Data-Centric Architecture (DCA) that transforms data from being system-specific to being universally available. Data is organized and unambiguously represented in data domains within a Semantic layer. 

Understanding the Graph Center of  Excellence 

The Knowledge Management community has gotten good at extracting and curating  knowledge. 

There is a confluence of activity – including generative AI models, digital twins and shared ledger capabilities that are having a profound impact on enterprises. Recent research by analysts at Gartner places contextualized information and graph technologies at the center of their impact radar for emerging technologies. This recognition of the importance of these critical enablers to define, contextualize and constrain data for consistency and trust is all part of the maturity process for today’s enterprise. It also is beginning to shine light on the emergence of the Graph Center of Excellence (CoE) as an important contributor to achieving strategic objectives.  

For companies who are ready to make the leap from being applications centric to data centric – and for companies that have successfully deployed single-purpose graphs in business silos – the CoE can become the foundation for ensuring data quality and reusability. Instead of transforming data for each new viewpoint or application, the data is stored once in a machine-readable format that retains the original context, connections and meaning that can be used for any purpose.  

And now that you have demonstrated value from your initial (lighthouse) project, the  pathway to progress primarily centers on the investment in people. The goal at this stage of development is to build a scalable and resilient semantic graph as a data hub for all business-driven use cases. This is where building a Graph CoE becomes a critical asset because the journey to efficiency and enhanced capability must be guided.  

Along with the establishment of a Graph CoE, enterprises should focus on the creation of  a “use case tree” or “business capability model” to identify where the data in the graph  can be extended. This is designed to identify business priorities and must be aligned with the data from initial use cases. The objective is to create a reusable architectural framework and a roadmap to deliver incremental value and capitalize on the benefits of content reusability. Breakthrough progress comes from having dedicated resources for the design, construction and support of the foundational knowledge graph.  

The Graph CoE would most logically be an extension of the Office of Data Management and the domain of the Chief Data Officer. It is a strategic initiative that focuses on the adoption of semantic standards and the deployment of knowledge graphs across the enterprise. The goal is to establish best practices, implement governance and provide expertise in the development and use of the knowledge graph. Think of it as both the 

hub of graph activities within your organization and the mechanism to influence organizational culture.  

Some of the key elements of the Graph CoE include: 

• Information Literacy: A Graph CoE is the best approach to ensure organizational understanding of the root causes and liabilities resulting from technology fragmentation and misalignment of data across repositories. It is the organizational advocate for new approaches to data management. The message for all senior executive stakeholders is to both understand the causes of the data dilemma and recognize that properly managed data is an achievable objective.  Information literacy and cognition about the data pathway forward is worthy of being elevated as a ‘top-of-the-house’ priority.  

• Organizational Strategy: One of the fundamental tasks of the Graph CoE is to define the overall strategy for leveraging knowledge graphs within the organization. This includes defining the underlying drivers (i.e., cost containment,  process automation, flexible query, regulatory compliance, governance  simplification) and prioritizing use cases (i.e., data integration, digitalization,  enterprise search, lineage traceability, cybersecurity, access control). The opportunities exist when you gain trust across stakeholders that there is a path to ensure that data is true to original intent, defined at a granular level and in a format that is traceable, testable and flexible to use. 

• Data Governance: The Graph CoE is responsible for establishing data policies and standards to ensure that the semantic layer is built using wise engineering principles that emphasize simplicity and reusability. When combining resolvable identity with precise meaning, quality validation and data lineage – governance shifts away from manual reconciliation. With a knowledge graph at the foundation, organizations can create a connected inventory of what data exists,  how it is classified, where it resides, who is responsible, how it is used and how it moves across systems. This changes the governance operating model – by simplifying and automating it. 

• Knowledge Graph Development: The Graph CoE should lead the development of each of the knowledge graph components. This includes working with subject matter experts to prioritize business objectives and build use case relationships.  Building data and knowledge models, data onboarding, ontology development,  source-to-target mapping, identity and meaning resolution and testing are all areas of activity to address. One of the critical components is the user experience and data extraction capabilities. Tools should be easy to use and help teams do  their job faster and better. Remember, people have an emotional connection to the way they work. Win them over with visualization. Invest in the user interface.  Let them gain hands-on experience using the graph. The goal should be to create value without really caring what is being used at the backend. 

• Cross-Functional Collaboration: The pathway to success starts with the clear and visible articulation of support by executive management. It is both essential and meaningful because it drives organizational priorities. The lynchpin, however,  involves cooperation and interaction among teams from related departments to deploy and leverage the graph capabilities most effectively. Domain experts from  technology are required to provide the building blocks for developing  applications and services that leverage the graph. Business users identify and prioritize use cases to ensure the graph addresses their evolving requirements.  Governance policies need to be aligned with insights from data stewards and compliance officers. Managing the collaboration is essential for orchestrating the  successful shift from applications-centric to data-centric across the enterprise.  

After successfully navigating the initial stages of your project, the onward pathway to  progress should focus on the development of the team of involved stakeholders. The first  hurdle is to expand the identity of data owners who know the location and health of the  data. Much of this is about organizational dynamics and understanding who the players are, who is trusted, who is feared, who elicits cooperation and who is out to kill the activity.  

This coincides with the development of an action plan and the assembly of the team of skilled practitioners needed to ensure success. Enterprises will need an experienced architect who understands the workings of semantic technologies and knowledge graphs to lead the team. The CoE will need ontologists to engineer content and manage the mapping of data. Knowledge graph engineers are needed to coordinate the meaning of data, knowledge and content models. This will also require a project manager to be an advocate for the team and the development process.  

And a final note, organizations working on their AI readiness must understand it requires being ready from the perspective of people, technology and data. The AI-ready data component means incorporating context with the data. Gartner points this out by noting that it necessitates a shift from the traditional ETL mindset to a new ECL (extract,  contextualize and load) orientation. This ensures meaningful data connections. Gartner advises enterprises to leverage semantic metadata as the core for facilitating data connections.  

The Graph CoE is an important step in transforming your lighthouse project or silo deployment into a true enterprise platform. A well-structured CoE should be viewed as a driver of innovation and agility within the enterprise that facilitates better data integration, improves operational efficiency, contextualizes AI and enhances the user experience. It is the catalyst for building organizational capabilities for long-term strategic advantage and one of the key steps in the digital transformation journey. 

CASE STUDY: Morgan Stanley , Global Fortune 100 Financial Institution, Transforms Information & Knowledge Management 

Morgan Stanley is one of the largest investment banking and wealth management firms with offices in more than 42 countries and more than 60,000 employees, ranking 67th on the 2018 Fortune 500 list of the largest US corporations by total revenue. Headquartered in New York City, the organization faced challenges for better information retrieval, records retention, and legal hold capabilities or potentially face steep compliance fines. Securing data from outside threats is critical, but information from within the friendly firewall’s hamstrings business ability to operate, even without regulatory pressures. With worldwide data to swell by 10-fold by  

2025, a better solution needed to be addressed. Leadership at Morgan Stanley solicited several consulting experts and chose  Semantic Arts to guide in strategic resolution of this massive information sprawl while enabling greater information retrieval and easier user consumption. 

“Information management” as part of the legal department took lead as it was chartered with knowing about all data sets within the firm: Structured, Unstructured and everything in between. A major undertaking for any group yet alone a global giant with divisions all over the world. 

PROBLEM STATEMENT: Information management determined that existing traditional architectures and relational data structures were failing to keep pace with data growth and management of information assets. A solution that offered scale, extend-ability, and an enhanced user search experience was the primary objectives. Like other organizations entrenched in data silos and single ownership, information resided in many data sources (SQL, Oracle, SAP, SharePoint, Excel, PDF, videos, and shared files, to name a few), making for difficult data aggregation with accuracy. Decades of integration have resulted in highly dependent systems and applications. In fact, changes to any data schemas were laborious coding and testing exercises that yielded little business benefit. In short, it was problematic to access the right data and costly to make even simple changes.  

STRATEGY: By collaborating with Semantic Arts, experts in Data /Digital transformation, a data strategy was established for better  information management. Implementation of Semantic Knowledge Graphs and a flexible Ontology for future information growth  was decided after lengthy evaluation. It offered strategic value for supporting numerous domain areas simultaneously; including risk management, regulatory compliance, asset management, adviser information retrieval while linking data from each domain.  Additionally, an important use case of a Semantic Knowledge Graph approach is the architectural advantage of limitless extendibility across the enterprise for reuse. This factored into the long-term reasoning and vision of becoming Data Centric. 

APPROACH: Starting strategic initiatives like this can be particularly tricky in that achieving a balance between building a foundation for future success and immediate results can be a high wire act in organizational politics. With the advice of Semantic Arts, a “Think Big and Start Small” initial phase of work was proposed and accepted. This involved building a core Ontology in parallel with a Domain model, whereby both will be connected for building data relationships in future phases. This strategy will address the mission of contextually enriching the data organizationally, which in turn can be leveraged for greater insights in making business decisions and improved data governance.

Semantic Arts represents professional management consulting services for untangling the ad hoc patchwork of systems integration; turbo-charging new Knowledge and Information initiatives. We call it the “Data-Centric Revolution” that inverts the dependency between data models and application code. In short order, the code will become dependent on the shared information model. Join the Revolution!  

RESULTS: A small team of consultants and Morgan Stanley SME’s assembled for a 6-month assignment. During the engagement,  results came quickly. Within the initial weeks, after loading the data into a Triple Store and applying some very simplistic natural language processing routines, the team took the firm from 0.5% tagging of information to 25%, a 50-fold increase in information classification with relatively nominal effort. 

By incorporating Semantic Arts strategy of instituting a flexible Ontology and Knowledge Graphs, the improved visibility and harmonization of the information across multiple data sets quickly captured the attention of business capability owners. Amazing is the fact that only 1% accuracy was in place by leveraging existing technologies. Collaboratively, the team captured hundreds of regulatory jurisdictions used for promoting rules. By linking this data with billions of internal documents from disparate databases, it gave contextual information surrounding a document or repository for a self-assembling capability. Previously, aggregation was manually driven, inaccurate, clumsy and time-consuming. 

OTHER DOMAINS JOIN IN: Follow up engagements with Equity Research, and Operations Resiliency soon followed as the changes made a tangible impact. Those domain teams have taken on smaller use case purposes to answer difficult questions while leveraging the functionality from the core Ontology foundation developed by the Semantic Arts consultants during the first initiative. The inherent nature of Knowledge Graphs linking data relationships can transform into a Siri like experience by offering answers, recommendations and learn when tied to AI capabilities. Furthermore, the information within the Graph enriches the contextual value because its connected, resulting in a single model. The business value of capturing knowledge for expanding wisdom growth multiples as the connections become realized between domains. Beginnings are taking form: removal of data silos,  replication of data, and costly integration of application functionality.  

CHANGING WALL STREET: Combining a strategic data plan and incorporating Knowledge Graphs as a companion solution is making a difference. Wall Street reports are now being unlocked with AskResearch Chatbot capabilities to extract value by delivering hard to find information from hundreds of data sources. With coaching in best practice Ontology development, the  Equities Research team has successfully continued expansion of this graphs initial use case. 

“You have this historical archive sitting in a library and   there is so much value embedded in it, but traditionally   it has been hard to unlock that value because insights   and data are fixed in monolithic PDFs.” -D’Arcy Carr, the global head of research, editorial, and publishing

Claims of future time savings in (Billions/year) are hard to quantify but clearly usage of the Chatbot is steadily increasing. Leveraging Knowledge Graphs as the backbone for information retrieval was critical for intuitive search functionality and giving realization to self-service capability for users.  

FINANCIAL INDUSTRY INFORMATION FUTURE: The ability to leverage AI and Machine Learning in tandem with Knowledge  Graphs according to Forbes, is the financial industry future. Use will soon shift from a competitive edge to a must-have. Further discussion between Semantic Arts and Marketing and HR innovators at Morgan Stanley are in flight with more dynamic results pending.  

Semantic Arts represents professional management consulting services for untangling the ad hoc patchwork of systems integration; turbo charging  new Knowledge and Information initiatives. We call it the, “Data-Centric Revolution” that inverts the dependency between data models and application code. In short order, the code will become dependent on the shared information model. Join the Revolution!