Top 3 Tech Trends for the Future of Finance: How Process Tempo + Neo4j Compares

In this three part series, we dive into the top three relevant technology trends for finance in the next decade ( as identified by Gartner's hype cycle ) and describe how the Process Tempo + Neo4j partnership stacks up.

The finance function is changing quickly into an environment where people and machines collaborate to transform finance and business capabilities, drive process efficiency, and automate data pipelines, according to Gartner, Inc.

“There are three broad themes to the technologies on this hype cycle,” says Mark D. McDonald, senior director, Gartner. “First, there are technologies, such as decision intelligence , that drive effective and efficient organizations. Second, there are a group of transformational technologies, such as composable applications , that can drive new digital business capabilities. Third, there are technologies, such as augmented data quality , that automate the collection, storage and retrieval of data and increase accuracy.”

Read Part I: Decision Intelligence

Read Part II: Composable Applications

Read Part III: Augmented Data Quality

Part I: Decision Intelligence

Gartner says:

Decision intelligence (DI) is at the Innovation Trigger of the Hype Cycle. DI is a practical discipline used to improve decision making by explicitly understanding and engineering how decisions are made, and how outcomes are evaluated, managed and improved via feedback. The current hype around automated decision making and augmented intelligence, fueled by AI techniques in decision making has revealed the brittleness of legacy business processes in this new environment.

An increasingly complex business environment, with an increasingly uncertain pace of business, and ever more decisions taken by machines have created a sense of unease from the human and also regulatory perspective. There is a need to transparently represent how decisions are being made.

Recommendations:

• Curtail unstructured ad-hoc decisions that are siloed and disjointed
• Properly harmonize collective decision outcomes across an entire organization
• Enable organizations to practically implement DI projects and strategies.

How can Process Tempo + Neo4j help curtail unstructured ad-hoc decisions that are siloed and disjointed? How can the technology help properly harmonize collective decision outcomes across an entire organization?

Using a no-code data platform like Process Tempo + Neo4j makes it easier to identify and trace the source of any decisions, enabling organizations to properly audit decision-making processes.

By representing data as nodes and edges within a graph structure, it’s possible to create interconnected models that can be used to monitor and track decisions made throughout the organization. This approach creates a single source of truth with a unified view into organizational information, helping to eliminate siloed decision-making, improve the consistency and accuracy of decisions, and ensure that everyone is on the same page. As decisions are made and implemented, they can then be compared against each other in order through the platform to determine how they align with organizational objectives.

This allows organizations to make data-driven decisions while maintaining alignment with corporate responsibilities and goals, and, help ensure decisions are being made in a consistent and responsible manner, allowing for better compliance with corporate policies and regulations.

How can Process Tempo + Neo4j enable organizations to practically implement DI projects and strategies?

Process Tempo + Neo4j can help facilitate the practical implementation of Decision Intelligence projects and strategies in a number of ways:

• First, by enabling users to quickly and easily create complex visualizations that allow decision makers to gain better insight into the interconnections between decisions and outcomes. This can help inform key strategic decisions by providing more information about causes, effects, trade-offs, and potential solutions.
• Second, by making it easy to capture real-time feedback from stakeholders and incorporate it into decision models. This helps ensure that decision models stay up to date with changing circumstances and remain relevant during times of rapid change.
• Third, by providing powerful analytical capabilities that enable users to evaluate the performance of different decision making strategies and identify areas for improvement. This analysis can then be used to refine decision models, allowing them to evolve over time and keep pace with changes in the environment.

Part II: Composable Applications

Gartner says:

"In a departure from the monolithic and inflexible technology applications commonly associated with enterprise technology, composable applications have arisen in response to greater demand for business adaptability in more volatile times.

Composable applications are modular in nature and are built to support fast, safe, and efficient application changes in the face of frequent disruption and new opportunities. The improved agility of business technology drives resilience and adaptability throughout the business.

Composable applications are built as flexible compositions of well-packaged modules of business application capabilities. The “composers” tend to be a business-IT fusion team while the creators of the modules may central IT software engineering teams .”

How is the Process Tempo + Neo4j solution modular in nature? And how does it help support fast, safe, and efficient application changes in the face of frequent disruption and new opportunities?

The Process Tempo + Neo4j offering is highly modular in nature, meaning that users are able to quickly and easily create their own custom flexible applications without having to write code. This allows for fast changes and updates to the application as new requirements or opportunities arise.

The modularity of the platform also allows for efficient scalability and deployment. As new requirements come up, users can simply update certain features, like workflows, instead of having to go back and rework existing logic or code. This can save both time and money as developers don't have to start from scratch each time they need to make a change.

Additionally, the modular structure of the platform is inherently secure, since components do not affect each other unless specifically designed to do so. This allows for rapid, safe changes to applications without worrying about unexpected consequences or security vulnerabilities.

How does the Process Tempo + Neo4j solution help business-IT fusion teams? How does it help central IT software engineering teams?

Process Tempo + Neo4j helps Business-IT fusion teams by allowing them to quickly develop and deploy applications with greater agility and flexibility. By leveraging the power of graph databases to model complex relationships between entities and the data they produce, business-IT fusion teams can create powerful, business-focused applications that require minimal coding or engineering knowledge.

These applications are ideal for business-IT fusion teams that need to rapidly process large volumes of data while providing insights into complicated datasets. These applications provide an intuitive user experience, allowing business analysts and IT professionals alike to easily access and manage their data. And, by using no-code development to build these applications, business-IT fusion teams can decrease costs by eliminating the need for expensive coding projects while still deploying high-quality, enterprise-grade applications.

With Process Tempo + Neo4j, fusion teams can increase efficiency by streamlining processes across departments, increasing collaboration among stakeholders, and building reliable applications quickly and cost-effectively.

Process Tempo + Neo4j helps Central IT Software Engineering Teams in a number of ways:

First, by allowing software engineers to use prebuilt components and tools, such as data modelers, to create powerful solutions that take advantage of the relationships between the various elements of their data. This helps them rapidly develop and deploy elegant applications that are tailored to their specific needs.

Second, by making it easy for developers to access data from multiple sources, allowing them to integrate other services more efficiently and quickly than they could with traditional development approaches. This flexibility gives software engineering teams the power to rapidly develop and deploy innovative applications without the need for long development cycles. In this way, central IT software engineering teams can quickly build robust applications that meet their needs and maximize ROI.

Third, by delivering enhanced security options compared to traditional coding approaches. Graph-native solutions like Process Tempo + Neo4j are designed to facilitate secure connections between the various elements of an application, which ensures the safety and integrity of the data being stored or processed within it. This allows developers to ensure secure access to sensitive data by using authentication protocols such as OAuth2 and OpenID Connect. Furthermore, the platform provides additional layers of encryption for further protection from cyber attacks. With these added layers of security, IT software engineering teams can rest assured that their applications are secure and compliant with industry standards.

Part III: Augmented Data Quality

Gartner says:

"Traditional data quality tools fall short of fulfilling comprehensive and timely data quality needs due to significant manual effort, limited scope and out-dated technologies. Modern data quality solutions offer augmented data quality capabilities to disrupt how we solve data quality issues. This disruption — fueled by metadata, artificial intelligence/machine learning (AI/ML) and knowledge graphs — is progressing and bringing new practices through automation to simplify data quality processes.

Emerging and future data ecosystems need augmented data quality solutions that can integrate with cohesive designs like data fabrics , support operational excellence and improve overall governance.

Data and analytics leaders must leverage these solutions and related best practices to evolve their data quality capabilities.”

How does Process Tempo + Neo4j leverage metadata, AI/ML, and knowledge graphs to simplify data quality processes and to improve data quality?

Metadata: Process Tempo + Neo4j leverage metadata to simplify data quality processes and improve data quality in a number of ways. By cataloging the meaning, context, origins and uses of all the data stored within an organization’s databases, Process Tempo + Neo4j applications allows users to understand their data on a deeper level, enabling them to rapidly assess the quality of their information assets. Metadata organized by Process Tempo + Neo4j can be used to create a unified view of the different datasets, making it easy for business users to quickly identify anomalies or incorrect entries that could lead to erroneous analysis. Additionally, it can help document relationships between different datasets and enable users to draw insights from cross-referencing diverse sources—all without having to write any code. Finally, the platform can be used to automate processes such as data security, compliance and auditing, allowing organizations to ensure their data is compliant with government regulations. Metadata through Process Tempo + Neo4j delivers an unprecedented level of transparency into the quality of a company’s data assets, empowering leaders to take corrective action early and reduce the risk of costly errors due to low-quality information.

AI: Process Tempo + Neo4j uses techniques such as natural language processing (NLP) and machine learning (ML) to detect patterns in data that might indicate errors or inconsistencies. By leveraging these techniques, they are able to identify potential issues quickly and accurately, without requiring manual review from a human analyst. Additionally, Process Tempo + Neo4j uses AI to automate the validation process for incoming data by quickly comparing it against expected schemas and standards. This helps ensure that only clean, accurate information is stored within the system. In addition to this automated quality control, the platform’s AI can also be used to analyze existing datasets and suggest improvements or modifications based on findings. This provides organizations with an easy way to constantly monitor and optimize their data quality levels over time.

Knowledge graph: Knowledge graphs are becoming increasingly popular for improving data quality because they provide organizations with an easy way to link multiple sources of information together. An organization can leverage knowledge graphs within Process Tempo + Neo4j to ensure data accuracy and integrity across all their repositories while also allowing them to quickly identify any inconsistencies that may exist. Additionally, knowledge graphs within Process Tempo + Neo4j can help automate certain processes related to data verification and validation; for example, detecting if an individual record has duplicate values and suggesting corrections accordingly. With automated checks like this in place, data quality can be significantly improved as fewer errors and inconsistencies will slip through the cracks.

Does Process Tempo + Neo4j integrate with data fabrics? How does Process Tempo + Neo4j help support operational excellence and improve overall data governance?

Data Fabrics: A data fabric is a unified data architecture and set of tools used to connect, store, manage, and derive insights from data sources in real-time. It provides an integrated experience for managing large amounts of heterogeneous data by making it available across various applications and systems. The combination of Neo4j's graph-native back-end and Process Tempo's unified D&A front-end allows users to quickly establish their own advanced data fabric on any given use case without the need for coding or technical expertise. In addition, the data and information generated by a Process Tempo + Neo4j application can integrate seamlessly into other existing data fabrics.

Supporting Operational Excellence: A graph-native, no-code data application like Process Tempo + Neo4j provides the ability to quickly create and deploy a highly interactive and interconnected data models. By leveraging the power of relationships across multiple data sets, powerful visualizations can be created that allow users to explore data in an intuitive way. This type of application supports operational excellence by making it easier to access and analyze key performance indicators (KPI) as well as monitoring trends over time. The information is presented in a visually engaging manner, allowing users to have greater insight into the underlying processes or systems they are managing.

Improving Governance: In addition to improving operational efficiency, this type of application can also improve governance. Having all stakeholders involved in decision making with full visibility into their respective areas of responsibility creates a more organized and accountable process. With the ability to query and analyze data in real time, organizations can quickly identify potential risks or anomalies. This helps ensure that any changes are properly documented, tracked, and managed according to established standards. The application also allows users to more easily gain insight into the impact of their decisions before they are implemented, reducing the likelihood of costly mistakes down the line.

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