AI and Data - Incedo Inc.

Faster and better decisions with AI-driven self-serve Insights

Incedo Lighthouse^TM with Self-Serve AI is a cloud-based solution that is creating a significant business impact in Commercial Effectiveness for clients in the pharmaceutical industry. Self-serve entails empowering them with actionable intelligence to serve their business needs by leveraging the low-code AI paradigm. This reduces dependency on data scientists and engineers and makes faster iterations of actionable decisions and monitoring their outcomes by business users.

As internal and external enterprise data continues to grow in size, frequency, and variety, the classical challenges such as sharing information across business units, lack of a single source of truth, accountability, and quality issues (missing data, stale data, etc.) increase.

For IT teams owning diverse data sources, it becomes an added workload to ensure the provisioning of the enterprise-scale data in requisite format, quality, and frequency. This also impedes meeting the ever-growing analytics needs of various BU teams, each having its own request as a priority. Think of the several dashboards floating in the organizations created at the behest of various BU teams, and even if with great effort they are kept updated, it is still tough to extract the insights that will help take direct actions to address critical issues and measure their impact on the ground. Different teams have different interaction patterns, workflows, and unique output requirements – making the job of IT to provide canned solutions in a dynamic business environment very hard.

Self-service intelligence is therefore imperative for organizations to enable business users to make their critical decisions faster every day leveraging the true power of data.

Enablers of self-service AI platform – Incedo Lighthouse^TM

Our AWS cloud-native platform Incedo Lighthouse^TM, a next-generation, AI-powered Decision Automation platform t arms business executives and decision-makers with actionable insights generation and their assimilation in daily workflows. It is developed as a cloud-native solution leveraging several services and tools from AWS that make the journey of executive decision-making highly efficient at scale. Key features of the platform include:

Customized workflow for each user role: Incedo Lighthouse^TM is able to cater to different needs of enterprise users based on their role, and address their specific needs:
- Business Analysts: Define the KPIs as business logic from the raw data, and define the inherent relationships present within various KPIs as a tree structure for identifying interconnected issues at a granular level.
- Data Scientists: Develop, train, test, implement, monitor, and retrain the ML models specific to the enterprise use cases on the platform in an end-to-end model management
- Data Engineers: Identify the data quality issues and define remediation , feature extraction, and serving using online analytical processing as a connected process on the platform
- Business Executives: Consume the actionable insights (anomalies, root causes) auto-generated by the platform, define action recommendations, test the actions via controlled experiments, and push confirmed actions into implementation
Autonomous data and model pipelines: One of the common pain points of the business users is the slow speed of data to insight delivery and share action recommendation, which may take even weeks at times for simple questions asked by a CXO. To address this, the process of insights generation from raw big data and then onto the action recommendation via controlled experimentation has been made autonomous in Incedo Lighthouse^TM using combined data and model pipelines that are configurable in the hands of the business users.
Integrable with external systems: Incedo Lighthouse^TM can be easily integrated with multiple Systems of Record (e.g. various DBs and cloud sources) and Systems of Execution (e.g. SFDC), based on client data source mapping.
Functional UX: The design of Incedo Lighthouse^TM is intuitive and easy to use. The workflows are structured and designed in a way that makes it commonsensical for users to click and navigate to the right features to supply inputs (e.g. drafting a KPI tree, publishing the trees, training the models, etc.) and consume the outputs (e.g. anomalies, customer cohorts, experimentation results, etc.). Visualization platforms such as Tableau and PowerBI are natively integrated with Incedo Lighthouse^TM thereby making it a one-stop shop for insights and actions.

Incedo Lighthouse^TM in Action: Pharmaceutical CRO use case:

In a recent deployment of Incedo Lighthouse^TM, the key users were the Commercial and Business Development team of a leading Pharma CRO. The company had drug manufacturers as its customers. Their pain point revolved around the low conversion rates leading to the loss of revenue and added inefficiencies in the targeting process. A key reason behind this was the wrong prioritization of leads from conversion propensity and total lifetime value perspective. This was mainly due to manual, human-judgment-driven, ad-hoc,, static, rule-based identification of leads for the Business Development Associates (BDA) to work on.

Specific challenges that came in the way of the application of data science for lead generation and targeting were:

The raw data related to the prospects – that was the foundation for f for e predictive lead generation modeling – was in silos inside the client’s tech infrastructure. This led to failure in developing high-accuracy predictive lead generation models in the absence of a common platform to bring the data and models together.
Even in a few exceptional cases, where the data was stitched together by hand and predictive models built, the team found it difficult to keep the models updated in the absence of integrated data and model pipelines working in tandem.

To overcome these challenges, the Incedo Lighthouse^TM platform was deployed. The deployment of Incedo Lighthouse^TM in the AWS cloud environment not only brought about real improvements in target conversions but also helped transform the workflow for the BDAs. By harnessing the power of Data and AI, as well as leveraging essential AWS native services, we achieved efficient deployments and sustained service improvements.

Combine the information from all data sources for a 360-degree customer view, enabling the BDAs to look at the bigger picture effortlessly. To do so effectively, Incedo Lighthouse^TM leveraged AWS Glue which provided a cost-effective, user-friendly data integration service. It helped in seamlessly connecting to various data sources, organizing data in a central catalog, and easily managing data pipeline tasks for loading data into a data lake.
Develop and deploy AI/ML predictive models for conversion propensity using Data Science Workbench which is part of the Incedo Lighthouse^TM platform, after developing the data engineering pipelines that create a ‘single-version-of-the-truth ’ every time raw data is refreshed. This was done by leveraging the pre-built model accelerators, helping the BDAs sort those prospects in the descending order of their conversion propensity, thereby maximizing the return on the time invested in developing them. The Data Science Workbench also helps with the operationalization of various ML models built in the process, while connecting model outputs to various KPI Trees and powering other custom visualizations. Using the Amazon SageMaker Canvas, Incedo Lighthouse^TM enables machine learning model creation for non-technical users, offering access to pre-built models and enabling self-service insights, all while streamlining the delivery of compelling results without extensive technical expertise.
Deliver key insights in a targeted and attention-driving manner to enable BDAs to make the most of the information in a short span of time. Incedo Lighthouse^TM leverages Amazon QuickSight, a key element in delivering targeted insights, that provides well-designed dashboards, KPI Trees, and intuitive drill-downs to help BDAs and other users make the most of the information quickly. These tools allow leads to be ranked based on model-reported conversion propensity, time-based priority, and various custom filters such as geographies and areas of expertise. BDAs can double-click on individual targets to understand deviations from actuality, review comments from previous BDAs, and decide on the next best actions. QuickSight seamlessly integrates with Next Gen Stats apps, and offers cost-effective scalable BI solutions, interactive dashboards, and natural language queries for a comprehensive and efficient user experience. This resulted in an increased prospect conversion rate due to data-driven automated decisions leveraging AI that are disseminated to BDA in a highly action-oriented way.

Complexity of decision making in the VUCA world

In today’s VUCA (Volatile, Uncertain, Complex and Ambiguous) business environment, the decision makers are increasingly required to make decisions at speed, in a dynamic and ever evolving uncertain environment. Contextual knowledge including cognizance of dynamic external factors is critical, and the decisions need to be made in an iterative manner employing ‘test & learn’ mindset. This can be effectively achieved through Decision Automation Solutions that leverage AI and ML to augment the expert human driven decision-making process.

Incedo Lighthouse^TM for Automated Decision Making

Incedo Lighthouse^TM an AWS cloud native platform has been designed and developed from the ground up to automate the entire process of decision making. It has been developed with the following objectives:

Distill signal from noise: The right problem areas to focus on are identified by organizing KPIs into a hierarchy from lagging to leading metrics. Autonomous Monitoring and Issue Detection algorithms are further applied to identify anomalies that need to be addressed in a targeted manner. Thereby, effectively identifying crucial problem areas that the business should focus its energy on, using voluminous datasets that are updated at frequent intervals (typically daily).
Leverage context: Intelligent Root Cause Analysis algorithms are applied to identify the underlying behavioral factors through specific micro-cohorts. This enables action recommendations that are tailored to specific cohorts as opposed to generic actions on broad segments.
Impact feedback loop: Alternate actions are evaluated with controlled experiments to determine the most effective actions – and use that learning to iteratively improve outcomes from the decisions.

Incedo Lighthouse^TM is developed as cloud-native solution leveraging several services and tools from AWS that make the process of executive decisions highly efficient and scalable.

Incedo Lighthouse^TM implements a powerful structure and workflow to make the data work for you via a virtuous problem-solving cycle with an aim to deliver consistent business improvements through automation of 6-step functional journey of Problem Structuring & Discovery to Performance Improvement to Impact Monitoring.

Step 1: Problem Structuring – What is the Problem?

In this step, the overall business objective is converted into a specific problem statement(s) based on Key Performance Indicators (KPIs) that are tracked at the CXO level. The KPI Tree construct is leveraged to systematically represent problem disaggregation. This automation enhances the decision making process by enabling a deeper understanding of the issue and its associated variables. Incedo Lighthouse^TM provides features that aid the KPI decomposition step, such as KPI repository, self-serve functionality for defining the structure of KPI trees and publish those with latest raw data automatically.

Step 2: Problem Discovery – Where is the problem?

Here the objective is to attribute the anomalies observed in performance, which are significant deviations from the performance trend, to a set of customers / accounts / subscribers. Incedo Lighthouse^TM provides features, which are a combination of rule-based and anomaly detection algorithms, that aid in identifying most critical problem areas in the KPI trees, such as Time Series Anomaly Detection Non-time series Anomaly Detection, Cohort Analyzer and Automated Insights.

Step 3: Root Cause Analysis – Why is there a problem?

Once the problem is discovered at the required level of granularity, identification of the root causes that drive the business performance becomes critical. To automate the root cause identification for every new or updated data set the Root Cause Analysis must be packaged into a set of pre-defined and pre-coded model sets that are configurable and can be fine-tuned for specific use case scenarios. Incedo Lighthouse^TM enables this using pre-packaged configurable model sets, the output of which is presented in a format that is conducive for the next step, which is, action recommendations. These model sets include Clustering, Segmentation and Key Driver Analyzer.

Step 4: Recommended Actions

However sophisticated the algorithms are, if the workflow stops at only delivering the insights using anomaly detection and root cause analyzer etc, it would still be a lost cause. Why? Because the executives are not supported with recommendations to take corrective, preventive or corroborative actions based on the insights delivered. Incedo Lighthouse^TM incorporates the Action Recommendation module that enables the actions to be created at each cohort (customer microsegment) level for a targeted corrective or improvement treatment based on its individual nuance. The Action Recommendation module helps define and answer questions for each cohort: What is the action, Who should be the target for the action, and When the actions should be implemented and state the Goal of the action in terms of KPI improvement target.

Step 5: Experimentation

Experimentation is testing various actions on a smaller scale, and being able to select the optimal action variant that is likely to produce the highest impact when implemented on full scale. Incedo Lighthouse^TM has a Statistical Experimentation engine that supports business executives to make informed decisions on actions to be undertaken. Some of the key features of the module are: Choice of the experiment type from the options such as A/B Testing, Pre vs. Post etc., Finalization of the target population and Identification of the success metrics and define targets.

Step 6: Impact Monitoring

Post full scale implementation of actions, through their seamless integration into organization’s operating workflows, tracking their progress on an ongoing basis is critical for timely interventions. Our platform ensures that the actions are not merely implemented but are continuously monitored for their impact on key performance indicators and business outcomes.

A two-way handshake is required between Incedo Lighthouse^TM and the System of Execution (SOE) that is used as an operations management system to continually monitor the impact of the actions on ground. Incedo Lighthouse^TM covers the following activities in this step – Push Experiments/Actions, Monitor KPIs, and Experiment Summary.

Incedo Lighthouse^TM in AWS environment

Infrastructure to host the Incedo Lighthouse^TM platform plays an important role in the overall impact that the platform creates on business improvements through better and automated decision making. In cases where the clients are already leveraging the AWS Cloud, the Incedo Lighthouse^TM implementation takes advantage of the following AWS native services that provides significant efficiencies for successive deployments and ongoing service to the business users. A few of the AWS Services prominently used by Incedo Lighthouse^TM are:

AWS Compute: AWS provides scalable and flexible compute resources for running applications and workloads in the cloud. AWS Compute services allows the companies to provision virtual servers, containers, and serverless functions based on application’s requirements, and enable pay for what you use, making it a cost-effective and scalable solution. Key compute services used in Incedo Lighthouse^TM are: Amazon EC2 (Elastic Compute Cloud), AWS Lambda, Amazon ECS (Elastic Container Service) and Amazon EKS (Elastic Kubernetes Service).

AWS Sagemaker: There are various ML models that are the brain behind various modules in Incedo Lighthouse^TM the Anomaly Detection, Cohort Analyzer, Action Recommendation and Experimentation etc. All these models are developed, trained, validated and deployed via AWS Sagemaker.

AWS Glue: The large amount of frequently updated data in various source systems that is needed by the ML models is brought into the common analytical storage (data mart/ data warehouse/ data lake) etc. using AWS Glue jobs that implement ETL or ELT logic along with value add processes such as data quality checks and remediation.

Incedo Lighthouse^TM boosts effectiveness and efficiency of executive decision making with the power of AI. As a horizontal cloud-native platform powered by AWS, it is the key to achieving consistent business improvements across domains and use cases.

The financial services industry has undergone immense disruption in recent years, with fintech innovators and digital giants eroding the market share of traditional banking institutions. These new players are championing enhanced customer experiences through personalization as a common strategy to drive user adoption and build market share. In this cloud-centric narrative, we’ll explore the value of personalization in retail banking and how AWS services can be leveraged to empower this transformation with Incedo Lighthouse^TM.

The Value of Personalization for Retail Banking

The adoption of personalization strategies has become a central focus for banks to enhance customer experience and deliver significant business impact. This includes:

Building a Growth Engine for New Markets and Customer Segments
New-age fintech companies have leveraged data-driven products for expedited underwriting, utilizing data like bureau scores, digital KPIs, and social media insights to assess a prospect’s creditworthiness. Traditional banks must swiftly adopt similar data-driven approaches for faster loan fulfillment and to attract new customer segments. AWS cloud services can facilitate this transition at speed by offering scalable, flexible, and secure infrastructure.

Maximizing Customer Lifetime Value
To maximize the share of customers’ wallets, banks are now focusing on improved cross-selling, superior servicing, and higher retention rates. Next-generation banks are employing AI-driven, next-best-action recommendations to determine which products to offer at the right time and through the most effective channels. This shift involves transitioning from reactive retention strategies to proactive, data-driven personalized approaches.

Improved Risk Assessment and Mitigation Controls
Personalization is not confined to marketing; it extends to risk management, fraud detection, anti-money laundering, and other control processes. Utilizing sophisticated AI models for risk, fraud, and AML detection, combined with real-time implementation, is crucial to establishing robust risk defense mechanisms against fraudsters.The impact of personalization in retail banking is transformative, with opportunities to enhance experiences across all customer touchpoints. Incedo’s deployment of solutions for banking and fintech clients showcases several use cases and potential opportunities within the cloud-based landscape.

Building a Personalization Engagement Engine with AWS

A successful personalized engagement engine necessitates integrated capabilities across Data, AI/ML, and Digital Experiences, all hosted on the AWS cloud. The journey begins with establishing a robust data foundation and a strategy to enable a 360-degree view of the customer:

Data Foundation to Support Decision Automation
Traditional banks often struggle to consolidate a holistic customer profile encompassing product preferences, lifestyle behavior, transactional patterns, purchase history, preferred channels, and digital engagement. This demands a comprehensive data strategy, which, given the extensive storage requirements, may require building modern digital platforms on AWS Cloud. AWS services and tools facilitate various stages of setting up this platform, including data ingestion, storage, and transformation.
AWS Glue: A large amount of frequently updated data in various source systems that are needed by the ML models is brought into the common analytical storage (data mart/data warehouse/data lake, etc.) using AWS Glue jobs, implementing ETL or ELT logic along with value-add processes such as data quality checks and remediation.

AI, ML, and Analytics-Enabled Decisioning
Identifying the right product or service to offer customers at the ideal time and through the preferred channel is pivotal to delivering an optimal experience. This is achieved through AI and ML models built on historical data. AWS offers services like Amazon SageMaker to develop predictive models and gain deeper insights into customer behavior.
AWS Sagemaker: The brain of Personalization lies in the Machine Learning models that take advantage of the wealth of customer-level data across demographic, behavioral, and transactional dimensions to develop insights and recommendations to enhance the customer experience significantly, as explained in the use cases above.

Optimal Digital Experience
Personalization goes beyond data; it requires the right creatives and effective communication to drive customer engagement. AWS services for data integration and analytics enable A/B testing of digital experiences, ensuring the creation of best-in-class customer journeys.While data, AI, and digital experiences are the core building blocks of a personalized engagement layer, the orchestration and integration of these capabilities are essential for banks to realize the full potential of personalization initiatives. Building these capabilities from scratch can be time-consuming, but the AWS Cloud provides the scalability and flexibility required for such endeavors.
AWS Compute: AWS provides scalable and flexible computing resources for running applications and workloads in the cloud. AWS Compute services allow companies to provision virtual servers, containers, and serverless functions based on the application’s requirements, enabling pay for what you use, and making it a cost-effective and scalable solution. Key compute services used in Incedo Lighthouse^TM are Amazon EC2 (Elastic Compute Cloud), AWS Lambda, Amazon ECS (Elastic Container Service), and Amazon EKS (Elastic Kubernetes Service).

Turning Personalization into Reality with Incedo Lighthouse^TM and AWS

Building personalization capabilities is just the first step; embedding personalization recommendations into enterprise workflows is equally critical. This integration ensures that personalized experiences are not just theoretical but are actively implemented and drive customer engagement.

Incedo’s Lighthouse^TM solution for CX personalization accelerates the journey, offering an enterprise-grade solution that significantly reduces time-to-market for data/AI-enabled marketing personalization. It automates AI/ML-enabled decisions from data analysis to customer outreach, ensuring personalized offerings are delivered to customers at the right time. Incedo’s solution includes a prebuilt library of Customer 360-degree data lakes and AI/ML models for rapid implementation, supported by digital command centers to facilitate omnichannel engagement.

No matter where banking clients are in their personalization journey, Incedo’s solution ensures that they experience tangible benefits within weeks, not years. The implementation is complemented by a personalization roadmap that empowers organizations to build in-house personalization capabilities.

In the fast-paced world of banking, personalization is essential for acquiring new customers, maximizing their value, and retaining the best customers. Trust, combined with personalization capabilities, ensures traditional banks maintain their competitive edge against fintech players and digital giants.

In the ever-evolving landscape of financial services, personalization powered by AWS offers banks a strategic advantage in acquiring and retaining customers. Incedo’s Lighthouse^TM solution, hosted on AWS Cloud, enables rapid implementation and ensures that banks can quickly harness the benefits of personalization. This approach is not just a trend but a necessity for banks looking to stay competitive and provide a superior banking experience.

The explosion of data is a defining characteristic of the times we are living in. Billions of terabytes of data are generated every day, and million more algorithms scour this data for patterns for our consumption. And yet, the more data we have, the harder it becomes to process this data for meaningful information and insights.

With the rise of generative AI technologies, such as ChatGPT, knowledge workers are presented with new opportunities in how they process and extract insights from vast amounts of information. These models can generate human-like text, answer questions, provide explanations, and even engage in creative tasks like writing stories or composing music. This breakthrough in AI technology has opened up possibilities for knowledge workers.

Built with powerful LLMs, Generative AI has taken the world by a storm and led to a flurry of companies keen to build with this technology. It has indeed revolutionized the way we interact with information.

And yet, in this era of ever-increasing information overload, the ability to ask the right question has become more critical than ever before.

While the technology has evolved faster than we imagined, its potential is limited by the ways we use it. And while there is scope for immense benefits, there is also a risk for harm if users don’t practice judgment or right guardrails are not provided when building Gen AI applications.

As knowledge workers and technology creators, empowering ourselves and our users relies heavily on the ability to ask the right questions.

Here are three key considerations to keep in mind:

1. The Art of Framing Questions:

To harness the true potential of generative AI, knowledge workers must master the art of framing questions effectively. This involves understanding the scope of the problem, identifying key variables, and structuring queries in a way that elicits the desired information. A poorly constructed question can lead to misleading or irrelevant responses, hindering the value that generative AI can provide.

Moreover, knowledge workers should also consider the limitations of generative AI. While these models excel at generating text, they lack true comprehension and reasoning abilities. Hence, it is crucial to frame questions that play to their strengths, allowing them to provide valuable insights within their domain of expertise.

2. The Need for Precise Inquiry:

Despite the power of generative AI, it is essential to remember that these models are not flawless. They heavily rely on the input they receive, and the quality of their output is heavily influenced by the questions posed to them. Hence, the importance of asking the right question cannot be overstated.

Asking the right question is a skill that knowledge workers must cultivate to extract accurate and relevant insights from generative AI. Instead of relying solely on the model to generate information, knowledge workers need to approach it with a thoughtful mindset.

3. Collaboration between Humans and AI:

Generative AI should be viewed as a powerful tool that complements human expertise rather than replacing it. Knowledge workers must leverage their critical thinking, domain knowledge, and creativity to pose insightful questions that enable generative AI to augment their decision-making processes. The synergy between human intelligence and generative AI has the potential to unlock new levels of productivity and innovation.

Think of Gen AI as a powerful Lego block, a valuable component within the intricate structure of problem-solving. It’s not a replacement but an enhancement, designed to work in harmony with human capabilities to solve a problem.

In conclusion, in the age of generative AI, asking the right questions is fundamental. Careful framing of queries unlocks generative AI’s true power, enhancing our decision-making. Cultivating this skill and fostering human-AI collaboration empowers knowledge workers to navigate the information age and seize new growth opportunities.

A Complementary Partnership

“Data is the new currency.”— has gained immense popularity in recent years as data is now a highly valuable and sought-after resource. Overtime data continues to be accumulated and is becoming increasingly abundant. The focus has now shifted from acquiring data to effectively managing and protecting it. As a result, the design and structure of data systems have become a crucial area of interest, and research into the most effective methods for unlocking its potential is ongoing.

While innovation and new ways keep coming to the fore, the best of the ideas currently consists of two distinct approaches in the form of data mesh and data fabric. Although both aim to address the challenge of managing data in a decentralized and scalable manner, they have different approaches and benefits, and they differ in their philosophy, implementation, and focus.

Data Mesh

The architectural pattern was introduced by Zhamak Dehghani for data management platforms that emphasize decentralized data ownership, discovery, and governance. It is designed to help organizations achieve data autonomy by empowering teams to take ownership of their data and provide them with the tools to manage it effectively. Data mesh enables organizations to create and discover data faster through data autonomy. This contrasts with the more prevalent monolith and centralized approach where data creation, discovery, and governance are the responsibility of just one or a few domain-agnostic team(s). The goal of data mesh is to promote data-driven decision-making and increase transparency, break down data silos, and create a more agile and efficient data landscape while reducing the risk of data duplication.

Building Blocks of Data Mesh

Data Mesh Architecture

Since data mesh involves a decentralized form of architecture and is heavily dependent on the various domains and stakeholders, the architecture is often customized and driven as per organizational needs. The technical design of a data mesh thus becomes specific to an organization’s team structure and its technology stack. The diagram below depicts a possible data mesh architecture.

It is crucial that every organization designs its own roadmap to data mesh with conscious and collective involvement of all the teams, departments, and line of Business (LoBs), with a clear understanding of their own set of responsibilities in maintaining the data mesh.

Data Fabric

Data Fabric is not an application or software package; it’s an architectural pattern that brings together diverse data sources and systems, regardless of location, for enabling data discovery and consumption for a variety of purposes while enforcing data governance. A data fabric does not require a change to the ownership structure of the diverse data sets like in a data mesh. It strives to increase data velocity by overlaying an intelligent semantic fabric of discoverability, consumption, and governance on a diverse set of data sources. Data sources can include on-prem or cloud databases, warehouses, and data lakes. The common denominator in all data fabric applications is the use of a unified information architecture, which provides a holistic view of operational and analytical data for better decision-making. As a unifying management layer, data fabric provides a flexible, secure, and intelligent solution for integrating and managing disparate data sources. The goal of a data fabric is to establish a unified data layer that hides the technical intricacies and variety of the data sources it encompasses.  

Data Fabric Architecture

It is an architectural approach that simplifies data access in an organization and facilitates self-service data consumption. Ultimately, this architecture facilitates the automation of data discovery, governance, and consumption through integrated end-to-end data management capabilities. Irrespective of the target audience and mission statement, a data fabric delivers the data needed for better decision-making.

Principles of Data Fabric

Parameters	Data Mesh	Data Fabric
Data Ownership	Decentralized	Agnostic
Focus	High data quality and ownership based on expertise	Accessibility and integration of data sources
Architecture	Domain-centric and customized as per organizational needs and structure	Agnostic to internal design with an intelligent semantic layer on top of existing diverse data sources
Scalability	Designed to scale horizontally, with each team having their own scalable data product stack	Supports unified layer across an enterprise with the scalability of the managed semantic layer abstracted away in the implementation

Both data mesh and data fabric aim to address the challenge of managing data in a decentralized and scalable manner. The choice between the two will depend on the specific needs of the organization, such as the level of data ownership, the focus on governance or accessibility, and the desired architecture.

It is important to consider both data mesh and data fabric as potential solutions when looking to manage data in a decentralized and scalable manner.

Enhancing Data Management: The Synergy of Data Mesh and Data Fabric

A common prevailing misunderstanding is that data mesh and data fabric infrastructures are exclusive to each other i.e., only one of the two can exist. However, fortunately, that is not the case. Data mesh and data fabric can be architected to complement each other in a way that the perquisites of both technologies are brought to the fore to the advantage of the organization. 

Organizations can implement data fabric as a semantic overlay to access data from diverse data sources while using data mesh principles to manage and govern distributed data creation at a more granular level. Thus, data mesh can be the architecture for the development of data products and act as the data source while data fabric can be the architecture for the data platform that seamlessly integrates the different data products from data mesh and makes it easily accessible within the organization. The combination of a data mesh and a data fabric can provide a flexible and scalable data management solution that balances accessibility and governance, enabling organizations to unlock the full potential of their data.

Data mesh and data fabric can complement each other by addressing different aspects of data management and working together to provide a comprehensive and effective data management solution.

In conclusion, both data mesh and data fabric have their own strengths but are complementary and thus can coexist synergistically. The choice between the two depends on the specific needs and goals of the organization. It’s important to carefully evaluate the trade-offs and consider the impact on the culture and operations of the organization before making a decision.

The client is one of the top 5 leading provider of technology, communications, information and entertainment products and services, and a global leader in 5G technologies.

Client was looking for a competent modernization partner to enable effective metrics collection and analysis that provides ways to improve automated flows in network troubleshooting and trouble ticket management for its 5G network.

See how Incedo’s solution helped the client with: