The term “data” refers to the collection of facts, statistics, and information used for analysis, reference, and decision-making. Data used and stored digitally is of a wide variety – personal, corporate, and retail are a few examples. Organizations use data in multiple ways to enable informed business decisions to align with their goals, objectives, and initiatives. Data is analyzed to be used in a wide variety of use cases – healthcare to improve patient treatment outcomes, retail to enable personalized sales, and hospitality to deliver personalized guest experiences, to name a few.   

The Challenge 

Data is being used widely, especially personal data, and as such, the need to protect against data vulnerability is stronger than ever. Unauthorized use of data is a reality, and hackers are continuously developing the tools to access data and use it to harm people and organizations. Sensitive data in the hands of such criminals can lead to major security incidents, known as data breaches. Data classification is a major step an organization can take to understand the risk and exposure based on the data it stores. Data in an organization can be classified into four major categories based on the sensitivity level: 

• Public  
• Internal  
• Sensitive or Confidential 
• Highly Confidential  

Data exposed at each sensitivity level also carries with it a level of impact; sensitivity level combined with the anticipated impact helps an organization develop a risk assessment. A data risk assessment facilitates efficient data management, making it easier to manage and protect data, ensuring resources are allocated effectively. 

Data classification positions an organization to manage data security risk. Some of the biggest data security risks can be categorized as follows: 

• Accidental data exposure 
• Insider threats 
• Phishing attacks 
• Malware 
• Ransomware 
• Cloud data storage breach 

Each incident of a cyberattack and a subsequent loss of data can have some very dire implications for a person and/or organization. Your data, in the hands of the wrong people, can be used maliciously to cause personal harm. Identity theft, emotional trauma, and reputational damage are just a few examples. For organizations, the loss could be in terms of business downtime, data loss, monetary loss, reputation impact, and legal consequences. The impact can be long-lasting and may even threaten the survival of the organization. 

How can data security be improved to minimize cyber threats? 

Organizations must ensure data security so that cyberattacks can be prevented and intercepted before they cause any harm. This is not only a technical solution. Enterprise data security measures fall into three categories: administrative, physical, and technical. A well-rounded information security program includes safeguards in all three categories, and such measures help address the prevention of cybercrimes. There are standards which indicate which measures should be in place to have what is considered a “strong” program; these include ISO/IEC 27001, NIST, SOC2, and HIPAA, among many others.   

• Administrative Measures include policies, procedures, and practices designed to manage and protect information systems. This includes training of employees on cybersecurity best practices to improve the strength of the “human firewall.” There are cyberthreats, such as phishing and social engineering, that a technical firewall cannot easily prevent, but knowledge about how to avoid falling prey to a phishing scheme can protect against such dangers. 
• Physical Measures include measures to protect data in electronic systems, equipment, and facilities from threats, environmental hazards, and unauthorized intrusion. A few common physical measures include physical locks and barriers, security guards, surveillance cameras, lockable cabinets and safes, fences, and lighting. 
• Technical Measures are the most obvious security safeguards that protect systems and data from unauthorized access, attacks, and other cyberthreats. Most people, when they think of data security, may think of “technical measures” first. These include encryption, access controls, data backup and disaster recovery, data loss prevention (DLP), and antivirus/anti-malware, to name just a few. 

How Fresh Gravity Ensures Data Security 

Fresh Gravity drives digital success for our clients by enabling them to adopt transformative technologies that make them nimble, adaptive, and responsive to the changing needs of their businesses. We enable our clients to achieve informed data-driven business outcomes by implementing Data Management, Analytics & ML, and Artificial Intelligence solutions. For all our solutions, we ensure that we adhere to the best practices of data security. We comply with the data security compliance requirements of our clients when implementing solutions for them. We also handle a lot of our clients’ data during analysis and implementation, so within Fresh Gravity, we have ensured that all the measures are strictly followed to ensure that the data is safe. Our team members’ data is also treated with the same level of security as our clients’ data. Fresh Gravity follows ISO27001 standards, and we have achieved a Silver certification by Cybervadis. We thus have a holistic Information Security Program in place to ensure maximum security and protection against cyber threats. 

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In this age of information, organizations are inundated with data from countless sources – social media, emails, customer feedback, IoT devices, and much more. While this abundance of data holds immense potential, much of it is unstructured, making it challenging to analyze and leverage for decision-making. Organizing unstructured data effectively is key to transforming complexity into clarity, unlocking insights that drive innovation and growth. 

What is Unstructured Data? 

Unstructured data refers to information that doesn’t follow a predefined structure or format. Unlike structured data, which is neatly organized in rows and columns, unstructured data can include text files, images, videos, audio recordings, and other formats that are less straightforward to process. Various estimates suggest that unstructured data makes up approximately 80% to 90% of all data generated today, with sources such as MIT Sloan and Forbes highlighting its rapid growth and critical role in enterprise data management. This underscores its significance in the modern digital landscape and the need for effective strategies to manage and analyze it. 

The Challenges of Unstructured Data 

Organizing unstructured data is no small feat due to its: 

1. Volume: The sheer amount of unstructured data can overwhelm traditional systems. 
2. Variety: Unstructured data exists in diverse formats, requiring different processing techniques. 
3. Velocity: The rapid generation of data demands real-time or near-real-time processing. 
4. Veracity: Ensuring the accuracy and reliability of unstructured data can be difficult, especially when dealing with noisy or incomplete information. 

Strategies for Organizing Unstructured Data 

To harness the power of unstructured data, organizations must implement robust strategies that make it accessible and actionable. Here are key steps to achieving this: 

1. Data Classification and Tagging: Start by categorizing your data based on its type, source, or relevance. Automated tools powered by machine learning can analyze data attributes and apply tags or metadata, making future retrieval and organization easier. 
2. Implement Natural Language Processing (NLP): For text-heavy unstructured data, NLP can extract meaning, detect sentiment, and identify patterns. From customer reviews to support tickets, NLP helps uncover actionable insights hidden in text. 
3. Leverage AI and Machine Learning: Advanced algorithms can identify relationships, trends, and anomalies within unstructured data. These technologies excel in processing images, videos, and even voice data, offering deeper analysis beyond human capabilities. 
4. Adopt Scalable Data Storage Solutions: Cloud-based storage systems designed for unstructured data, such as data lakes, provide the scalability needed to manage large volumes. These platforms support integration with analytics tools for streamlined processing. 
5. Data Governance and Security: Effective data organization requires robust governance policies to ensure data quality, consistency and cohesiveness, privacy, and compliance. Assigning ownership and implementing access controls protect sensitive information while maintaining clarity in data management. 

Real-World Applications of Organized Unstructured Data 

• Healthcare: Analyzing unstructured data from patient records, medical imaging, and clinical notes aids in disease diagnosis and personalized treatment. 
• Retail: Insights from customer reviews, social media, and purchase history enable retailers to refine their offerings and enhance customer experiences. 
• Finance: Fraud detection and risk assessment benefit from analyzing unstructured data, such as emails, transaction records, and voice calls. 
• Entertainment: Media companies can benefit from organizing unstructured data like video, metadata and viewer preferences to recommend content and improve engagement. 

The Road Ahead 

Organizing unstructured data is an ongoing journey that requires technological innovation, strategic planning, and a commitment to continuous improvement. By embracing tools and techniques that simplify the complexity of unstructured data, organizations can transform overwhelming information into a strategic asset. 

In a world driven by data, the ability to turn complexity into clarity is not just a competitive advantage – it’s a necessity. Whether it’s improving decision-making, enhancing customer experiences, or driving operational efficiency, organized unstructured data is the foundation for a smarter, more agile future. Reach out to us at info@freshgravity.com if you are ready to unlock the power of your data. 

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As data engineering and analytics pipelines become more complex, organizations need efficient ways to manage deployments and enhance collaboration. Traditional approaches often involve redundant code, scattered dependencies, and inconsistent environments.  

Databricks Asset Bundles (DABs) provide a structured, streamlined way to package, share, and deploy Databricks assets, simplifying collaboration across teams and environments. By integrating GitLab CI/CD, we can automate the entire development lifecycle, ensuring efficient version control, validation, and controlled deployments across multiple Databricks workspaces.

In this blog, we’ll explore how DABs can enhance data projects and streamline workflows, empowering organizations to navigate the complexities of modern data engineering effectively. 

Who Can Leverage DABs? 

DABs are particularly useful in scenarios where: 

1. Infrastructure as Code (IaC) is required for managing Databricks jobs, notebooks, and dependencies
2. Complex code contribution and automation are essential to avoid redundancy
3. Continuous Integration and Continuous Deployment (CI/CD) are a requirement for rapid, scalable, and governed workflows

Scenarios for DAB Implementations 

Consider a scenario where multiple data engineers work on a pipeline following the Medallion architecture. This pipeline involves: 

• Numerous metadata files 
• Redundant code spread across multiple notebooks
• Challenges in maintaining and scaling workflows

By using DABs, developers can: 

• Modularize workflows by creating generic notebooks that dynamically execute with different base parameters
• Eliminate redundant code, making pipelines more scalable and maintainable
• Collaborate efficiently by packaging all necessary assets into a single bundle that can be versioned and deployed easily

CI/CD Workflow for DABs with GitLab 

The following diagram represents the Databricks CI/CD pipeline using GitLab as the repository and CI/CD tool, enabling a structured and approval-based deployment process: 

Figure 1. DAB deployment Workflow with GitLab CI/CD 

• Development in Local Environment 
  • Developers create notebooks, job configurations, and dependencies in their local environment. 
  • These are packaged into a Databricks Asset Bundle (DAB), which includes: 
    • Notebooks 
    • Configurations 
    • Library dependencies 
    • Job definitions 

• Version Control with GitLab Repository 
  • The DAB files are pushed to a GitLab repository to maintain: 
    • Version history for rollback and tracking 
    • Collaboration among teams 
    • Automation triggers for the CI/CD pipeline 

•   CI/CD Pipeline Execution with GitLab CI/CD 
  • Once the DAB files are committed, GitLab CI/CD triggers the pipeline, which automates: 
    • Code validation (linting, static analysis) 
    • Unit testing to verify notebook functionality
    • Packaging and artifact creation 

• Deployment to Databricks Development Workspace 
  • Successfully validated DABs are deployed to the Development Workspace
  • Developers test and refine their code before moving forward

• Deployment to Databricks Staging Workspace 
  • The CI/CD pipeline deploys the bundle to the Staging Workspace, where: 
    • Integration testing 
    • Performance testing 
    • User Acceptance Testing (UAT) takes place

• Approval-Based Deployment to Production Workspace 
  • Final deployment to production requires explicit approval from: 
    • Management 
    • DataOps Leads 
    • Security & Compliance Teams 
  • Once approved, the Release Manager or an automated approval workflow in GitLab CI/CD triggers deployment to the Databricks Production Workspace. This ensures: 
    • • Governance & compliance 
      • Risk mitigation 
      • Controlled and auditable releases 

Advantages of Using Databricks Asset Bundles (DABs)  

• Efficient Code Versioning and Collaboration 
  • Developers can systematically version control their code and collaborate seamlessly using GitLab repositories
• Declarative and Simple Deployment 
  • DABs use a simple YAML-based declarative format, allowing the deployment of multiple resources like jobs, pipelines, and Unity Catalog objects with minimal configuration
• Automated Software Development Lifecycle 
  • Enables organizations to apply agile methodologies and enforce a structured SDLC (Software Development Lifecycle) for Databricks projects
• Approval-Based Governance for Production Deployments 
  • Prevents unauthorized changes by enforcing a structured approval process before deploying to production
• Scalability & Maintainability
  • Reduces code complexity by allowing reusable components and standardized configurations, making large-scale data pipelines easier to manage

In the ever-evolving world of data engineering, ensuring efficiency, scalability, and consistency across Databricks environments is essential for organizations aiming to stay competitive. By leveraging Databricks Asset Bundles (DABs) and integrating GitLab CI/CD, businesses can streamline their workflows, improve collaboration, and automate deployments, ultimately reducing operational overhead and accelerating time-to-market. 

At Fresh Gravity, we understand the challenges companies face in modernizing their data infrastructure. Our team of experts is committed to helping organizations optimize their Databricks workflows through tailored solutions and industry best practices. From designing custom CI/CD pipelines to implementing governance controls and automating infrastructure provisioning, we provide end-to-end support to ensure your Databricks environment operates at its highest potential.

Reference:
1. https://docs.databricks.com/en/dev-tools/bundles/index.html 

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A Tale of Two Analysts: The Power of Good Data 

Meet Sarah and Jake. Both are data analysts at different companies, each tasked with providing insights to drive business decisions. Sarah spends her time confidently pulling reports, analyzing trends, and delivering reliable insights. Jake, on the other hand, is constantly questioning the data—he’s chasing down missing fields, struggling with inconsistent formats, and getting different answers for the same question depending on the system he pulls the data from. 

Sarah’s company has a robust Data Governance (DG) framework in place. Jake’s? Not so much. While Jake is firefighting data issues, Sarah is providing valuable recommendations that her leadership team can trust. 

But here’s the thing—Jake’s company isn’t a mess. They have great people, solid tools, and ambitious goals. They just don’t have the right guardrails to make data a true asset. That’s where Data Governance comes in.  

Why Data Governance Matters for Business Intelligence & Analytics 

Data Governance (DG) isn’t just about control—it’s about enabling better, faster, and more confident decision-making. Without it, Business Intelligence (BI) and analytics are built on a shaky ground. Here’s how DG directly enhances BI: 

• Data Quality & Consistency: Ensuring data is clean, standardized, and trustworthy means analytics reports are accurate and meaningful. Without high-quality data, reports can be misleading, leading to incorrect business strategies. With governance, businesses can establish standardized definitions, formatting, and validation rules to maintain integrity across all data sources. 
• Data Accessibility & Security: DG helps define who can access what data, striking the right balance between openness and protection. Organizations can ensure that sensitive information remains secure while still making valuable data available to those who need it, promoting efficiency and compliance. 
• Data Lineage & Trust: When decision-makers ask, “Where did this number come from?” DG ensures there’s a clear, documented answer. Transparency in data lineage means that any anomalies can be quickly traced back to their source, reducing errors and instilling trust in analytics. 
• Compliance & Risk Reduction: With increasing regulations like GDPR and CCPA, organizations can’t afford to overlook data governance. Regulatory requirements demand strict data management, and proper governance ensures  that companies avoid hefty fines while maintaining a strong reputation. 
• Efficiency & Productivity: Analysts spend less time cleaning and validating data and more time delivering actionable insights that drive business growth. 

Fresh Gravity: Helping Companies Take Charge of Their Data 

At Fresh Gravity, we specialize in making Data Governance practical, achievable, and impactful. We don’t believe in just handing over theoretical frameworks—we work alongside organizations to implement governance models that actually work for their business. 

Our Approach to Data Governance 

To ensure organizations achieve real, lasting success with their data governance initiatives, Fresh Gravity follows a structured, yet flexible approach as shown in the figure below. The structured methodology provides a robust framework for consistency, while the flexibility allows clients to customize and tailor the approach to their specific needs and goals. 

Figure 1. Our Data Governance Approach – A Snapshot 

• 01 Data Maturity Assessment & Benchmarking: We evaluate your current data landscape, identify gaps, and benchmark against industry best practices to ensure your governance strategy is competitive and effective. Our assessment provides a clear understanding of where your organization stands and what steps are needed for improvement. 
• 02 Strategic Roadmap & Actionable Recommendations: We provide practical, achievable governance strategies that align with business goals. Rather than overwhelming organizations with complex frameworks, we focus on actionable, high-impact changes that drive real improvements in data reliability and usability. 
• 03 Seamless Implementation & Enablement: Fresh Gravity works closely with the client’s teams to establish governance frameworks, define policies, and integrate governance into everyday workflows. From selecting the right tools to embedding governance processes, we ensure a smooth and effective rollout. 
• 04 Change Management & Socialization: Governance is only successful when people adopt it. We actively engage stakeholders, promote awareness, and integrate governance into company culture through structured communication, training, and advocacy efforts. We help teams see governance as an enabler, not a blocker. 
• 05 Ongoing Governance Support & Optimization: Data governance is not a one-time project—it’s an evolving discipline. We provide continued support, monitoring, and training to ensure governance efforts stay effective as business needs change. Our goal is to embed governance as a sustainable and valuable practice. 

Bringing It All Together 

Back to our story—imagine if Jake’s company had a well-defined Data Governance strategy. Instead of spending hours validating reports, Jake could be delivering powerful, data-driven insights. Leadership wouldn’t have to second-guess reports, and decisions could be made faster and with confidence. 

That’s the power of Data Governance in Business Intelligence and Analytics—not just fixing problems but unlocking true business value from data. 

The organizations that succeed today and will continue to do so in the future are the ones that turn their data into a strategic asset rather than a liability. At Fresh Gravity, we help businesses take control of their data—ensuring it’s trusted, secure, and actionable. 

If your organization is ready to move from data chaos to data confidence, Fresh Gravity is here to help. Let’s work together to build a Data Governance Model that fuels smarter decisions, drives competitive advantage, and secures long-term success. Reach out to us at info@freshgravity.com and start your DG journey today.

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Data is a key foundational pillar for any digital transformation and is often regarded as the new currency for strategic decision-making. For organizations aiming to harness their data as a strategic asset, developing a cohesive data strategy is essential to meet current and future needs. A well-defined and effectively executed data strategy enables businesses to transform data into actionable insights, driving long-term success. 

A comprehensive data strategy extends beyond data collection, governance, storage, and compliance. It focuses on managing and maximizing the full potential of data to deliver meaningful value and insights. 

A well-defined data strategy outlines a vision for transforming an organization into a data-driven organization. To realize this vision, organizations must effectively understand, access, and connect their data; leverage the latest data science tools and techniques; nurture data talent and skills; and establish robust, organization-wide practices for data governance, management, and policy oversight. 

Why We Need a Data Strategy 

• Recognizing Data as an Asset – In the digital age, data is a valuable asset that can drive insights, innovation, and decision-making. A data strategy ensures that data is treated as a strategic resource. 
• Aligns with Business Goals – A data strategy aligns data initiatives with organizational objectives, ensuring that data efforts support and enhance business outcomes. 
• Establishes Data Governance – It establishes data governance practices, including data quality, security, and compliance, to maintain data integrity and protect sensitive information. 
• Increases efficiency – A data strategy streamlines data operations and reduces redundancies, leading to cost savings and operational efficiency. 
• Data Monetization – It enables organizations to monetize their data assets by identifying opportunities for data-driven products or services. 
• Competitive Edge – A well-executed data strategy can give a competitive edge by enabling data-driven decision-making, personalization, and predictive analytics. 

Benefits of a Well-Defined Data Strategy 

• Better Decision-Making – With a strong data strategy, organizations can make more informed, data-driven decisions by analyzing current and historical data. 
• Competitive Advantage – Leveraging advanced data analytics allows companies to identify trends, optimize operations, and develop new products faster than competitors. 
• Improved Data Quality – Data governance policies ensure higher data accuracy, consistency, and reliability across the organization. 
• Regulatory Compliance – A data strategy that addresses compliance ensures that organizations adhere to legal frameworks like GDPR, HIPAA, or CCPA, reducing the risk of fines and penalties. 
• Cost Optimization – Efficient data management and infrastructure can lead to cost savings by eliminating data silos, reducing storage costs, and optimizing resource usage. 
• Enhanced Customer Experience – By using data to personalize offerings, optimize supply chains, and improve services, organizations can better meet customer needs and expectations 

Developing a data strategy can be a complex and challenging endeavor. It’s important to recognize that creating and implementing a data strategy is not merely an IT project but rather a holistic, organization-wide process. Data strategy development should be inclusive, leveraging the organization’s priorities and expertise while fostering buy-in from key stakeholders. 

As the data strategy takes shape, it should be formally articulated and published, at least for internal use. If it isn’t documented and shared, it ceases to be a strategy and becomes a secret. Lastly, organizations must be prepared to allocate the necessary resources to support both the data strategy and the infrastructure required to sustain it. 

Building an effective data strategy hinges on establishing strong data management practices from the outset. Fresh Gravity’s Data Management Capability provides a solid framework to achieve this, serving as the cornerstone for transforming into a data-driven organization and crafting a resilient data strategy. To know more about our offerings, please write to us at info@freshgravity.com. 

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In today’s fast-paced and dynamic business landscape, staying ahead of the curve requires more than just traditional methods. Organizations must adapt to the digital age by leveraging the power of data and technology to enhance productivity and drive growth. Whether you’re a small startup or a multinational corporation, integrating data-driven strategies and innovative technologies into your operations can provide numerous benefits and give you a competitive edge in the market. 

Harnessing the Power of Data 

Data is often referred to as the new oil, and for good reason. It holds immense potential to uncover valuable insights, optimize processes, and make informed decisions. However, the key lies not just in collecting data but in effectively analyzing and interpreting it to drive actionable outcomes. 

Implementing robust data analytics tools and techniques allows organizations to: 

• Gain Insights: By analyzing large datasets, organizations can uncover patterns, trends, and correlations that provide valuable insights into customer behavior, market trends, and operational inefficiencies.
• Optimize Operations: Data analytics can help identify bottlenecks and inefficiencies in various processes, enabling organizations to streamline operations and allocate resources more effectively.
• Improve Decision-Making: Relying on data-driven decision-making diminishes the need for guesswork. Instead, it empowers leaders to make well-informed choices supported by solid evidence and thorough analysis.
• Enhance Personalization: Understanding customer preferences and behaviors through data analysis enables organizations to tailor products, services, and marketing campaigns to individual needs, driving customer satisfaction and loyalty.
• Predictive Capabilities: With advanced analytics techniques such as predictive modeling and machine learning, organizations can anticipate future trends and outcomes, enabling proactive rather than reactive strategies.

Embracing Innovative Technologies 

In addition to leveraging data, embracing innovative technologies is essential for organizations looking to enhance productivity and efficiency. From automation and artificial intelligence to cloud computing and the Internet of Things (IoT), there is a myriad of technologies that can revolutionize how businesses operate. 

Figure 1. Technology Drivers that Enhance Productivity 

• Automation: Automating repetitive tasks and workflows frees up time and resources, allowing employees to focus on high-value activities that require human intervention. Whether it’s automating data entry processes or scheduling routine maintenance tasks, automation improves efficiency and reduces the risk of errors.
• Artificial Intelligence (AI): AI-powered solutions can analyze vast amounts of data at incredible speeds, uncovering insights and patterns that would be impossible for humans to discern manually. Whether it’s chatbots providing customer support, predictive analytics forecasting future demand, or algorithmic trading optimizing financial transactions, AI is transforming industries across the board.
• Cloud Computing: Cloud-based services offer scalability, flexibility, and cost-effectiveness, allowing organizations to access computing resources and storage capabilities on demand. Whether it’s hosting applications, storing data, or collaborating on projects, the cloud provides a centralized platform for streamlined operations and enhanced collaboration.
• Internet of Things (IoT): IoT devices interconnected via the Internet can collect and exchange data in real time, enabling organizations to monitor and control physical processes remotely. Whether it is tracking inventory levels, monitoring equipment performance, or optimizing energy consumption, IoT technologies offer endless possibilities for efficiency gains and cost savings.

Creating a Data-Driven Culture 

To fully harness the potential of data and technology, organizations must foster a culture that embraces innovation, collaboration, and continuous learning. 

Figure 2. Building a Data-driven Culture

• Leadership Buy-In: Leadership must champion the importance of data and technology initiatives and allocate resources accordingly. They should lead by example and demonstrate a commitment to embracing digital transformation.
• Employee Training and Development: Providing employees with the necessary skills and training to leverage data analytics tools and technology platforms is crucial. Investing in ongoing education ensures that teams are equipped to adapt to evolving technologies and best practices.
• Cross-Functional Collaboration: Breaking down silos and fostering collaboration between departments encourages knowledge-sharing and interdisciplinary problem-solving. By working together, teams can leverage diverse perspectives and expertise to drive innovation and achieve common goals.
• Continuous Improvement: Embracing a mindset of continuous improvement means constantly seeking ways to optimize processes, enhance efficiency, and innovate. Encouraging feedback and experimentation empowers employees to identify areas for improvement and implement solutions proactively.

In conclusion, in an increasingly digital world, data and technology are essential drivers of organizational productivity and competitiveness. In partnering with Fresh Gravity, organizations can effectively navigate their digital transformation journeys, from strategy to implementation. Fresh Gravity’s comprehensive suite of services and deep expertise in data analytics, AI, cloud computing, and process automation provide the necessary tools and guidance to enhance productivity, streamline operations, and drive growth. To know more about our offerings, write to us at info@freshgravity.com. 

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In the rapidly evolving world of data analytics and business intelligence, organizations are increasingly turning to integrated platforms that streamline their processes. Microsoft Fabric stands out as a unified analytics solution that combines the capabilities of Power BI, Azure Synapse, and Azure Data Factory into one cohesive environment.  

This blog explores the implementation of Continuous Integration and Continuous Deployment (CI/CD) in Microsoft Fabric, leveraging its integration with Azure DevOps Repos and deployment pipelines. 

Understanding Microsoft Fabric 

Microsoft Fabric is designed to facilitate end-to-end analytics workflows, enabling organizations to manage their data lifecycle efficiently. By providing a single platform for data integration, transformation, and visualization, Microsoft Fabric allows teams to collaborate effectively on business intelligence projects. The key components of Microsoft Fabric include: 

• Lakehouses: A unified storage layer that combines the best features of data lakes and data warehouses
• Data Warehouses: Structured storage for analytical workloads
• Data Integration: Tools for ingesting and transforming data from various sources
• Business Intelligence: Capabilities for creating reports and dashboards

With these features, Microsoft Fabric empowers organizations to break down silos and foster collaboration among teams. 

The Importance of CI/CD in Data Analytics 

Continuous Integration (CI) and Continuous Deployment (CD) are essential practices in modern software development. They enable teams to deliver high-quality solutions quickly by automating the integration and deployment processes. In the context of Microsoft Fabric, CI/CD practices enhance collaborative development and streamline the release cycles for analytics solutions. 

Benefits of CI/CD in Microsoft Fabric 

• Faster Delivery: Automating the deployment process allows teams to deliver updates more frequently 
• Improved Collaboration: CI/CD practices encourage collaboration among team members by integrating source control with deployment pipelines
• Reduced Errors: Automated testing during the CI process helps identify issues early, reducing the likelihood of errors in production
• Enhanced Flexibility: Teams can quickly adapt to changes in requirements or feedback from stakeholders 

Integrating Microsoft Fabric with Azure DevOps 

One of the standout features of Microsoft Fabric is its seamless integration with Azure DevOps Repos. This integration allows developers to leverage source control capabilities while working on their analytics projects. 

Setting Up Azure DevOps Repos 

• Create a Repository: Start by creating a new repository in Azure DevOps where your project will reside 
• Branching Strategy: Establish a branching strategy that suits your development workflow (e.g., feature branches for new developments)
• Integrate with Fabric: Use the Fabric interface to connect your workspace with the Azure DevOps repository

Syncing Workspaces with Git 

Fabric allows developers to sync their workspaces with Git branches easily: 

• Each developer can commit changes made in their respective workspaces back to the repository 
• Pull requests can be created in Azure DevOps to merge changes into the main branch 

This workflow enhances collaboration by allowing team members to review each other’s work before merging changes into the main project. 

Building Deployment Pipelines in Microsoft Fabric 

Deployment pipelines are a critical component of the CI/CD process in Microsoft Fabric. They facilitate the movement of content between different environments (e.g., Development, Testing, Production) in a controlled manner. 

Creating Deployment Pipelines 

Creating deployment pipelines in Microsoft Fabric is straightforward: 

• Define Pipeline Stages: Teams can define various stages within a pipeline, such as Development, Test, and Production 
• Codeless Process: The deployment process is codeless, allowing users to configure pipelines using a graphical interface without extensive coding knowledge 
• Content Comparison: Before deploying content from one stage to another, teams can compare items between stages to identify any missing deploys or discrepancies 

Steps to Create a Deployment Pipeline 

• Access Deployment Pipelines: Navigate to the Deployment Pipelines section within Microsoft Fabric 
• Create New Pipeline: Select “Create New Pipeline” and assign meaningful names based on your project needs 
• Configure Stages: Set up stages such as Development, Test, and Production by associating them with existing workspaces or creating new ones 
• Schedule Pipelines: You can schedule pipelines to run at specific times or trigger them based on events 

Figure 1. Development and Deployment Scenario using Microsoft Fabric and an Azure DevOps Repo  

Best Practices for Deployment Pipelines 

To maximize the effectiveness of deployment pipelines in Microsoft Fabric, consider the following best practices: 

• Meaningful Naming Conventions: Assign clear and descriptive names to deployment pipelines for easy identification 
• Use Existing Workspaces: Leverage existing workspaces or create new ones specifically for different pipeline stages
• Content Comparison: Regularly compare content between stages to ensure all necessary items are deployed correctly 
• End-User Testing: Publish applications from any workspace in the pipeline for end-user testing before final deployment 
• Branch Management: Maintain separate branches for each developer or feature to avoid merge conflicts during development 

Real-World Use Cases 

Use Case 1: Retail Analytics Project 

A retail company uses Microsoft Fabric to analyze sales data from multiple sources. The data engineering team creates a lakehouse in a Fabric workspace where they ingest raw sales data from various systems. Data analysts then build reports using Power BI integrated with Microsoft Fabric. With Azure DevOps integration, the team manages version control effectively while collaborating on report development. Deployment pipelines enable them to move reports from development to production seamlessly. 

Use Case 2: Marketing Campaign Management 

A marketing agency leverages Microsoft Fabric for managing campaign performance metrics. They create separate workspaces for each campaign and use deployment pipelines to test new reports before launching them publicly. Integration with Azure DevOps allows marketing analysts to collaborate on report creation while ensuring that only approved metrics are published. 

Use Case 3: Patient-Centric Care in Healthcare 

In the healthcare sector, a hospital utilizes Microsoft Fabric to enhance patient care by integrating data from various sources, such as electronic health records (EHRs), lab results, and imaging systems. By creating a unified lakehouse, healthcare professionals can access comprehensive patient profiles in real time, enabling better-informed decision-making. The platform’s advanced analytics capabilities allow for predictive modeling, helping clinicians anticipate patient needs and improve treatment outcomes. Additionally, the integration with Azure DevOps facilitates collaboration among multidisciplinary teams working on clinical research and patient engagement initiatives. 

Use Case 4: Clinical Research and Data Management 

A research institution employs Microsoft Fabric to streamline clinical research processes by consolidating diverse healthcare data sources into a single platform. The institution can analyze large datasets including clinical trial information, genomic data, and patient demographics to uncover insights that drive innovative treatments. Utilizing deployment pipelines, researchers can test analytical models and share findings with stakeholders efficiently. The ability to harmonize unstructured and structured data enhances the institution’s capacity to conduct comprehensive studies while ensuring compliance with regulatory standards. 

Adopting best practices for CI/CD within Microsoft Fabric ensures high-quality results, making it a vital tool for staying competitive in today’s data-driven environment. By utilizing workspaces, Git integration, and deployment pipelines, organizations can streamline processes and boost productivity across industries.

Fresh Gravity can play a key role in helping organizations leverage the integration of Microsoft Fabric with Azure DevOps to optimize their analytics workflows. With our expertise in data engineering, DevOps, and cloud platforms, we offer end-to-end support, including:

• CI/CD Implementation and Best Practices
  • We can assist in setting up robust Continuous Integration/Continuous Deployment (CI/CD) pipelines within Microsoft Fabric. We ensure your analytics workflows are automated, efficient, and adhere to industry best practices, improving both productivity and quality.
• Workspace and Git Integration
  • Our team can help seamlessly integrate Microsoft Fabric workspaces with Azure DevOps and Git repositories, enabling better version control, collaboration, and governance across projects. We ensure that your development and deployment processes are tightly aligned with your business goals.
• Customization for Industry-Specific Use Cases
  • We understand the nuances of different industries. Whether it’s healthcare analytics, manufacturing optimization, or financial compliance, we customize Microsoft Fabric implementations to cater to specific use cases, ensuring you get the most out of your investment in the platform.
• Deployment Pipelines for Scalable Solutions
  • By leveraging deployment pipelines, we ensure that your data solutions are scalable, secure, and ready for production. Our team can help you manage and optimize resources to handle varying workloads and user demands.
• Ongoing Support and Optimization
  • As businesses grow and technology evolves, we provide ongoing support, helping you adapt to changing requirements and ensuring that your Microsoft Fabric and Azure DevOps implementations continue to deliver value.

Fresh Gravity’s deep technical expertise, combined with our commitment to client success, positions us as a strategic partner for organizations looking to enhance their data-driven decision-making processes through Microsoft Fabric.

References 

• Microsoft Fabric Integration with ADO Repos and Deployment 

• Get Started with Deployment Pipelines in Microsoft Fabric 

The post Implementing CI/CD in Microsoft Fabric: A Comprehensive Guide appeared first on Fresh Gravity.

In a B2B landscape where data-driven business decisions are pivotal, effectively harnessing and utilizing data is necessary. Availability of data is no longer a problem for firms but identification of relevant information among vast amounts of data is certainly a puzzle. To address this critical need, Fresh Gravity, in partnership with Reltio Inc. and Dun & Bradstreet (D&B), has developed a pre-built integration between Reltio and D&B Data Blocks, providing seamless data enrichment of your enterprise Master Data. 

What is Data Enrichment? 

Data Enrichment is the process of enhancing customer data by adding additional, relevant information from trusted and reliable third-party source. The additional information could be attributes, relationship etc. In the context of Master Data Management, data enrichment is a common practice to augment customer data with trusted information, fill gap and additional information. The goal of data enrichment is to improve the data quality, leading to improved decision making, enforce compliance and enhance the customer experience. 

Reltio – D&B Data Blocks Pre-Built Integration 

The pre-built integration was created to make the most of the latest D&B data enrichment functionality. This integration is designed in accordance with Reltio’s Customer Data (B2B) velocity pack, which maps D&B data points to industry-specific data models. It also empowers the user to customize the integration between Reltio MDM and D&B to fulfil their business needs. This integration is built on top of Reltio Integration Hub (RIH), which is a component of the Reltio Connected Customer 360 platform. 

This pre-built integration supports the following modes of data enrichment: 

• Batch enrichment with a scheduler or API-based triggers 
• Real-time enrichment leveraging Reltio’s integrated SQS queue 
• An API-based trigger for on-demand enrichment. This can be useful for UI button-based integration 
• Monitoring an automated process to ensure records registered for regular updates are constantly refreshed 

Key Highlights: Why This Integration Outshines Existing Solutions 

• Leverages the latest D&B product, Data Blocks 
• Offers consistent functionality across all enrichment modes 
• Supports enrichment from the following Data Blocks: 
  • Company Information Data Blocks include communication details, key financial figures, and industry codes 
  • Hierarchies and Connections Data Blocks provide an upward corporate hierarchy 
  • Diversity Insights Data Blocks provide socio-economic information 
  • Principals and Contacts Data Blocks provide details of the principal contacts of the organization 
• Includes company information such as communication details, key financial figures, and industry codes 
• Provides upward corporate hierarchies and connections
• Provides socio-economic information and diversity insights
• Provides details of principal contacts of the organization 
• Supports attribute-level transformations and validations 
• Eliminates the “URI mismatch” error 
• Uses unique cross-reference syntax for enriching different versions 
• Supports a “Potential Matches Only” mode 
• Offers a platform to customize and extend D&B offerings, such as full hierarchy and enrichment of multiple entity types 
• Includes configurable properties for enhanced flexibility 

Why This Pre-Built Integration Matters 

In a world where data drives decision making, the quality, speed, and reliability of that data can make or break a business. The new D&B integration for Reltio MDM is built with these priorities in mind, delivering: 

• Implementation Best Practices: The integration is designed in accordance with implementation best practices, leveraging Fresh Gravity’s expertise in the field of MDM. 
• Precision Data Integration: Seamlessly connect with D&B’s expansive global database, ensuring that the data is as accurate and comprehensive as possible. 
• Lightning-Fast Processing: Experience unparalleled performance of RIH recipes with optimized design to ensure RIH task utilization, memory consumption and reliability, even in high-volume data environments. 
• Scalability Without Limits: Designed to scale alongside the business, this integration can handle anything from day-to-day new records to bulk data enrichment. 
• Effortless Integration: Enjoy a hassle-free setup and smooth integration with the Reltio MDM platform, minimizing disruption and maximizing productivity. 
• Intuitive User Experience: Benefit from a user-centric interface that simplifies complex data tasks, allowing the data teams to focus on what matters most. 
• Better User Experience: Provides access to detailed logs, statistics, and email notifications. 

Transformative Use Cases: 

In the ever-evolving world of data management, the pre-built integration of Dun & Bradstreet (D&B) data blocks with Reltio MDM offers transformative capabilities for businesses. This integration enhances not only data accuracy and completeness but also delivers powerful insights across customer profiles, corporate hierarchies, risk management, and key contact management, enabling businesses to stay ahead in a data-driven landscape. Here are a few of the many use cases for this integration: 

• Holistic Customer Views: Integrate D&B data to create enriched, 360-degree customer profiles that drive holistic view, loyalty programs, sales analytics and many more. 
• Corporate Hierarchy Management: Leverage D&B’s corporate hierarchy to redefine your customer strategy, rebuild company hierarchy to fulfil business needs. 
• Proactive Risk Management: Leverage golden data of key financial and revenue to anticipate and mitigate risks before they impact your business. 
• Streamlined Compliance: Maintain accurate and compliant records effortlessly, meeting global data regulations with confidence. 
• Key Contacts: Use principal contact details to advance customer relationships. 
• Reliable Data Management: Endure benefits of pre-built integration designed for Reltio’s B2B velocity pack which complements your data modelling, data enrichment, data quality, data completeness and data enrichment needs.

Join the Data Revolution: Ready to take your data strategy to the next level? Discover the full potential of the new D&B Integration for Reltio MDM, designed and developed by Fresh Gravity. Contact us for a personalized demo or to learn how this revolutionary tool can be a game-changer for your business. 

For a demo of this pre-built integration, please write to info@freshgravity.com or ashish.rawat@freshgravity.com. 

Key Technologies  

• Reltio MDM: Connected Data Platform 

Reltio is a cutting-edge Master Data Management (MDM) solution that enables an MDM solution with an API-first approach. It offers top-tier MDM capabilities, including Identity Resolution, Data Quality, Dynamic Survivorship for contextual profiles, and a Universal ID for all operational applications. It also features robust hierarchy management, comprehensive Enterprise Data Management, and a Connected Graph to manage relationships. Additionally, Reltio provides Progressive Stitching to enhance profiles over time along with extensive Data Governance capabilities.  

• Reltio Integration Hub: No-Code, Low-Code Integration Platform 

Reltio offers a low code/no code integration solution, Reltio Integration Hub (RIH).  RIH is a component of the Reltio Connected Customer 360 platform which is an enterprise MDM and Customer Data Platform (CDP) solution. RIH provides the capabilities to integrate and synchronize data between Reltio and other enterprise systems, applications, and data sources. 

• Dun & Bradstreet (D&B) 

The leading global provider of B2B data and analytics, specializing in business information and insights. An AI-driven platform that helps organizations around the world grow and thrive. Dun & Bradstreet’s Data Cloud, which comprises more than 500 million records, was founded in 1841, D&B offers a comprehensive range of solutions designed to help organizations manage risk, drive growth, and improve decision-making. 

• D&B Data Blocks 

D&B Data Blocks enable users to retrieve data on a specific entity or category. In a single online API request, multiple data blocks can be pulled. Monitoring is supported for all elements of standard data blocks. Data Blocks have various levels and versions, designed to pull information from any organization based on license.

The post Elevate B2B Data Management: Discover the Enhanced D&B Data Blocks Pre-Built Integration with Reltio MDM appeared first on Fresh Gravity.

Data engineering is the backbone of any data-driven organization. It involves designing, constructing, and managing the infrastructure and systems needed to collect, store, process, and analyze large volumes of data and helps maintain the architecture that allows data to flow efficiently across systems. It serves as the foundation of the modern data ecosystem, enabling organizations to harness the power of data for insights, analytics, decision-making, and innovation. 

At its core, data engineering is about transforming raw, often unstructured data into structured, accessible, and usable forms. This involves a wide range of tasks such as creating data pipelines, setting up data warehouses or lakes, ensuring data quality, and maintaining the integrity of data as it flows through various systems. 

Why Is Data Engineering Important? 

As organizations collect more data from various sources—such as customer interactions, business processes, IoT devices, and social media—the need to manage and process this data effectively becomes crucial. Without the infrastructure and expertise to handle large-scale data, companies risk drowning in information overload and failing to extract actionable insights. 

Data engineering bridges the gap between raw data and meaningful insights by ensuring that data flows smoothly from various sources to users in a structured manner. It enables businesses to be data-driven, unlocking opportunities for innovation, optimization, and improved decision-making across industries. 

In the age of big data and artificial intelligence, data engineering is a key enabler of the future of analytics, making it an indispensable part of the data ecosystem. 

Role of Data Engineers in Data Engineering 

Data engineers in this space are mainly responsible for: 

• Data Pipeline Development: Creating automated pipelines that collect, process, and transform data from various sources (e.g., databases, APIs, logs, etc.). 
• ETL (Extract, Transform, Load): Moving data from one system to another while ensuring that it’s correctly formatted and cleaned for analysis. 
• Data Storage Management: Designing and optimizing databases, data lakes, and warehouses to store structured and unstructured data efficiently. 
• Data Quality and Governance: Ensuring that data is accurate, reliable, and consistent by implementing validation, monitoring, and governance frameworks. 
• Collaboration: Working closely with data scientists, analysts, and business teams to ensure the right data is available and properly managed for insights and reporting. 

Best Practices in Data Engineering 

Whether one is working on building data pipelines, setting up data lakes, or managing ETL (Extract, Transform, Load) processes, adhering to best practices is essential for scalability, reliability, and performance. 

Here’s a breakdown of key best practices in data engineering:

• Design for Scalability

As data grows, so must the infrastructure. The design of data pipelines and architecture should anticipate future growth. Organizations should choose scalable storage solutions like cloud platforms (e.g., AWS S3, Google Cloud Storage, Azure Blob Storage) and databases (e.g., BigQuery, Redshift) that can handle an increasing volume of data. While working with large datasets that require parallel processing, we recommend considering distributed computing frameworks such as Apache Spark or Hadoop. 

• Focus on Data Quality

Data quality is paramount. If the data is inaccurate, incomplete, or inconsistent, the insights derived from it will be flawed. Organizations must implement validation checks, monitoring, and automated alerts to ensure data accuracy.  

Some key aspects of data quality include: 

• Accuracy: Ensure data is correct and reflects real-world entities 
• Consistency: Uniform data across different systems and time frames 
• Completeness: Ensure no critical data is missing 
• Timeliness: Timely availability of data

At Fresh Gravity, we have developed DOMaQ (Data Observability, Monitoring and Data Quality Engine), a solution which enables business users, data analysts, data engineers, and data architects to detect, predict, prevent, and resolve data issues in an automated fashion. It takes the load off the enterprise data team by ensuring that the data is constantly monitored, data anomalies are automatically detected, and future data issues are proactively predicted without any manual intervention. This comprehensive data observability, monitoring, and data quality tool is built to ensure optimum scalability and uses AI/ML algorithms extensively for accuracy and efficiency. DOMaQ proves to be a game-changer when used in conjunction with an enterprise’s data management projects such as MDM, Data Lake, and Data Warehouse Implementations.   

To learn more about the tool, click here.

• Embrace Automation

Manual processes are often error-prone and inefficient, especially as systems grow in complexity. Automate your data pipelines, ETL processes, and deployments using tools like Apache Airflow, Prefect, or Luigi. Automation reduces human error, improves the reliability of the pipeline, and allows teams to focus on higher-level tasks like optimizing data processing and scaling infrastructure.

• Build Modular and Reusable Pipelines

Design your data pipelines with modularity in mind, breaking down complex workflows into smaller, reusable components. This makes it easier to test, maintain, and update specific parts of your pipeline without affecting the whole system. In addition, adopt a framework that facilitates code reusability to avoid redundant development efforts across similar processes. 

Databricks as a unified, open analytics platform can be leveraged in building efficient data pipelines. Together, Databricks and Fresh Gravity form a dynamic partnership, empowering organizations to unlock the full potential of their data, navigate complexities, and stay ahead in today’s data-driven world.  

To learn more about how Databricks and Fresh Gravity can help in this, click here.

• Implement Strong Security Measures

Data security is crucial, especially when dealing with sensitive or personally identifiable information (PII). Encrypt data both at rest and in transit. Ensure that data access is limited based on roles and privileges, adhering to the principle of least privilege (PoLP). Use centralized authentication and authorization mechanisms like OAuth, Kerberos, or IAM roles in cloud platforms. 

In addition, ensure compliance with privacy regulations such as GDPR or CCPA by anonymizing or pseudonymizing PII and maintaining audit trails.

• Ensure Data Governance and Documentation

Data governance establishes the policies, procedures, and standards around data usage. It ensures that the data is managed consistently and ethically across the organization. Having proper documentation for your data pipelines, architecture, and processes ensures that your systems are understandable by both current and future team members. 

Good practices include: 

• Establishing data ownership and stewardship 
• Maintaining a data catalog to document data lineage, definitions, and metadata 
• Enforcing data governance policies through tooling, such as Alation, Collibra, or Apache Atlas 

At Fresh Gravity, we have extensive experience in data governance and have helped clients of different sizes and at multiple stages in building efficient data governance frameworks.  

To learn more about how Fresh Gravity can help in Data Governance, click here.

• Optimize Data Storage and Query Performance

Efficient storage and retrieval are key to building performant data systems. Consider the format in which data is stored—parquet, ORC, and Avro are popular columnar storage formats that optimize space and speed for big data. Partitioning, bucketing, and indexing data can further improve performance for queries. 

Use caching mechanisms to speed up frequent queries and implement materialized views or pre-aggregations are appropriate to improve performance for complex queries.

• Adopt Version Control for Data and Pipelines

Version control, often associated with software development, is equally critical in data engineering. Implementing version control for your data pipelines and schemas allows for better tracking of changes, rollback capabilities, and collaboration. Tools like Git can manage pipeline code, while platforms such as DVC (Data Version Control) or Delta Lake (in Databricks) can help version control your data.

• Build Monitoring and Alerting Systems

Ensure that you’re continuously monitoring your data pipelines for failures, performance bottlenecks, and anomalies. Set up monitoring and alerting systems with tools like Prometheus, Grafana, Datadog, or CloudWatch to track pipeline health and notify data engineers of any issues. This can help detect and address problems before they escalate to larger issues like delayed reporting or failed analysis.

• Testing

Testing is critical in ensuring the reliability and correctness of your data systems. Implement unit tests for individual components of your data pipelines, integration tests to verify that the system as a whole works, and regression tests to ensure that new changes don’t introduce bugs. Test data quality, pipeline logic, and performance under different load conditions. 

Some popular testing frameworks include PyTest for Python-based pipelines or DbUnit for database testing.

• Choose the Right Tools for the Job

There’s no one-size-fits-all solution for data engineering. Choose tools that align with your organization’s needs and goals. Whether it’s batch processing with Spark, stream processing with Apache Kafka, cloud services like AWS Glue or Google Dataflow, or a managed unified analytics platform like Databricks (that gives a collaborative environment with Apache Spark running in the background), select the stack that meets your use cases and data volumes effectively.  

When evaluating new tools, consider factors like: 

• Ease of integration with existing systems 
• Cost-efficiency and scalability 
• Community support and documentation 
• Ecosystem and toolchain compatibility 

 How Fresh Gravity Can Help 

At Fresh Gravity, we have deep and varied experience in the Data Engineering space. We help organizations navigate the data landscape by guiding them towards intelligent and impactful decisions that drive success across the enterprise. Our team of seasoned professionals is dedicated to empowering organizations through a comprehensive suite of services tailored to extract actionable insights from their data. By incorporating innovative techniques for data collection, robust analytics, and advanced visualization techniques, we ensure that decision-makers have access to accurate, timely, and relevant information.   

To know more about our offerings, please write to us at info@freshgravity.com or you can directly reach out to me at debayan.ghosh@freshgravity.com.  

Please follow us on LinkedIn at Fresh Gravity for more insightful blogs. 

The post Data Engineering and Best Practices appeared first on Fresh Gravity.

In today’s ever-evolving healthcare industry, managing vast amounts of data is crucial. Healthcare organizations face challenges in managing and enriching customer data to meet sales, compliance, and commercial needs. Traditional data enrichment approaches to solve the above often fail to integrate multiple data sources effectively, compromising data quality and consistency.  

To address these data challenges in the healthcare industry, Fresh Gravity aimed to integrate MedPro Systems and Reltio MDM. Many external data providers enrich organizational data. Reltio MDM, however, is widely used to master an organization’s HCP and HCO data while MedPro Systems offers verified data enrichment for HCPs (Healthcare Professionals) and HCOs (Healthcare Organizations) in North America. MedPro Systems’ enrichment for HCP and HCO data further enhances and improves the quality of this data. 

Fresh Gravity is pleased to unveil a pre-built integration of MedPro Systems’ database with Reltio MDM, leveraging Reltio Integration Hub (RIH). The solution is designed to streamline healthcare data management and improve data quality. A brief description of the various technological components of this solution will be presented in this blog. 

Reltio MDM:  Connected Data Platform 

Reltio is a cutting-edge Master Data Management (MDM) solution that enables an MDM solution with an API-first approach. It offers top-tier MDM capabilities, including Identity Resolution, Data Quality, Dynamic Survivorship for contextual profiles, and a Universal ID for all operational applications. It also features robust hierarchy management, comprehensive Enterprise Data Management, and a Connected Graph to manage relationships. Additionally, Reltio provides Progressive Stitching to enhance profiles over time, as well as extensive Data Governance capabilities.  

Reltio Integration Hub: No-Code, Low-Code Integration Platform 

Reltio offers a low code/no code integration solution, Reltio Integration Hub (RIH).  RIH is a component of the Reltio Connected Customer 360 platform which is an enterprise MDM and Customer Data Platform (CDP) solution. RIH provides the capabilities to integrate and synchronize data between Reltio and other enterprise systems, applications, and data sources. 

MedPro Systems: Your Source for Reliable Healthcare Data 

MedPro Systems offers an extensive data set of 28 million records including Healthcare Practitioners and HealthCare Organizations. The MedPro Systems’ database and its solutions help customers meet their sales, compliance, and commercial needs for engaging the Healthcare and Life Sciences market. The MedProID database consists of data on Practitioners and Organizations in the United States and Puerto Rico. 

Practitioner Database 

• Includes 28 healthcare practitioner designations across 28 million records 
• Regular updates from state licensing boards 
• Extensive cleansing of all licensing data 
• The MedProID HCP database is matched to the Drug Enforcement Administration (DEA) and National Provider Identifier (NPI) data 

Organization Database 

• Includes 15 healthcare organization types across 800k records 
• Regular updates from State Boards of Pharmacy, the Department of Health, and additional authoritative licensing sources within each state
• The MedProID organization database is matched to DEA and NPI data

Reltio Enrichment with MedPro Systems

Fresh Gravity, in partnership with Reltio Inc. and MedPro Systems, has developed an automated data exchange between Reltio MDM and MedPro’s databases to enrich your HCO and HCP data. We have applied our years of expertise in the Life Sciences and Healthcare domains, coupled with our expertise in MDM implementation to deliver a high-performance, automated batch data interface that will improve and enhance your data. 

Key Features 

• Automated and high-performance data exchange 
• Data enrichment from 10-12 sources for HCP/HCO provided by MedPro Systems 
• Optimized integration pipeline according to implementation best practices 
• Enhanced Reltio Integration Hub (RIH) task execution techniques 
• Comprehensive data cleansing and validation 
• Default mapping based on Reltio’s Life Sciences Velocity Pack 
• Configurable data mapping to support customer-specific data models 
• Configurable properties for connections, job types, mode of enrichment, enrichment source 
• Retry mechanism and single-click re-processing of failed records 
• Detailed job statistics and extensive logging 
• Email notifications for job completion and failure 
• Dashboard for job monitoring 
• Easy access and management via Reltio Integration Hub (RIH) 

Connector Process Flow 

• The process begins with the scheduler initiating the master data export from Reltio MDM.
• The exported data is downloaded and extracted by the Reltio Integration Hub. 
• The extracted data is transformed into MedPro’s expected Standard-10 input format. 
• This transformed data is then processed and exported to the MedPro SFTP server. 
• Within MedPro, customer data is maintained by the MedPro system as a customer universe. 
• MedPro uses its efficient algorithms to provide data enrichment of new and existing records in the Standard-80 output format. 
• An automated file monitoring process picks the Standard-80 file from MedPro’s SFTP server. 
• Enriched data is segregated into HCPs and HCOs. 
• These enriched records are transformed back into Reltio JSON format. 
• Finally, the transformed data is returned to Reltio MDM as data enrichment. 

How Can Fresh Gravity Help? 

This pre-built integration leverages Fresh Gravity’s expertise in the Life Sciences and Healthcare sectors, ensuring robust data management and improved operational efficiency. Fresh Gravity has decades of experience in end-to-end MDM implementations and product development. We help clients implement this integration and customize it to meet their organization’s business needs.  

For a demo of this integration, please write to info@freshgravity.com or ashish.rawat@freshgravity.com. 

The post Streamlining Healthcare Data Management: Reltio – MedPro Systems Pre-built Integration appeared first on Fresh Gravity.