Author: Fresh Gravity


Data Management, Industry-agnostic

Implementing CI/CD in Microsoft Fabric: A Comprehensive Guide

October 23rd, 2024 WRITTEN BY Fresh Gravity Tags: , ,

Written By Ashutosh Yesekar, Consultant, Data Management

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 

Life Sciences

Pharma Industry Trends in 2024 & Beyond

July 18th, 2024 WRITTEN BY Fresh Gravity Tags: , , , , , , ,

Written By Sunayan Banerjee, Director, Data Management

Pharmaceutical companies are at a significant crossroads in terms of how they engage with customers, innovate, and adopt digital capabilities. On one hand, there is an increasing demand for personalization of drugs, while on the other hand, global inflation and uncertain market trends are causing challenges for the industry. These factors, in addition to a lower number of clinical trials during the pandemic, caused a downturn in 2023.  

2024 has seen a revival for pharmaceutical companies, driven by increased consumer spending and stabilization of inflation in the US and EU regions. Clinical trials have also resumed as companies strive to develop and launch safer and more effective drugs while containing their R&D and manufacturing costs. We believe personalized drugs and patient care plans, technology adoption by pharmaceutical companies, improved quality and access to drugs, and mergers and acquisitions are the significant trends that will affect the pharmaceutical industry in 2024 and beyond. 

Personalized Drugs and Patient Care Plans 

As the industry evolves, a combination of new therapeutic developments and changing healthcare policies is reshaping its landscape. The future of this industry will not only be focused on developing new drugs but also on providing more holistic and focused patient care. Personalized patient care helps healthcare providers make accurate diagnosis and tailor a suitable treatment plan for patients resulting in more effective recovery and results. This can also reduce healthcare costs by avoiding unnecessary procedures and medications that may not be effective for an individual patient. This trend is expected to continue this year and beyond. Areas such as weight management/obesity, autoimmune diseases, oncology, and diabetes are expected to grow. In addition to treating diabetes, the inclusion of GLP-1 drugs for treating obesity and weight management issues is expected to be a significant growth driver and the demand for such drugs for weight loss has never been higher. Companies have also started to look for personalization opportunities in this domain.  For example, Eli Lilly, which manufactures Tirzepetide – an advanced GLP-1 drug, has started their distribution network along with personalized patient coaching. Innovations such as cell therapy and precision medicines will also play an important role. 

Technology Adoption 

Digital transformation and data can help pharma companies to increase their productivity and operational efficiency. Developing and establishing a secure digital core that includes a modern data foundation, flexible AI architecture, and smart business applications are key to driving growth and innovation. These capabilities allow companies to collect and integrate data across various platforms and locations. The data can then be leveraged to provide key business insights to decision-makers and managers. AI and ML can be of great use to generate intelligent business reports to reflect key business KPI’s and interpret market trends which in turn helps optimize business processes and maintain organizational agility. AI can also be adopted to deliver personalized patient care plans and has the potential to revolutionize disease detection and prevention. By analyzing large data sets, AI algorithms can detect subtle trends, patterns, and risk factors which can potentially contribute to certain diseases and epidemics. As per recent reports, Lilly is collaborating with OpenAI to discover novel medicines for treating drug-resistant bacterial infections. Such innovations will continue to revolutionize the way pharmaceutical companies operate. 

In the coming years, the ability to leverage AI and ML (Machine Learning) technologies will remain a critical differentiating factor which will provide pharmaceutical companies a key lever for gaining strategic advantage in the market. Such technologies can potentially be adopted across the drug development cycle, from the discovery of candidate molecules to streamlining clinical trials, resulting in faster time to market, while helping to reduce development, manufacturing, and logistical costs. Pharmaceutical companies also generate a huge amount of data from various sources. Managing such a huge volume of data efficiently while maintaining various regulatory compliances like IDMP, HIPAA, and GDPR is an ever-increasing challenge. Technology can play a key role in effectively managing this data in a secure and regulated manner while ensuring adequate monitoring and governance. There is also a need to integrate with third-party data providers to validate, enrich, and cleanse the data pharmaceutical companies are accumulating. Companies realize the value of maintaining high-quality data to drive their futuristic digital initiatives. Hence, as companies look to embrace newer technologies, a strong focus will remain on traditional areas such as master data management, data governance, and data quality. Senior industry leaders, such as Vas Narasimhan, CEO of Novartis, continue to emphasize the need for having access to high-quality, trusted data and the importance it has in the success of any digital initiative.  We expect companies to continue to invest in consolidating and streamlining their data assets, and this trend will only get stronger as more companies strive to make their business customer-focused, and data-driven. 

Improved Quality and Access 

Name-brand drugs are often expensive due to high initial investments in R&D and hence over a period of time, customers tend to move to cheaper options. Such migrations increase patient’s access to drugs while increasing pressure on pharma companies to provide high-quality generics at competitive prices. In addition to this, there are several existing and proposed policies focused on drug price reform and control. These factors mean that making high-quality drugs available at reasonable prices is still a challenge. The entry of Chinese pharmaceutical companies in Europe is also contributing to the evolving dynamics and pricing of pharmaceuticals. Per the European Parliament, the EU is 85-90% dependent on the Chinese market for all ingredients and 33% dependent on it for active ingredients. This poses critical challenges in terms of access and quality control, hence forcing policymakers to reevaluate their strategy to counter over-dependence on a single source.   

As per the 2023 report published by the WHO, 2021 saw a new high in global spending on healthcare. This reached US$ 9.8 trillion or 10.3% of global gross domestic product (GDP). Hence, debates around drug affordability, accessibility, and the strategies of payers in managing healthcare costs continue to remain a challenge for all parties concerned. Changes in policies and formulary decisions are also evident. Such adjustments are typically aimed at mitigating costs while avoiding drug overuse/abuse, indicating a greater level of scrutiny for access to critical and high-cost medications. The emphasis on policies to optimize access will continue to compete with efforts to ensure drugs are medically necessary. 

Mergers and Acquisitions  

Big Pharma will continue to focus on consolidating its market share and product pipelines through M&A. One such example is Pfizer’s acquisition of Seagen in a deal worth $43 billion. However, due to pushback from the United States Federal Trade Commission (FTC) on M&A, and high debt leverage, such large acquisitions will remain at moderate levels. At the same time, greater activity can be expected in smaller-sized acquisitions. Per a report published by S&P Global, the pharmaceutical industry will see robust growth through 2027.  

To conclude, the pharmaceutical industry will continue to see sturdy growth through 2024 and beyond.  Innovation and technology adoption will play a crucial role in this growth story while personalized health plans and policies to optimize access to high quality drugs will be significant contributing factors. M&A will continue to play a role but at relatively moderate levels; large acquisitions may be few and far between. However, as discussed above, there are some challenges and pitfalls that the industry needs to remain conscious of to ensure sustained growth and development.  

How can Fresh Gravity help?  

At Fresh Gravity, our team of domain experts and technology consultants have extensive experience working with some of the biggest pharmaceutical companies. We strive to enable and empower our customers by providing business-focused solutions on master data management, data quality, and governance. We have experts with hands-on experience working with and building cutting-edge solutions using AI/ML tools and various data platforms. We have also developed several in-house solutions to help our customers in areas such as clinical study protocol digitization, clinical study automation, IDMP compliance, and clinical data repositories. To know more about our offerings, please reach out to us at info@freshgravity.com

Data Management, Industry-agnostic

Understanding Product Data Management: Product MDM vs. PIM Solutions

May 15th, 2024 WRITTEN BY Fresh Gravity Tags: , , , , , , ,

Written By Monalisa Thakur, Sr. Manager, Client Success

In today’s evolving business landscape, trusted product data is crucial for accurate decision-making, customer satisfaction, and operational optimization. With the growth of digital commerce and multiple sales channels, organizations must ensure consistent and accurate product information across touchpoints. Flexible product data solutions drive personalized experiences and revenue growth. However, choosing between Product Master Data Management (Product MDM) and Product Information Management (PIM) can be confusing and challenging due to their subtle differences. 

Product MDM and PIM:  Key Capabilities and Benefits 

Both Product MDM and PIM solutions aim to establish a trusted “golden record” of product data. However, they differ in their objectives and hence, functionalities. 

Track #1: Product Master Data Management (Product MDM)

A Master Data Management (MDM) system is an enterprise-wide solution that focuses on managing and maintaining master data that can include ‘product’ as a domain, amongst other master data domains such as customers, suppliers, locations, and more. MDM aims to provide a single source of truth for data consistency and accuracy across the organization. A key purpose of MDM is also to create relationships, whether horizontal (for example, between multiple domains such as products, customers, vendors, locations, etc.), or vertical (for example, patients and products) that help fuel analytical business applications. 

The following is an illustrative diagram to depict the functional layout of a multi-domain MDM system, that consumes data from multiple sources and distributes the mastered data to consuming applications. 

Fig. 1: Sample multi-domain MDM including product as a domain 

The key benefits of a Product MDM solution are as follows: 

  • Gain a trusted and comprehensive 360° view of organization-wide product data.  
  • Consolidate siloed product data from diverse organizational systems. 
  • Create a single unique version of an organization-wide used Product (or a Product Family) record 
  • Establish clear relationships between products and other entities.  For example, products-customers (insurance industry) or product family-substances-ingredients (life sciences) 
  • Boost business efficiency and IT performance by enabling data profiling, discovery, cleansing, standardizing, enriching, matching, and merging in a single central repository. 
  • Leverage reporting and analytics for informed decision-making. 

Track #2: Product Information Management (PIM)

On the other hand, a Product Information Management (PIM) solution centralizes the management of product data – not necessarily just master data but hundreds of product attributes such as color, size, style, price, packaging, reviews, images, nutritional labeling, or digital assets – enabling streamlined collaboration and data enrichment. PIM standardizes and automates product information, ensuring trusted, enriched, and high-quality data for customer touchpoints, sales, and marketing channels. It might often uncover hidden customer and sales opportunities that may have been overlooked due to disconnected product data. 

The following is an illustrative diagram to depict the functional layout of a PIM solution, and the various aspects of product information that it may encompass.

Fig. 2:  Sample PIM solution 

A PIM solution aims to: 

  • Streamline collaboration on product content internally (within the organization) and externally (at all customer touchpoints). 
  • Automate workflows for product information management and approval. 
  • Accelerate time-to-market for new products. 
  • Enhance omnichannel capabilities and publish consistent, relevant, and localized product content. 
  • Supply any channel with correct and up-to-date product information. 
  • Expand sales and marketing reach to new channels. 
  • Securely exchange product data via data pools. 
  • Increase sales through rich product information, engaging customer experiences, and improved cross-selling opportunities. 

How do you decide if you need a PIM or MDM for your business? 

Let us try to figure this out by citing some common use cases businesses face:

Use Case Scenarios Product Master Data Management (P-MDM) Product Information Management (PIM)
Scenario 1:   A retail company with a large product catalog expanding its online presence 
Product Catalog Management  Not the primary focus, but can support catalog creation  Centralized product data repository for catalogs 
Scenario 2:   A manufacturing company wants to gain insights into product performance, sales trends, and customer behavior to make data-driven decisions 
Business Analytics and Reporting  Offers advanced analytics and insights for master data  Not the primary focus, but can provide some analytics support 
Scenario 3:   A global e-commerce company plans to expand its operations into a new region, requiring localized product catalogs, marketing materials, and language support 
Expansion into New Locations  Not the primary focus, but can support data expansion  Ready-to-use catalogs and assets for multiple regions, marketplaces, and storefronts 
Scenario 4:   A financial organization needs to establish data governance policies for managing product data, ensuring data security, privacy, and compliance with industry regulations. 
Establishing Data Policies  Focuses on data governance, roles, responsibilities, and controls  Not the primary focus, but can support data guidelines and policies 
Scenario 5:  An e-commerce company aims to increase sales by improving product visibility, enhancing product descriptions, and optimizing pricing strategies 
Increasing Sales  Not the primary focus, but can support sales optimization  Enables omnichannel engagement and quick creation of price rules 
Scenario 6:  A fashion brand wants to provide a seamless customer experience across online and offline channels by ensuring consistent product information and compelling marketing collateral 
Cross-Channel Consistency and Marketing Collateral  Not the key focus, might help to get accurate info, but is limited  Ensures accurate and up-to-date information is available across all customer touchpoints 
Scenario 7:  A retail company aims to provide personalized product recommendations, tailored pricing, and consistent experiences across different channels and touchpoints 
Personalized Customer Experiences and Omnichannel Engagement  Lacks the specialized focus on marketing and sales activities required for delivering personalized customer experiences across multiple channels  Creates and manages enriched product data for marketing purposes, supporting omnichannel engagement and personalized customer interactions

Therefore, while both Product MDM and PIM have overlapping capabilities, they are best suited for different needs and scenarios. Product MDM focuses on managing master data, data governance, and advanced analytics, while PIM specializes in catalog management, omnichannel engagement, and quick creation of price rules. 

At Fresh Gravity, we offer robust technological and functional expertise in implementing product data solutions, whether it is Product Master Data Management or Product Information Management. With a solid understanding of the intricacies of managing product data, we excel in designing and deploying tailored solutions to meet the unique needs of our clients. Our team’s proficiency extends across various industries, allowing us to leverage best practices and innovative strategies to optimize data quality, governance, and accessibility in this space. Through our commitment to excellence, we empower organizations to harness the full potential of their product data to drive efficiency, competitiveness, and growth.

Are you considering Product MDM or PIM?  Contact us at info@freshgravity.com and we will be happy to set up a session to answer your questions. 

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