Three pillars of the retail industry: Replenishment, allocation, and transportation 

The retail industry is one of the most dynamic and fast-paced sectors, comprised of a variety of stakeholders engaged in selling finished products to end-user consumers. In 2022, the U.S. retail sector was estimated at more than seven trillion dollars. The sector is projected to continue to grow, and by 2026 U.S. retail sales are expected to reach approximately USD 7.9 trillion. With the increased demand for consumer goods in different sectors and the ever-increasing choices of products at low costs, investments in the retail sector have also grown over the past few years.

As there is always an element of change in this industry, new challenges are encountered every day. Take product stockouts, for example. Suppose a customer walks into a grocery store to purchase items of their favorite brand but discovers that the product is not available. Frustrated by this, the customer chooses to either buy another brand or postpone the purchase; both scenarios are unfavorable to the business. The brand image and sales of the product are damaged because of this out-of-stock issue. The out-of-stock situation occurs when the inventory of a particular product is exhausted; this causes a problem for both suppliers and retailers.

There can be multiple reasons that would cause the product stockout, such as inaccurate inventory data, lack of demand forecasting, or an unseasonal spike in purchasing. Many of these underlying causes of stockouts can be avoided if the business implements adequate processes to be carried out every month.

To avoid situations like the stockout example above, retail companies need to develop a methodology for streamlining the following operations:

  1. Replenishment
  2. Allocation
  3. Transportation

3 pillars of retail industry

    These three operations create the three pillars of the retail industry that help monitor real-time insight into customer behavior and understand their buying patterns hence strengthening the retail foundation.


    Replenishment refers to a situation where the amount of stock left in the store is counted so that the right products are available at an optimal quantity. It is considered an essential aspect of inventory management as it ensures that the right products are being reordered to meet the customer demand.

    In operational terms, the efficiency of store replenishment has a significant impact on profitability. The effectiveness and accuracy of store ordering affect sales through shelf availability and storage, handling, and wastage costs in stores and other parts of the supply chain. By optimizing demand forecasting, inventory management, and setting of order cycles and order quantities by making them more systematic, the gains obtained are significant, often amounting to savings of several percent of total turnover.

    For companies that must manage a large number of SKUs, one of the most effective ways of making store replenishment more accurate, efficient, and cost-effective is by using a replenishment system specifically tailored to business operations. When many different products need to be managed, manual ordering is highly labor-intensive and expensive; this results in companies using the replenishment system.

    An efficient replenishment system reduces process costs, improves inventory turnover, and provides higher service levels. The system constantly monitors the stock, sales, and demand while considering the forecast changes in demand and adjusting the replenishment orders. Recognizing the sales frequency, sales value, or profit margin, the company can control its inventory in such a way that ensures long-term profitability. The replenishment system calculates the safety stock level for each SKU separately and sets them to meet the service level targets with efficiency, considering the predictability of demand.


    In allocation, the new stock of products is distributed to individual store units, such that they maximize the sale of the product and prevent any stock out situation in the future. This process enables the assigning of supplies so that they support the organization’s strategic goals. Having sufficient stock levels is an essential component for any retail business; with the changing consumer habits, it becomes crucial for the stock to be available in the right place at the right time.

    To meet new and increasing demands, retailers need an efficient process to gather, interpret, and analyze data from customer behaviors and habits, which would help get a more localized and specific idea of what is sold at a larger quantity in different locations. Items that are high sellers in one particular area may not sell well in others, so recognizing and monitoring this can ensure that the stock is allocated to the most needed location. Due to this, an opportunity is provided for the retailers to encourage sales by pushing stock of a similar type that a customer may favor at a particular location.


    Transportation plays a significant role in delivering the right stock of products at the right point of delivery. It connects the business to its supply chain partners and influences the customers’ satisfaction with the organization. With the ever-changing customer preferences and as their expectations continue to evolve, the transportation industry is undergoing a dramatic transformation to meet these demands.

    Today, data plays a vital role in shaping how the industry will progress amidst tough competition. Due to the maturation of automation technologies, AI will help the transportation industry to manage drivers and fleet managers. By employing the techniques of AI, fleet and truck adjustments will offer data in real-time, eventually improving the industry’s standard. The safety and retention of the drivers will also increase from these newly acquired standards, and with enhanced access to data, there will be transparent communication between drivers and carriers.

    The average time between goods purchasing and delivery decreases by using real-time findings, making retailers focus on transportation to improve their business performance. The ability to automate insights, alerts, and data exchange more quickly will be the game-changer for this industry.

    These three pillars of retail can be strengthened by adopting in-house solutions and capabilities like Trial Run, customer analytics, supply chain analytics, and store operation analytics.

    How could these solutions help the retail industry?

    Trial Run is a data-driven, cloud-based test management product used to test business ideas for sites and markets using Google Cloud capabilities combined with advanced technologies to improve customer experience, enhance product recommendations, streamline operations, optimize inventory, and enhance the supply chain.

    Trial Run helps in scientific and systematic testing, which can unlock insights and provide direction with the following tests:

    • Marketing and Merchandizing tests
    • In-store experience tests
    • Store operations tests

    Customer Analytics is an AI-driven suite that helps a retailer to know their customers in a better way by understanding the customer needs and preferences from every angle, like acquisition, engagement, retention, and growth, gaining insights that can fuel growth in marketing initiatives, loyalty programs, or eCommerce platforms.

    Supply-chain Analytics is an advanced analytics and intelligent automation solution that helps in end-to-end supply chain visibility to stay competitive, keeping the distribution networks customer-oriented and efficient while reducing environmental impact. It helps in streamlined operations, which results in better cost savings, ultimately delivering more value in every step of the supply chain process.

    Store Operation Analytics helps boost sales productivity and reduce costs across every facet of store operations – from labor, facilities, and inventory management to enhanced customer service and satisfaction.

    All these solutions and capabilities help understand the customer motivations, preferences, and desires to meet their demands and increase sales effectively, hence strengthening the pillars of the retail industry.


    To meet these growing customer expectations, retailers should give priority to collecting the customer data and analyzing it to support business decisions throughout their value chain. The inventory stocking patterns and shipping routes will shift in relation to patterns informed by this data. Retailers should make a concentrated effort to leverage the data while making critical business decisions, and to remain efficient; they must remain flexible and transform their operations as they capture more insights from their data.

    Over the past 20+ years, Fractal has helped many retail companies make their replenishment, allocation, and transportation operations more efficient by leveraging AI, engineering, and design. If you would like to learn more about optimizing these aspects of your retail business, please contact us to speak with one of our experts.

    Find how Trial Run enabled decision-making and helped clients with increased accuracy in test analysis and better ROI measurement resulting in an annual financial gain of $25Mn.

    4 min. read Enabling AI in personalized healthcare at scale

    The medical field has always been at the forefront of innovation, constantly seeking new ways to improve patient care. AI is reshaping industries, with the healthcare sector leading the way in harnessing Generative AI for drug discovery and personalized healthcare practices. But how exactly is it changing healthcare? In this article, we will explore the future of healthcare with Generative AI and how companies like Fractal leverage AWS to transform healthcare.

    Role of GenAI in the healthcare industry

    The healthcare industry is one of the most heavily regulated industries in the world. Developing new drugs and enabling new healthcare practices is a lengthy and expensive process that requires extensive testing and approval from regulatory bodies such as the Food and Drug Administration and the Department of Health and Human Services. However, generative AI has the potential to revolutionize the healthcare process by reducing the time and cost required to bring a new healthcare practice to market.

    Personalizing the data to decision journey with GenAI

    From targeted marketing campaigns to customized healthcare diagnoses, GenAI personalizes the data journey and enables the data to generate insights and provide real-time data-driven decisions.

    • Turning mountains of data into gold: AI shifts through vast clinical trials and research papers, unearthing insights and connections humans might miss. These insights empower quicker drug discovery, more effective healthcare practices, identification of promising therapies, and optimized trial design.
    • Personalizing the patient journey: Imagine an AI assistant crafting targeted care plans, explaining treatments in plain language, and even helping patients find financial assistance. Generative AI puts the power of knowledge in patients’ hands, empowering them to make informed decisions.
    • Marketing that’s more magic than medicine: AI helps craft personalized messages for healthcare professionals, predicting future trends and empowering data-driven strategies. By offering transparent patient data and crafting personalized care plans, AI ensures the right information reaches the right people for optimal treatment outcomes.

    GenAI can accelerate time to delivery in core business processes. It also helps improve product availability and increase product yield. For example, one use case in clinical trial operations involves the creation of protocols and related study documents. The large language model (LLM) can generate initial protocols and documents that fit the purpose by providing sufficient information as input to a LLM. This approach actively reduces the time required to create and revise documents such as legal agreements, patient reports, contracts, etc., ultimately accelerating trial launch.

    GenAI allows healthcare companies to access insights more quickly, benefiting the entire company’s supply chain, including research, trials, manufacturing, and commercialization. This enables faster decision-making, ultimately leading to improved product availability.

    Challenges of GenAI in the healthcare industry

    While Generative AI has enormous potential in healthcare, it has challenges. It is essential for healthcare companies to carefully evaluate these challenges and ensure that they have the necessary resources and expertise to implement this technology successfully. Addressing these challenges necessitates a multidisciplinary approach involving experts in AI, healthcare research, regulatory affairs, and data privacy.

    Challenges of GenAI in healthcare

    • Data quality: Poor quality data can lead to incorrect models and inaccurate predictions.
    • Regulatory compliance: Generative AI in healthcare may require regulatory approval, which can be time-consuming and costly.
    • Ethical concerns: The main concern is the potential misuse of Generative AI to create new drugs without proper testing and evaluation. Another drawback is the use of Generative AI to create drugs that are only effective for specific demographics, which can lead to inequality in access to healthcare.
    • Validation and accuracy: Establishing robust validation methodologies specific to the healthcare domain can be complex and time-consuming.
    • Personalized recommendations: Creating effective strategies and transparent patient treatment plans needs expertise in creating a holistic view of the customer’s journey, which is time-consuming and requires a dedicated workforce to achieve it.

    Fractal’s Gen AI solutions in action

    Currently, most healthcare companies deploy market intelligence analysts to mine information for insights, which can take time to retrieve information, incurring operational costs.

    Our GenAI solutions are helping the healthcare industry automate their workflow, saving time and energy and reducing costs.

    These are Fractal’s solutions, which use AWS services to transform healthcare.

    NBA Logo Next Best Action is an AI-powered B2B and B2C automated omnichannel orchestration engine that delivers personalized recommendations for healthcare clients. The solution integrates with data platforms like Cassandra and CRMs like Salesforce and Veeva to generate weekly recommendations automatically, creating a holistic view of customers’ journeys.
    Patient Jarvis Logo Patient Jarvis is an AI-powered tool that helps the sales team develop effective strategies, provides transparent patient treatment information, and helps create personalized care plans. It overcomes data format issues to ensure accurate information, leading to better care plans.
    GenAI Tagging Logo GenAI tagging serves as a transformative force in healthcare content analytics and personalization. Integrating rapid manual tagging and AI-generated descriptive metadata ensures data integrity and streamlines analytics through strategic data warehouse integration. The platform addresses challenges in maintaining efficiency and accuracy when scaling to diverse markets and content volumes.

    Check out other GenAI-powered Fractal solutions for the Healthcare industry on the AWS Marketplace.    

    Unlocking AWS and Fractal partnership to scale healthcare industry

    Fractal and AWS work together to leverage their combined strengths in the healthcare industry, tackling complex business challenges across marketing, sales, supply chain, and more.

    The healthcare industry has been facing challenges in reaching out to its target audience. With AWS services, Fractal delivers personalized marketing messages to their customers at the right time and in the right place.

    Using AWS services, Fractal is changing the game in the healthcare industry by improving marketing effectiveness, reducing costs, and enabling companies to comply with regulatory requirements. Its unmatched reliability, security, and data privacy make it a trusted technology and innovation partner to the global healthcare and life sciences industry.

    The COVID-19 pandemic has accelerated the trend towards virtual health, and this trend is expected to continue in the future. AWS services can help healthcare companies deliver virtual health services and stay ahead of the curve.

    Contact us to get started!

    Learn how we leveraged Fractal’s Next Best Action to enhance productivity for a global healthcare firm in this case study.

    4 steps to improve the data quality of your organization

    The world today is swimming with data, and organizations need to handle a large amount of it to derive valuable insights for decision-making. But data is only helpful if it is of good quality. According to SAP, bad data costs the US $3 trillion annually. These costs include employee’s time and effort to correct bad data and errors.

    Why is data quality important?

    Data quality is a base for data analytics and data science. It measures how well-suited your data is to accomplish a particular task accurately and consistently. Good data helps an organization make key spending decisions, improve operations, and develop growth tactics. Even though technologies like AI and machine learning have enormous potential to handle large volumes of data, they need good quality data to produce reliable results quickly.

    As data is an integral part of an organization, data quality impacts many aspects, from marketing to sales to content creation.

    Good quality data helps you make informed decisions, target the right audience, drive effective marketing campaigns, strengthen customer relationships, gain competitive advantage, and so on.

    Benefits of improving data quality

    In this competitive era, organizations try to understand their customers better and make better financial, marketing, and development decisions based on accurate data for a better ROI. Bad data is unstructured data that may show quality issues like inconsistency, inaccuracy, insufficiency, or even duplicate information. It could be misleading and even more harmful for a business than a lack of data.

    Improving the data quality of your company can result in the following advantages:

    Benefits of improving data quality

    • Data-driven decision-making: Decision-making is based on solid reasoning, and the correct data can only help make wise business decisions and provide the best outcomes.
    • Customer intimacy: Drive marketing and customer experience by analyzing entire consumer views of transactions, sentiments, and interactions by using data from the system of record.
    • Innovation leadership: Learn more about your products, services, usage trends, industry trends, and competition outcomes to help you make better decisions about new products, services, and pricing. ​
    • Operational excellence: Make sure the correct solution is provided quickly and dependably to the right people at a fair price.

          Challenges faced while maintaining data quality

          Poor data quality can lead to financial losses, increased complexity, and limited usefulness of real-world evidence. Understanding data quality challenges is crucial for informed decision-making, reducing business risks, and achieving data-driven goals. Explaining a few challenges below.

          • Data debt reduces ROI: Data debt refers to the negative consequences of accumulating poorly managed data. This includes inaccurate, inconsistent, incomplete, or inaccessible data. If your organization has a lot of unresolved data-related problems, such as issues with data quality, categorization, and security, it can hinder you from achieving the desired Return on Investment (ROI).
          • Lack of trust leads to lack of usage: A lack of data confidence in your organization leads to a lack of data consumption, which has a detrimental impact on strategic planning, KPIs, and business outcomes.
          • Strategic assets become liabilities: Poor data puts your company in danger of failing to meet compliance standards, resulting in millions of dollars in fines.
          • Increased expenses and inefficiency: Time spent correcting inaccurate data equals less workload capacity for essential efforts and an inability to make data-driven decisions.
          • Adoption of data-driven technologies: Predictive analytics and artificial intelligence, for example, rely on high-quality data. Delays or a lack of ROI will come from inaccurate, incomplete, or irrelevant data.
          • Customer experience: Using bad data to run your business can hinder your ability to deliver to your customers, increasing their frustration and reducing your capacity to retain them.

          Improving data quality with Fractal’s methodology 

          Maintaining high levels of data quality allows organizations to lower the expense of finding and resolving incorrect data in their systems. Companies can also avoid operational errors and business process failures, raising operating costs and diminishing revenues. 

          Good data quality enhances the accuracy of analytics applications, leading to improved business decisions that increase sales, improve internal processes, and provide firms with a competitive advantage over competitors. High-quality data can also help increase the use of BI dashboards and analytics tools. If analytics data is perceived as trustworthy, business users are more inclined to depend on it instead of making judgments based on gut feelings or their spreadsheets. 

          Fractal has developed a process that significantly improves data quality across enterprises. Here’s how we do it. 

          Data Preparation and Rule Calculations 

          Fractal helps handle large volumes of data, preparing it for further processing. It also performs calculations based on data rules, identifying defective records in the data. 

          Data extraction and preparation 

          Fractal leverages a back-end engine for data extraction, data preparation, and data rules configuration on various data sets. 

          Optimized process 

          The focus is an optimized process with minimal processing time, parallel processing, and data aggregations to reduce the storage space and provide the user’s best dashboard performance. 

          Data quality improvement 

          Fractal helps transform the data cleansing operation with faster reduction of data defects, improved data quality, and tracking key KPIs like asset score, coverage, and conformance. 

          How can Fractal help maintain data quality with Google Cloud?

          Fractal leverages all the services provided by Google Cloud and supports integrations with the Google Cloud Platform. It also determines which Google Cloud services best meet Fractal’s data quality needs for each project.

          Here are some ways Google Cloud can help maintain data quality.

          • Data Governance: It helps automatically detect and protect sensitive information in data, ensuring data privacy and compliance. It also helps enable granular control over data access, preventing unauthorized modifications or deletions.
          • Data Profiling & Cleansing: It offers a user-friendly interface for data cleaning and transformation, including outlier detection, data standardization, and data validation. It also provides AI-powered tools for data profiling and anomaly detection, helping identify data quality issues proactively.
          • Data Monitoring & Improvement: It offers comprehensive dashboards and alerts for monitoring data quality metrics, allowing for proactive identification and resolution of issues. It also helps run large-scale data quality checks and analysis, providing deeper insights into data quality trends.
          • Machine Learning & AI Integration: It provides tools for developing and deploying custom AI models for data quality tasks, such as entity extraction, classification, and matching. It also helps build serverless data quality functions that can be triggered on specific data events, ensuring real-time data quality checks.


          In today’s data-driven world, maintaining high-quality data is no longer an option but a necessity. Poor data quality can lead to financial losses, operational inefficiencies, and inaccurate decision-making. By leveraging Fractal’s data quality methodology and Google Cloud’s powerful tools and services, organizations can effectively address data quality challenges and unlock their full potential.

          Fractal empowers organizations to achieve data quality excellence. When combined with Google Cloud’s data quality capabilities, Fractal delivers a comprehensive solution for managing and improving data quality throughout the entire data lifecycle.

          Are you seeking help to improve the data quality of your organization? Contact us to get started!


          Trail Run illustration

          Nowadays, companies want to be able to test business decisions and ideas at a scale large enough to believe the results but also at a scale small enough to reduce the large investments and risks that come with full-scale execution.

          Trial Run helps conduct tests such as altering store layouts and remodeling, loyalty campaigns, and pricing to recommend the best possible tailored rollout to maximize gains. You can now implement new ideas with minimal risk and maximum insight with the power of business experimentation. Trial Run helps you:

          • Test each business idea at scale to generate customer insights without excessive spending.
          • Find out why your customers behave the way they do.
          • Learn how your customers will react to your new big idea.

          What is Trial Run?

          Trial Run is a data-driven, cloud-based test management platform used to test business ideas for sites, customers, and markets. It is intuitive for beginners and experts alike and helps companies scale experimentation efficiently and affordably.

          Trial Run supports the entire experimentation lifecycle, which includes:

          1. Design: Build a cost-effective and efficient experiment that gives you the data you need to proceed with confidence.
          2. Analyze: Work with variables that provide you with targeted and actionable insights.
          3. Act: Use the generated insights to ensure that your new rollout provides your stakeholders with the precise ROI.

          Trail Run illustration

          Trial Run offers valuable support across various operational and administrative departments, including Retail, Consumer Packaged Goods (CPG), and Telecommunications.

          Through its scientific and methodical testing approach, Trial Run can uncover fresh perspectives and guide decision-making through a range of tests, including:

          • Marketing and merchandising strategies.
          • Enhancing the in-store experience.
          • Examining store operations and processes.

          These tests are carried out at the store operations and process, product, or consumer levels.

          Trial Run offers a dynamic, affordable, and modern way of experimentation so you can stay relevant in a rapidly changing business environment. Trial Run also helps you to drive experiments through:

          • Driver Analysis: Identify key factors that are significant in driving the business outcomes
          • Rollout simulator: Maximize the ROI of a campaign
          • Synthetic Control Algorithm: Determine the right number of control stores with appropriate weights to create the replica of the test store
          • Experiment calendar: Avoid overlaps in experiments
          • Clean search: Let Trial Run parse the experiment repository and find entities that are available for a test.

          What you can expect from Trial Run

          • Graphical design elements make it easy to use the program as an expert or a beginner
          • Automated workflows can guide you through the process from start to finish
          • Highly accurate synthetic control results with automated matching processes that only require minimal human intervention
          • Experiments at speed and scale without the hassle of expert teams or expensive bespoke solutions
          • Training, troubleshooting, and best practices from the best in the business
          • Easy pilots to help your new idea go live in as little as 6 to 8 weeks.

          Trial Run stands out from other solutions by offering a transparent methodology and easily explainable recommendations. Trial Run utilizes a cutting-edge technique called “synthetic control” for matching, ensuring precise results. Trial Run can be used as a SaaS offering that is easily scalable based on demand and can be hosted on the cloud of customer’s choice. With Trial Run software, customers have unlimited test capabilities, enabling them to design and measure numerous initiatives without any restrictions. Finally, Trial Run success is proven in enterprises, with over 1,000 use cases deployed on our platform.

          How do I get started?

          Are you ready to implement cutting-edge technology to help you build cost-effective and efficient experiments that provide you with the data you need to make decisions with confidence?

          If you want to learn more about the key concepts behind successful Trial Run implementation, check out the solution page:

          Interested in learning more about how Fractal can help you implement Trial Run, contact us to get in touch with one of our experts.

          Role of data & analytics in alleviating health inequities

          The COVID-19 pandemic and the resulting global recession caused a significant increase in poverty around the world as many families lost their sources of income. The poorest families, especially those without access to social protection, were hit the hardest. As a result, the World Health Organization urged governments and health organizations to pay attention to the social determinants of health to take steps to optimize community well-being.

          Social determinants of health (SDOH) are any of a variety of non-medical factors that influence health outcomes. They encompass the conditions in which people are born, grow, live, learn, work, play, worship, and age — shaping their overall health. These factors include attributes associated with one’s environment, patterns of social engagement, and one’s sense of security and well-being. Collectively, SDOH influences health, working life conditions, and quality of life.

          In this article, we’ll discuss how social determinants of health impact overall wellness across populations, including disparities in healthcare, and the role data can play in alleviating these inequities and shaping healthcare outcomes.

          Social determinant factors

          Patients’ health depends not only on the care they receive in a provider’s office but also on social determinants of health such as individual lifestyle choices, living situation, and access to healthy food.

          According to a study by the CDC, social determinants of health fall into five broad groups:

          HIMSS Illustration



          Key benefits of studying social determinants

          Addressing social determinants is important for improving health and reducing health disparities. Understanding social factors gives valuable insight into possible negative health outcomes for patients in many domains. Below are the key benefits of studying social determinants of health.

          Holistic healthcare: Incorporating social determinants into healthcare practices fosters a more holistic and comprehensive approach to patient care. For instance, factors such as a patient’s education, income level, and environment should be considered when providing treatment and care.

          Addressing health inequities: Social determinants have an important influence on health inequities – the unfair and avoidable differences in health status seen within and between countries.

          Resource development: Acknowledging social determinants can initiate the development of resources to solve inequality and strengthen the overall health of the community.

          Influencing health outcomes: Research shows that social determinants can be more important than healthcare or lifestyle choices in influencing health outcomes.

          The impact of social determinants of health

          Social determinants of health have a significant impact on people’s health, well-being, and quality of life. A growing body of research indicates:

          • Children born to parents who haven’t completed high school are more likely to live in environments that contain barriers to health.
          • Poor individuals who are white are less likely to live in areas of concentrated poverty than poor racial and ethnic minorities.
          • As income decreases, the likelihood of premature death increases.
          • There is a direct link between the likelihood of smoking, shorter life expectancy, and lower income.
          • The environment in which an individual lives may impact future generations.
          • Stress related to disparities has a direct link to health and often results from overlapping factors.

          Negative social determinants of health can lead to disparities in healthcare, which can be costly and inhibit the overall quality of care and population health. This can result in added healthcare expenses, loss of productivity, and premature death. According to the Kaiser Family Foundation, 30% of direct medical costs for black, Hispanic, and Asian Americans are unnecessary costs incurred due to inefficiencies, disparities, or inequities in the healthcare system. In addition, the US economy loses an estimated $309 billion annually due to the direct and indirect costs of disparities.

          Role of data & analytics in alleviating healthcare inequities

          Barriers like a lack of data standards and costly datasets can hinder an organization’s access to social determinants information. However, developing an approach for more holistic patient care will be necessary for organizations looking to improve patient and population health, whether the data is complete or not.

          Data and analytics are vital in helping to end these disparities and ensuring that all populations have the same access to services and care, not only for COVID-19 but also for all diseases and disorders that threaten public health.

          Through data analytics and population health management, providers can improve patient outcomes, enhance care management, and address social determinants of health. Nowadays, data analytics are helping providers replace the “one size fits all” care mentality to deliver value-based care. Providers can assess which processes are the most effective methods for wellness and prevention within value-based care models. With population health management, organizations can consider physical and social determinants of health that may impact individuals and focus on “well care” rather than waiting for a patient to become ill.

          Building a better healthcare system with Fractal

          Health disparities and inequities are shaped by a multitude of factors in an individual’s socio-economic and healthcare journey. The health outcomes of an individual are significantly influenced by social determinants of health of the community in which they reside.

          Fractal’s RAISE, powered by AWS, is an AI-powered solution that helps organizations speed up population health and health equity journeys. RAISE combines a member’s community data with clinical and social needs, assisting organizations in crafting the right interventions. It also creates a multifaceted approach involving data, analytics, and AI to advance health equity. It helps identify the drivers of inequities and disparities that are specific to the members of the organization.

          AWS equips Fractal’s AI/ML solutions with the scalability, reliability, and security needed to deliver its solutions to healthcare providers and patients worldwide. By leveraging AWS, Fractal is helping to make a real difference in the lives of people affected by health inequities by supporting community-based healthcare organizations, advocating for policies that address the social determinants of health, and promoting health education and literacy.

          Fractal recently led an on-demand webinar in collaboration with the Healthcare Information and Management Systems Society (HIMSS) to discuss health inequities. It explained how Fractal has been solving various healthcare-related issues using advanced AI/ML solutions.

          The webinar featured Fractal’s Chief Practice Officer, Matt Gennone, and Dr. David Nash, an advisor at Fractal. They discussed the economic impact of health inequities and delayed care and how healthcare can return to its “true north” of providing high-quality care.

          To know more, watch this session.

          Contact us to learn more.

            Model development diagram

            Operationalizing and scaling machine learning to drive business value can be challenging. While many businesses have started diving into it, only 13% of data science projects actually make it to production. Moving from the academics of ML to real-world deployment is difficult, as the journey requires finetuning ML models to fit the practical needs of a business and ensuring the solution can be implemented at scale.

            Many organizations struggle with ML operationalization due to a lack of data science and machine learning capabilities, difficulty harnessing best practices, and insufficient collaboration between data science and IT operations.

            Common challenges with ML operationalization

            Many organizations get attracted to buzzwords like “machine learning” and “AI,” and spend their development budgets pursuing technologies rather than addressing a real problem. ML projects are an investment, and obstacles in operationalizing the solution make it even harder for the business to realize value from these solutions.

            Here are some common ML operationalization bottlenecks and the solutions to tackle them.

            • Lack of communication, collaboration, and coordination: Proper collaboration between the data scientist team and other teams, like business, engineering, and operations, is crucial. The ML project may not add real-world business value without proper alignment and feedback.
            • Lack of a framework or architecture: When ML models lack the proper framework and architecture to support model building, deployment, and monitoring, they fail.
            • Insufficient infrastructure: ML models use vast data to train the model. Most of the time is spent preparing data and dealing with quality issues without the proper infrastructure. Data security and governance are crucial factors that must be considered in the initial phase.
            • The trade-off between prediction accuracy and model interpretability: Complex models are generally harder to interpret but provide more accurate predictions. The business must decide what’s an acceptable tradeoff to get a “right-sized” solution.
            • Compliance, governance, and security: The data science team may not always consider other issues like legal, compliance, IT operations, and others that occur after the deployment of ML models. In production, setting up performance indicators and monitoring how the model can run smoothly is important. So, understanding how ML models run on production data is a crucial part of risk mitigation.

            Unfortunately, many ML projects fail at various stages without ever reaching production. However, with the correct approach and a mix of technical and business expertise, such as that provided by Fractal’s data science team, it is possible to avoid or quickly resolve many of these common pitfalls. Fractal can help organizations deploy more ML models to production and achieve a faster time to value for ML projects with the tools, practices, and processes of MLOps.

            Starting with the business objective

            Fractal’s proven MLOps methodology helps streamline and standardize each stage of the ML lifecycle from model development to operationalization. It allows collaboration between technical and non-technical users and empowers everyone to participate in the development process actively.

            We have helped many organizations leverage MLOps, allowing them to overcome their challenges. It includes a process for streamlining model training, packaging, validating, deployment, and monitoring to help ensure ML projects run consistently from end to end.

            Our successful 5-stage model

            Model development diagram

            1. Train: We create and train an initial model based on available data, business requirements, and desired outcomes.
            2. Package: Once the model is trained, we package up the model to make it easy to test, iterate, and deploy at scale.
            3. Validate: Later, we help validate models by measuring candidate models against predefined KPIs, deployment testing, and testing application integrations.
            4. Deploy: On validating, we deploy models by identifying the deployment target, planning the deployment, and then deploying the models to their production environment. We ensure that the services are implemented to support scalability, data pipelines are automated, and a model selection strategy is implemented.
            5. Monitor: Finally, we monitor models to track behavior, continuously validate KPIs, measure accuracy and response times, watch for drift in model performance, and more.

            Google Cloud services for ML model deployment

            We can help teams successfully deploy and integrate more ML models into production and achieve a faster time to value for ML projects with more efficient model training using Google Cloud services such as:

            • Google Cloud Storage: It enables organizations to store, access, and maintain data so that they do not need to own and operate their own data centers, moving expenses from a capital expenditure model to an operational expenditure.
            • Cloud Functions: It provides a simple way to run code responding to events with minimal configuration and maintenance. Cloud Functions are event-driven, meaning they can be triggered by changes in data, new messages, and user interactions.
            • Big Query: A fully managed enterprise data warehouse helps you manage and analyze your data with built-in features like machine learning, geospatial analysis, and business intelligence.
            • Kubernetes engine: A solution to help organizations achieve zero ops. Kubernetes is an open-source container orchestration system for automating software deployment, scaling, and management.
            • Data Proc: It is a fully managed and highly scalable service for running Apache Hadoop, Apache Spark, and 30+ open-source tools and frameworks.
            • Vertex AI: It is a machine learning platform that lets you train and deploy ML models and AI applications and customize large language models (LLMs) for use in your AI-powered applications frameworks.

            Leveraging both business and technical expertise

            MLDEVOps diagram

            Our ML model lifecycle unifies data collection, pre-processing, model training, evaluation, deployment, and retraining to a single process that teams maintain. It allows businesses to quickly tackle obstacles faced by the data scientists and IT operations team while providing a mix of technical and business expertise.

            How Fractal’s methodology benefits organizations

            • Eliminates guesswork
            • Supports consistency
            • Enables continuous packaging, validation, and deployment of models to production
            • Rapid time to value
            • Accelerate time-to-value and time-to-deployment
            • Efficiently manage data error and model performance
            • Increase model scalability during training and serving


            As we continue through 2023, the MLOps market is surging rapidly. As ML applications become a key component for maintaining a competitive advantage, businesses realize they need a systematic and reproducible way to implement ML models. According to the analyst firm Cognilytica, MLOps is expected to be a $4 billion market by 2025. Fractal has deep expertise in MLOps and can help deliver solutions for unique business challenges across virtually all industries and sectors.

            Ready to begin leveraging MLOps in your organization? Contact us to get started.

            Benefits of DevOps

            Technology projects require maintenance throughout the entire lifecycle, from development to deployment and monitoring. Maintaining a project from version to version can become a manual and strenuous process. Developers must take special considerations at each stage to ensure smooth rollouts. Failure to do so can result in extended-release planning cycles ensuring the software is ready for use by end users.

            Development and IT Operations teams may end up spending unnecessary cycles supporting and fixing buggy software. And even worse, failed software releases can impact a company’s financial performance through operations inefficiencies, lost sales, and customer attrition. Failure to maintain working software can impact SLAs, regulatory compliance for some industries, resulting in fines or legal action.

            Successful organizations have adapted and created a set of best practices for governing projects, called DevOps.

            DevOps illustration

            What is DevOps?

            DevOps aims to create a common culture that brings together the people, processes, and technology to deliver value (i.e., working software) to end-users.

            It has also come up with procedures for automating many manual maintenance steps to reduce the time it takes to develop, test, and deploy software. Many companies are rushing to implement DevOps to avoid the high costs associated with manually maintaining projects.

            Benefits of DevOps on Google Cloud

            If you’re asking this question, keep reading. Outlined below are three key benefits of implementing DevOps in your organization.

            Benefits of DevOps

            1. Improved quality

            • Standardized tools and processes (i.e., Google Cloud DevOps and Agile Project Management) help keep quality consistent across projects and releases
            • Increased DevOps flow on Google Cloud helps improve software quality, delivery, and operations which leads to maintained security and faster deployment from the start
            • Quality control implemented through source control branching, code reviews, environment management, release management, etc.
            • Reduced fire drills and break-fix measures because of following DevOps best practices

            2. Reduced effort

            • Fewer manual processes and interventions through improved automation
            • Improved software stability leads to faster deployment with minimum manual intervention
            • Lower effort to support/maintain because solutions have gone through the appropriate governance and management processes
            • Leverage templates for common infrastructure builds/configurations to accelerate new projects going forward

            3. Increased collaboration

            • Agile project management structure encourages frequent collaboration among team
            • Google Cloud provides robust CI/CD capabilities, allowing teams to automate the entire software delivery pipeline. By integrating development, testing, and deployment processes, DevOps teams can collaborate more effectively and achieve faster and more reliable software releases
            • Improved communication channels enable the team to identify, track, and manage risks as they arise
            • Clear direction and prioritization through collaboration between stakeholders, developers, and end-users

            Hopefully, this helps you better understand some of the benefits that implementing DevOps using Google Cloud can bring to your business.

            Implementing DevOps is a journey and is not as easy as installing a package, flipping a switch, or buying new technology. Fractal specializes in helping our customers through the process of implementing DevOps, no matter what their current maturity level is. Fractal can provide strategic planning, technology assessments, proof of concepts, as well as technical resources to get you started on your DevOps journey.

            Interested in learning more about how Fractal can help you implement DevOps? Please contact us for additional information from our experts.