Key advantages of managed services

Access to Specialized Skills

Access to Specialized Skills

Organizations can tap into a pool of specialized talent without the need to hire and train an in-house team, overcoming the shortage of unique IT skills.

Cost Savings and Predictable Expenses

Cost Savings and Predictable Expenses

Customers pay only for the work done and can easily scale the team without the lengthy hiring process. Clients can avoid hefty upfront costs associated with hardware, software, and infrastructure investments.

Focus on Core Business Activities

Focus on Core Business Activities

By offloading the responsibility of managing specific IT areas to a third-party service provider, organizations can concentrate on their core business functions. This allows better focus on strategic initiatives and core competencies, while IT experts handle the complexity of data processing.

Scalability and Agility

Scalability and Agility

Managed IT services are designed to scale with the needs of the clients’ business. Whether it is expanding operations, adding new users, or adapting to technological advances, there is no problem to quickly adjust resources to accommodate changing requirements.

Data & Analytics Services

AI Services

The Scope of Services provided by Comarch BI

  • Data modelling

    • Data model design - Creating the structure of a data model, taking into account dimension hierarchies, relationships between them, and complex analytical categories.
    • Technology and tools selection - Determining the appropriate technologies and tools for implementing an analytical model, considering organizational requirements and data availability.
    • Data integration - Combining data from various sources to create a coherent and comprehensive dataset for analysis, including the creation of multi-source models.
    • Multidimensional models implementation - Developing multidimensional models that facilitate data analysis from various perspectives.
  • Data migration execution and advisory

    • Data assessment and profiling - Analyzing the structure and characteristics of migration data to ensure compliance with reporting requirements. Profiling data to identify potential issues that may affect reporting performance.
    • Data mapping and transformation - Establishing connections between individual elements of source data and target data. Applying transformations to ensure compatibility between source and target systems. 
    • Data acquisition - Extracting data from various sources, focusing on extracting relevant information for BI analysis and loading transformed data into a DWH/data lake.
    • Data testing - Verifying the accuracy and completeness of migrated data. Testing data consistency between sources and the reporting environment.
    • Migration validation - Ensuring the accuracy of analyses after migration. Conducting acceptance tests by BI Tool users.
    • Cloud data migration - Creating a personalized data migration plan to the cloud. Adapting or designing application/DWH architectures for efficient operation in the cloud. Configuring performance monitoring tools for cloud application operation and optimizing operating costs.
  • Data governance services

    • Defining data standards - Establishing uniform standards for data structure, format, and quality to ensure consistency across the organization.
    • Data access control - Implementing access control mechanisms to ensure secure data access is available only to authorized users.
    • Monitoring data quality - Establishing real-time data quality monitoring processes, including detection and correction of data inaccuracies.
    • Developing data policies - Defining policies related to data collection, storage, processing, and sharing to ensure compliance and confidentiality.
    • Developing a data lifecycle management plan - Defining a data lifecycle management strategy covering data collection, processing, and storage.
  • Data quality services

    • Defining data quality rules - Creating rules and standards that specify which data should be considered correct and incorrect.
    • Creating and managing data dictionaries - Developing and maintaining a data dictionary that contains definitions, descriptions, and dependencies between different data elements.
    • Establishing data cleansing mechanisms - Designing and implementing processes for automatic data cleansing to eliminate errors and inconsistencies.
    • Monitoring data quality - Implementing tools and processes for real-time monitoring of data quality.
    • Automating data quality assessment - Developing scripts for regular data quality assessment, identifying issues, and generating reports.
  • Master data management services

    • Data identification and standardization - Establishing clear identifiers for data and standardizing formats.
    • Data deduplication - Implementing mechanisms to remove duplicate data, ensuring the uniqueness and integrity of stored information.
    • Creation of data hierarchies - Constructing data hierarchies to facilitate complex analyses, particularly in the context of data relationships.
    • Data protection - Implementing security measures to safeguard data against unauthorized access.
  • BI dashboard and report development

    • Creating SQL queries - Developing queries that retrieve the necessary data from source systems for reporting purposes.
    • Data modelling - Preparing data models optimized for business analysis, facilitating the easy combination of different datasets for generating reports and dashboards.
    • Report and dashboard design - Designing the structure of analyses, including selecting appropriate forms of data visualization, to present data in a user-friendly manner for end-users.
    • Performance optimization - Optimizing report performance through database indexing and query optimization. Verifying analyses for speed of generation and data loading to ensure smooth operation even with large datasets and user numbers.
  • Data warehouse, data lake and data lakehouse design and implementation

    • Business requirements analysis - Conducting detailed analysis of business needs and data requirements. Identifying the type of data to be stored, its structure, and relationships.
    • Architecture design - Determining the appropriate architecture for the data warehouse/data lake, considering the type of data, scalability, and performance. Choosing suitable technologies and tools, such as databases, data processing tools, cloud computing, etc.
    • Data modelling - Developing data models, including schemas, tables, relationships, and keys.
    • Data integration - Implementing “extract, transform, load” (ETL) processes to move data from multiple, disparate sources to the data warehouse/data lake. Creating data pipelines for continuous data streaming and updates.
    • DWH/data lake testing - comprehensive testing set, which can include ETL/ELT testing, BI testing, DWH performance testing and security testing.
    • DWH/data lake implementation and support - building a DWH tailored to each customer’s unique data consolidation/storage needs and implementing it with the  target ecosystem. Providing DWH support to identify and solve performance issues, ensuring stability for timely and high-quality data flow to business users, and reducing storage and processing costs.
  • ETL (extract, transform, load) development and support

    • Optimization of existing ETL processes - Analysis, optimization, and automation of existing processes to increase efficiency, reduce data processing time, and improve data quality and consistency.
    • ETL process design and implementation - Creating comprehensive processes that enable data extraction from various sources, transformation according to business requirements, and loading into appropriate target systems.
    • Maintenance and technical support for ETL processes - Providing continuous technical support for ETL processes, including performance monitoring, troubleshooting, system updates, etc.
    • Monitoring and error management - Implementing real-time process monitoring mechanisms, identifying and managing errors, and deploying rollback and data reprocessing mechanisms in the event of failures.
    • Data integration - Designing and implementing solutions to integrate data from multiple, disparate sources such as databases, text files, web applications, APIs, etc., to create a cohesive dataset.
  • Performance tuning services

    • SQL query analysis - Conducting detailed analysis of SQL queries used to retrieve data from databases for reporting and analysis. Identifying and optimizing queries that impose a heavy load on the database can significantly improve performance.
    • Indexing - Evaluation and optimization of index structures in the database. Proper indexing can shorten query response times and speed up data analysis processes.
    • Data model optimization - Analysis and optimization of the data model used in the BI system. Verifying whether the relationships between tables are optimized and the data structure is tailored to reporting needs.
    • Server configuration - Optimization of report server settings, including memory management, connection settings, and data access. Correct server configuration can significantly speed up report generation.
    • Monitoring and tuning hardware resources - Monitoring the performance of hardware resources such as CPU, RAM, and hard drives, and adjusting hardware configuration to the needs of the BI system.
  • BI audit and technology advisory

    • Audit of existing analytical solutions - The audit encompasses various areas extracted from the BI environment and focuses on the toolset used in the company, their specifics, and technical capabilities. Monitoring and evaluating the performance of existing analytical solutions allow the identification of bottlenecks and proposing ways to improve them.
    • Data processing analysis - Optimization of data warehousing and reporting processes. Recognizing potential threats and risks associated with data processing, such as data loss, security breaches, or legal compliance issues.
    • Evaluation of reporting availability and performance - Analysis of the availability of existing reports and the ability to quickly create new ones. Validating the report generation process, from data acquisition and storage in the database, through the ETL process, construction of views and reports, enables assessing the availability of each of these points in terms of the ability to quickly implement changes and responsiveness.
    • Data quality evaluation - Analysis of data quality in the system, including completeness, accuracy, consistency, and timeliness. With a primary focus on examining the completeness of data sets, verifying proper mapping for building utilized indicators, or evaluating the method of data acquisition from sources.
    • Technical advisory - Providing advice on selecting the optimal analytical solutions, creating an implementation schedule, and setting future project development directions.
    • BI architecture design - Developing a comprehensive solution architecture, encompassing technical components and integrations with existing systems.
  • BI project management

    • Project assessment and analysis - Analysis of project goals, required timeframes and budgets, workflow and communication flows. This also includes conducting feasibility studies, estimating resource needs, conducting interviews with project participants, and assessing project risk.
    • BI project planning - Developing BI project plans, including defining project goals, schedules, budgets, roles and involvement of individual project members. An important element is creating a project roadmap with phases, tasks, and outcomes, as well as planning the solution architecture and its functional scope.
    • Project resource management - Assigning project tasks according to employee skills, coordinating teams, monitoring progress, and managing human resources within the project. Creating a project staffing plan, as well as developing an optimal communication process scheme.
    • Monitoring and managing risk - Identifying, assessing, and managing risks associated with BI projects, including technical, operational, and business risks. Determining measurable key performance indicators (KPIs) for the entire project and for individual project milestones. Monitoring deviations within the assumed schedule and budget, and, if necessary, implementing contingency plans or corrective actions.
  • Proof of concept (PoC) preparation

    • Analysis of business requirements - Assessment of customer needs and identification of goals to be achieved through the PoC.
    • Design and concept development - Creation of a preliminary solution concept to be tested as part of the PoC.
    • Prototype implementation - Building a prototype or demonstrative version of the solution to test its functionality and performance.
    • Testing and gathering feedback - Conducting tests to assess whether the solution meets business and technical expectations.
    • Reporting and results analysis - Development of a final report containing conclusions and recommendations for further actions based on the proof of concept results.
  • The Scope of Services provided by Comarch BI

    Determining regulatory compliance requirements for machine learning (ML)

    Regulatory landscape analysis - Conducting thorough analysis of relevant regulations and compliance standards that apply to the specific domain and geographic location where the ML service will be deployed.

    Data governance and privacy assessment - Evaluating data governance practices to ensure compliance with regulations governing the collection, storage, and processing of sensitive information.

    Ethical AI and bias mitigation - Implementing mechanisms to address and mitigate biases in ML models to comply with regulations emphasizing fairness and ethical considerations.

    Security and model robustness - Conducting vulnerability assessments to identify and address potential threats to the ML system, aligning with regulations that mandate robust cybersecurity practices.

    Determining regulatory compliance requirements for machine learning (ML)

    Machine learning model development

    Machine learning model development

    Data preprocessing - Cleaning, transforming, and organizing the raw data to ensure suitability for training a machine learning model. 

    Feature selection - Choosing the most relevant features from the dataset that contribute the most to the predictive power of the model. 

    Model selection/architecture design - Selecting the appropriate machine learning algorithm or model architecture for the problem at hand. Choosing between different types of models (e.g., decision trees, neural networks) and tuning hyperparameters to optimize model performance.

    Model training - Training the selected model on the preprocessed data and evaluating its performance using appropriate metrics (e.g., accuracy, precision, recall, F1-score). 

    Model deployment and monitoring - Integrating the model with existing systems, setting up monitoring to track its performance over time, and implementing mechanisms for model updates and maintenance.

    Audit of existing AI/ML environment

    Audit of existing AI/ML environment

    Data governance and quality assessment - Examining the quality, completeness, and accuracy of the training and testing datasets used in the machine learning models. Evaluating data governance policies and practices, including data access controls, encryption, and data lineage.

    Model performance - Checking the performance of machine learning models, considering metrics such as accuracy, precision, recall, and F1 score.

    Algorithmic fairness and bias assessment - Verifying the AI/ML models for biases in predictions and outcomes. Implementing fairness metrics and assess the fairness of model outputs across different demographic groups.

    Security and privacy analysis - Conducting a security audit to identify vulnerabilities in the AI/ML infrastructure, including model deployment, API endpoints, and data storage. Ensuring that proper encryption measures are in place for data both at rest and in transit.

    Monitoring and maintenance procedures - Implementing  monitoring procedures to ensure ongoing performance and identify issues promptly. Verifying the existence and effectiveness of mechanisms for model versioning, updates, and retraining to adapt to evolving data patterns.

    Development of custom AI solutions

    Development of custom AI solutions

    Problem definition and requirement analysis - Understanding the problem domain, defining clear objectives for the AI solution, and gathering requirements from stakeholders.

    Data collection - Collecting relevant data from various sources, such as databases, APIs, or sensors. It also includes data preprocessing tasks such as cleaning, filtering, and transforming the data to ensure suitability for training machine learning models.

    Algorithm selection and model design - Designing the architecture of the AI model, including the selection of layers, nodes, and activation functions. Optimizing hyperparameters and considering factors like model interpretability, scalability, and resource requirements.

    Implementation and integration - Developing APIs or libraries for model inference, integrating the model with existing software infrastructure, and ensuring compatibility with other components of the system.

    AI Solutions Advisory

    AI Solutions Advisory

    Strategic AI roadmapping - Formulating an AI strategy that aligns with the enterprise's goals and objectives. This encompasses the identification of potential AI use cases, assessment of the feasibility and impact of AI adoption, and the creation of a roadmap for its implementation.

    Governance frameworks - Setting up policies, guidelines, and best practices for the management of data, model development, and deployment. This facilitates the implementation of mechanisms ensuring transparency, accountability, and compliance.

    AI processes optimization - Reviewing existing processes and workflows to precise opportunities for effective AI integration. Providing recommendations on optimizing processes, streamlining data pipelines, and fostering a culture of continuous learning and improvement.

    Predictive modelling

    Predictive modelling

    Exploratory data analysis (EDA) - Conducting a thorough exploration of the data to understand patterns, relationships, and potential insights. Identifying potential variables that may have significant impact on the predictive modelling task.

    Model selection - Selecting the most suitable predictive modelling techniques based on the problem at hand and the nature of the data. Common techniques include linear regression, logistic regression, decision trees, random forests, support vector machines, neural networks, etc.

    Model validation - Splitting the data into training and testing sets, cross-validation, and using metrics such as accuracy, precision, recall, F1-score, ROC curve to evaluate the model's performance.

    Model monitoring - Implementing monitoring systems to track the model's performance over time and identify any degradation in predictive accuracy.

    Training and workshops

    Expert-led training and workshops in the fields of business intelligence and data science. Our training programs cover a wide range of topics, including ETL solutions, data warehousing technologies, and the latest advances in analytical tools and methodologies. Designed to empower professionals with practical skills and insights, our training sessions are conducted by seasoned industry experts and offer hands-on learning experiences.

    Training and workshops

    About Comarch Business Intelligence

    Comarch BI, as an integrator of data warehousing solutions and third-party systems, has been building dedicated Enterprise-class projects for over 18 years, implemented in 33 countries worldwide. During this time, Comarch's specialists have completed over 100 BI projects supported by references from satisfied clients, serving global and local brands.

    Importantly, Comarch's specialists, through implementations across various sectors, possess specialized industry knowledge, significantly facilitating and expediting the project realization process regardless of the industry. Leveraging acquired experience, qualifications, and know-how, Comarch BI also provides managed services in the areas of business intelligence, advanced data analytics, artificial intelligence, machine learning, and related domains.

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    Comarch cooperation models

    Comarch cooperation models empower clients to seamlessly adjust their team size according to project complexity and timelines, providing unparalleled flexibility and agility in resource allocation. Leveraging Comarch BI's vast pool of IT specialists, clients gain access to a diverse array of expertise, spanning business analytics, data visualization, data quality, master data management, data migration, big data, data warehousing, machine learning, artificial intelligence, data science, and more. 

    With Comarch BI's demonstrated track record in delivering top-tier IT solutions and services, clients can confidently rely on the expertise and reliability of our team to achieve their goals.

    With Comarch BI's demonstrated track record in delivering top-tier IT solutions and services, clients can confidently rely on the expertise and reliability of our team to achieve their goals.

    Body leasing

    Body leasing is a flexible solution that enables businesses to hire skilled resources on an hourly basis. With this model, companies can seamlessly scale their teams according to project needs, without the commitment of maintaining full-time employees in-house. Comarch specialists are available to join your team temporarily, providing invaluable support.

    Project team leasing

    Comarch provides a dedicated team to handle specific tasks, seamlessly collaborating with your team or other vendors. This accelerates development initiatives and delivers cost savings with a reliable team of IT professionals. Our approach ensures efficient project progress and high-quality results aligned with your objectives.

    Full project outsourcing

    Full outsourcing is a comprehensive service, where Comarch  takes on the responsibility of managing all aspects of your project from start to finish. With this service, we assemble a dedicated team of skilled IT professionals who are committed to handling specific project tasks from inception to completion.

    Why Comarch BI?

    • 18 + years of experience in the global market 
    • Providing services and successful projects across five continents in over 33 countries  
    • References from all over the world   
    • Extensive experience across multiple industries and sectors - retail and FMCG, telecommunications, finance, banking and insurance, public administration, manufacturing, healthcare, fuel and power engineering, CLM and more.
    • Comprehensive expertise and experience across various technologies and diverse technological domains.
    • Top-class professionals - graduates of the best Polish and foreign universities are available to work for you under the body leasing cooperation model.
    • Strategic partnership with IBM, and more leading tech companies
    • ISO 9001 and ISO27001 certifications confirming robust quality management and information security approaches.

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    Why Comarch BI?

    Our tech stack for managed solutions

    Comarch's team of experts offers extensive experience across a diverse range of technologies, delivering tailored solutions to meet your specific needs. We excel in renowned platforms such as IBM (DB2, Cognos, Data Stage, Watson, and SPSS), Microsoft (SQL Server, SSIS, SSAS, Reporting Services, and Power BI), and Oracle (Database and Business Intelligence solutions). We are proficient in leading visualization tools like Tableau and Qlik, as well as modern cloud-based data warehousing platforms such as Snowflake. Leveraging cloud computing platforms like AWS and Azure, we deliver flexible, scalable, and cost-effective solutions to drive innovation within your organization. With expertise in analytical tools such as R Studio and Python, we enable advanced data analysis and predictive modelling.


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