Enterprise business intelligence: capabilities, key components, and best solutions

Enterprise business intelligence: capabilities, key components, and best solutions

Enterprise BI services we offer

We help companies that want to implement or enhance their existing BI solution by developing business intelligence roadmaps, conceptualizing BI solutions, choosing optimal technology, as well as creating data governance policies and rules.

We design and integrate multi-component enterprise business intelligence solutions within a set time and budget by covering every project step from business needs analysis to BI solution development and launch.

Development

We create custom full-scale enterprise BI solutions from the ground up as well as develop and deliver separate BI components to solve non-trivial BI tasks and meet unique business requirements.

About Itransition

15+ years of experience with business intelligence

25+ years in enterprise software development

40+ successful BI projects

Strategic partnerships with Microsoft, AWS, and Google Cloud

ISO 27001 and ISO 9001-certified to ensure services quality and customer data safety

Selected success stories

Pharmaceutical BI for a US multinational

10x

faster data processing

We redeveloped the customer’s business intelligence platform and migrated it to the cloud. Through this, we streamlined data processing, introduced market-forecasting algorithms, helped the customer get insights into employee performance, and reduced infrastructure costs.

Cloud BI for vehicle manufacturers

70%

faster time-to-market

We transformed the customer’s legacy BI system to help them achieve a 15-20x higher system throughput, 100% predictability and transparency of working processes, and 50% higher productivity.

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Key capabilities of enterprise BI

Data consolidation and storage

    Consolidating diverse data sets in a unified data storage, making data accessible and ensuring its appropriate format and quality for analytics and reporting.

    • Seamless connection to existing data sources located on-premises and in the cloud 
    • Ingestion of structured, semi-structured and unstructured data
    • Batch and streaming data processing
    • Data transformation and quality management enabled by the ETL/ELT processes 
    • Consolidation of company-wide data in an enterprise data warehouse and business line data in subordinate data marts
    • Accompanying analytics data stores with an operational data store and a data lake for operational and high-volume business data
    • Storing business data in a relational, columnar, and multi-dimensional format

    Essential integrations for enterprise BI

    Core integrations
    CRM
    ERP
    HRM
    Supply chain management
    Finance and accounting
    Ecommerce platforms

    CRM

    • Customer behavior monitoring and modeling
    • Customer satisfaction analysis
    • Customer churn prediction
    • Customer value analytics
    • Analysis of marketing campaigns profitability

    ERP

    • Business process monitoring and evaluation
    • Cause-effect analysis and bottleneck recognition 
    • Operational performance analysis, including driver analysis and gap analysis
    • Operational performance forecasting

    HRM

    • Employee performance monitoring and evaluation 
    • Employee satisfaction, engagement, and productivity analysis
    • Employee turnover prediction 
    • Employee retention analysis
    • Workforce planning and scheduling

    Supply chain management

    • Procurement analytics
    • Supplier performance analysis
    • Demand forecasting
    • Identification of optimal inventory levels 
    • Prediction of order fulfillment rates 
    • Supply chain risk analytics

    Finance and accounting

    • Financial performance monitoring and evaluation
    • Profitability analysis
    • Financial planning and budgeting
    • Financial risk forecasting

    Ecommerce platforms

    • Sales performance analysis
    • Customer behavior analytics
    • Evaluation of marketing campaigns effectiveness 
    • Customer churn analytics and forecasting
    • Market trends analysis

    Enterprise BI architecture and its components

    Customer data
    Financial data
    KPI data
    Sensor data
    Flat files
    Log data
    ETL
    Data transfer
    Data transfer
    Enterprise data warehouse
    Operational data store
    consolidated operational data
    ETL
    Data lake
    raw unstructured data semi-structured data unstructured data
    ETL
    ETL
    Data mart
    Data mart
    OLAP cubes
    OLAP cubes
    Self-service BI
    OLAP
    Statistical analysis
    Data visualization
    Data mining
    Machine learning
    Reports
    Dashboards
    Scorecards
    Governance, metadata, and security management
    1
    Data sources

    These include internal and external data sources generating information for a BI system. Data sources range widely and include corporate solutions and applications supporting day-to-day business workflows, relational databases, IoT devices, internal documents and archives, a corporate website, surveys and statistics, in addition to information from business partners and competitors. Data sources for a BI system are defined by business needs, so not all digital information a company has access to should be analyzed by the BI solution.

    2
    Data integration and quality management

    Before analysis and reporting, data has to undergo some modifications to make it consistent, accurate, relevant, and complete. The choice of data integration methods (ETL, ELT, data replication, change data capture, streaming data integration, etc.) and the complexity of data quality management depend on the data type, format, volume and business analytics requirements. 

    3
    Data repositories

    When cleaned and consolidated, business information is further structured in an enterprise data warehouse according to the predefined data model. Traditionally, an EDW is complemented with data marts, built to cater to the needs of individual business departments and divisions. In addition to these analytics data repositories, an enterprise BI solution can include an operational data store (to store actual data for real-time reporting) and a data lake (to store various volumes of raw data for ML and big data workloads).

    4
    BI and analytics layer

    This component encompasses a set of solutions, such as query tools, OLAP software, data mining tools, and data visualization software, that enable both power and casual enterprise BI users to access and manipulate business data. The functional scope of this layer is based on the enterprise’s analytics maturity. 

    5
    Data governance

    The aim of this component is to govern the BI workflow and ensure data security, availability, and quality, as well as proper and beneficial use.

    Enterprise BI implementation roadmap

    The typical procedure companies follow when implementing a BI solution includes the following steps:

    1

    Business needs analysis

    2

    Business and technology environment evaluation

    3

    Conceptualization

    4

    Architecture design

    5

    Deployment environment and tech stack selection

    6

    Project planning

    7

    Components development and delivery

    8

    QA and end users training

    9

    Launch

    Our industry BI expertise

    We create full-scale BI solutions for the retail sector to increase customer satisfaction, craft effective marketing and sales campaigns, and quickly identify new sales opportunities.

    • Inventory levels optimization
    • Inventory planning, including purchasing, replenishment, and allocation
    • Product assortment assessment and planning
    • Dynamic price modeling
    • Customer satisfaction and loyalty analysis
    • Customer lifetime value calculation
    • Customer behavior modeling
    • Customer attrition and retention analysis
    • Supplier performance assessment and risk forecasting
    • Analysis of marketing campaign effectiveness
    • Marketing and sales KPIs tracking
    • Sales performance analytics
    • Sales forecasting and planning

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    Best ready-made software for enterprise BI

    Key features
    • Ingestion of structured, semi-structured and unstructured data
    • Streaming data ingestion
    • Analytics querying of data from a data lake, an operational data store, and relational databases
    • Support of big data workloads
    • Native integration with the AWS ecosystem (AWS Lake Formation, Amazon EMR, Amazon QuickSight, and Amazon SageMaker)
    • Built-in fault tolerance and disaster recovery
    • Granular access controls
    • Multi-factor user authentication
    • Dynamic data masking
    • End-to-end data encryption
    • Compliant with SOC1, SOC2, SOC3, PCI DSS Level 1 and others
    • Features for storage decoupling and resources computing (RA3 node type)
    Category
    • Data warehousing software
    Platform pricing

    On-demand pricing

    from $0.25/hour

    Reserved instance pricing

    depends on the location type and region, term length, and payment option

    Data storage (RA3 node type)

    $0.024/GB/month

    Free trial

      Enterprise BI: adoption best practices

      Balance agility and data governance

      On the one hand, enterprise business intelligence drives data democratization at all corporate levels and data-driven culture expansion, especially when paired with self-service capabilities. On the other hand, if not managed properly by a solid data governance framework, the solution’s validity can be compromised by sensitive data exposure, lack of trust in data, and data inconsistency.

      Open architecture

      When implementing a BI solution for an enterprise, make sure it scales easily without performance bottlenecks and a sharp increase in spending. Enterprise BI architecture should allow for a seamless integration with new data sources, the addition of new data repositories (a data lake and/or an operational data store, and data marts), and an increase in the number of users and analytics complexity.

      Focus on end users

      The success of enterprise BI implementation depends on user adoption rates. To increase user adoption, introduce self-service capabilities, conduct tailored user trainings and workshops on a regular basis, track user activity to spot problems early on, and ensure data quality from the very beginning to foster trust in the new software.

      Enterprise BI cost factors

      The cost of implementing an enterprise business intelligence solution ranges across companies due to a number of factors, which include:

      Enterprise BI cost factors
      • Data sources - their number, attributes and integration flexibility
      • Data complexity – data structure (structured, unstructured, semi-structured), data variety, and data volume
      • Data cleansing complexity - is defined by the amount of corrupt, duplicate or inconsistent data 
      • Data storage requirements – if analytics data stored are to be complemented with a data lake or an ODS
      • Data analytics complexity – if real-time, self-service, or augmented analytics is required 
      • Data reporting complexity – number of reports and dashboards, their complexity and frequency, custom data visualization, and self-service capabilities
      • Data security complexity - compliance requirements, complexity of access management and controls, and data backups

      Need help with choosing the optimal tech stack for your enterprise BI solution?

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      Benefits of implementing enterprise BI

      Data consolidation

      Integration of different types of enterprise data from siloed systems with varying data models into a single unified storage.

      Enhanced data quality and value

      Automation of data management and integration of a centralized data governance approach. This helps ensure data safety, consistency, accuracy, timeliness, completeness, and validity across different departments and divisions.

      Data-driven decision-making

      Quick access to high-quality relevant business data, elimination of communication bottlenecks, and convenient self-service capabilities to meet varying analytics and reporting needs of different-level users.