en flag +1 214 306 68 37

Operational Analytics

Key Features, Must-Have Integrations, Main Cost Factors, and More

In data analytics since 1989, ScienceSoft helps companies design and build effective data analytics software to derive operational insights.

Operational Analytics Solution - ScienceSoft
Operational Analytics Solution - ScienceSoft


Alex Bekker
Alex Bekker

Head of Data Analytics Department, ScienceSoft

Marina Chernik

Business Analyst and BI Consultant, ScienceSoft

Operational Analytics: The Essence

Operational analytics is a way to get prompt insights on optimizing business processes, including customer, supply chain, finance, and HR management. ScienceSoft drives the success of operational analytics projects by scoping the solution in line with case-specific requirements (e.g., operational KPIs to be measured, reporting frequency), drawing a robust risk mitigation strategy, and applying other project management practices we’ve developed and polished over 35 years.

  • Development time: from 2—6 months for an MVP.
  • Core integrations: A service management system, a CRM, a financial management system.
  • Costs: from $100,000 to $1,000,000, depending on the scope of the solution. Use our online calculator to get a custom cost estimate. It's free and non-binding.


Data Analytics Solution Architecture to Enable Operational Analytics

Architecture of an operational analytics solution - ScienceSoft

Data staging layer

To extract, transform and load operational data in the format suitable for storing in a data store and a data warehouse. The staging area should include tools for resolving data redundancy, data cleansing, checking data integrity, etc., to maintain data reliability.

Data storage layer

To store operational data in its raw/preprocessed format in the data store and keep processed and structured operational data in the data warehouse and data marts.

Data analytics layer

  • To run simple queries on raw real-time operational data.
  • To run complex analytical queries on processed and structured operational data.
  • To build machine learning and data mining models to carry out predictive and prescriptive operational analytics.

Data visualization layer

  • Pre-built and custom operational analytics reports and dashboards for executives and managers.
  • Operational analytics embedded directly into applications used by operational workers on a daily basis.
  • Automated alerts and notifications on any disruptions in operations, ML-based recommendations sent directly to applications.
  • Self-service dashboards to conduct additional ad-hoc analysis.

Key Features of an Operational Analytics Solution

At ScienceSoft, we form feature sets of operational analytics solutions for our customers based on their unique business needs. Below, we outline the core functionality of operational analytics software that accommodates the majority of real-life use cases.

Operational data integration

  1. Ingestion of structured, unstructured and semi-structured data.
  2. Ingestion capacity of up to petabytes of operational data to support the lowest granular queries.
  3. In-batch data extraction.
  1. Continuous data load into the destination repository.
  2. Automated data validation, cleansing and transformation of varying complexity (summarization, derivation, etc.) to remove extraneous or erroneous data, ensure data integrity, etc.

Operational data storage

  1. Storing consolidated raw/preprocessed real-time operational data in the operational data store.
  2. Storing time-variant, subject-oriented and integrated operational data in a data warehouse.
  3. Storing time-variant, subject-oriented and integrated data in data marts to provide more focused data to a specific business line or team and improve query performance.
  1. Storing operational data in a multidimensional format for online analytical processing (OLAP).

Operational data analysis

  1. Support for batch and streaming data analytics modes.
  2. Querying data directly from the operational data store to support time-sensitive operational processes:
    1. Product delivery
    2. Customer support
    3. Manufacturing activities
    4. Marketing and sales activities, etc.
  1. Enabling historical and trend analysis on operational data from the data warehouse to help managers plan and optimize operations by:
    1. Devising new sales and marketing strategies.
    2. Planning finances.
    3. Optimizing equipment maintenance strategies, etc.
  2. Pre-built ML models to enable forecasting of operational bottlenecks and disruption and next-best actions recommendations.

Operational data reporting

  1. Scheduled and ad hoc reporting.
  2. Pre-built and custom reports and dashboards.
  3. Capability to embed operational analytics content into daily-used applications.
  1. Interactive operational dashboards with self-service capabilities (dynamic filters, drag-and-drop interface, etc.).
  2. Mobile reporting.
  3. Natural language processing user interface.

Get a Tailored Solution for Operational Analytics

ScienceSoft’s data analytics experts will help you design and develop a comprehensive solution for operational analytics to ensure the prompt delivery of analytics insights to target users (front-line workers, managers, business analysts, etc.).

Sample Integrations for an Operational Analytics Solution

Data locked in silos is not what our customers want, therefore ScienceSoft always takes special care of integrating operational analytics solution with other corporate systems. Below, we share the examples of essential integrations:

Sample Integrations for an Operational Analytics Solution - ScienceSoft

Service management system

The integration enables:

  • Change impact analysis to define how changes in the service/project delivery affect its schedule or progress.
  • Resource utilization analysis.
  • Resource demand forecasting in real time.
  • Project/service risk assessment.
  • AI-based suggestions on resource allocation.

Customer relationship management system

The integration enables:

  • AI-based sales rep next best action suggestions, sales rep response suggestions, etc.
  • Detection of the most profitable customers.
  • Recognition of customer experience bottlenecks in real time and next-best-action recommendations.
  • Customer behavior analysis.
  • Customer satisfaction analysis.
  • Customer churn analysis.

Financial management system

The integration enables:

  • Recognizing how operational bottlenecks affect corporate finances.
  • Uncovering drivers of profitability.
  • Working capital and business expenditures analysis.

Key integrations for manufacturing enterprises

Key Integrations for Manufacturing Enterprises - ScienceSoft

Procurement management system

The integration enables:

  • AI-based recommendations on supplier assignment to purchase orders.
  • Supplier performance assessment and analytics.
  • Spend analysis and forecasting.
  • Purchasing trend analysis, etc.

Production operations management system

The integration enables:

  • Product demand vs. capacity analysis.
  • Capacity utilization analysis (on the machine and workforce utilization).
  • Analysis of the production outputs at a given period.
  • Production and inventory costs analysis.
  • Product demand forecasting.
  • Workforce demand forecasting.

The integration enables:

  • Real-time recognition of manufacturing process bottlenecks and their root-cause analysis.
  • Recommendations on production process improvements, waste management optimization, etc.

Computerized machine maintenance management system (CMMS)

The integration enables:

  • Predictive maintenance.
  • Automatic recommendations on equipment utilization.
  • Equipment maintenance strategy optimization, etc.

Inventory management system

The integration enables:

  • Recommending optimal inventory levels.
  • Forecasting inventory demand, etc.

Warehouse management system

The integration enables:

  • Dynamic data-driven inventory allocation.
  • Automatic alerting for shelf replenishment.
  • Warehouse labor demand forecasting, etc.

Data Analytics in Operations Management: Success Factors

ScienceSoft's 19-year experience in business intelligence lets us define the success factors that should be covered in operational analytics solutions:

Robust data security

Comprehensive security features such as operational data anonymization, end-to-end encryption, fine-grained access control, data masking, etc., to ensure data compliance with specific regulations (e.g., GDPR, HIPAA).

Timely delivery of analytics insights

Instantly available in pre-configured reports and charts for managers; analytics embedded into applications of operational employees (sales reps, equipment inspectors, customer service agents, etc.).

Self-service capabilities

AI-based data preparation and analysis, natural language processing, dynamic filters, and drill-down capability enable end users with no tech expertise to apply the solution in their daily operations.

Costs of Operational Analytics Implementation

The cost of operational analytics implementation may vary from $100,000 to $100,000,000+ and depends on a number of factors, including the following:

  • The number of operational data sources for integration (CRM, OMS, a supplier management system, project management software, etc.).
  • Operational data complexity (defined by its structure, volume, etc.).
  • The required level of data processing and analytics speed (near real-time, hard real-time, etc.).
  • Complexity of the data storage layer (examples of repositories include operational data store, a data warehouse, data marts, etc.).
  • Complexity of data cleansing.
  • Whether ML and AI capabilities are required.
  • Complexity of the operational data reporting layer (the number and complexity of reports (including ad hoc reports), the number of dashboards, if custom data visualization is required, etc.).
  • Operational data security requirements.
  • User training.
Pricing Information

Finding it difficult to match the cost factors mentioned above with required investments? Don’t you worry. We took care to make this step easier for you. Please feel free to use our cost calculator to get an estimate for your project.

Want to know the average implementation cost for your case?

Get a free quote

Benefits of Operational Analytics

Streamlined decision-making due to increased visibility into operations.

Fine-tuned operational processes due to quick detection of potential issues.

Personalized customer experience and improved customer service with quickly delivered operational information.

Improved productivity and collaboration of employees involved in operations.

Early detection and forecasting of payment fraud.

Staying competitive due to quickly identifying market changes and getting AI-based recommendations on how to act on them.

Operations Analytics Tools ScienceSoft Recommends

Below, we share the tools that we frequently use while designing and implementing operational analytics solutions:

Microsoft Power BI

Best for

Operational data reporting.


  • Ingestion of operational data with 120+ native data source connectors, including pre-built connectors for operational databases and a data lake.
  • Self-service data preparation and analytics capabilities for Power BI users to create tailored operational data dashboards in minutes.
  • Real-time streaming with Power BI REST APIs, Streaming Dataset UI, Azure Stream Analytics to display and update real-time data.
  • Incorporating Power BI content into other applications with Power BI Embedded.

DEMO: Watch our Power BI demo.


  • Free plan.
  • Power BI Pro - $9.99/user/month.
  • Power BI Premium: $4,995/dedicated cloud storage and compute resources/month, $20/user/month

Azure Synapse Analytics + Azure Cosmos DB

Best for

Operational data warehouse (hybrid transaction/analytical processing).


  • Integrating operational data from hundreds of data sources across the company’s divisions, subsidiaries, etc.
  • Running fast, cost-effective no-ETL queries on large operational real-time data sets without copying data and impacting the performance of the company’s transactional workloads.
  • Flexible indexing options (primary and secondary indexes) to execute complex analytics queries on operational data.


Azure Synapse Analytics:


  • On-demand pricing: $1.20/hour (DW100c) - $360/hour (DW30000c).
  • Reserved instance pricing can save up to 65% over the on-demand option (in a 3-year term).
  • Data storage: $122.88/TB/month.

Azure Cosmos DB analytical store:

  • Storage - $0.02/GB/month
  • Write Operations (per 10,000 operations) - $0.05
  • Read Operations (per 10,000 operations) - $0.005

Azure Synapse Link pricing includes the costs incurred by using the Azure Cosmos DB analytical store and the Synapse runtime.

Amazon Redshift

Best for

Operational big data warehousing.


  • SQL querying of exabytes of structured, semi-structured, and unstructured operational data across the data warehouse, operational databases, and a data lake.
  • Accommodating operational analytics workloads with Advanced Query Accelerator, result caching, materialized views, and ML-based workload management.


  • On-demand pricing$0.25 - $13.04/hour.
  • Reserved instance pricing offers saving up to 75% over the on-demand option (a 3-year term).
  • Data storage (RA3 node type): $0.024/GB/month.

Note: No charge for the amount of data processed.

Consider Professional Services for Operational Analytics Implementation

With 35 years in data analytics, ScienceSoft helps businesses design and develop or modernize analytics solutions to capture, aggregate, store and analyze operational data for data-driven operations planning, management, and optimization.

Operational analytics consulting

  • Analysis of operational analytics needs.
  • Conceptualization and design of a solution for operational analytics.
  • Implementation planning (milestones, risk management planning, defining KPIs for measuring software quality, etc.).
  • Business case creation, including cost estimation, time budget estimates.
Go for consulting

Operational analytics implementation

  • Analysis of operational analytics needs and drawing up software requirements.
  • Conceptualization and tech selection for an operational analytics solution.
  • Solution development and quality assurance.
  • After-launch support and optimization.
Go for implementation

About ScienceSoft

ScienceSoft is an IT consulting and software development company headquartered in McKinney, Texas. We advise on and implement tailored data analytics solutions to help businesses gain visibility into their operational environment, conduct fast operational amendments, and plan deeper operations optimization. Being ISO 9001 and ISO 27001 certified, ScienceSoft relies on a mature quality management system and guarantees that cooperation with us does not pose any risks to our customers’ data security.