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.
Contributors
Head of Data Analytics Department, ScienceSoft
Business Analyst and BI Consultant, ScienceSoft
Schedule a meeting
What is an Operational Data Analytics Solution?
An operational analytics solution aggregates data from an operations management system, a supply chain management system, CRM, a financial management system, an HRM system, etc., and provides prompt analytics insights to managers and operational employees (sales agents, equipment inspectors, dispatchers, etc.).
Data Analytics Solution Architecture to Enable Operational Analytics
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.
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:
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
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 18-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.
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?
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.
Description
- 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.
Pricing
- 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).
Description
- 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.
Pricing
Azure Synapse Analytics:
Compute:
- 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.
Description
- 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.
Pricing
- 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 34 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.
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.
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.