Best Analytics Tools

Interactive Dashboard Analytics

With InetSoft's analytics tools, users have the unique ability to take the data that they have stored away in data warehouses and other sources and make it easily accessible from any device to all company users, regardless of skill level.

InetSoft's dashboard visualizations makes analytics far more efficient than looking at traditional static reports, enabling users to perceive and interpret performance changes as well as predict future trends using unique drill down features that allow for the isolation and further exploration of desired data aspects. Drill down happens using a simple point and click interface, allowing users of any technical level to interact with data and collaborate with other business users.

Data Mashup for Analytics Tools

A key strength that makes InetSoft one of the best analytics tools in the BI industry is its depth of support for data mashups. Users can combine data from one or more data warehouses with data from other relational databases such as XML, SOAP, Java beans (POJO), EJB beans, OLAP cubes, Hadoop/HIVE, SAP ERP, PeopleSoft, Siebel CRM, Google AdWords & Analytics, JDE, salesforce.com, MapR, SAP HANA and more.

InetSoft offers unique self-service capabilities to users for enterprise mashups, meaning you do not have to be an IT professional to create advanced data mashups and create professional dashboards and reports. In fact, InetSoft's analytics tools make this kind of data mashup as easy as dragging and dropping desired blocks of data into visualizations in a matter of seconds. By combining data from disparate sources, users have the ability to analyze previously disparate data and spot trends that were not easily found before by looking at traditional reports, and therefore making data analytics much more efficient and effective than before.

Interested in finding out more about the best analytics tools? Request a personalized demo of InetSoft's award-winning technology today!

A Plastic D Rings Manufacturer with InetSoft’s StyleBI: Analytics for Executives and Line Managers

This article explores how the manufacturer leverages InetSoft's open-source analytics tool, StyleBI, to empower both executives and line managers with actionable insights. Tailored for an IT audience, we'll dive into the technical capabilities of StyleBI, its deployment, and its impact on the organization's decision-making processes.

The Business Challenge

Plastic D-rings, though small, are vital components requiring precision manufacturing to meet stringent quality and delivery standards. The manufacturer faced several challenges:

• Data Silos: Production data resided in an on-premises SQL Server database, sales data in a cloud-based CRM (Salesforce), and inventory records in an ERP system (Epicor). Integrating these disparate sources was a manual, error-prone process.
• Limited Visibility: Executives lacked real-time insights into key performance indicators (KPIs) like production yield, defect rates, and supplier performance. Line managers struggled to monitor shop floor metrics, such as machine downtime or material usage, in a timely manner.
• IT Dependency: Traditional reporting required IT to generate custom SQL queries or Excel-based reports, creating bottlenecks and delaying decision-making.
• Scalability: As order volumes grew, the existing manual processes couldn't keep up, risking delays and quality issues.

The company needed a business intelligence (BI) solution that was cost-effective, easy to deploy, and capable of serving both strategic (executive) and operational (line manager) needs. After evaluating several options, they chose InetSoft's StyleBI for its open-source flexibility, robust data mashup capabilities, and user-friendly interface.

Why StyleBI?

StyleBI, InetSoft's flagship BI platform, is a web-based, zero-client solution designed for self-service analytics. Its open-source version, available on GitHub, offers most features of the commercial edition, making it ideal for organizations with skilled IT teams looking to minimize costs. For the plastic D-rings manufacturer, StyleBI stood out due to:

• Data Mashup Engine: StyleBI's patented Data Block technology allows seamless integration of disparate data sources, enabling the IT team to unify SQL Server, Salesforce, and Epicor data without a full-scale data warehouse.
• Drag-and-Drop Interface: Business users, including non-technical line managers, can create reports and dashboards using a visual designer, reducing IT dependency.
• Real-Time Analytics: High-speed parallel processing supports real-time monitoring of production metrics, critical for shop floor operations.
• Web-Based Architecture: As a Java-based server application, StyleBI runs on any modern browser (Chrome, Firefox, Edge) across Windows, Linux, or macOS, ensuring accessibility for distributed teams.
• Customizability: The open-source nature allowed the IT team to tailor the platform to specific manufacturing needs, such as custom KPI calculations for defect rates.

Implementation: A Technical Overview

The IT team, consisting of two developers, a data analyst, and a system administrator, deployed StyleBI on an on-premises Linux server with 16 GB RAM and a quad-core processor. The implementation took six weeks, leveraging the open-source version to keep costs low. Here's how they set it up:

1. Data Integration

The IT team used StyleBI's data mashup engine to connect to:

• SQL Server 2016: Hosted production data, including machine output, defect logs, and material consumption.
• Salesforce: Provided sales orders, customer feedback, and demand forecasts.
• Epicor ERP: Contained inventory levels, supplier delivery times, and procurement costs.

StyleBI's data adapters standardized disparate formats, allowing the team to create reusable data blocks. For example, they merged production defect data with supplier records to analyze material quality trends. The IT team also implemented automated data caching to minimize the load on operational systems during peak hours.

2. Dashboard Design

Using StyleBI's drag-and-drop designer, the team built two primary dashboard suites:

• Executive Dashboard: Focused on strategic KPIs, such as:
• Revenue by product line (e.g., D-rings for pet collars vs. automotive).
• Gross margin by customer segment.
• Supplier performance (delivery times, defect rates).
• Visualizations included heatmaps for inventory levels and line charts for sales trends.
• Line Manager Dashboard: Tailored for shop floor operations, with real-time metrics like:
• Machine uptime/downtime.
• Defect rates by production batch.
• Material usage vs. planned consumption.
• Gauges displayed live sensor data from IoT-enabled molding machines, while bar charts tracked defect trends.

The IT team embedded custom JavaScript hooks to enable automated alerts. For instance, line managers received email notifications when defect rates exceeded 2%, allowing immediate intervention.

3. Security and Access Control

StyleBI's data security model was configured to enforce role-based access:

• Executives accessed high-level dashboards with aggregated data.
• Line managers viewed granular, department-specific metrics.
• Data cell-level security ensured that managers only saw data relevant to their production lines.

The IT team integrated StyleBI with the company's existing LDAP system for single sign-on, streamlining user authentication.

4. Mobile Accessibility

With many line managers working on the shop floor, the IT team optimized dashboards for mobile devices using StyleBI's responsive design features. Managers accessed real-time metrics on iPads, improving responsiveness to production issues.

Impact on Executives

For the C-suite, StyleBI transformed strategic decision-making. Key benefits included:

• Holistic Visibility: The executive dashboard provided a single control panel for monitoring sales, production, and supply chain performance. For example, a heatmap revealed overstocked raw materials, prompting a 15% reduction in inventory costs.
• Faster Insights: Real-time KPI tracking replaced monthly Excel reports. Executives could drill down into underperforming product lines, identifying a 10% drop in pet collar D-ring sales due to a competitor's pricing strategy.
• Predictive Analytics: Using StyleBI's what-if analysis tools, executives modeled the impact of increasing production capacity, leading to a data-driven decision to invest in two new molding machines.
• Supplier Optimization: By correlating supplier delivery times with defect rates, the company renegotiated terms with a key resin supplier, improving material quality and reducing defects by 8%.

Impact on Line Managers

Line managers, responsible for daily production, saw immediate operational improvements:

• Real-Time Monitoring:Dashboards displayed live defect rates, enabling managers to halt production runs when issues arose. This reduced scrap rates by 12%.
• Proactive Maintenance: IoT sensor data integrated into StyleBI flagged machines nearing maintenance thresholds, cutting unplanned downtime by 20%.
• Material Efficiency: Visualizations of material usage helped managers identify overuse in specific batches, saving $50,000 annually in resin costs.
• Empowered Decision-Making: The self-service interface allowed managers to create ad hoc reports without IT support, such as analyzing defect patterns by shift, leading to targeted training for night-shift workers.

Technical Benefits for IT

The IT team reaped significant advantages from StyleBI's architecture:

• Low Maintenance: The zero-client, web-based design eliminated the need for client-side installations, reducing support tickets by 30%.
• Scalability: StyleBI's parallel processing handled growing data volumes as the company expanded to new markets.
• Open-Source Flexibility: The IT team customized the platform to integrate with a legacy quality management system, avoiding costly commercial upgrades.
• Rapid Deployment: The drag-and-drop interface and pre-built connectors accelerated dashboard development, freeing IT resources for other projects.

Challenges and Lessons Learned

The implementation wasn't without hurdles:

• Data Quality: Inconsistent data formats in Epicor required initial cleansing, which delayed deployment by one week. The IT team used StyleBI's data transformation tools to standardize inputs moving forward.
• User Adoption: Some line managers resisted transitioning from Excel. The IT team conducted hands-on training, emphasizing StyleBI's ease of use, which boosted adoption to 90% within three months.
• Performance Tuning: High-frequency IoT data initially slowed dashboard rendering. The team optimized queries and enabled caching, resolving the issue.