Querying Text Data Sources Using InetSoft's BI Software

Walkthrough

To create a query for a text data source:

1. Select the ‘New Query’ button to create a new query. Select ‘OldCRM’ as the data source, and enter the query name, “Customer List”.

2 Add ‘Customers’ to the request list. Using the Fields tab in the Wizard you can choose the database fields you want to include in the query. Click ‘Finish’.

3. If necessary, text file format options and column information can be changed for the query.

4. ‘Save’ the query.

More Resources About InetSoft

• Significant Shift Shows Analytics Have Moved Beyond The Data Warehouse

  The article argues that analytics can no longer be confined to traditional data warehouse architectures because modern use cases involve streaming data, sensor inputs, and event processing. Organizations begin monitoring real‑time production, customer calls, and IoT devices, making legacy staging models less effective. The write‑up explains that analysis must occur closer to the data creation source and sometimes within the stream rather than after storage. It emphasises that simply storing everything in a warehouse may waste resources and delay insight. Companies embracing this shift gain agility in spotting anomalies, optimizing operations, and reacting faster to business events.

• Comprehensive Guide To Analyze The Performance Of Google Shopping Ads

  This guide details how e‑commerce businesses evaluate campaign effectiveness for their Google Shopping ads by tracking metrics such as CTR, conversion rate, CPC, CPA and ROAS. It explains how visual dashboards help merge ad metrics with web analytics and CRM data to gain deeper insights. E‑commerce teams benefit from segmenting product, geography and time dimensions to optimise spend and product feed quality. The article also explores integrating with Google Analytics 4 for enhanced behavioral tracking post‑click. Organizations using these insights reported improved ad efficiency, better inventory alignment, and stronger attribution of marketing spend.

• New Approaches For Analyzing Streaming Data In Real Time

  The piece addresses how increasingly organizations are dealing with high‑velocity data streams from sensors, service calls or transactions and need analytics that run in real time. It describes how teams are shifting from batch data processing to event‑based, streaming analytics that detect anomalies and trigger immediate action. The article emphasises that reactive analytics—once daily or hourly—is insufficient when fast response is required. Tools that bring analytics closer to the source enable more timely decision making and business responsiveness. As a result, firms implementing streaming analytics reported improved operations monitoring, early‑warning capability, and cost savings from avoiding failures.

• Key Dashboard Reporting Development For Apartment Rental Software Platforms

  This article focuses on how property‑management and rental software vendors incorporate dashboards to provide portfolio overview, occupancy rates, rental income and tenant metrics. It details must‑have features such as real‑time monitoring of leases, overdue rent, assets/fixtures and marketing conversion metrics for listings. Stakeholders like property owners and managers benefit from visualisation of KPIs rather than purely spreadsheets. The write‑up gives guidance on defining user needs, selecting relevant KPIs and designing interactive dashboards that reflect operations. Companies using these dashboards reported greater transparency, faster decision cycles, and improved financial tracking across multiple properties.

• Applying Machine Learning To Improve Customer Service And Support

  The article explains how machine‑learning models are embedded within dashboards to analyse customer support interactions, classify ticket types, and predict resolution times. It shows how analytics reveal patterns in service data that manual review might miss—such as recurring issues, sentiment shifts or agent performance trends. By blending ML predictions with live dashboards, customer‑service teams can prioritise high‑risk cases and allocate staffing dynamically. The content emphasises the need for data quality, proper feedback loops and user adoption. Organisations applying these techniques reported shorter resolution times, better customer satisfaction scores and reduced operational cost.

• Modern Automotive Industry Data Dashboards Enabling Transformation

  This piece discusses how vehicle manufacturers facing telematics, electrification and global operations must adopt dashboards that go beyond profit statements to agility, resilience, employee experience and risk. It recommends that automotive companies start with a strategic roadmap and clear KPIs before deploying dashboards. Visual tools enable tracking metrics like customer experience, production throughput, and logistic disruptions, not just unit sales. The narrative emphasises that executives must approve changes and that dashboard tools should be simple and interactive. Companies implementing these dashboards reported better alignment between engineering, production and sales, improved responsiveness and enhanced operational transparency.

• Practical Balanced Scorecard Examples And Dashboard Alignment

  This article offers examples of balanced scorecards embedded within dashboard platforms to align metrics across strategic, financial, customer, internal‑process and learning perspectives. It explains how organisations map strategy to KPIs and visualise them using thresholds, alerts and drill‑downs. The balanced scorecard framework supports executive views and links to operational metrics so that strategy and execution are connected. The write‑up emphasises customization and flexibility to fit different industries and roles. Firms using these scorecard dashboards reported improved alignment of goals, clearer accountability and faster tracking of strategic initiatives.

• Critical Considerations Before Starting A Machine Learning Project

  This thoughtful piece emphasises that before launching a machine‑learning initiative one must define success criteria such as cost savings, revenue impact, or knowledge generation, and build those into the project from the start. It argues that many ML projects fail because they begin without business context or measurable objectives. The article outlines the need for feedback loops, training data, and human‑business alignment prior to rollout. It also discusses how business users, not just data scientists, must be part of the process to ensure adoption. Organisations following this advice reported fewer failures, more actionable results and better business buy‑in into ML efforts.

• Tangible Benefits Of Dashboards For Customer Service Centers

  This article illustrates how dashboards for service‑centre operations increase visibility across metrics like first‑contact resolution, average response time, ticket volume and CSAT. It describes how managers access unified dashboards to monitor agent workload, queue trends and service‑level performance rather than scattered spreadsheets. The content emphasises flexibility, clarity and customizable views that enable staff to adapt as customer behaviour changes. It positions dashboards as a virtual command centre enabling remote, distributed teams to collaborate more effectively. Organisations deploying such dashboards reported faster issue resolution, better service quality and lower operational costs.