InetSoft Webinar: Bring Business Intelligence With You
This is the continuation of the transcript of a Webinar hosted by InetSoft on the topic of "Mobile BI: iPads & Tablets" The speaker is Mark Flaherty, CMO at InetSoft.
The new ability of sales managers and other involved in partner management or supply chain management to bring business intelligence with them means that they no longer need to download reports in advance or refer to out-of-date data. This results in more opportunities in performance and business relationships.
Even though some kind of information access may have existed in the form of Blackberrys and other such mobile devices, the amount of interaction and the scale of functionality is new and is groundbreaking.
In fact, if we look at a variety of research that has been done on this topic, we see that they all show the same trends. One survey of 277 companies with business intelligence systems showed that employee usage of these systems doubled once they started using mobile BI.
So we see that in the future mobile business intelligence systems will allow us to be in the places where we haven't been to before. In the future with mobile business intelligence, there’ll never be an excuse not to know something. That’s the direction we’re going in. Information will literally follow the business user.
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Business Intelligence at Their Fingertips
End users will have business intelligence at their fingertips whether they are at their desks, in meetings, or offsite. Wherever they go they will always be able to know what's going on all the time. So here is something that we need to consider when we are looking at mobile business intelligence.
There are three main things. One is the purpose of why we are using mobile BI. What is the business purpose? Because the idea here is not to have specific technical solutions. That’s not what's interesting here. What’s interesting here is how it helps our business to become a better business and to utilize the information it has to both improve what we have today and also foster some new business capabilities. You should always look at what's your business need, how using mobile BI will advance your business and who are the people who gain the most out of it.
The second thing to look at is data security. Data security, of course, exists in all organizations today. Mobile devices add another layer of complexity that you must take into account. Security policies in mobile devices need to be tight, but the reality is that there is such mix of devices in a company that you need to think up some new procedures and processes as to how to enforce them, especially taking into account that some of the mobile devices are not even owned by the company, they are owned by the users and actually their own individual devices.
The third thing is to consider business continuity issues. It’s very important that mobile BI will not be some standalone application that has nothing to do with the other applications that the businesses are using. Nobody uses such systems anymore. Mobile applications need to be tied into the whole enterprise infrastructure and need to be tied into how users use their computers and their laptops.
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Continuity Across Devices
There needs to be a continuity between using applications while they are at their desk and using applications while they are on the go. Also for when users using their application while they are away from work, and all they have is their mobile phones. So they need these three types of devices. There needs to be continuity and there needs to be continuity between the BI applications and of course other applications within the organization.
So it’s very important that the business intelligence application will be a unified solution. With focus shifting towards mobile BI offerings that meet business needs and offer a higher level of interactivity, some service models are also becoming more important. Mobile business intelligence requires ease-of-use and a spectrum of BI applications that provide end users with the autonomy to work on their own, self-service BI. Vendors that are meeting these needs are developing interactive dashboards that give the same features and functionalities as a Web-based application, but with desktop quality alternative capabilities.
How HTML5 Enables Desktop App Quality
HTML5 has revolutionized web development by empowering browser-based applications to deliver a user experience that rivals native desktop software. With enhanced capabilities like offline storage, multimedia support without plugins, and faster rendering, HTML5 allows developers to create highly responsive and interactive applications. APIs such as WebSockets, Web Workers, and the Canvas element enable real-time data communication, background processing, and advanced graphics, all within the browser.
Modern CSS3 and JavaScript frameworks built on HTML5 further enrich the interface with animations, transitions, and dynamic layouts that adapt seamlessly to user inputs. Features like drag-and-drop support and device access (via the File and Media APIs) bring the tactile responsiveness of desktop environments to the web. HTML5’s ability to cache data locally ensures continuity and performance even without an internet connection, a capability long exclusive to desktop apps.
As a result, web applications built with HTML5 no longer feel constrained by browser limitations. Instead, they deliver high-performance, full-featured experiences that blur the line between traditional desktop software and modern web platforms. This has opened the door for enterprise-grade applications to go fully web-based without compromising quality or usability.
What Are Some Best Practices for Improving Data Quality?
Improving data quality is essential for ensuring reliable analytics, accurate reporting, and sound decision-making. Poor data quality can lead to costly errors, misinformed strategies, and lost opportunities, so applying best practices is not just a technical concern—it's a business imperative. Here are some of the most effective practices for improving and maintaining high-quality data across an organization:
1. Define Clear Data Standards and Governance
Establish well-documented data definitions, naming conventions, and formatting rules across all systems. This includes specifying how dates, names, units of measure, and other fields should be entered and maintained. Implementing a data governance framework—with roles such as data stewards and owners—helps ensure accountability and consistency throughout the organization.
2. Perform Regular Data Audits and Profiling
Data profiling tools help assess the accuracy, completeness, consistency, and timeliness of datasets. By profiling data regularly, organizations can identify anomalies, duplicates, or missing values that may otherwise go unnoticed. Audits should not be one-time events—they must be scheduled and integrated into operational workflows.
3. Validate Data at the Point of Entry
The earlier you catch bad data, the cheaper and easier it is to fix. Use input validation rules in user interfaces, such as dropdowns, required fields, format checks, and real-time feedback. Automated validation in APIs and ETL pipelines also reduces the chance of corrupt data entering your systems.
4. Centralize and De-Duplicate Data Sources
Siloed data often results in conflicting or redundant records. Consolidating databases, integrating systems through ETL or data mashup tools like StyleBI, and maintaining a single source of truth help eliminate discrepancies and duplication. Master data management (MDM) strategies can further standardize core entities like customers, products, or vendors.
5. Establish Automated Data Cleaning Workflows
Use scripts or data quality platforms to identify and correct issues such as inconsistent capitalization, outdated entries, and typographical errors. Implementing fuzzy matching for names or addresses and applying transformation logic (e.g., trimming whitespace, reformatting dates) improves consistency and usability.
6. Enable Real-Time Monitoring and Alerts
Set up automated alerts that notify stakeholders when data anomalies, missing values, or unexpected patterns are detected. Dashboards and exception reports can surface issues as they arise, helping teams act before problems escalate. Tools like InetSoft’s Style Intelligence can facilitate real-time monitoring tied to visual dashboards.
7. Track Data Lineage and Provenance
Understanding where data comes from and how it has been transformed is essential for ensuring trust. Lineage tracking tools provide transparency into how data is collected, modified, and consumed. This also makes it easier to troubleshoot issues and comply with data privacy regulations.
8. Involve Business Users in Data Stewardship
Data quality isn't just IT’s responsibility. Business users understand the context and nuances of the data better than anyone, so involving them in flagging, correcting, and annotating errors adds valuable domain expertise. Providing accessible tools for non-technical users to inspect and manage data fosters collaboration.
9. Measure and Report on Data Quality Metrics
Define and track metrics such as completeness, accuracy rate, duplication rate, and error frequency. These KPIs help organizations benchmark improvements over time and demonstrate the impact of data quality initiatives. Reporting on these metrics also reinforces accountability.
10. Prioritize Data Quality During Integration Projects
Migrations, mergers, and system upgrades often introduce data quality risks. Include thorough data cleansing and validation steps in your project plans, and never assume source data is clean. Run mock data loads, user acceptance testing, and validation exercises before going live.
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