Key Factors In Successful Business Intelligence
This is the transcript of a podcast recorded by InetSoft on the topic of "Key Factors In Successful Business Intelligence" The speaker is Jessica Little, Marketing Manager at InetSoft.
One of the key factors in successful business intelligence is a sound infrastructure. You are not going to be successful in operation BI unless you have a sound infrastructure to deliver data on time so your business can analyze that data in a timely way.
So I think what InetSoft's BI solution brings to operation BI is a sound infrastructure that ties together disparate data sources via our proprietary data mashup technology. This also includes different ways of dealing with high workloads which becomes critical for delivering responsiveness in operational BI.
How important is operational BI to predictive analytics? If we look at some of the applications in operational BI such as risk management, fraud detection, which are good examples of operational BI, money laundering would be another one, you have business rules that define potential problem situations.
And predictive analytics is used to create those rules. When they’re loaded into the operational monitoring system, you’re able to detect fraud, for example, almost in real-time. So rigorous analysis and data mining is very important.
Critical BI Success Factors
What are some of the critical success factors when you’re implementing a business intelligence solution? From an operational BI viewpoint, you need a good infrastructure to support the kind of data volumes that are involved. You also need operational BI to coexist with other kinds of business intelligence applications such as tactical BI. Therefore you need good, strong load management capabilities that support both operational BI and strategic intelligence requirements.
What is one of the biggest growth areas in operational BI? There are a number of different industries that have taken advantage of operational BI. One of the more interesting ones is Internet commerce. I think that sector of the marketplace, web analytics, will grow dramatically over the next few years. The kind of analytics going on there is mind-blowing compared to anything that has been done before. These companies are providing good examples of where we are going with operational BI.
Another factor in long-term success of a BI implementation is scalability and workload management. You have to be able to support the kind of workloads that operational BI needs. There are different ways of doing operational BI within an existing data warehousing environment. Certainly there are some very good case studies.
Frequent Shipment Monitoring
We’ve done several studies. One interesting one was a railroad company that was tracking shipments and service levels for customers that are important for customer satisfaction measurement and a competitive viewpoint. It is important that those shipments arrive on time and meet certain service levels. Inevitably issues impact the service of the railroad, mechanical failures or weather, for example.
They are monitoring on a daily or intra-daily basis shipments, and if delays occur, they can look for ways of rerouting shipments to ensure they meet service level goals. That is a very good example of the use of InetSoft in operational business intelligence.
Why else might I recommend InetSoft to companies looking for an operational BI solution? In today’s tough economic environment, I think business intelligence can be used to help reduce costs and improve efficiency. I think in the past, BI has tended to be used to increase revenues, to become more competitive. That is still important in today’s environment. BI can also inevitably be used to lower costs.
I think looking for quick short-term solutions focused very much towards specific business problems, you can actually optimize cost structures. InetSoft's role here as a very flexible quick to implement and easy to use solution can be applied in a new department, for example, to tackle a new problem quite easily. This way the subject matter experts can focus on specific business processes to analyze how to make them more efficient.
How an Acorn Products Company Uses Analytics & Reporting Software
At the heart of the business, analytics knit together disparate streams of operational data — harvest volumes from seasonal collection sites, moisture and tannin measurements from the processing line, inventory levels for acorn flour and oil, and sales from both wholesale channels and direct-to-consumer e-commerce. Reporting software ingests these feeds and normalizes them so managers can compare across time and location: which groves produced the best-quality nuts this autumn, which leaching processes produced the lowest residual tannin, and how drying times correlate with shelf life. Instead of chasing spreadsheets, the business relies on a set of live dashboards that highlight anomalies (a sudden spike in moisture) and trends (steady growth in artisan bakery orders), turning raw sensor and transactional data into immediate operational intelligence.
Different teams use tailored reports that reflect their decisions. Production supervisors view shift-level KPIs — throughput, reject rates, average tannin ppm, and energy consumed per kilogram — enabling quick adjustments to drying schedules or leaching recipes. The supply chain team monitors inbound collection forecasts, routings for mobile collection crews, and cold-chain integrity when needed, reducing spoilage and lowering logistics cost. On the commercial side, marketing and sales teams use cohort analyses and channel reporting to identify the fastest-growing retail segments, assess price elasticity of acorn flour versus oil, and refine promotions that move slow SKUs without eroding margin. Crucially, role-based access means each department sees the same underlying truths but framed for their choices.
The company also benefits from self-service analytics: smallholder cooperatives and field managers can run ad hoc queries and generate simple visualizations without IT support, surfacing local insights that would otherwise be lost in centralized reports. Predictive models baked into the reporting layer forecast harvest yield based on weather, flowering patterns, and historical collection rates; they also estimate tannin risk under different processing timelines, so teams can preemptively reallocate capacity. These capabilities convert seasonal uncertainty into planning leverage — the company can decide whether to accept a large wholesale contract months in advance or reserve capacity for high-margin artisanal customers during peak demand.
Finally, analytics improve governance and product storytelling — traceability reports tie a bag of acorn flour back to the exact grove and processing batch, which satisfies food-safety audits and becomes marketing gold for premium labeling. Operational dashboards quantify ROI from improvements (reduced rejects, shorter turnaround, lower freight cost) and make the business case for further automation or investment in quality sensors. In short, reporting software turns an obscure but craft-driven industry into a data-savvy operation: more resilient, more efficient, and better able to scale while preserving the artisanal qualities that differentiate its products.