Webinar: How to Implement Business Analytics
This is the transcript of a Webinar hosted by InetSoft on the topic of "How to Implement Business Analytics." The speaker is Abhishek Gupta, Product Manager at InetSoft.
We are here today to talk about how to implement business analytics. You know through the course of my career here at InetSoft I have been around many analytics projects and certainly have developed some of my own opinions as to why it matters but at the end of the day my opinion probably doesn’t matter that much to you. What I think matters more are the opinions and more importantly the experiences of your peers who are using analytics effectively, in some cases, to drive double digit growth in major performance metrics like profitability and cash flow.
Hopefully, today the data that I show will give you some ideas as how you might build a stronger analytical environment within in your own organizations and navigate the waters of Big Data as the title suggests and produce some real results at the end of the day.
So here I’ve put together a fairly straight forward agenda for the presentation today. The world of business analytics is not surprisingly somewhat in flux. I want to start by talking through a couple of the high level data points that help show the current state of analytics. We will really highlight the value of analytics from the data side to the front end delivery of insights. And finally, I want to close things up by talking through a couple of recommendations and takeaways that will hopefully help you inform your own journey or voyage of analytics as it were.
The Three Factors of Urgency Driving the Need for Effective Business Analytics
When people talk about Big Data what is it that they are referring to? Is it a technical strategy for handling data? Is it a problem they are looking to solve? Is it a competitive advantage they are touting? It could be any and all of the above, but from the perspective of a business, it boils down the urgency.
Is there urgency around capturing more data? Or is there urgency around maintaining it in an IT environment that is typically very disparate and siloed? Is there urgency to actually do something with that data rather than let it languish as under-utilized. Another factor is the ever changing complexion of the typical analytical user base. We’re seeing more user types and more job roles and more functional areas raising their hands and asking for analytical capabilities. Analytics is increasingly in demand from business decision makers.
And third there is really a clear urgency around time, specifically less of it, regardless of the chicken and egg debate about the speed of business versus the proliferation of technology and what caused what. The fact is the increased pace of business is very real as is the increasingly demanding nature of customers. And as a result business leaders simply have less time to make decisions. What we call that “decision window” is really shrinking.
One interesting report I found surveys companies about these assertions. First, on the data side, whether they are small, mid size and large companies, you see the simple point that the data challenge as an urgency is noticeable regardless of company’s size or IT budget. In terms of volume growth small companies are actually seeing the highest year-over-year growth, 42%. And then in terms the number unique data sources that are used for analysis. It averages out at about 8 for small companies and 36 for large enterprises. So it’s some pretty substantial growth and also disparity there.
Second on the user side, I talked about an increased need for analytics among a variety of job roles and functional areas. What the research shows is there are the traditional use cases for analytics such as finance and marketing but also in some non-traditional areas like say human capital management. The business users themselves have moved beyond just being consumers of reports and dashboards but creators of tailored insight and using analytics for everyday functionally specific activities like customer analytics or workforce management or what have you.
And third, I alluded earlier to the so-called “Decision Window” or the window of time during which the delivery of information can really materially impact the business decision. Not surprisingly this window is shrinking for the vast majority of decision makers. You know, “I need it by the end of the day” has really turned into “I need it by noon,” and in some cases I need it within an hour. I need it within ten minutes now. So there is simply less time to make decisions.
These are really the three factors of urgency driving the need for effective business analytics. The real crux of the question is about understanding what it is that leads to best-in-class performance in order to provide a roadmap for average and laggard companies to improve their analytical strategy and draw closer to the best-in-class.
How a Saffron Cultivator Sped Up Decision-Making with StyleBI
Saffron cultivation is an intricate and labor-intensive agricultural process where timing and precision directly affect yield and quality. From monitoring flowering cycles to tracking labor-intensive hand-harvesting and drying processes, every decision must be informed by accurate, up-to-date data. Traditionally, saffron cultivators relied on manual record-keeping and siloed spreadsheets, which slowed the decision-making process and increased the risk of errors. By adopting StyleBI, a saffron cultivator was able to centralize and visualize data from multiple sources—including weather forecasts, soil sensors, irrigation systems, labor logs, and market demand trends—into a single, interactive dashboard.
With StyleBI’s real-time data mashup capabilities, the cultivator could quickly correlate environmental factors with flowering patterns and yield projections. For instance, by combining soil moisture data with temperature trends, the system highlighted which fields required immediate irrigation to prevent reduced stigma quality. Previously, such analysis might have taken days of manual calculation and consultation with field managers; with StyleBI, the team could make informed, actionable decisions within hours, ensuring optimal use of resources and maximizing yield quality.
StyleBI’s self-service analytics also empowered the cultivator’s team to explore data independently without waiting for IT or data engineering support. Harvest managers could drill down into labor allocation, adjusting schedules in real time to ensure that peak flowering periods were fully covered. Finance and sales teams could simultaneously monitor market demand and pricing trends, allowing the company to adjust distribution and marketing strategies faster than competitors. This cross-functional visibility reduced delays in decision-making and eliminated bottlenecks that had historically slowed operational responsiveness.
Moreover, the cultivator leveraged StyleBI to create predictive insights, such as estimating harvest volumes based on flowering patterns and historical yield data. These forecasts allowed management to plan storage, drying capacity, and shipment logistics well in advance, avoiding last-minute crises and costly inefficiencies. By integrating real-time monitoring, self-service dashboards, and predictive analytics, StyleBI transformed the decision-making process from reactive and fragmented to proactive and data-driven, significantly speeding up responses to both operational and market challenges while enhancing overall business agility.