This is the continuation of the transcript of a Webinar hosted by InetSoft on the topic of "Data Discovery Tools and End User Mashup". The speaker is Abhishek Gupta, product manager at InetSoft.
Well let’s create a globally consistent way to integrate, report and analyze data and then just open up lots of local franchises throughout the company in either the sales, service, marketing, HR, finance or some of the different lines of business units.
And then by bringing it to the local market it’s going to be more responsive. It’s going to have more domain expertise, but there is still a sort of global consistency. So that might be more right for a more centralized self-service BI approach.
Now I have seen other folks, and a lot of manufacturers come to mind who tend to be very decentralized, base their BI strategy upon lines of business units. They wanted to be even more decentralized, and they almost had what I would almost view as a bottoms-up approach where essentially the different departments get to do whatever they want.
They can integrate, report and analyze data any way they want, and they had full autonomy and full control. And the job of the centralized team is really just to kind of watch what the decentralized teams are doing.
When they are doing something really useful, identify that and promote that, and disseminate that out to the other departments and say hey, this team over here is doing something really cool, let’s make this more widely available to other aspects of our enterprise.
So what I guess I am preaching in this message is there is no one size fits all. This is a very complex issue, and I think depending upon the culture of your company, and by giving examples from different industry verticals, retail, government and manufacturing, you may find yourself more in the centralized end of the spectrum of more on the decentralized end of the spectrum or somewhere in the middle like the government agency who used the divide and conquer approach.
But recognizing the problem, like they did, bringing all the parties together and talking about what’s the right way to solve this problem is the place to start. Identify any subject areas that need to be modeled, and then identify how we are going to go about doing it. I think those three approaches I mentioned, the divide and conquer, the franchise model and the bottoms-up model are all ideas that have many permutations to them. They could be adopted depending upon the culture of your organization, and we are going to go in lot more detail on this on subsequent slides.
Now another key point I want to hit on in the strategy section is this notion of finding the right sweet spot for BI. Now I have been using this slide for years, if you have ever seen me present this. We have probably used this slide so long because this was a big epiphany for me a number of years ago from a BI tool perspective and from a data insights capability perspective.
On the last slide I was talking more about balancing central and de-central from an organizational standpoint, but here I am talking about it from more of from a BI reporting tool and data discovery capability and how we are provisioning it to end users. I think we look at the far left of the spectrum as obviously not optimal as the dull side. These static reports that we build, they are just not agile enough at all. They are too controlled. They are too centralized. They never really give the information our business users want.
Along the same lines the far right of the spectrum is the opposite. It’s the data dumps in Excel and Access. It’s just wild-wild west scenario. It’s completely ungoverned. There is no way to really do any type of audits and ensure consistency or guarantee any type of data quality in this world. So if the extremes are bad, I am going to focus smack dab in the middle.
A microalgae-based nutraceutical conglomerate operates across multiple countries, managing cultivation facilities, processing plants, and distribution networks that span diverse regulatory and market environments. Balancing centralized and decentralized reporting is critical for such an organization to maintain strategic oversight while empowering local managers to make timely operational decisions. Centralized reporting ensures consistency in financial, regulatory, and quality data, allowing headquarters to monitor global performance, enforce compliance, and plan capital investments across regions. At the same time, decentralized reporting enables individual facilities or country units to access real-time, context-specific dashboards to track production yields, quality metrics, and local market performance.
To achieve this balance, the conglomerate often uses a hybrid reporting architecture. Core operational and compliance data—such as nutrient analysis, product purity, and cross-border regulatory filings—are standardized and funneled into a centralized system to ensure consistency and facilitate executive-level analysis. Meanwhile, local units maintain self-service reporting capabilities for daily operational decisions, such as optimizing harvest schedules, adjusting nutrient feeds, or managing logistics and sales pipelines. By integrating these layers with a robust BI platform, the company can maintain a single source of truth while allowing managers at each facility to act quickly on operational insights without waiting for headquarters to process requests.
Modern analytics tools, like StyleBI, further facilitate this hybrid approach by providing flexible data mashup, dashboarding, and role-based access features. Executives can access consolidated, global dashboards for strategic planning and risk assessment, while local managers interact with operational dashboards tailored to their site or region. Alerts and KPI monitoring can be configured at both levels, ensuring that headquarters is informed of critical deviations while local teams receive actionable insights for immediate interventions. This approach enables the conglomerate to maintain operational agility, regulatory compliance, and consistent performance across multiple geographies, while avoiding the inefficiencies and bottlenecks often associated with fully centralized reporting systems.