Different Use Cases of Analytics
This is the continuation of 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.
Enterprises have different use cases of analytics. Analytics has become an absolute expectation in the business across every organization. The people who are driving analytics are very knowledgeable. They have their own webinars, and everyone joins to hear the next best use case that’s available to them around this data. So it’s absolutely making a huge impact.
How do you enable a real time enterprise that simultaneously analyzes, acts and transacts at the same time? That is the only way for you to be successful in this new generation of Big Data. An absolute pre-requisite is the real time data platform. What I mean by real time data platform is that until today the technologies that have typically supported the transactional storage and data warehousing have been distinct and separate.
They have each been optimized for the particular niche and nicely separated, very nicely in their star schemas by the different teams responsible for these different applications. But looking into the future we now see a new possibility that the real time data platform can serve both. I’ve read recently that over a fourth of organizations are now beginning to bring analytics and transactions together, and this is going to be absolutely critical to be successful.
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Building a Network of Truth
We must be able to handle both the analytic and transactional loads of organizations and deliver in what I call that network of truth that everyone can contribute to and leverage. A true real time data platform allows any analytical view to be generated without incurring additional storage costs, and this is key to enabling the network of truth, because using data mashup technology, which is what we do, allows the analytical content to be endlessly replicated, modified and re-imagined.
That’s important because everyone should be able to re-imagine it any time and not have to ask their IT department to go back and rebuild their database. You need to be able to do this without incurring the traditional storage and processing costs associated with data warehousing. Any analyses need to be dynamically generated from the original records without requiring the aggregations to be pre planned or pre calculated.
I know I am getting a bit technical, and so those technical people out there, you know what I’m talking about. The only way to get a network of truth is to have a one place to store, one place to calculate, and to have a real time platform that doesn’t mean you have to go back and rebuild constantly. This is a dynamic world we’re living in, and this will enable you to achieve new possibilities that you are never be able to before.
So this is the only slide where I would tell you what InetSoft has to offer because I don’t want this to be sales presentation. I want this to be more of education around big data and helping you understand what’s going on out there. I wanted to show you some examples of how InetSoft might be able to help you. But I will tell you that we are highly focused on Big Data right now. We have a Big Data platform that will help you run your real time enterprise. Big Data apps and analytics are where there is a lot of attention now.
I want to help you engrain insights into your DNA, and that’s what's critical to be successful. You do not have to rely on a data scientist working in the back room to get those calculations. These Big Data tools allow you to get the data where you need it to be that help you get the right calculations out to the outer edges of your business so that everyone can be predicting. Everybody can be leveraging complex calculations. And they do not even have to know that they are doing so.
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Imagine the Possibilities
So I am going to summarize by just saying imagine the possibilities. Imagine what you can do to make Big Data real and make it something that is available today. This is not something that is ten years from now. This is something you can do today. We have so many customers who are doing the coolest things because they look at data differently, and so I encourage you to think differently.
Put the customer first. Put your people first and figure out how you are going to achieve new possibilities when you enable Big Data solutions. Become that state-of-the-art enterprise like the people that are performing and leveraging analytics today. So with that I am going to wrap up, and I think it is time for Q&A, and we’re happy to answer any of the questions that you may have.
Frequently Asked Questions About Analytics Software
1. What is analytics software, and how does it differ from traditional reporting tools?
Analytics software goes beyond static reporting by enabling interactive exploration, real-time data visualization, predictive modeling, and self-service dashboards. Traditional reporting tools typically present historical data in pre-defined formats, while modern analytics platforms allow users to ask new questions, drill down into specific data slices, and discover patterns on the fly. The difference lies in agility and depth—analytics software empowers decision-makers to respond dynamically to changing business environments.
2. Does analytics software require programming or technical expertise to use effectively?
Not necessarily. Many modern analytics platforms—especially those designed with business users in mind—offer drag-and-drop interfaces, natural language queries, and guided data exploration tools. While technical expertise can enhance customization and integration capabilities, well-designed software should allow non-technical users to build dashboards, run reports, and explore data independently. This democratization of analytics is key to fostering a data-driven culture across an organization.
3. How does analytics software integrate with existing data sources and business systems?
Most enterprise-grade analytics tools are built to connect with a wide array of data sources—including SQL and NoSQL databases, cloud storage, ERPs, CRMs, APIs, and spreadsheets. Integration typically involves setting up secure data connections, mapping schemas, and establishing update schedules or real-time syncs. The best platforms offer native connectors, data mashup capabilities, and transformation layers that allow organizations to blend and cleanse data without building complex pipelines from scratch.
4. What kind of ROI can businesses expect from implementing analytics software?
Return on investment can manifest in multiple forms: improved decision-making speed, reduced reliance on IT for routine reports, greater insight into customer behavior, and the identification of cost-saving or revenue-generating opportunities. For example, companies using analytics to optimize supply chains or sales pipelines often report measurable performance improvements within months. While the ROI depends on adoption, data quality, and organizational alignment, well-executed analytics strategies typically yield both financial and operational gains.
5. Is cloud-based analytics software secure and scalable for enterprise use?
Yes, most cloud-based analytics solutions are built with robust security features including encryption, user authentication, role-based access control, and audit trails. Reputable vendors comply with data privacy regulations such as GDPR, HIPAA, or SOC 2. Scalability is a core advantage of cloud-native systems—they allow organizations to expand storage, users, and compute resources seamlessly, making them ideal for enterprises with growing data volumes or distributed teams.
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