The Need to Aggregate Information from Multiple Sources
This is the continuation of the transcript of a Webinar hosted by InetSoft on the topic of "Agile BI: How Data Virtualization and Data Mashup Help" The speaker is Mark Flaherty, CMO at InetSoft.
We will come and take a look at that. But even here, it's important to recognize that there are some BI applications that are truly analytic or data mining in nature. For instance, you might be trying to find patterns of purchase behavior among customers to get a strategic understanding of customers.
In this case, you really need to mine lots of transactional data or click-stream data in a warehouse. This contrasts to reporting dashboards or operational business intelligence, which is not analytically heavy, but it still needs to aggregate information from multiple sources.
You will see that that distinction is somewhat important because today people are using replication-based strategies to serve a lot of business intelligence work, but in reality, much of this kind of work can actually be done through virtualization, saving cost and time plus giving you the ability to deliver faster changes to the product.
So having said that, it’s not an either/or question. You can store the same kind of data if you replace this with MDM, and we will see that shortly.
Virtualization Serves That BI Dashboard
You could have some of the information in a master data or a customer master, but a lot of the rest of the information is being brought together through virtualization to eventually serve that BI dashboard. What data virtualization can do is either supplement or in some cases replace the need for physical replication and therefore provide virtualized data marts for reporting and dashboarding.
The data mashup platform can provide contextual information to support transactions in the process world or customer service world. Information that’s gathered and presented for reporting could be abstracted away into another kind of review and provide, for example, a backdrop to customer service, to provide let's say up-sell, cross-sell marketing intelligence. An example is to be able to manage analysis of customers viewing patterns on cable television in order to match that with product data in order to propose upgrade packages to customer service representatives.
I am getting a little ahead of myself, so what I am going to do now is use this example as the launching point to start talking about use cases for data mashup. We have found different ways of thinking about use cases. On the previous slide, we talked about informational applications and transactional applications. In this slide, we have picked out the four top uses for data virtualization based on our 15-plus years of expertise in this area.
Dashboards Using Virtual Data Marts
Generally speaking, agile business intelligence is a key area where real-time reporting, performance dashboards using virtual data marts, either instead of, or as a way of extending the enterprise data warehouse, or in fact, to create a virtual data warehouse. These are common goals. We are talking about orders of magnitudes in better speed, flexibility, and lower costs in terms of building those options compared to traditional methods.
We are not going to completely replace the need for certain data mining requirements. You will still want that, but a lot of work can be accomplished with much less money. And competitive and social BI, this is gaining popularity. Typical traditional data integration tools are simply not capable of dealing with that. When it comes to customer focus, providing a 360-degree view of the customer and feeding that into a self-service portal, is another challenge for traditional BI tools.
There is a whole class of applications that can be enabled through a data virtualization platform like ours. With it, you can access Web data in a two-way Web automation, such as a cloud-based SaaS application with no API, or data aggregation from the Web, such as reputation monitoring, or things like that. And then finally, all of these are projects that are often a starting point for building a data services capability. Here a data mashup tool can provide logical abstraction for migration and creation of the virtual data layer. This becomes an enterprise kind of capability. So let’s go through a few applications in each case.
StyleBI for Reputation Monitoring: A Case Study
A leading fashion retailer, TrendVibe, has leveraged StyleBI, an AI-powered business intelligence platform, to enhance its online reputation monitoring, transforming how it engages with customers and manages its brand image. StyleBI aggregates data from social media platforms, review sites, and news outlets, providing TrendVibe with real-time insights into customer sentiment and brand mentions. By analyzing millions of posts, comments, and reviews, the platform identifies trends, such as a surge in positive feedback about a new clothing line or complaints about delivery delays. This allows TrendVibe to respond swiftly to customer concerns, amplify positive narratives, and maintain a strong digital presence in the competitive fashion industry.
StyleBI’s advanced analytics, powered by machine learning and natural language processing, enable TrendVibe to dissect unstructured data from diverse sources like Instagram, X, and Yelp. For example, when a negative hashtag about a product quality issue began trending, StyleBI’s sentiment analysis flagged it within hours, enabling TrendVibe’s PR team to address the issue with a targeted campaign before it escalated into a crisis. The platform’s customizable dashboards also allow the company to filter mentions by region, demographic, or platform, revealing that younger audiences on TikTok were driving positive buzz about a sustainable fashion initiative. This granular insight helps TrendVibe tailor its marketing strategies to specific audience segments.
The impact of using StyleBI extends beyond crisis management to proactive reputation building. By tracking competitor mentions and industry trends, TrendVibe identified an opportunity to differentiate itself by emphasizing eco-friendly practices, which resonated strongly with its audience, as shown by a 15% increase in positive social media mentions. StyleBI’s automated alerts also ensure that TrendVibe’s customer service team responds to reviews within minutes, with AI-generated response templates maintaining a consistent brand voice. This responsiveness has boosted customer satisfaction scores and helped TrendVibe maintain a 4.7-star rating across major review platforms, directly contributing to increased sales and brand loyalty.
Despite its strengths, integrating StyleBI presented challenges, including aligning the platform with TrendVibe’s existing CRM systems and ensuring data privacy compliance with regulations like GDPR. Initial setup required significant training to interpret StyleBI’s complex analytics, but the platform’s user-friendly interface and dedicated support mitigated these hurdles. Moving forward, TrendVibe plans to use StyleBI’s predictive analytics to anticipate shifts in consumer sentiment, potentially integrating data from emerging platforms like virtual reality marketplaces. By harnessing StyleBI, TrendVibe exemplifies how big data analytics can elevate reputation management, fostering trust and driving business success in a hyper-connected digital landscape.
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