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Tips + Tricks

3 Ways Big Data is Changing How You Analyze Data

By Marissa Davis | March 15, 2016
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Big data has really changed how people approach business intelligence and analytics today. Valuable business data no longer just comes from business applications or is solely managed by IT. You have applications in the cloud that you subscribe to one day then switch off the next. Other sources of data such as social media and video are pushing data volumes and velocity at a much greater scale, which plays a big part of the big data story.

As a result, big data analytics applications need to do more than just reporting. These apps need to offer greater levels of interactivity with the data, and need to be developed with higher levels of agility. With the right tools, your organization can analyze 100% of its data quickly and easily, regardless of how far the data sits below the surface. You can blend disparate data sets and empower users to explore that data on their own without technical assistance.

Let’s take a look at some common use cases that work with big data.

Internet of Things

The growth of the Internet of Things (IoT) has been exploding – changing the way businesses and consumers interact with the physical world. With so many connected devices generating so much data, there’s often a need to derive insights and meaning from this data.

Use cases might include a data center with thousands of machines generating machine logs, or a healthcare facility with medical devices or sensors monitoring activity.

Another example is our customer Glassbeam, a big data applications company specializing in multi-structured machine data analytics for IT and business users. Glassbeam needed an embeddable application that would enable their customers to monitor device usage and performance. They also needed more developer control over options like placement of charts and filters within reports.

With Logi, Glassbeam was able to build and customize dashboards, which they can frequently enhance and modify with new visualizations, interactivity, and data sources. They’re also able to provide value through capacity planning – helping end-users ensure their devices have enough memory, disk space, and processing power to operate and to proactively predict device failures and ensure uptime. Users can also utilize this data to perform audits and intrusions where unauthorized access can be detected in real time.

Measuring Brand Performance

Social intelligence is necessary for gaining insight on how consumers think and behave. As social technology matures, social intelligence can help companies overcome some of the limits of older intelligence-gathering approaches. These are often used with traditional reporting and business intelligence methods to help organizations make better data-driven decisions.

Let’s take a look at local social media agency as an example. This business has brand managers and customer support agents who needed help tracking everything that was said about their company, and understand if the sentiments posted were positive or negative. They wanted to proactively monitor the health of their brands and engage with individuals coming through their numerous channels (Facebook, Twitter, blogs, forums, etc.).

Logi’s big data technologies made it possible for the organization to get value from all the data collected at rates much faster than ever before, making complex problems much easier to digest and take action on.

Business Process Complexity

Technology can wrangle the complexity in a business process to deliver results faster. Service warranties, as an example, are provided by many different agents and channels, and sorting through these relationships can be quite complex.

What’s more, different warranties may have different terms, and with the business expanding, these documents are constantly evolving.

For this use case, let’s consider a mid-size global warranty services organization. This organization faced many challenges when trying to bring service documents together, and structure them into a relational database. They needed Logi to help create those complex joins which before, had proved to be a long, time-consuming process for them. Ultimately, solving this problem required a NoSQL data store in order to efficiently store and query such documents.

Logi was able to help them deliver much higher value to their business by helping them identify potential opportunity by policy renewals, up-sales, and cross-promotions of warranty products.

As consumers, we all take for granted the excellent user experiences offered by the Facebooks, LinkedIns, Amazons, and the Googles of the world. We don’t necessarily sit back and thank them for using big data. We simply enjoy an intuitive, seamless user experience. In turn, this heightens our expectation that business applications will provide as much utility as consumer applications provide for us.

Ultimately, that is what makes big data relevant to those who are looking to implement big data projects.

Looking for additional tips on how to leverage big data technologies to build a viable long-term big data strategy? Check out our ‘What’s the Big Deal about Big Data?’ eBook.

 

About the Author

Marissa Davis is the Corporate Communications Manager at Logi Analytics. She was previously an Account Manager at LEWIS PR, where she managed the public relation activities for a number technology companies. Marissa holds Bachelor degrees in Communication Studies and Technical and Scientific Communications from James Madison University.

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