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Making the Most of Mobile Intelligence
BI Strategies for Data on the Move

by Don DeLoach, www.infobright.comWednesday, May 18, 2011

New iPhone releases spark mass pandemonium and buyers lining up around the block. The term “CrackBerry” is so overused it’s nearly passé. Clearly, the age of mobile data has arrived. And now that millions of people around the globe are carrying their Internet connection with them as they shop, socialize and travel, businesses have even more streams of information that they need make sense of in order to provide quality service, better understand consumer habits and compete in a crowded marketplace.

The always-on, dynamic nature of mobile data is challenging traditional approaches to gathering business intelligence. Communications service providers, content generators, retailers and other companies active in the mobile arena must be able to analyze ever growing volumes of information, and they need to extract intelligence in near real-time. But with new challenges come new opportunities. Mobile technologies such as GPS open the door to innovative marketing and sales strategies, such as location-based promotions. The trick is in transforming raw data into insight fast enough for it to be useful. Here are three key requirements for making the most of mobile intelligence:   

1. Deliver “On Demand”

The problem with traditional data warehousing and management solutions is that they were built for a different era, when speed-to-information was measured in months and weeks, rather than hours, minutes and seconds. Built to crank out pre-configured reports (such as quarterly sales histories or monthly call center activity) they are not well suited for mobile channels where near-real-time analysis and quick decision making is critical. For example, to be effective, content and location-specific marketing offers need to be served up in the short amount of time that a mobile user is interacting with a particular Web site or gaming application, or when they walk in to a retail store or entertainment venue. This requires up-to-the-minute insight into the “who, what, when and where ” of mobile behavior:  i.e., Who is this user?  What device are they using?  When and where are they using it? Additionally, mobile intelligence needs are highly dynamic. Today, an advertiser might need to know how many “clicks” a specific mobile banner received. Tomorrow, the same company might want to understand user profiles for smart phone apps in order to identify the best venues for targeted offers. Retrofitting traditional database solutions to handle constantly changing queries often requires an enormous amount of manual fine-tuning, as IT professionals must create indexes, partition data and perform other customized tasks to ensure a fast response. This approach is simply too time consuming and expensive in to be practical in a mobile environment. Thus, a simple yet flexible solution for turning around insight “on demand” is critical—whether analytic queries were prepared in advance or thought up on the fly.     

2. Embrace Data Diversity

The number and type of mobile devices that generate potentially valuable business intelligence are proliferating at a swift pace. As basic cell phones have given way to smartphones and tablets, the diversity of mobile data now spans e-mails, Web clicks, music and video downloads, interactive gaming and more. Mobile intelligence is also being produced by devices like  eReaders and Xboxes—even sensors that capture enormous volumes of machine-generated information on everything from cars passing through toll booths to electricity traveling through transmission lines. In addition, mobile data needs to be combined with other back-office sources of information (from CRM, inventory, accounting systems, etc.) in order for businesses to extract the most complete intelligence. For example, mobile geolocation data alone might tell a sporting goods company that a potential customer has walked into one of its retail stores. While useful, this information would be even more valuable when linked with historical data that reveals the individual is a member of the company’s rewards program and frequent purchaser of skiing equipment. A single, holistic view of data coming from diverse sources and information silos is needed—one that is able to convert raw content into contextually relevant business intelligence. This means that data must be properly integrated and transformed before running queries, requiring tools that facilitate ETL (extract, transform, load) activities, as well as analytic capabilities that can dig deeply into multiple types of information.

3. Prepare for Petabytes

Mobile channels are a growing contributor to the terabytes (and in the near future, petabytes) of data that today’s businesses now need to capture, track, analyze and store. Machine-generated information in particular—call detail records, Web and event logs, GPS info, RFID readings—is increasing at an exponential pace. Not surprisingly, rapidly expanding data volumes are bumping up against the ability of most organizations to store and manage it all. Traditional, hardware-centric approaches to information management are no longer enough:  continuing to throw more servers and storage systems at the problem creates massive infrastructure footprints that are extremely costly to scale, house, power and maintain. Organizations need tools that can help them load data faster, store it more compactly and reduce the cost, resources and time involved in analyzing and managing it.

Fortunately, there are new approaches that today’s businesses can take to extract useful intelligence from mobile data. Columnar databases (which store data column-by-column versus traditional row-oriented databases) are a strong choice for high-volume analytics because they can help reduce disk I/O and computing resources while also enabling significant data compression and accelerated query processing. This means users don’t need as many servers or as much storage to analyze the same volume of information—a particularly compelling capability when it comes to mobile intelligence. Also helpful are integration solutions specifically tuned for the efficient preparation and transformation of data for business intelligence. With so many diverse sources coming into play, it is especially useful to have ETL capabilities that are open enough to handle an array of data requirements.

Finally, it’s important to think about where mobile data actually resides. Much of the data that organizations need to look at is not necessarily “owned” by them—it exists within Twitter and Facebook feeds, it’s hidden within Web logs, sensor output and call detail records. Finding exactly what’s needed within such an enormous stream can be like finding a needle in a haystack. Information consumers need to be able to define a set of queries and get the summary information needed in a much smaller, more digestible form. This requires technology able to use knowledge about the data itself to intelligently isolate the relevant information and make queries more efficient. It’s a fundamentally different way of approaching analysis. 

Mobile data presents yet another rich vein of information that organizations can mine for valuable business insight. But in this fast-paced environment, the answers need to come fast.  When it comes to mobile analytics, accelerated query performance, streamlined integration and an ability to handle “big data” volumes will help businesses uncover the gems of intelligence that drive better decisions, greater profitability and competitive advantage.   

Author Biography

Don DeLoach is CEO of Infobright, the open source analytic database company. Infobright is being used by enterprises, SaaS and software companies in online businesses, telecommunications, financial services and other industries to provide rapid access to critical business data. For more information, please visit www.infobright.com.

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