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4/1/2013
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5 Data Sources For Visualization Beginners

Confused by the buzz around data analytics and visualization? Start with these five information sources.

The information age is chock full of jargon: Big data, analytics, business intelligence, visualization, graph databases, sentiment analysis and so on down the lingo list. We've even got "long data" now.

It's all a little overwhelming for small and midsize businesses (SMBs) that are doing little -- or nothing -- to translate "data" into "stuff that actually helps us make money." Justin Langseth, founder and CEO of data visualization startup Zoomdata, notes a growing gap between the data haves and data have-nots. According to Langseth, around 1% of business users are doing sophisticated Hadoop-level data analysis. That elite group is in "hog heaven," Langseth said in an interview. Another 4% are using traditional business intelligence tools.

And the rest of us?

"Then there's the 95% of people who either don't have access to anything or just don't use anything," Langseth said. Firms like Zoomdata, Datahero, Tableau and others are hoping to change that with data visualization tools that don't require a lot of heavy lifting by IT. Or, as Langseth puts it, "Making it easier for normal businesspeople to get stuff out of Hadoop and do stuff with it."

[ What is long data? Read Big Data, Meet Long Data. ]

Visualization plays an important part in that democratization of data, sometimes because of the sheer volume of information. "The human mind is very good at looking at large sets of data visually and finding patterns," Langseth said.

Indeed, visualization can be a powerful way to translate all those bytes into meaningful business information -- enough so that some companies are even deploying "viz walls" -- large banks of high-definition monitors that display corporate data in or near real-time.

But if you're among that 95% -- and the math says you are -- where do you begin? Sometimes the number of potential data sources alone is enough to halt a would-be project in its tracks.

Langseth recommends starting with things like Google Analytics and the built-in capabilities of cloud platforms like Salesforce.com. Most of these services offer APIs for extracting your data for visualization and other purposes. Doing this can better allow you to integrate and benchmark your critical information and add increasing layers of sophistication to what you do with it. You've got to start somewhere; consider these five data sources as a reasonable Phase One.

1. Salesforce.com

Few businesses succeed without making the cash register ring, so tracking the sales lifecycle -- whether a bag of groceries or a six-figure contract -- is crucial for strategic decision making. Salesforce.com is the sales data source for many SMBs these days. While the platform includes its own built-in analytics, extracting it from Salesforce can offer more flexibility in visualization and integration. "There's a lot of value in sucking that data out of Salesforce through the various APIs they offer and smashing [and] mashing it together with other data," Langseth said.

2. Your Own Website

Here's your no-brainer data source for that "smashing and mashing" Langseth referred to: Your own external website(s). If you use Google Analytics, that's a good place to start. According to Langseth, visualizing website data and weaving it with Salesforce and other sources helps to better illuminate customer behavior beyond your actual transactions: Where people are coming from, where they're going, what they're doing after their online experience with your business, and so on.

3. Customer Care & CRM

There's a vast world of post-sale, customer care-related data to be had: Call center interactions, online chats via LivePerson or similar channels, online surveys, and so on. (The same kinds of interactions can take place with prospective customers, too.) "There's a lot of [available] information about what your customers are saying or trying to tell you about your products or service," Langseth said. "Listening to that and trying to understand that is really interesting."

The historical technical challenge here has been that much of that data is in text form, but Langseth said that advances in text and sentiment analysis tools are starting to fulfill on the bottom-line goal: Turning that text into usable data.

4. Social

The same concept applies to the expanding, noisy universe of social media and related sites. Langseth noted that social data should include not just your own prospects and customers but also your competitors and broader industry. "Being able to understand those trends and figuring out quickly how they [apply] to your business [is important]," Langseth said.

Properly interpreting the tea leaves can become a critical input into areas like marketing, product development and design, pricing and so forth. "It's becoming even more important now as the pace of business and the pace of change and innovation is much, much faster than it's ever been," Langseth said. "You really have to be able to react quickly to not just what's happening inside of your business but to stuff that's happening outside of your business."

5. Internal Performance

In the era of social, mobile and other exploding data sets, it can be easy to overlook the old reliables: Your organization's own internal operations. Longstanding fundamentals like supply chain and other logistics, for example, can become invaluable data sources for monitoring organizational health and performance. Likewise, consider things such as development bug trackers or call-center support tickets; visualizing the flow of those processes can sometimes unearth hiccups and headaches that are quite easy to resolve once identified. If your firm uses business process modeling (BPM) software or something similar, that's a logical place to look to as a data source.

"Looking at the under-the-hood view of those BPM systems can really show you where things are backing up or stuck, or where there are inefficiencies in a business," Langseth said.

There's a decided advantage in underpinning all of the above in historical and forward-looking data, according to Langseth. That's especially true in terms of making the visual representations of current information more valuable for those aforementioned "normal businesspeople."

When set against benchmarks, just about any kind of data can reveal insights when it deviates from past or predicted norms. "Then you can immediately spot things that are new or divergent from what would be predicted or expected to happen," Langseth said. He added that such visualizations can be especially useful in areas like social, where "listening" and "understanding" are two very different things. (Lots of businesses do the former; fewer can say they do the latter.)

He offered an example of a retail customer that had deployed visualization as a means to improve its Black Friday operations. Had the store just been looking for traffic surges or unusual activity, well, they'd have found them. That's just business as usual during the post-Thanksgiving shopping craze, so visualizing the spikes would have been akin to confirming that water is wet. But by overlaying their real-time data over historical trends and predictable variables, they were able to spot issues -- which included a promotional code that stopped working, inciting a rash of angry customers -- almost immediately. The predictive piece of the equation comes into play when factoring in variables like changes in staffing or locations, for example, or major marketing or advertising campaigns.

The same principle holds true just about any "like for like" period, especially for companies doing real-time visualization. "If you were trying to visualize what's happening now, you wouldn't want to compare it to [the same time] yesterday because yesterday was Easter Sunday -- but you might want to compare it to the average of the last five Mondays," Langseth said.

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