Business Intelligence

Big Data, Part Two: A Happy Medium

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Business Intelligence, Marketing Musings

Welcome to the second part of my journey to explore the world of big data (read part one here). Though I am an experienced database developer familiar with both transactional and business intelligence systems, the world of high-volume, complex, fast-moving data blithely labeled “big” is sending me back to school. Join me in my efforts to beat back ignorance and ship cool solutions.

At this point, you may be convinced that your company has big data that is underused or undervalued; and you want to find a way to import it, organize it, and start mining it for its incalculable business value. Allow me to step in and give you a single word of advice:

Don’t.

What? Consultants are supposed to write about why you should buy what they’re selling. But let’s look at the implementation details. Jumping into the deep end of your first big data project involves quoting and procuring huge amounts of storage; choosing, buying, and acquiring skills on new data analysis tools; and almost certainly staff augmentation, in terms of new head count or outside vendors. With that many variables, there is a lot of potential downside. One or two costly mistakes could cripple the project.

For your first adventure, I would advise using the tools and storage you already have, and learning to sip from the fire hose. Just because something is possible doesn’t mean it’s feasible, especially for a team charting new territory. Here are four ways to cut big data down to size, so you can acquire skills and demonstrate business value using only a few hundred gigabytes of data, instead of petabytes.

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Weekly Roundup: 9/14/12

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Business Intelligence, Marketing Musings, Social Media

Welcome to the Projectline Weekly Roundup. We know that the week can move pretty fast. Since Fridays sometimes offer a chance for a breather, we wanted to share links to some of the articles we liked this week. As always, we’d love to get your take, so feel free to leave a comment or chat us up on Twitter. Happy reading and have a great weekend!

Weekly Roundup

Business Intelligence
Data Is Useless Without the Skills to Analyze It—I’ve highlighted a couple of these kinds of blog posts, but it is so important. Machines can only do so much with data, and humans are critical to using it to your advantage.

Social Media
Facilitating Social Business in Large Organizations—This the transcript from this week’s #mmchat Twitter chat. It is worth it to scan through and pick up some ideas.

Traditional Strategy Is Dead. Welcome to the #SocialEra—I was really drawn to the opening of this post. “When I say, “Social is and can be more than media,” people resist. It’s as if the two words (social and media) are now permanently fused together. But they shouldn’t be.”

Projectline Posts
What’s The Big Deal? The World of Big Data—Just what is “big data?” Norm Bowler from our business intelligence group, shares his perspective in this blog post.

What’s The Big Deal? The World of Big Data

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Analytics, Business Analytics, Business Intelligence, Marketing Musings

I’m an engineer, for better or worse. I live by what I can weigh and measure. When someone tells me they have “big data” and are going to “put it in the cloud,” I put my hand on my wallet. I can’t help it—I get a fairyland feeling. To me, “too big to measure” sounds suspiciously like “too big to fail,” and both terms make me nervous. What is this stuff? My engineer brain rebels.

But there really must be such a thing as big data, because suddenly everyone is talking about it. And people are sure they need it, even if they’re not exactly sure what it is. Join me as I try to shed a little light on the latest buzzword.

How Big Is Big?
Let’s start with a practical example: Take a piece of paper and write out your name, address, phone number, and e-mail, then count the characters. (Engineers like me love this stuff; give yourself a gold star if you used graph paper.) It will probably be a little under 200 characters. Using that as a baseline, we can extrapolate: five address book records per 1000 characters (a kilobyte, or “K”); five thousand entries per million characters (a megabyte, or ”Meg”); and five million entries per billion characters (a gigabyte, or “Gig”). Multiply that by another factor of 1000, and you’ve got five billion address book entries and a trillion characters of data—a terabyte. Almost one entry for each person in the world. That’s a lot of data, but it’s not “big” data—a terabyte or two is well within the capabilities of current SQL databases.

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Picture of the Week: Serious/Fun with the BI Team

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Business Intelligence, Pic of the Week

Here at Projectline, we’ve recently started a project to capture all the places we work and bring together our team around the world. Every weekday morning at 10:42 am, our team is invited to send in a picture of where they are, what they’re doing, or who they’re with. Each Monday, we’ll choose our favorite picture of the previous week and share its story here.

10:42 March 7, 2012 - EricM , we really work in BI too....

Bearded water-gun slinger holds BI Analyst hostage high over the city of Bellevue. Actually, just an average day in the BI outpost of our Eastside fort.

 

Taming Social Media Data

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Business Analytics, Marketing Musings, Social Media

I’m a Business Intelligence Analyst. I’m also a data geek. So when a client recently asked me to oversee the development of a comprehensive Social Media report, for monthly presentation to their upper management, I was both excited and wary.

Excited Me: Cool! The latest thing! A vast untapped resource of dialog and opinion straight from customers!

Wary Me: This stuff is mostly free-form! How are we ever going to glean relevant, actionable data from these sources? It’s like the Wild West out there…

Some basic social metrics are easy to gather and interpret. How many Facebook likes did we add this month? How many unlikes? How many Tweets were made with a hashtag we might expect, like #ProductName? How many people liked our client’s latest Facebook post? It’s a good start, but our client’s management wanted more in-depth insight.

There are several tools available for social media analytics. Visible Intelligence, Radian6, Sysomos (MAP and Heartbeat), Nielsen’s BuzzMetrics, Crimson Hexagon, etc. They all offer valuable insight but go about it in very different ways. Instead of picking a tool and prying out what we could, we worked backward from our client’s requirements. We tried various tools, analyzed the results, and chose the solutions that provided the most relevant analyses for their needs. Here’s a partial list of some of the in-depth analyses we were able to generate:

Buzz Volume: Using keyword searches across Facebook, Twitter, and select blog, video, and photo sites, we came up with a “buzz” volume that we could track over time. This is good to watch for overall social interest—for example, to see the buzz from rolling out a redesigned website or a new product version.

Sentiment: Some tools are quite good at classifying posts by sentiment: positive, negative, or neutral. Tracking counts of posts by sentiment month-to-month proved to be very useful, particularly around new product version releases and press releases. For further insight we picked out specific sample posts, positive and negative, and provided links to the actual posts.

Topics by Media Type: This breaks out a percent of posts by where they occurred: Facebook, Twitter, blogs, etc. Watching shifts in these volumes proved valuable. For example, we knew that discussions in this client’s forums tended to be mostly about troubleshooting technical issues, so an increase in forum buzz proportionally to other sources could indicate an emerging technical issue.

Top Contributors: We identified the URLs of forums, URLs of blogs, and handles of Twitter accounts providing the highest number of posts in the month. This provides a nice short list for further investigation of who is “buzzing” about our client’s product the most and might be worth approaching for a more formal relationship. For example, a person who frequently blogs about usability of the product might be an excellent person to have as a beta tester.

Competitive Analysis: Why just track buzz volume for the client’s product? We track buzz volume for competitors’ products as well, then combine and track over time to see how our client stacks up. The percentage that our client’s buzz contributes to the overall buzz in their category could be termed “Share of Voice” and is a good metric to track. When our client releases a new version, does their Share of Voice go up? If not, do they need to retool their launch marketing efforts?

Website Visits by Referring Domain: Most common web traffic tracking tools can report the domain from which a visitor came. Because many of our client’s social media posts include a call-to-action to click on a link to our website, we can track referrals from twitter.com, linkedin.com, etc. to determine how much web traffic is being driven by their social campaigns.

Success Events by Referring Domain: Going one layer further with web analytics, we worked with our client to define specific “success events” we wanted to track. These were desirable actions taken once the visitor landed on the client’s website, such as watching a video, reading a whitepaper, or downloading a software trial. Analyzing these success events by referring domain allowed us to see the level of engagement of visitors from social sites. Combining Visits and Success Events, both by Referring Domain, we calculated a “Success per 100 Visits” metric-another good way to see how effective our client’s social media posts are at engaging customers.

Blog Traffic Analysis: We used standard web analysis tools to track interaction on our client’s blog. Initially, their blog showed entire articles on the main page. To enable more detailed analysis, we drove changes to their site structure so that the home page shows only the first several lines of each article with a “Read more” link. This way, each full article receives a unique URL, and we’re able to track how many in-depth reads each article is receiving.

Blog Traffic by Referring Domain: Again, it’s interesting to see how people are getting to our client’s blog. This analysis reveals some noteworthy sources of links to their blog articles, and it can also identify sites that could be approached for a more formal relationship.

In its infancy, the process of analyzing social media data can yield some very helpful insights for marketers and managers. It’ll be exciting to see how the tools evolve—no doubt providing richer and more accurate data in the future—and to discover how we can further leverage this rich source of customer feedback data to benefit our clients.

Picture of the Week: Hello, Webtrends!

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Business Analytics, Company, Marketing Musings, Pic of the Week

Here at Projectline, we’ve recently started a project to capture all the places we work and bring together our team around the world. Every weekday morning at 10:42 am, our team is invited to send in a picture of where they are, what they’re doing, or who they’re with. Each Friday, we’ll choose our favorite picture of the week and share its story here.

10:42 June 14, 2011 - Eric Larson in Portland to meet with Webtrends

This week it was hard to choose just one favorite—we’ve seen pictures of the Inc. Leadership Conference, an adorable baby, and the art near our lovely downtown neighborhood.

But Eric’s picture from Portland was irresistible, because we’re very excited about our new partnership with Webtrends (which is based in Portland). You can now find us on their partner page, and we can’t wait to see how their analytics tools enrich our ability to help clients with customer intelligence. (Not to mention giving us an excuse to visit Portland more often!)

Master Data Management for Marketing

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Business Analytics, Marketing Musings

Projectline recently released a white paper on Master Data Management (MDM) for the healthcare industry. Although it’s packed with content, much of it may not seem obviously applicable to our more marketing-based thinking. But it does offer some great ways to think about data, and we’d like to pull out two of those points from the white paper and apply them to the difficult process of measuring return on investment in marketing.

Point 1: People, Process, and Technology:

“The Projectline approach has three core dimensions, which revolve around people, processes, and tools. While many consulting firms focus primarily on tools and processes, Projectline believes the role of people is just as important because they possess the institution’s collective knowledge and are responsible for implementing information tools and processes. These core dimensions are Data Stewardship, Data Governance, and Technology. All of them must be taken into account as an organization undertakes a project to discover its master data and to design, build, and implement a solution.”

As in healthcare, new marketing measurement initiatives often focus on process or tools, but underplay the vital role of people in getting good data that leads to valuable insights. Without full participation from marketers, the data going into the system will always be flawed—and the stories the data tells will always be untrustworthy. People need to trust the technology and understand the process. In turn, the technology needs to be created in a way that honors existing processes. And the processes need to be developed with a full understanding of the technology and how it fits into people’s work.

Point 2: Quick Wins:

“Develop effective implementation plans for ‘quick wins.’ Quick wins are particularly important because they lead functional areas to recognize the importance of MDM and accept it right away.”

When you create a long-term system to optimize marketing by measuring marketing initiatives, it can be easy to get focused on crafting a pure system or shifting ingrained behaviors and taxonomies. But to gain the confidence of teams—and executives—it is vital to define early success and make it achievable. It will be much easier to justify continued investment in a program if you can point to success within the first quarter after implementation, rather than pointing to numbers far into the future.