These are just a couple of the questions that have been plaguing industries and enterprises worldwide since the “Big Data” phenomenon surfaced. By now, most of us have heard this buzzword/phrase that has been penetrating the minds of IT and analytics professionals alike. However, many organizations are still unsure how to effectively analyze and gain new insights from it. Luckily, there are expert specialists in this field who are eager to join and guide them through their journey.
What is “Big Data?”
I’ll spare you the formal definition and put it simply: “Big Data” is everything, and it’s everywhere. “Big Data” is defined by (at least) three ‘Vs’: Volume, Velocity and Variety. And you might even hear about a fourth ‘V’ depending on which “Big Data” solution provider you’re talking to.
- zettabytes = as much information as there are grains of sands on all the world’s beaches
- Veracity (IBM) — Accurate, truthful and trustworthy data
- Variability (SAS) — Data flows that may be unpredictable, inconsistent and anomalous
Now that we have a better grasp of what exactly “Big Data” is, I’d like to explore some of the complexities and challenges companies face because of it, as well, as the opportunities it presents.
Challenges & Complexities
The size, requirements, boundaries and resources of an organization, as well as the industry it’s in, can dictate the adoption of “Big Data” in addition to which obstacles will prevent them from extracting high-value impact and gaining new business insights that were previously unattainable.
However, there are a few common challenges despite the nature of the business:
- An abundance and variety of data sources and the information collected
- Inherent complexity in processing, management and aggregation
I intentionally left out a fundamental part of the “Big Data” definition when I talked about the three or four ‘Vs’ of this concept, but this is a perfect place to sneak it in.
IDC’s definition of “Big Data” embraces the hardware, services and software that integrate, organize, manage, analyze and present the data that is characterized by the ‘Vs’ discussed at the beginning of this post.
This is why new technologies and architectures, advanced tools and platforms are needed and are continuing to be developed. These appliances will allow enterprises to leverage “Big Data” and (you guessed it) analytics.
- Technical: Data scientists with an unparalleled level of skill to understand the interactions of a new class of technologies
- Analytics: Data mining; statistics; business analytics; problem solving; creativity
Although there are some hindrances to enterprises fully embracing this new era of “Big Data” and analytics, there are evolving approaches to conquer them. For example, the Google Analytics Premium and BigQuery integration that will be taking place toward the end of this year was just announced at the Google I/O a couple weeks ago. If you’re a GA Premium user, I’ll venture to guess that this made you smile — even if you’re not 100% sure what it’s going to mean for your business.
Check back next week when I’ll discuss what value, advantages, opportunities and possible use cases can arise from utilizing more advanced technologies, solutions, and analytics strategies such as the “Big Data” movement. Stay tuned!
One of my favorite web sites on the entire planet is Woot.com. They sell one item every single day. There’s no way to predict what the item will be or how many are in stock or how much it will cost – just visit their site at 12 AM CST every day to find out what great sale they will promote next!
Let’s pretend that you are the senior data / web analyst for Woot.com, and my online behavior and interactions with your site(s) were representative of the average, everyday visitor. It wouldn’t be long before you cracked open Google Analytics, WebTrends, Quantcast, or your favorite measurement tool to have the equivalent of a heart-attack. Here’s my personal estimation of my lifetime statistics on Woot.com and its family of sites:
Bounce Rate: 99.5%
Average Time on Site: 0:00:20
Abandonment Rate: >99%
Conversion Rate: <0.00%
Average Order Value: ~$17.50
Visits to Purchase: 300+
Revenue Per Visit: ~$0.02
Now I don’t know about you, but I don’t know many folks who wouldn’t frown upon looking at those depressing statistics. I can make it even worse for Woot.com by subscribing to their RSS feed and never actually visiting any of their sites in the first place.
The interesting thing about me is that I don’t visit Woot.com to purchase items. If there is something interesting, something that I need, or some cheap gadget that I have no use for but I really have the itch to spend, then yes, I’ll make a purchase. But if you were to ask me what my top 5 reasons for visiting Woot.com would be, I would tell you that I visit Woot.com to:
1. See (not buy) what the item of the day is
2. View purchasing statistics (Geo and hourly breakdowns)
3. Read the product overview (they are VERY clever and funny!)
4. See what’s on shirt.woot, wine.woot, and sellout.woot (their network of sites)
5. If I am remotely interested in the product, read their message boards to see what people are saying about the product
Bonus Reason #6: To see if they are doing a Woot Off!
So if my usage statistics and reasons for visiting Woot.com are representative of the average, everyday visitor, what happens now? Do you sound the general alarm and have a fire sale? Redesign your entire web site? Drop your prices to a ridiculous level? Use a lifeline and phone a friend?
Or, maybe you start including the “why” factor into your data analysis.
Google Analytics, Omniture SiteCatalyst, and every other web analytics package can give you every usage statistic imaginable, but it can’t directly tell you why people search for what they search for on Google and why they are on your site. To fill in the gaps left behind by your favorite web analytics platform, you’ll need to really think about what your web site has to offer its visitors, and what they can possibly do you on site – other than whatever your site’s main objective is. If you sell products of any kind, they could be coming to your site to simply read reviews, or window-shop, or read your company blog, and not even think about purchasing an item at this time. If you are a B2B company, they could be finding out about the history of your company, your board of directors, or to read client case studies, and not to immediately request an RFP and do business with you. And, if you’re a non-profit organization, they could simply be learning more about your causes and getting fact-sheets, and not visiting with the intention of donating to your cause.
There are some tools and some ways that you can help yourself in including the “why” factor in your daily / weekly data analysis. These include (but are most definitely not limited to):
1. Visitor Loyalty reports in Google Analytics
2. Site Search usage reports (usage on your site’s internal search function)
3. “Voice of Customer” tools (4Q by iPerceptions is an excellent online survey tool)
4. Google Insights for Search and Google Trends for Websites (get a feel for visitor behavior trends)
5. Offline focus groups / user-experience studies
Whether you’re the senior web analyst for Woot.com, the National Football League, CNN.com, or marketing for Jennifer’s local flower shop or Louie’s Pizza Joint, it’s critical to include the “why” factor in your data analysis, or you’ll be working off of faulty assumptions. Always determine why people are visiting your site.