Ask enough data professionals to identify common realities about analytics, and you're likely to experience déjà vu. Certain themes come up over and over.
We have summarized some of the key issues, as well as comments we're hearing from analysts, data scientists, and others struggling to maximize the value of big-data.
It's challenging to work with inconsistent data sources. It can be difficult, time-consuming, or both to aggregate data in an efficient way. Collecting and analyzing massive amounts of data from sources such as social media, social media monitoring reports, paid media, and site side traffic reports can reveal interesting and useful patterns. However, we still lack the ideal tools to select, combine, and visualize these disparate datasets easily, as well as to perform simple queries and selections to allow meaningful data analysis.
An associate media director in New York put it this way:
Pulling all that data together takes time, often as much as 10 hours -- and aggregating seven or eight sources of data is not a sexy job. It's not fun. We need better ways to quickly import various data sources and create the charts and visualizations that will drive decision making. And in the meantime, we have to decide whose job is it to do this aggregation. You don't want to give the task to a high-level employee, but do you really trust an intern to do it unless you have a human-errorproof template in place?
Not all data is created equal. Each data source has its own biases and deficiencies. To make optimal use of that data, you have to understand the methodology behind it. You have to ask, "How is the data collected, and is it biased toward a specific perspective?"
Take panel data, for instance. A company may recruit Web participants, who install a piece of software that tracks all the person's browsing behavior and reports it to the company running the panel. In addition, the participants are usually asked to self-report demographic, salary, educational, and other personal information. But since almost all businesses, universities, and other institutions ban monitoring software (because of security and privacy concerns), most monitored behavior comes from home users. And users are often enticed to install monitoring software in exchange for sweepstakes entries, downloadable screensavers and games, or nominal sums of money. The type of people who participate in the panel may cause a bias in the data.
An oil and gas analyst in Bismarck, N.D., told us:
There's no shortage of data. The challenge is finding the best data to meet your objective. To do that, you have to understand exactly how the data is collected and understand both the sample size and sampling bias of the data reported to you. If you're comfortable with what you find, then and only then should you use the data.
Average people can be hugely influential. One person can have a surprisingly broad impact on companies and brands -- for good or bad. In his book The Tipping Point, Malcolm Gladwell dubbed the people who like to pass along knowledge "Mavens" and the sociable personalities who bring people together "Connectors." Someone who can do both is even more valuable, because that person has a large sphere of influence and higher social currency (or influence in the social media sphere). You need to understand the new metrics of social networking. It is possible to measure the connections and influence of average people, and perceptive companies and brands put their money where these mouths are -- on blogs, forums, and social media sites.
Today's biggest trends -- the mobile Web, social media, gamification, real-time data -- are changing the consumer landscape. Average people are empowered and influential.
Too much data can make you lose focus. Extraneous data can divert your attention from key performance indicators (KPIs), which support management decision making. In addition, all levels of an organization can use KPIs to measure success in achieving stated outcomes. The challenge is to identify the high-value data that supports each KPI without shifting attention to a secondary performance indicator. In other words, you have to make sure what you are measuring is a true indication of the values you wish to monitor.
Say your objective is to assess qualified traffic on a Website by looking at the average time spent on the site and the number of page views and uniques. If that's the case, it's important to focus on key pages driving the majority of traffic and where traffic is coming, from versus analyzing every single page on the site.
If your company has over-complicated data and analytics, it's time to take a step back, simplify, and reassess. Marketers [or executives of the companies that hire them] must put a few stakes in the ground -- business objective-setting -- from which they will be able to back into a few KPIs or metrics to support their goals.
@jewel.ascano, thanks for jumping into the conversation here, and I'm glad the piece resonated with you. You point out the human element -- if you had to weigh your "people" challenges alongside factors like data management and analytics tools, would you rate those as among the more difficult ones?
Thanks for simply summarizing the challenges we face every day. What's also interesting is that each of these points stresses the importance of having a human element to make the data valuable and actionable. Data alone will not give us the answers.
I like your Four Simple Truths about Analytics Noreen and I really like the point about " too much data can make you lose focus". I think it is easy to get excited and start to ask question after question which will probably take you off your initial objective.
I like the advice of keeping it simple, no matter how much it kills you, Choose a couple KPI and exhaust them, get a return on your efforts, strengthen the bottom line ( after all that is what business are trying to do with analytics) and after you have solidified your position as bring value to the company, then you can explore other ways to leverage your findings or investigate other aspects altogether,
The organization suggests that negative influencers exist one way or another -- and that by creating a strong WOM campaign, the positive voices will overcome the negative ones In fact, it contends, the widespread belief that you "tell three people" about a positive experience, and "ten people" about a negative one, isn't true.
This trnd study with 30,000 survey participants investigates the relationship between positive and negative word-of-mouth, as well as the role of social media in the relaying of purchase recommendations. By Martin Oetting, Monika Niesytto, Jens Sievert and Florian Dost, September 2010/Augut 2011.
Here is the survey results in a nutshell:
• Most people remember much more positive (89%) than negative (7%), word-of mouth.
• Negative word-of-mouth spreads barely more than positive (average of 8.25 people vs. 7.44 people).
• There is little neutral word-of-mouth – consumer statements almost always have either negative or positive polarity
There is no reason to avoid actively working with word-of-mouth. The frequentlyexpressed concern that you cannot prevent negative communication in the context of word-of-mouth marketing is in any case misleading – it suggests that negative. WOM would simply stop happening if you didn't work with consumer conversations.
Is WOMMA a real organization. I visited the website and it looks real ... but I still can't get over my suspicion that it is fake. What does the WOMMA say about anti-influencers, annoying people who cause other people to avoid buying the products and services they rave about?
LEADERS FROM THE BUSINESS AND IT COMMUNITIES DUEL OVER CRITICAL TECHNOLOGY ISSUES
The Current Discussion
Visual Analytics: Who Carries the Onus? The Issue: Data visualization is an up-and-coming technology for businesses that want to deliver analytical results in a visual way, enabling analysts the ability to spot patterns more easily and business users to absorb the insight at a glance and better understand what questions to ask of the data. But does it make more sense to train everybody to handle the visualization mandate or bring on visualization expertise? Our experts are divided on the question. The Speakers: Hyoun Park, Principal Analyst, Nucleus Research; Jonathan Schwabish, US Economist & Data Visualizer
Dynamic data visualizations let analysts and business users interact with the data, changing variables or drilling down into data points, and see results in a flash. Advance your use of data visualization with tools that support features like auto-charting, explanatory pop-ups, and mobile sharing.
No doubt your enterprise is amassing loads of data for fact-based decision-making. Hand in hand with all that data comes big computational requirements. Can traditional IT infrastructure handle the increasing number and complexity of your analytical work? Probably not, which is why you need a backend rethink. Big data calls for a high-performance analytics infrastructure, as Fern Halper, a partner at the IT consulting and research firm, Hurwitz & Associates, discusses here.
Redbox's bright-red DVD kiosks are all but ubiquitous these days, located in more than 28,000 spots across the country. Jayson Tipp, Redbox VP of Analytics and CRM, provides an insider's look at how the company has accomplished its phenomenal nine-year growth.
InterContinental Hotels Group (IHG), a seven-brand global hotelier, has woven analytics into the fabric of its operations. David Schmitt, director of performance strategy and planning, shares IHG's analytics story and his lessons learned.
Elizabeth Barth-Thacker, a BI and informatics technology manager at Humana, tells us how her team is creating data transparency and building engagement with the business – with the help of an internal collaboration portal called Humanalytics.
Whether working in major league sports, financial services, or healthcare, analytics, and data, professionals are checking out how visual analytics and high-performance technologies can help them optimize their environments, shrink their cycle times, and improve decision making, as attendees at the recent SAS Executive Briefing in New York share with us.
Jim Davis, SVP and CMO at SAS, talks with us at a recent SAS Executive Briefing about how high-performance analytics and visual analytics take away the concerns over big-data and let companies get down to business with their data.