Making Data Meaningful

Which is Better: Faster or Slower?

Dan Whitacre — Mon, 06 May 2013 14:03:16 +0000

I must admit I do enjoy Beck Bennett’s series of commercials for AT&T where he poses the question, “Which is better: faster or slower?” I find his deadpan approach to a variety of co-actors and situations very humorous. The question “Which is better: faster or slower?” has interesting application in today’s information and analytics environment. Faster has always been better, correct? The scenario holds true in every industry. If you can make better decisions at a faster pace than your competitor or adversary, then you will always hold an advantage over them. However, the key isn’t just faster, but better decisions faster!

An interesting event occurred last week that made the point that faster is not always better. A short-lived Twitter hoax briefly erased $200 billion of value from the US Stock Market. False reports of explosions in the White House triggered a set of algorithms monitoring news feeds into a two-minute selling spree. In this case, untethered analytics only increased the pace at which we can make mistakes and caused the DOW to drop 145 points. The error was quickly identified and the DOW bounced back, but who knows what losses were incurred by algorithms reacting to the news feed and potentially to other algorithms reacting to those algorithms.

I am fortunate to be in the information and analytics industry and am continuously astounded by the algorithms and analytics that I see people put together. However, this event continues to remind me that even the best algorithms need good data and solid IT development principles such as building in a failsafe. Perhaps we need to teach these algorithms to check their sources before taking action.

Real-Time Analytics

Nash Nath — Wed, 24 Apr 2013 14:40:16 +0000

If you have ever shopped at Amazon you may have noticed a “Featured Recommendations” section that appears after your initial visit. These recommendations get automatically updated after the system notices a change in the shopping pattern of a particular member. This is real-time analytics at work. The system is using the data at hand and coming up with suggestions in near real-time. With more companies investing into a mobile business intelligence initiative, real-time analytics is an essential requirement to ensure a good return on investment.

I think that the implementation of a solution to get real-time analytics could be a costly endeavor. This would require implementation of technologies like Master Data Management and delivery options like cloud and/or mobile BI. Cloud BI presents its own set of security concerns, which is why some of the region’s largest companies are hesitant to implement such a solution. According to one BI manager, the company’s executives do not support the notion of putting their data into the cloud without the implementation of certain security measures. Their need for a mobile BI strategy would require security that would enable the company to delete everything from a device if it is stolen or misplaced.

Insurance companies and retail stores can greatly benefit from such technology. The off-site sales reps will be able to see current information about potential customers including updated life changing events right on their mobile devices, which would increase the likelihood of either gaining a new customer or retaining an existing one*. In-store managers at grocery stores can get a real-time report about slow moving items allowing them to increase sales by changing displays. Real-time analytics can be on-demand where the system responds to a certain request by an insurance sales rep or it can be a continuous hourly report to the store manager of a grocery store**.

Overall, real-time analytics gives a company a competitive advantage over its rivals but requires heavy investment into the implementation of the technology and the guarantee of proper security measures being put in place with delivery options like the cloud. This information is helpful for quick decisions, but companies should still make all major decisions by looking at historical data and studying the trends.

Sources:

*Pat Saporito, “Bring your Best”, Best’s Review, September 2011

**Jen Cohen Crompton, “Real-Time Data Analytics: On Demand and Continuous”. http://blogs.sap.com/innovation/analytics/real-time-data-analytics-09636. August 2012

BI and Social Media: How to Integrate the Two

Nash Nath — Mon, 22 Apr 2013 13:59:46 +0000

With the increasing use of social media tools like LinkedIn, Facebook or Twitter for business, organizations are starting to look at different ways they can use the information gathered from these tools to create marketing strategies, new products or even improve existing products. Social business intelligence is gathering all positive and negative comments about a company from social networking sites and leveraging this data for visual dashboards, scorecards and much more. According to Arcplan President and CEO, Roland Hoelscher*, “If done correctly, integrating social media analysis and business intelligence gets you immediate insight into web activities that have an impact on business”.

Most organizations are keen on creating a 360-degree view of their customers. In order to effectively achieve that, they have to monitor or be part of the conversations on social media sites because that’s where their customers are sharing experiences about a product or service. These organizations have to find ways to engage their customers and then figure out how this information is going to impact their future strategy. Companies like Apple do a great job at engaging their loyal customer base. The create buzz about their next device which leads to multiple discussion threads on various blog sites and trends on Twitter and Facebook. Apple uses social media as a free marketing tool and its customers are doing all the work for them – if you think about it, it’s simply brilliant.

Social media gives good insight into consumer’s buying patterns that can benefit both small and large companies. These companies can now track what their customers are buying, what they like, what they think about certain brands, and then use those metrics to create a marketing strategy or new business initiative. One of the biggest mistakes that companies make is that they want to measure every possible metric. Measuring everything creates an overload of data making it nearly impossible to act on anything. Companies need to determine the most important factors to their strategy and start from there.

* http://biblog.arcplan.com/2011/05/qa-integrating-social-media-and-bi/

The Language of Bar Charts

Jeff Shaffer — Thu, 04 Apr 2013 16:30:38 +0000

It is always best to avoid rotated text when creating data visualizations, yet this seems to be one of the most common problems I see. This might be due to the fact that tools like Microsoft Excel rotate axis labels automatically in many situations and people don’t make any adjustments to these defaults. Even Tableau, which generally has better practices built into the defaults, rotates axis labels in many situations. In fact, Tableau doesn’t even allow the user to rotate the y-axis title and requires a work around to show the y-axis title horizontally.

The most common problem with rotated text is in the x-axis labels. Often times, it’s simply the length of the labels that force the software to rotate the text. Consider these bar charts below:

As I tell my data visualization students, the language of bar charts speaks both vertically and horizontally. In this case the easiest thing to do is to rotate the entire chart. By doing so the axis labels can be read easily, without tilting your head, and the bars have the same function as they did when they were vertical.

The only exception to rotating a chart from vertical bars to horizontal bars is when dealing with time series data. Time series data is always best on the x-axis (and typically in a line chart). Therefore, do not rotate or reorder time series data.

If you feel you must use vertical bars for some reason then consider other options to avoid rotating text (see the article Exploring All of Your Options for more detail on Time Series data and other options).

Here’s an alternative in this particular example.

I hope this information will help you avoid rotating text on your data visualizations.

Day to Day Data: March Madness

Nick Bikas — Wed, 27 Mar 2013 17:55:39 +0000

The Background

Love it or hate it, the madness is upon us. Every March, the country gets a healthy serving (or three) of College Basketball. Each year, approximately 40 million people fill out brackets for the NCAA Men’s Basketball Tournament and each year, every single one of those people swears that they picked everything perfectly. If you were about to Google, “What are the odds of completing a perfect bracket?” I will save you the trouble; it is 1 in 9.2 Quintillion. If you were about to Google, “What on Earth is a Quintillion?” the answer is a 1 with 18 zeros behind it. To put this in perspective, the odds of winning the Powerball are 1 in 175 Million. You have a better chance of winning the Powerball multiple times than picking that bracket correctly.

These however are just numbers, I began to wonder how I can slice and dice tournament history data. Sure, I can find what teams have won the most or lost the most. But can I dive even further, and find out what states, cities, or teams have the most wins or championships? Which teams constantly underperform and which teams exceed expectations?

The Research

Using a data dump of NCAA Tournament History from 1939 to 2012 I was able to dive in very quickly and start seeing results. I first wanted to see which states produced the most tournament victories. Using Tableau I was able to visualize what the Top Ten states were in terms of victories.

Using a filled map, I was able to visualize the amount of wins for the top ten states. North Carolina and California are the top two states, no doubt fueled by the powerhouse schools of North Carolina, Duke, and UCLA. I wanted to go even further and see which cities brought the championships home for their respective states. To create this visualization I used a dual axis map combining my filled map with a symbol map.

Using this visualization you can see which cities allowed the states to appear on my first map. Los Angeles and Lexington are homes to schools that have brought home the most national championships. Instead of using strictly numbers and labels, I was able to represent their success using a “Circle” Symbol. The bigger the symbol the more championships achieved.

I have a clear picture of what teams succeed, but how can I find out which teams succeed… Or don’t, when they are supposed to. To do this, I needed to find out how many upsets occurred over the years. Using the teams designated seeds at the beginning of the tournament I was able to determine every upset in tournament history. I took this data and created visualizations for teams that get upset, and teams that create the upset.

I was able to utilize a stacked bar chart to visualize when teams were a higher seed if they were upset more often than not, and vice versa, if they were a lesser seed were they prone to upset their competitor. The stacked bar also helped to show that while teams like Duke and North Carolina were upset the most, it was because they had the most opportunities to become upset. The data above shows that Kansas is an overachieving team. 34 times out of 49 possibilities they upset their opponent in the tournament.

The Analysis

History shows that our top performing states are North Carolina, California, and Kentucky. The cities that make those states successful are Lexington, Los Angeles, Chapel Hill, and Durham. We can also see that teams such as Brigham Young, Pennsylvania and Utah State have a habit of underperforming in the tournament. While teams such as Florida, Duke, and North Carolina, tend to over perform when they are the underdog.

The Conclusion

March Madness is an event loved by many, and the benefits of visualization allow me to recognize these findings very quickly. Imagine this type of data at your fingertips when you are filling out your bracket. I certainly wish I would have used it to my advantage. Now, imagine these types of visualizations fueled by your company’s data. Replace the “wins” data with company revenue data. You would be able to identify where you are successful, and then go further down to see what cities are producing that success. This allows a quick look at your business. Use sales leads data to fuel your stacked bar charts. See which of your offices is receiving/submitting leads and see how well they are closing them. Data is powerful, but using visualization tools makes data meaningful.

Hey You … Get Out of My Cloud!

Jill Cole — Wed, 13 Mar 2013 16:37:45 +0000

Do you remember these recent stories? On July 31, 2012 Dropbox admitted it had been hacked. (Information Week, 8/1/2012). Hackers had gained access to an employee’s account and from there were able to access LIVE usernames and passwords which could allow them to gain access to huge amounts of personal and corporate data. Just four days later, Wired® writer Mat Honan’s Twitter account was hacked via his Apple and Amazon accounts (story in Wired and also reported by CBS, CNN, NPR and others).

Did you notice the common theme behind these reports? Hackers didn’t get through the defenses of the Cloud by brute force. Instead, they searched out weak points and exploited other vulnerabilities led to by those entry points. In these examples – as in countless others – the weak points were processes and people.

The Dropbox hack was made possible by an employee using the same password to access multiple corporate resources, one of which happened to be a project site which contained a “test” file of real unencrypted usernames and passwords. Either one could be considered a lapse in judgment – I mean, who thinks it is a good idea to store unencrypted user access information on a project site??? – but added together, these lapses made a result much more dangerous than the sum of their parts.

Mat Honan’s hack was made possible in part by process flaws at large and popular companies. Again, each chink taken individually would likely not have been as damaging as the series of flaws building on each other. Apple or Amazon individually didn’t provide enough information for hackers to take over Mr. Honan’s account, but taken together their processes and individual snippets of data provided the opportunity.

My purpose in writing this isn’t to scare anyone away from the Cloud or its legitimate providers. The Cloud is cost-effective, portable, scalable, stable, and here to stay. And it is as secure as technology will allow. But as these stories illustrate, technology isn’t the risk. Information wasn’t compromised by brute-force hacking or breaking encryption algorithms. Data was put at risk by people and processes.

Have you ever worked with someone who messed up something royally by not following a documented process? Or do you know someone who clicked a link in a bogus email and infected their laptop – or even the whole company – with a virus? They might be working for your Cloud provider now. Don’t rely on those folks to protect your data in the Cloud. Instead, protect it yourself with Backups, Password Safety and Data Encryption before entrusting your precious data to the Cloud. If a hacker gets into your Cloud, at least you won’t be the easiest target.

Exploring All of Your Options: Data Visualization

Jeff Shaffer — Tue, 05 Mar 2013 15:29:43 +0000

Excerpt:

It is true about blogs and books suggesting line charts for time series data. In fact, when teaching data visualization at the University of Cincinnati I always reinforce to my students that time series data is best as a line chart. This is because we, as readers, typically understand time when plotted on the x-axis and we typically want to see a trend over time. This is the biggest advantage of a line chart as it shows trend over time better than any other chart type.

Click here to read Exploring All of Your Options: Data Visualization

ABOUT THE AUTHOR:

Jeffrey A. Shaffer is the Vice President of Information Technology and Analytics at Unifund. Mr. Shaffer joined Unifund in 1996 and has been instrumental in the creation and development of the complex systems, analytics and business intelligence platform at Unifund. Mr. Shaffer holds a BM and MM degree from the University of Cincinnati and an MBA from Xavier University where he was the winner of the 2006 Graduate Student Scholarly Project in Research. Mr. Shaffer has attended the Harvard Business School’s Executive Education Program, is a Certified Manager of Quality and Organizational Excellence through the American Society for Quality, a Certified Project Management Professional through the Project Management Institute and has completed Six Sigma Green Belt and Black Belt training with the Xavier Consulting Group. Mr. Shaffer is also Adjunct Assistant Professor at the University of Cincinnati in the Carl H. Lindner College of Business teaching Data Visualization in the Graduate Course series for Data Analytics. He is also a regular speaker at business intelligence conferences and symposiums on the topic of data visualization, writes for the data visualization blog at MakingDataMeaningful.com for LÛCRUM, Inc. and was a finalist in the 2011 Tableau Interactive Visualization Competition.

Business Intelligence to the Rescue?

Stephen Bishop — Fri, 01 Mar 2013 18:18:46 +0000

As a business undergraduate at the University of Cincinnati, I recently noticed an article in the Cincinnati Business Courier about P&G’s push into business intelligence and analytics. Why are CIOs of P&G, FedEx and Boeing just now beginning the push “to make business intelligence the way that business gets done”?

Business Intelligence is already a mature market and we’re beginning to see the next “maturity cycle.” Analytics has been a top priority for CIOs for many years, but some have yet to pull the trigger. This leads me to believe that these corporations are testing the waters, waiting to jump in when the analytics market is hot enough that competition rises.

Economically we know that competition ultimately dilutes the market with firms promoting higher quality, better services and lower prices. Firms will be at the mercy of these corporations who are trying to get the lowest price for analytics services, while business intelligence firms are trying to get as much as possible without cutting into their margins.

Gartner estimates a 7 percent increase in BI software revenue in 2013 at $13.8 Billion from 2012. By comparing BI service providers in 2011 and 2010 in terms of market share and growth, we can get a general idea of where the market is headed.

Company	2011 Revenue	2011 Market Share (%)	2010 Revenue	2010 Market Share (%)	2010-2011 Growth (%)
SAP	2,883.5	23.6	2,413.1	23.0	19.5
Oracle	1,913.5	15.6	1,645.8	15.7	16.3
SAS Institute	1,542.8	12.6	1,386.5	13.2	11.3
IBM	1,477.6	12.1	1,222.0	11.6	20.9
Microsoft	1,059.9	8.7	913.7	8.7	16.0
Other Vendors	3,363.8	27.5	2,931.1	27.9	14.8
Total	12,241.0	100.0	10,512.2	100.0	16.4

Will business intelligence be the star that burns the brightest? Will it become just another management methodology that will fade away like all the others?

Six Sigma seemed to work great for Jack Welch at GE but his protégés that took that methodology to other industries failed. Not every method works in every industry. Analytics has already been very successful in data overloaded industries like banking and insurance, even logistics, but how will it pan out for consumer packaged goods (CPGs) and airplane manufacturers? Most CEOs of large corporations are focused on quarter-to-quarter earnings and increased shareholder value just to keep everyone happy.

Although U.S. corporations are sitting on more cash than ever before they are more than hesitant to spend it. Apple has over $100 billion in cash, but cash won’t make a better IPhone, will it? If these companies opened up their wallets it would not be in their best interest to just throw money at their short-term problems by investing in new technology or hiring new people. Unfortunately throwing money at a problem only provides temporary relief.

On the flip side when corporations like P&G and FedEx begin to become more transparent with data and hopefully more profitable, shareholder value will rise tremendously. Business leaders understand that analytics, if implemented correctly with specific strategies and goals, will add to the business’ bottom line. For the investor time will only tell; it might be a good idea to keep an eye out for these companies by measuring performance five years before BI implementation and 5 years after.

Source: http://www.bizjournals.com/cincinnati/blog/2013/02/pg-ceo-mcdonald-business.html

When is BI not BI?

DennisFoster — Wed, 27 Feb 2013 20:05:26 +0000

Google Trends shows the term “Business Intelligence”, as a web headline topic, has declined since 2004. In the past two years it has been surpassed by the term “Big Data”. “Business Analytics” is emerging as the term some industry thought leaders, such as Gartner and IDC, are using as the catch-all term for software solutions that use data analysis to guide business decisions.

Despite the essential inclusiveness of all three terms, there is no shortage of discussion on the differences among these and a number of other contenders. Are the old terms so limited that they cannot contain the huge new advances in the field? Or have there been too many disappointments with attempts to deliver “Business Intelligence,” that we need new, exciting, and “untainted” terms.

It is important that we do not get distracted by new umbrella terms that cover the same mission, the same systems, and the same activities. It is like arguing over whether a Prius is an automobile or a car. The important thing is that there are exciting new technologies that can be applied to achieve the objectives of Analytics, Business Analytics, Business Intelligence or Big Data. It really does not matter which term is used. Let’s face it, When is BI not BI? If a term refers to ways of making data meaningful and profitable, it’s all BI.

Visualize a Better Flight Experience

Brittany Fong — Wed, 20 Feb 2013 17:06:42 +0000

As business travelers and Cincinnatians we have all witnessed firsthand the ever-increasing airline ticket prices from Northern Kentucky International Airport. All companies, big and small, are trying to keep down unnecessary costs. This visualization was created in Tableau Desktop in order to show how data visualization can make it easier to view trends and patterns of airline costs in order to cut costs. This type of analysis and visualization can be done in any industry in order to view the progress of the company against their goals and performance indicators.

Why are visualizations so useful and why create one? Visual.ly Blog was recently asked “Why is Data Visualization so Hot?” They responded saying that, “Visualization allows access to challenging data sets, it allows exploration, can be fun, and provides useful information in an efficient way.” I would agree and add that, as humans, we are already trained to recognize trends and patterns in graphs, which is why they are so efficient in translating data.

Some of the questions that I wanted to answer about round trip airline prices were:

How do airline ticket prices fluctuate over time?

Looking at this graph it is easy to visually see that some trip prices vary a lot and some are fairly constant. This led me to another question:

Do prices follow trends by departure airport or arrival airport?

Looking at the graph you can see that for San Francisco the ticket prices seem to follow the same general trend. The other arrival cities were generally the same as well.

Would it be worth it to fly out of the Columbus (CMH) or Dayton Airports (DAY) instead of Cincinnati?

Looking at the graph above I found that CVG was on average $50 more expensive than CMH or DAY airports.

How competitive are the airlines by round trip? Does the cheapest flight change airlines constantly or do they stay the same?

Using this visualization you can choose a trip to look at in order to see how often the color (airline) changes. If the color changes a lot then that is a competitive trip where the cheapest airline changes often. However, trips like CVG to Charlotte and Las Vegas are constant by airline.

If there is a cheaper airline which one is it and how many flights do they have?

The graph above shows the average price of an airline ticket (color) and the total number of flights recorded (size of circle). Overall, looking at the graph, Delta had the most round trip tickets but in the end the average price was the highest. Whereas on the other end of the scale, AirTran had the cheapest average prices but only a few tickets. In other words, the deals that AirTran does have are very good deals.

Lastly, everyone’s favorite question:

When should I buy my ticket in order to get the cheapest price?

Normally when this question is asked it seemed that Tuesday was the best day to buy airline tickets, but after looking at my data, it showed that Wednesday had the cheapest prices. However, by looking at the graph you can see that the price difference is not very significant.

Using Tableau made it very easy to view the trends and patterns in airline prices. It was easy to see the fluctuations of trip prices and compare airports. Data visualization is a hot topic and can greatly help your company to quickly find anomalies and progress of performance indicators.

**Sources:

http://blog.visual.ly/why-is-data-visualization-so-hot/

This data was collected by finding the cheapest round trip ticket in March regardless of day, length of stay, or airline using www.hipmunk.com.