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We are Having a Party!

Posted by: Grant Stanley on April 2nd, 2012 Leave a Comment

It is time to take a break, and celebrate our 4th Birthday! We want to celebrate everything we have accomplished, and kick off all the great things that are going to happen. In 2012 we will launch 5 web applications, expand our offices from 1098 square feet to 3850 square feet, and training 20 new employees to support all of our new clients. Join us for an open house on April 18th from 4:00 to 6:00pm. We will have free food and drinks. You can meet our expanding staff, take a tour of our offices and learn more about what we are working on. Please RSVP at http://can4thbirthday.eventbrite.com.

Tadd and Jefferson go Mining for Data in Wyoming

Posted by: Grant Stanley on January 30th, 2012 6 Comments

CAN is helping one of our clients improve their asset management strategy, by building predictive models to determine when heavy equipment is most likely to fail. CAN’s asset management models will allow our client save hundreds of thousands of dollars each year, by converting emergency repairs into scheduled maintenance. Imagine the money and time that can be saved if repairs can be preemptively made in several hours instead of the weeks or months it takes to make repairs in the field.

While we could have developed the model from our offices in the Old Market, we wanted to make sure that we understood the conditions on the ground. Jefferson and Tadd decided to take a trip to Wyoming and spend a week learning about the machines and interviewing the experts that use the equipment on a daily basis. Their goal was to make sure that we had political support from the people that were going to use our models, and that we could build balanced models that combine data, theory and math. The following are some of the photos from their trip. I hope you enjoy.

We might push paper for a living, but we love to get our hands dirty to build beautiful models.

Branden Collingsworth Joins CAN as a Data Scientist

Posted by: Grant Stanley on January 2nd, 2012 Leave a Comment

Branden Collingsworth has officially joined CAN’s team as a data scientist. Branden interned at Contemporary Analysis this summer as a data scientist. In December, he just graduated with an MBA and Law Degree from the University of Nebraska at Lincoln. This is on top of a bachelor’s degree in economics from UNL.

Branden is a true data geek as you can see. He loves scrapping data from the internet, designing surveys, building databases, testing models, and creating data visualizations. He will be working on models to power our clients and the next generation of CAN’s products. He is also looking forward to demonstrating his technical chops on CAN’s blog. We are very excited to have Branden on board.

Dashboard Design: Teaching Strategic & Analytical Thinking

Posted by: Jefferson on November 28th, 2011 Leave a Comment

At CAN, we exist to provide our clients with leading edge methodologies that are both effective and easy to use. This requires that we constantly learn about new tools and techniques, and hone a fine edge on the ones we keep to provide to our clients.

Previously on our blog, we have discussed the application of dashboards and aspects of dashboard design that facilitate rapid perception by the human brain. How about using dashboards as a way to teach users a way of thinking? In this blog, we will discuss using dashboards to promote strategic thinking through guided analysis.

One of our clients approached CAN with the following predicament. Their enterprise operates nationwide with several districts responsible for operations within their unique geographic region. Every year, the strategic planning division would produce a thick binder reviewing each districts market forecasts, opportunities, and past performance. The intent was to assist the non-technical managers and business development of each district to think about trends in the market and industry to get more sales. Although very well produced and full of useful information, these binders acted mostly as a reference and did little to encourage analysis by the end-user.

Our solution was to use the same information used to build the binders and create views using Tableau. At first, these views replicated the familiar visualizations found in binders with an added level of interaction. Then, we started to add new data sources into the existing information. We connected industry forecasts, census data, economic indicators, past performance and connected all this functionality to a dashboard where the end user is able to bring in these factors at their command. Populating the dashboard with the raw materials required for analysis, is the first stage.

The second stage is defining the business questions that the users need to answer to run their business. We interviewed the executives on the strategic planning team and in several of the district offices to define what the most important business questions they needed to answer to run their business. Instead of providing managers of each district with binders that pushed facts and figures at them, we created a work book of questions that needed to be answered and how the answers could be applied to running their district.

The third stage is doing most, not all, of the users’ work for them. What I mean by this is producing dashboards that are 90% completed for the types of questions the user will want to answer. Our goal is to support the user in asking questions and getting answers, not simply handing them the answers or making them build their own dashboards. So, we build pre-made views for them. For example, one aspect of our client’s business functions was closely related to population growth. We produced a dashboard that integrated population growth figures for the past several years with our client’s historical sales figures and billable hours. The district manager, interested in staffing requirements, can population changes across the region with his current staffing and identify where adjustments and hiring are likely to take place.

In designing guided analysis, the bottom line is producing dashboards that solve the business question that users need to answer. This requires that the designers understand the purpose of each dashboard, how it will be used, and what the user intends to get out of it. If your goal is to achieve data-driven decisions from non-technical managers, you must design so that the user is on the right track with the controls, but ultimately require their interaction and thinking to reach the outcome.

When to Apply Predictive Analytics

Posted by: Grant Stanley on September 14th, 2011 1 Comment

At CAN we love predictive analytics and if we are not careful it is easy to start reducing everything into predictive models. I have even caught Tadd standing at the window collecting primary data on the smoking habits of the people in our building. To make sure that CAN and predictive analytics experience continued success, we have developed a guide for when and where to apply predictive analytics. First, predictive analytics should not be applied if:

The cost of being wrong is low. You should not apply predictive analytics if reducing uncertainty does not provide enough value. Predictive models should only be applied in situations with a high cost and/or probability of being wrong and where predictive analytics can provide information to reduce uncertainty. To determine if predictive analytics is worth applying to a decision you need to calculate the expected value of information. In the book How to Measure Anything, Hubbard provides the following formula, expected value of information is equal to the difference between the expected opportunity loss before and after information. The expected opportunity loss is equal to the chance of being wrong multiplied by the cost of being wrong.

The relationships are obvious. A predictive model is basically a story about why something happens and what will most likely happen in the future. With this in mind, you do not need to develop models if people are able to accurately describe the relationships between variables well enough that they can tell stories about why certain things are or are not happening.

The model can not be Reproduced. Even if you develop a model, but it can’t be reliably reproduced then you should admit that predictive analytics can not be applied.

Predictive models find and tell stories that are difficult for people to discover because there are either too many variables or the events being studied are too rare. The value in knowing the story of why something happens is that you can reduce uncertainty, make better decisions and work smart. The following are situations when predictive analytics should be applied.

Too Much Information. Over the last 30 years there has been an explosion of data, and business intelligence has been focused on collecting and presenting that data. Predictive analytics is one of the BI tools that is capable of statistically filtering out what data is most important.

Customer management is an example of when predictive analytics can be used to find valuable patterns in an overwhelming amount of data. Predictive analytics can be used to determine how to group customers, and develop campaigns to improve specific behavior for each client segment. For example, CAN sorted through over 500 different variables and millions of observations to determine the 5 variables that have a significant impact on responses to specific market campaigns.

Rare but Important Events. When an event is rare and results in either major gains or losses, it can be very beneficial to gain a better understanding of why the event happens. Predictive analytics can help you develop plans to encourage or discourage specific rare events.

Predicting the failure of essential business processes is an example of when predictive analytics can be used to find valuable patterns in rare but important events. Using predictive analytics CAN’s clients are able replace equipment and parts before they breakdown so that essential business operations continue to move forward. Imagine being able to know and replace parts at risk of failing before you spend the time and money to install a piece of heavy construction, mining or logging equipment on site.

In conclusion, the next step in the evolution of business intelligence is to understand what is likely to happen. Predictive analytics allows executives to learn from the cumulative knowledge of their organization. This systematized learning has the potential to help businesses and executives to make decisions that are less wrong, so that they can work smart. However, it is important that predictive analytics is applied in the right applications, so that is produces the most value to the end users.

CAN’s 1st Lolcat

Posted by: Grant Stanley on August 30th, 2011 Leave a Comment

It is official, Drew Davies and Drew Gourley at Oxide Design have turned CAN into a Lolcat. This is quite out of the ordinary for an enterprise predictive analytics company, and we are unsure what this means for our brand and customer loyalty. So, our analysts are working hard to answer this new set of business questions, all because of a Lolcat.

Why I Support Startup Weekend

Posted by: Grant Stanley on August 15th, 2011 Leave a Comment

On September 16th, CAN will be sponsoring our 4th Startup Weekend, SWOmaha.

Startup Weekends are about building startups, however one of the primary reasons we choose to sponsor Startup Weekends is because they build communities. They allow creatives, developers and businesspeople to move beyond simple friendship & networking and work together under pressure to develop a business and a working prototype. It doesn’t matter what they build, or if they continue with their startup once the weekend is finished, but that they had an opportunity to work together and learn from each other.

Experimentation: Startup Weekend gives aspiring entrepreneurs the freedom to build something really innovative. Innovation is the application of technology to create value. When you reduce the cost of a technology, the barriers to entry are lowered, and people tend to find new applications, encouraging further innovation. Startup Weekend builds on this principal to encourage people to experiment, by reducing the cost of programming, design and business development for 54 hours. While it is unlikely that you will be able to build your dream product over a weekend, you should be able to know if your idea has merit.

Raw Material: It’s possible to start a great company without political support or money, but without a talented, experienced and driven pool of businesspeople, developers and creatives, it’s impossible. Startup Weekend flourishes by mimicking the constraints of a startup; the lack of money, time and resources provides a breeding ground for the talent essential to creating a great company. Even if you choose not to continue with your startup after the weekend ends, Startup Weekend will provide you with an opportunity to be exposed and hone your skills under a bit of the pressure of a full time startup.

Humility: Startup Weekend keeps me as the CEO of a successful startup humble. It reminds that if I get complacent with my success, that there is a new generation of entrepreneurs eager for their chance to compete. Each time I attend a Startup Weekend I am reminded of a quote by Andy Grove , the former CEO of Intel, that “Success breeds complacency, complacency breeds failure, only the paranoid survive”. Meeting the entrepreneurs of tomorrow helps keep me grounded and paranoid.

Community: Entrepreneurship can be very lonely. As an entrepreneur, you are trying to accomplish the impossible under less than ideal situations. (Related Post) Besides other entrepreneurs, no one, not family or employees, will understand why you are risking everything and working 12+ hours a day. At Startup Weekend, you are surrounded by people that are just as crazy as you, and it is really nice to know that you are not alone.

A Tour of CAN Head Quarters

Posted by: Grant Stanley on August 10th, 2011 6 Comments

Since we started January 2008, CAN has moved headquarters 8 times. We have spent time in employee’s kitchens, parent’s basements, a forgotten university cubicle, a hip technology incubator, and a low rent apartment. In April 2011, after 3 nomadic years, CAN moved into our first real office at 1209 Harney St., Suite 200, Omaha, NE 68102. The building was built in 1880 by an iron and hardware wholesaler, and now is very contemporary. We love that our building is just like us; blue collar roots with a contemporary future.

Don’t Just Count, Measure

Posted by: Jefferson on August 3rd, 2011 1 Comment

The United States faces a shortage of 140,000 to 190,000 people with deep analytical skills and 1.5 million managers and analysts to analyze data and make decisions based on their findings. – Big Data: The next frontier for innovation, competition, and productivity

Measurement can be defined as a quantitatively expressed reduction of uncertainty based on one or more observations, while counting is to determine the exact number or amount of something. Executives have become obsessed with counting; they track everything from inventory, hours worked vs. hours billed, number of Facebook fans, website visitors and much more. It is easy to like counting, because it is familiar and exact. However, it is time to move beyond counting, and start measuring.

Making great business decisions is not only about knowing exactly how much of something you have, but instead about reducing uncertainty about the future and your decisions. This is the definition of measurement, to reduce uncertainty based on one or more observations.

To be meaningful, measurement requires that you define the business question to be answered and select the right variables to measure. Unfortunately, most off the shelf metrics or so named “analytic packages” distract executives with counting, instead of helping them define the most important business questions and find the variables will help provide the right answers. The following is a quick introduction to moving beyond counting and applying measurement in your business.

Step 1: Define Your Most Important Business Question

Valuable measurement requires a lot of work, so you want to make sure that you are answering the right business question. Before you start working on selecting variables, writing a survey, or defining methodology, it’s essential to define what business question, if answered, would have the most significant impact on your business. For example, what business question would be the valuable to answer: what are customer perceptions of your company, why do people choose to purchase your product, or why are people purchasing product A and not B. The most important business question is specific to each organization, or even individual departments. Without being able to clearly define your most valuable business question, it’s a waste of time and resources to move forward with your measurement endeavors.

Step 2: Prioritize Variables

The variables you select should be chosen based on the business question you need to answer, and variables you choose should be prioritized based on their ability to reduce the uncertainty of the answer to the most important business question you seek to solve. Many organizations tend to focus on internal data sources, but make sure to consider data from external, as well as internal sources. A deep knowledge of your internal business processes is required, but big opportunities, threats and results tend to come from outside an organization. Make sure that you only collect the most important data by clearly defining your business question and prioritizing the variables required to answer the question you seek to answer. Related Post: How to Structure a Survey

Step 3: Answer the Question

Ideally, your answer should go beyond just answering the business question, and explain how the answer impacts the business then provide recommended next steps. For a CAN specific example, if you answer the question of “why people purchase Tracker more frequently than Capture”, your answer should also provide steps to improve the sales of Tracker and Capture, and recommend whether CAN should focus on selling more Tracker or on increasing Capture sales relative to Tracker. This requires more work, as well as the integration of internal strategy, external forces, and a deeper involvement with stakeholders that shape the outcome, but going the extra step allows for your answer to actually impact the organization. Rather than simply counting the number of sales for Tracker and Capture, by measuring vital aspects of the sales, you are able to better plan for the future by helping to explain away the uncertainty of the future.

Focus on the Business Question, not the Technology

Posted by: Grant Stanley on July 26th, 2011 2 Comments

This post is part of a series of interviews with experts in business intelligence, sales management, marketing, customer retention, management and strategic planning. Everyday, the CAN team interacts clients, mentors, and friends who are leaders in their fields, and we started this series to share their expertise.

Corporate business intelligence has hit a roadblock, according to Cameron Ludwig, the Director of Analytics at BlueCross BlueShield of Nebraska. “As a discipline, we have been more enamored about what we can do, and not what we should do”. Business intelligence of tomorrow needs to put less focus on technical capabilities, and instead, emphasize designing solutions that focus on answering essential business questions. This need for a shift in focus is due to the exponential increases in data availability and the increasing reliance of executives on data in their decision making. For example, in a recent study by McKinsey there is a projected 40% growth rate in the amount of new data generated per year, with many companies having hundreds of terabytes of data (link). As a discipline, business intelligence has matured to the point where we need to move beyond collecting and displaying of data. It is time to shift to the next level.

“Now the knowledge is taking the place of capital as the driving force in organizations worldwide, it is all too easy to confuse data with knowledge and information technology with information.”- Peter Drucker, 2005

In order for BI to make the transition from what is technically possible, what we can do, toward what is valued by business, what we should do, requires a shift in focus for the emerging field of data science. Although I am hesitant to say that data scientists should study business at the exclusion of technology, this shift requires that data scientists become students of business as opposed to technology. That is, their greatest value comes from studying technology to the point of knowing what is possible and how to apply technology to meet the needs of their end users. For example when a contractor builds a house, he doesn’t study the hammer, he studies architectural plans and creates a finished product from raw materials. The same goes for data scientists; they should focus on understanding the problems that need to be solved, then spend time studying how to use raw materials (data) to create a valuable finished product.

Keeping with the need for a shift in the field of business intelligence from technology to application, the valuable finished product is not a dashboard displaying metrics, but rather actionable intelligence focused on answering the business questions of the end user. This renewed focus of business intelligence requires that BI only provides decision makers with what is essential to answer their questions. All the slick user-interfaces, gauges and dials of flashy dashboards will never provide as much value as the algorithm behind an executive report that integrates ten different historical and environmental variables to advise which projects to bid on, including anticipated profit margins.

Tremendous value exists in the proper application of data science, but the maximum value comes from a deep understanding of the needs and objectives of the end user. Ensuring that the end product fits the end user requires the right feedback, and at least as much criticism as creation. When self- and peer-reviewing their work, Cameron recommends that data scientists should be required to justify the existence of each sentence, idea, graph, and model. This requires each BI report to be designed with simplicity in mind, but also maximizes value to the end user and builds trust in BI by focusing only on that which contributes to solving the problem at hand. If an artifact, tool or feature cannot be defended, it is most likely of little value and should be eliminated. In order for business intelligence to contribute maximum value to the organization, every element of business intelligence must justify its existence.

“Capital importance of criticism in the work of creation itself. Probably, indeed, the larger part of the labour of an author (programmer) in composing his work is critical labour; the labour of sifting, combining, constructing, expunging, correcting, testing: this frightful toil is as much critical as creative”- T.S. Eliot

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