Dashboard Design: Bullet Graph vs. Bar Chart

We invest a lot of time and energy communicating our research, because unless we can effectively communicate our findings they are useless.  When the goal is to communicate the most valuable information with the least amount of ink that can be understood with the least amount of effort.  For your reference, our major influences are Deirdre McCloskey on writing, Stephen Few on dashboard design, and Edward Tufte on data visualization.

Recently, CAN conducted a customer satisfaction survey for the Georgia Regional transportation Authority.  In addition to developing, deploying and analyzing the customer survey, CAN went above and beyond to improve how GRTA reported the results of their annual survey.  In this post, I will explain why we used a modified bullet graph instead of a bar chart to answer the business question.

The purpose of the graph is to help answer the business question of how does GRTA compare to two competitors across 17 different metrics.  While GRTA needs to continually improve, for the purpose of  answering the business question the exact score was not important, but instead the difference between each competitor and compared to others how does GRTA score.  Comparing each company by metric was the main influence behind the design on CAN’s graph.

The Original Graph


 
 

The CAN Graph


– In the original graph, the bold vertical lines focus the viewer how each metric scored, by encouraging the eyes to go up and down.  In the CAN graph, the light gray horizontal lines encourage the eyes to travel left and right to compare each companies performance.  Also, we used light gray lines so that we did not dominate the graph with supporting data.
– In the original graph, there is no simple way to show the spread between the different competitors, besides comparing each line together.  However, it important to know how competitive each metric is when answering the business question.  When designing the CAN Graph, we darkened a length of the light gray horizontal lines to show the minimum and maximum score on the service quality index.  This
– In the original graph, using four different colors made it difficult to make a memorable distinction between each company, take up an unnecessary amount of space, and impossible for color blind (10% of males) to make distinctions.  Using different shades of gray CAN made it easy for everyone, including the colorblind, to distinguish between different companies.  In addition to adding an additional way to differentiate between companies, using different shapes allowed for better distinction when multiple companies score close to each other.
– In the original graph, the overall low graphical quality such as broken vertical lines, faded colors and pixilated font created an unnecessary distraction, and reduce the credibility of the results.  While this might seem petty, producing graphs that are crisp and well designed help develop trust with the audience.  In the CAN Graph, we produced the entire graph in black and white, so that the report can easily be reproduced on either a color or black and white printer.
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November 29, 2011 News No comments yet

Dashboard Design: Teaching Strategic and Analytical Thinking

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.
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November 28, 2011 News No comments yet

Cold Calling Works Again

Cold calls used to work, then they didn’t and now they work again. I used to agree with most people, that cold calls do not work. In fact, I established my sales career on referral networking. However, I have rediscovered the power of cold calling and how to do it effectively.  Networking is still important, but now I don’t have to wait around hoping for referrals.
Before the internet cold calling was effective because talking to salespeople was the most effective way for most people to learn about new products and services. As long as you had a good product, solid reputation and solid sales skills you could be successful. If someone wasn’t willing to take the time to listen to your sales pitch then they weren’t open to learning about the latest and greatest innovations that could transform their company.
However, the Internet made cold calling ineffective. It provided a more effective alternative to talking with salespeople that typically didn’t value people’s time and attention. Now, people had the ability to learn about new product and didn’t need someone to “sell it to them”.
Cold calling no longer worked because people no longer had problems to solve that they couldn’t solve using the Internet. They didn’t want a salesperson to create a problem. They certainly did not have time to listen to another sales pitch. If they had a problem they could solve it themselves, and this essentially took the power away from salespeople.
Salespeople transitioned from cold calling to networking and developing referral relationships. This worked because it established trust with prospects, and trust was something that the Internet lacked. The buyer did all the research to find possible solutions to meet their need, and then asked friends for a referral to someone they could trust to make answer a couple questions, provide a recommendation, and take the order. However, it is difficult to build a reliable sales system through networking and referrals, because you are relying on someone else to make the first move and then making sure that you are positioned in cahoots with the first person that they would ask for advice.
What cold calling and the Internet had allowed buyers to do is find products and services that they had the need, willingness and resources to purchase. The secrete is talking to the right people at the right time. With the right timing cold calling can be effective again, and sales people can once again activity take control of their pipeline.
Once we realized that timing was the secret, CAN set out on a mission to get our timing right. How could we build a system that would allow sales people to find leads when they had the need, willingness and resources to purchase?  The solution is Predictive Lead Generation. Predictive Lead Generation allows you to build a detailed profile of your ideal client that identifies what factors trigger prospects to have the need, willingness and resources to purchase your product, and find leads that have the attributes of someone who is ready to purchase, and find supporting evidence you need to successful call and build trust.  Instead of calling 100 people to get one person that is interested in your product, CAN is able to give you a list of 10 people.  You still have to have a great product and solid sales pitch, but Predictive Lead Generation can help you focus on talking to the right people.
We have been using Predictive Lead Generation internally for four years. Before Predictive Lead Generation our sales team used to spend the entire week attending networking events hoping to snag a solid lead, and make up excuses about how sales is all about luck and can’t produce reliable results. Now, our sales team is focused on building relationships with the right people, and I am confident that my team will be able to deliver each month.
I encourage you to use Predictive Lead Generation to put cold calls back into your arsenal. If you want to try out cold calling search the Internet and find a company you decide needs, wants and has the resources to purchase what you sell, spend 10 minutes learning about the person you are calling, and then call someone who actually needs what you are selling, and will be glad you called. If that produces results, then you might be a good candidate for CAN’s Predictive Lead Generation system. While referrals may always be the easiest phone call, cold calls are now some of the most effective.

November 23, 2011 News No comments yet

Presenting Predictive Analytics

The nature of forecasting the future makes presenting predictive analytics unique and challenging.  There is no flashy server or dashboard that will make presenting analytics any easier.  There is only a model that tells a story about the future of users’ business, customers, non-customers and competitors.  While models are very valuable they are not your typical business intelligence artifacts.  To produce a meaningful return on investment you need to translate the details of the story into results that can be applied to a specific business question.

While you can not replace sound scientific and statistical methodology, CAN has found that users don’t care about a model’s Durbin-Watson, standard error, or R², or they are not familiar enough to properly understand the statistical nuances.  The key to proving that a model works and getting political support required for implementation is to ask the experts if the story the model tells reflects reality.  It is also valuable to prove that a model works by letting the prove itself over time.

It is important to note that predictive models typically do not provide solutions to business questions, but instead often offer incomplete answers and important insights.  When presenting predictive analytics your audiences expectations should be set on becoming less wrong, instead of finding the perfect solution.  CAN finds that users appreciate our philosophy of Less Wrong.  While it seems counter intuitive, our lack of hubris builds confidence in our models and sets realistic expectations.  The basic principle behind, Less Wrong is that in business winners are not right, they are simply less wrong.  There are no perfect answers in complex sciences, such as data science and predictive analytics, only less wrong answers.  The goal is to reduce the uncertainty of making the wrong decisions, not thinking uncertainty can be eliminated.

In conclusion when presenting predictive analytics don’t be afraid to kill your darlings.  If you can not justify an element of your presentation get rid of it.  This will help you focus your presentation, your audience will listen and the results of your hard work building predictive model will get implemented.

September 19, 2011 News No comments yet

Applications of Predictive Analytics

The following are some examples of how predictive analytics can be applied in financial services, retail and manufacturing.  This list is not comprehensive, but it provides some interesting applications.
In the financial services the cost of making the right decisions provides marginal benefits, while making the wrong decisions can have significant costs.  Most applications of predictive analytics in the financial services industry help companies avoid making the wrong decisions. For example, credit card companies are able to determine who is most likely to default on their credit cards in the next 6 months by applying predictive analytics to customers purchases and demographics.
In retail understanding customers is essential to success.  Retailers have provided customized shopping experiences by using predictive analytics to understand the drivers of profitability, loyalty and activity for each customer segment and develop specific campaigns for each segment.  This has allowed retailers to wow customers with personalized services while scaling and keep prices competitive.  Predictive analytics have helped both offline and online retailers determine which products to carry, optimize marketing plans, and develop promotional and loyalty programs.  Imagine only offering effective loyalty promotions to profitable customers at risk of leaving, while avoiding offering discounts to unprofitable or already loyal customers.  Another example is knowing what a customer is most likely to purchase next, so that your staff or website can make informed recommendations.
Manufacturing is about knowing what, how, and how much to produce.  Predictive analytics have helped manufactures manage their supply chain and production schedules by accurately forecasting demand, and have helped manufactures produce goods in the most effective way possible by predicting failure of equipment, monitoring workers, and identifying ways to eliminate inefficiencies.  For example, CAN has helped companies with tens of thousands of sales each month forecast sales within a 10 to 50 units, so that they can optimize production schedules and supply chains.  Also, imagine being able to understand why different managers have different levels of employee turnover, employee injuries, and equipment failure.
Beyond specific applications, predictive analytics has the unique ability to help companies become less wrong, scale decisions and systematized learning.
Less Wrong: The basic idea of Less Wrong is that in business, and almost anything in life, you can never be perfectly right, but you can be less wrong and by striving to continually become less wrong you get closer and closer to being right.  By using predictive analytics you will not get the perfect answer, but you can determine what is happening, what most likely happen and most is most likely the right thing to do.  For example it might be really nice to know exactly what commodities prices will be in a month, unfortunately this is not possible.  However, using predictive analytics you might be able to predictive with 70% accuracy which direction a commodity price will trend and if this is better than before predictive analytics then it most likely will be worth the investment.  Another example would be picking which commodity would most likely be the best investment in the next 6 months.  The reason that predictive analytics can’t produce exact results is because it is not a simple science, for more read my post on Simple vs. Complex Science.
Scale Decisions: Predictive analytics has the unique potential to allow executives to scale their decision making as organizations and decisions become increasingly more complex with ever thinner margins for error.  Predictive analytics can be used to create a model of the business based on the organization’s data and executives’ theories.  For example an experienced sales manager will be able to determine which sales leads will most likely respond, purchase and be profitable customers, however he or she does not have time to review every lead for a 200 person sales team.  Also, while a sales manager knows what make a lead worth pursuing, he or she most likely finds it difficult to communicate the rules and criteria to their team.  Using predictive analytics a sales manager can develop a model to score incoming sales leads.  This model can be coded directly into the company’s customer relationship management (CRM) system so that leads are scored as soon as they are entered into the system.
Systematized Learning: In the future, profits will be directly related to your company’s rate of metabolizing new knowledge, as opposed to renting out existing knowledge.  Predictive analytics can increase your company’s ability to metabolize new knowledge by continually studying the data produced by your company, customers, non-customers and competitors to find important patterns that will impact your business.  For example, predictive analytics can help executives identify when customers stop responding to a certain campaign and why.
While every company can benefit from becoming analytical, like any other tool, predictive analytics can not fix anything.  However, it is most certainly the next step in the evolution of business intelligence.  If properly applied, predictive analytics has the potential to help businesses work smart. Read a related post on when to and not to apply predictive analytics.

September 16, 2011 News 1 comment

When to Apply Predictive Analytics

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 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. (more…)

September 14, 2011 News 2 comments

Dashboard Design: Design for Parallel Processing

The value of dashboards and visualizations are that they allow users to shift from serial to parallel processing.  When reading a block of text you can only process the information serially by starting at the top left of the text and finishing the bottom right. Dashboards and data visualizations allow you to absorb information in parallel making it easier to absorb information quickly, identify relationships and trends.
Download our eBook, “Dashboards: Take a closer look at your data”.
However, the lack of serial processing requires that dashboards be effectively designed so that information can be absorbed as easily as possible.  This requires that dashboard be designed for pre-attentive processing or for “the unconscious accumulation of information from the environment” (Wikipedia).  Pre-attentive processing is specifically designed for parallel processing.  Pre-attentive processing allowed our ancestors to continually scan the horizon to identify opportunities and threats.  If well designed, a dashboard is modern-day equivalent of the horizon of the savanna, a data rich experience where it is easy to absorb the most important information, identify relationships and spot trends.
The basic principle of designing a pre-attentive dashboard that enables parallel processing is to keep element natural.  Replace bright bold colors with neutral and natural hues, and pie charts, gauges and traffic lights with hue, intensity, location, orientation, line length, line width, size, shape, added marks, enclosure, and motion.

September 12, 2011 News 5 comments

Three Types of Dashboards

A dashboard is a single display that in a glance provides essential information for a specific objective. Since you are limited to a single display capable of being monitored at a glance, the first step of dashboard design is to select the purpose of your dashboard. This provides you with a filter to make sure that your dashboard effectively accomplishes its intended purpose.

Will it be strategic, analytical or operational? Answering this question will keep your dashboard from falling victim to trying to be everything to everyone.

Strategic dashboards provide managers and executives at all levels of the organization the information they need understand the health of the organization and help identify potential opportunities for expansion and improvement. Strategic dashboards do not provide all the detailed information needed to make complex decisions, but instead help executives identify opportunities for further analysis. A strategic dashboard should be simple and contain aggregate metrics the represent the over all health of the organization. Typically there is no need for interactive features and the data should be updated no more than monthly.

Analytical dashboards provide users with the data they need to understand trends and why certain things are happening by making comparisons across time and multiple variables. Analytical dashboards often contain more information per square inch than both strategic and operational dashboards. Since understanding is the goal analytical dashboards can be more complex than strategic or operations dashboards. Also, while analytical dashboards should facilitate interactions with the data, including viewing the data in increasing detail, it is important to maintain the ability to compare data across time and multiple variables. If you lose the ability to compare data then an analytical dashboards is no longer able to accomplish the goal of allowing users to understand trends and why things are happening.

Operational dashboards are used to monitor real time operations and alert the users to deviations for the norm. This often means that operational dashboards need to be updated frequently if not in real time, contain less information than analytical or strategic dashboards, and make it nearly impossible to avoid or misunderstand an alert when something deviates from the acceptable standards.  Operational dashboards should provide users with specific alerts and provide them with exactly what information they need to quickly get operations back to normal.

Download our eBook to find out more about using dashboards to get a better look at your data:

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September 9, 2011 News 6 comments

Gain Support for Predictive Analytics

The decisions you make in business may never be perfectly right, but, you can strive to become less wrong.  Predictive analytics provides decision makers with a system to continually improve decision-making, while eliminating some of the inefficiencies of non-analytical trial and error.

However, the ability of predictive analytics to systemize an organization’s cumulative knowledge can be threatening to experts who value their accumulated knowledge over continual learning.  When beginning a predictive analytics initiative, the most vital key for gaining political support is to maintain focus on the business problem, and never the technology.  By focusing on the specific problem, you will deploy predictive analytics only if it is the right tool for the job.  This ensures that the initiative has a greater likelihood of success, has the support of key internal stakeholders, and because predictive capabilities are leveled at a specific target the initiative gains executive buy-in.  “A business problem exists, and this is how we are going to solve it.”

While predictive analytics may be ‘the best man for the job’, expect there to be resistance to implementation.  Often, this comes from individuals who rely on their accumulated knowledge as a competitive advantage against their team-mates.  In light of this situation, quite possibly the most effective method of gaining support, is to focus predictive analytics on a specific, defined business problem.  Your initiative must be dialed in on solving vital, business critical issues.  This way, dissenters to implementation will be seen in the light of hindering the future success of the organization.
Consider the following case study.  One of our clients had a division that is responsible for sourcing materials for production.  They had a group of commodity traders that were responsible for sourcing materials at the best price possible.  While their expert traders had a great track record of forecasting the market, their best traders were nearing retirement.  Also, while the company had made significant investments into business intelligence, the amount of data required to make an informed trade had been growing exponentially for the last ten years.  The future of the organization required developing a system that made learning from the cumulative knowledge of the organization easier.  However, trying to come in with predictive analytics was politically challenging, as it could be threat to both business intelligence and the company’s best commodity traders.  To overcome that perceived threat, we had to focus on the business problem and make a case that eliminating the problem was essential to the continued success of the organization.
Our client had to systematize the knowledge of those traders nearing retirement, and also develop a solution to find valuable patterns in the increasing flow of data.  The solution that we developed was simple.  We built a model that forecasted the direction of commodity prices. Before our model, traders and business intelligence had been spending a significant amount of time determining the direction of the market.  Now, by leveraging predictive analytics, traders and business intelligence have moved up the value chain.  Instead of spending the majority of their time trying to determine the direction of the markets, they spend the majority of their time quantifying the direction of the change.  This resulted in the improved performance and value of both business intelligence and the commodity traders.
In conclusion, the key to gaining political support is to define the business problem in the context of its importance to the continued success of the organization.  If solving the business problem is not essential to the success of the organization, it may not be worth addressing.  If it truly is important, and predictive analytics is the best candidate for the job; internal opposition will be seen as coming from selfish protectionists who threaten the continued success of the organization.

September 8, 2011 News 2 comments

Predictive Analytics — The Evolution of Business Intelligence

Predictive analytics is the next step in the evolution of business intelligence.  Most companies, even local small business, have already implemented business intelligence systems that help them understand what has happened, why it happened and what is currently happening.  For example, most small businesses have implemented Quickbooks and Google Analytics that allow them to report, analyze and display data about their finances, operations and marketing. (more…)

September 6, 2011 News 9 comments
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