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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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.

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.

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…)

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.

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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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…)

Grant and Jefferson on the Grow Omaha Radio Program

Check out Grant and Jefferson‘s interview on the Grow Omaha Radio Show.  We talked about starting CAN, some of the interesting projects we have worked on, Startup Weekend Omaha, why we selected Omaha as our headquarters, and the importance of UNO for the future of Omaha.

Contemporary Analysis was founded on the premise that there is always a better way. In fact, we exist to help you find better ways to work smart. We do this using a methodology called predictive analytics.
Predictive analytics involves collecting data about your business and customers, and then applying theory and math to build simple systems to help you work more effectively and efficiently.
Our systems are tailored to fit your company no matter how big or small or what industry you are in. We have built simple systems for fast-growing technology companies, Fortune 500 companies as well as small companies in a variety of industries including community colleges, insurance companies, software companies and engineering firms.

CAN's 1st Lolcat

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.


Focus on the Business Question, not the Technology

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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