CAN’s success depends on our ability to provide great jobs to innovative and creative professionals. However, managing these highly intelligent and creative people can be challenging. Smart people want clear career paths, frequent meaningful promotions, and competent managers. If smart people perceive a position lacking, they quickly lose motivation. For example, smart people quickly lose respect for a manager they perceive as being incompetent or less intelligent. CAN has tried to address the challenges of hiring smart people.
First, we have limited the number of positions that are not core to the business. If possible we have outsourced any position that is not sales or operations. Essentially we have outsourced for quality not price. This has created an organization were almost every position has a clear career path. This has allowed CAN to focus on building in-depth training programs that allow us to develop our people so that they can earn frequent and meaningful promotions. (more…)
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Empower the great the build something greater.
Managing Smart People: Leaders and Experts
What to put on your business card
I received a business card from a networking event. I do what I always do, went to my CRM to add it to my contact list. I wanted to send them a “thank you for coming to our event” email. There was a problem. No last name. I have never, in my career as a sales person, seen the likes of this. I can usually catch someones first name because its usually simple. Bob, Frank, Susie, etc and this one was no different. A simple first name. I know they told me their last name, however I can’t remember much less spell it. After hitting the ceiling about not knowing their last name, I thought, I will just look at their email address. Most of the time its first and last name (me excluded), and this is where I really shook my head. There was no email address…
<shocked face>
<more shocked face>
It is 2012. We email now. We connect on Linked In, Facebook, Google Plus, and ten others I have never heard of. Without the right information on your business card it is impossible for me to connect with you. If I can’t connect with you then I will never find out about you, learn to like you, exchange leads with you, let alone buy something from you.
I pitched the card in the trash. If they can’t make it easy for me to contact them, I’m not going out of my way to try to contact them. I can’t even find that person on LinkedIn, Facebook, or Google plus. I don’t have their last name. I will not be giving my money to this person.
I still am shaking my head. They had a lot of other things on their card, such as what they do, name of business, and their website address. However, no last name and no email.
Here is what to put on your business card. You can have other information, graphics or pictures on your card, but you need to have the following. Also, it needs to be in a size, font and color that makes it easy to read.
Your name — First and Last.
I am disappointed that I have to point this out. Do not hand out a business card with just your first name. Who do you think you are, Snuffleupagus?
Your Title.
In business, your title is a filter that people can use when contact you. Working at a small company I used to think I didn’t need to include my title. I had so many different roles, and I didn’t want to be egotistical. However, I realized that including my title wasn’t for my benefit, and that it helps people decide how to interact with me.
Your company name.
I have had cards before with no company name. Please don’t had me your personal card. I want your business card. If I meet you while networking I want to hire you or refer you as a business not a individual. Also, don’t hand me a stack of cards, pick one card. Don’t be the Dealer.
Your address.
I might want to come by and see you in person or on Google. I understand you might have a home office, then put a PO box. At an absolute minimum put your city and state.
Your phone number and email address.
You need to make it as easy to contact you as possible. Some things are best communicated over the phone, and somethings are best communicated using email.
Your website address.
You must have one of these. We can discuss how you succeed without a website, but you will lose the argument. Have a website, even if it is a simple one or a template. WordPress.com is more than acceptable. You don’t have to spend tens of thousands of dollars, but it is important that you have a web presence so that I can get to know you.
When you hand me your business card please make it easy for me to contact you. Until next week, Happy Hunting!
Find out more about finding the right prospects for your business, download our eBook:
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A Preattentive Dashboard
The visual world is extraordinarily complex. For example a quick scan of my desk reveals hand-written notes, dry erase markers and USB thumb-drive. While I recognize these objects rapidly, I experience them at a basic visual perceptual level long before I can label or describe them. This low level of perception is what is called preattentive processing, or visual processing that occurs without deliberate attention. Preattentive processing can be used to create dashboards that easily communicate extraordinary amount of information per pixel and need very little effort to understand. Download our eBook, “Dashboards: Take a closer look at your data”.
Characteristics such as shape, size, color, contrast, luminosity and motion are examples of features that are perceived at this low level of perception. These factors are referred to as preattentive visual cues and help our brains categorize and filter our visual environment. Simply put, preattentive features are the information we gain from a visual scene before we direct attention to salient features to extract deeper meaning.
Humans are very good at extracting meaning from complex visual environments. However, this does not mean that we should be required to. This is certainly the case when designing dashboards and data visualizations. To keep things simple, CAN designs visualizations that focus on using preattentive imagery. Preattentive imagery allows us to communicate complex information in a rapid and concise manner. Our lives are complex enough. We deserve simple dashboards.
CAN recently competed a project for one of our clients examining the accuracy of industry level forecasts for every Metropolitan Statistical Area (MSA) in the United States. The report contained over 600 pages! Six hundred page reports do not get read, and consequently are rarely of value. We decide to go back to the white board. A 600 page report contrasts with our goal of making complex information easy to understand and act upon.
We needed a way for our client to explore and understand the meaning of our complex analysis. The result of our research are meaningless if they are not implemented. We started our design process by defining the business question our client needed to answer, “Which forecasts are inaccurate, and why?” Our client needed to navigate forecast accuracies by geography, industry sector, and the duration for which the forecasts are accurate. The dashboard we developed presents a 600 page report on one screen and can be fully navigated with three clicks.
Users explore the data by selecting areas on the map, concepts or MSAs individually or in group. This action updates the State, MSAs, forecast accuracy durations and industry sections for the selected region.
For our client, forecasts with greater than 90% accuracy are deemed acceptable, and closer examination is need for forecasts with 75 to 80% accuracy. We built these tolerances into our design. Notice the positioning of the grey crosses in each pane. The thin pink line shows 90% accuracy while the pink band shows 75-80% accuracy. As users explore the dataset, this relationship allows them to quickly identify and focus on values which are below the desired range. Glancing at the MSA window, it is clear that forecasts for Yuba City and Merced are suspect, and MSAs like Modesto should be examined more closely.
Let’s take a closeup look.
We’re looking at an overview of all Californian MSAs across several industry Concepts and at the Duration of Forecast. It’s immediately clear that the accuracy for Concept #4 is ‘in the red’. At this point, end users who are experts in the data can ask questions about what is going on in Concept #4, and discuss how this accuracy impacts future planning.
When elements on a page are judged to a similar standard, it is useful to maintain consistent visualization techniques. For example, we kept the theme of the reference lines constant across the Concepts and Duration of Forecast window. This helps reduce the effort required to use the dashboard and frees up some cognitive bandwidth to focus on the meaning of the data.
To visualize the Duration of Forecast, we carried over the reference line theme used in other panes. The purpose of this window is to let the user decide; for the region or categories they have selected, how accurate are the forecasts X quarters out. All the user needs to do is watch for where the grey line crosses the pink lines. This is a simple graphic. Users know they can expect this combination of forecasts to be 90% accurate up to 14 quarters out, and after 18 quarters the usefulness of the forecasts dissolve.
This approach strikes true to CAN’s goal of helping businesses work smarter. We turned a 600 page report into a single page that can be navigated with three clicks. Rather than increase complexity, we just built simplicity.
Inspect What You Expect
Several weeks ago I had a meeting with Raz Zehnacker. Raz is the former President of First Data. We met to talk about things I needed to be aware of as CAN grows, because according to Jim Collins “most companies don’t die of starvation but of indigestion”. Some of the best advice he gave me was to “Inspect What You Expect”.
Raz explained that as a leader you need inspect what you expect. During his time at First Data he used audits to make sure that he could stand by his promises, and that he could coach and grow his team. He used audits to encourage a culture of fixing things before they were delivered, instead of making excuses after it was too late.
I have been practicing Raz’s advice to “Inspect What You Expect”. At the beginning and end of each day I ask myself, “What do I expectt my team to accomplish?” I use the answers to inform my communication with them. I start by collecting as much information as possible, such as timelines, budgets and issues. I want to be able to ask the right questions to make sure that my team has thought through the project, that they have all the resources they need to overcome any issues to deliver on time, and learn how I can help them.
Inspecting what you expect can be uncomfortable, especially if someone has something to hide or they feel as if they are going to be judged. If they have something to hide, then it is essential that you inspect what is going on. However, it is also important that you communicate that you are not trying to judge, but that you want to help them. If your team is uncomfortable, it can be very tempting to leave them alone. In the end you as the leader are response. You need to make sure that you inspect what you expect so that you can stand by your promises.
Using Tableau Reference Lines to Explore Data
At CAN, as needed we use the visualization software Tableau to create reports and dashboards for our clients. Also, because Tableau is capable of handling large amounts of data very quickly, we’ve started using it to explore data visually during the data discovery stage of each project. We use Tableau to check the quality of data, find outliers, and get a sense of the properties of a data set, such as dispersion, central tendency, clustering, etc., before we apply statistical analysis or build predictive models. A Tableau feature, especially useful for exploring data, are Reference Lines.
This blog post explains a few ways that CAN uses Tableau to explore a data set.
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Use Email Signatures, They are Important
I get emails all the time from clients, potential clients, and people who want me to buy something from them. What constantly amazes me is the lack of email signatures. I know for a fact that all email programs allow you to make an email signature with your name, rank, email address, phone number, Skype number, LinkedIn page, web page, blog address, and business address.
Why then, don’t people use them? It can’t be because they are lazy. Not having one loses you business. After all, it is not always best to respond to an email with an email, especially when a topic is new, complicated, or sensitive. (more…)
Why Jefferson Decided to Join CAN
Jefferson joined CAN before we had this blog, our website, our products, or our office at 1209 Harney St. This video is about how and why he decided to join Contemporary Analysis. He knew we had potential and decided to become apart of CAN’s future and the future of data science. CAN specializes is 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
Tadd and Jefferson go Mining for Data in Wyoming
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 needed 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.
Mining for Data
We might push paper for a living, but we love to get our hands dirty to build beautiful models and to understand your business! Please contact us to learn how we can help you.
Rethinking why and where to network.
Its amazing when you have a target market how it changes everything you do. I realized a few weeks ago, some of the networking I was doing was not a good use of my time. The problem was not that there was a lack of good people there, but rather my target market wasn’t there. It was time to adapt. All networking has an expiration date, but this was different. I looked at my sales philosophy, the one written on a sticky note behind my computer that tempers everything I now do, and realized I needed to change how I network. The sticky note reads: (more…)
Using Mean Absolute Error for Forecast Accuracy
Using mean absolute error, CAN helps our clients that are interested in determining the accuracy of industry forecasts. They want to know if they can trust these industry forecasts, and get recommendations on how to apply them to improve their strategic planning process. This posts is about how CAN accesses the accuracy of industry forecasts, when we don’t have access to the original model used to produce the forecast.
First, without access to the original model, the only way we can evaluate an industry forecast’s accuracy is by comparing the forecast to the actual economic activity. This is a backwards looking forecast, and unfortunately does not provide insight into the accuracy of the forecast in the future, which there is no way to test. Thus it is important to understand that we have to assume that a forecast will be as accurate as it has been in the past, and that future accuracy of a forecast can be guaranteed.
As consumers of industry forecasts, we can test their accuracy over time by comparing the forecasted value to the actual value by calculating three different measures. The simplest measure of forecast accuracy is called Mean Absolute Error (MAE). MAE is simply, as the name suggests, the mean of the absolute errors. The absolute error is the absolute value of the difference between the forecasted value and the actual value. MAE tells us how big of an error we can expect from the forecast on average.
One problem with the MAE is that the relative size of the error is not always obvious. Sometimes it is hard to tell a big error from a small error. To deal with this problem, we can find the mean absolute error in percentage terms. Mean Absolute Percentage Error (MAPE) allows us to compare forecasts of different series in different scales. For example, we could compare the accuracy of a forecast of the DJIA with a forecast of the S&P 500, even though these indexes are at different levels.
Since both of these methods are based on the mean error, they may understate the impact of big, but infrequent, errors. If we focus too much on the mean, we will be caught off guard by the infrequent big error. To adjust for large rare errors, we calculate the Root Mean Square Error (RMSE). By squaring the errors before we calculate their mean and then taking the square root of the mean, we arrive at a measure of the size of the error that gives more weight to the large but infrequent errors than the mean. We can also compare RMSE and MAE to determine whether the forecast contains large but infrequent errors. The larger the difference between RMSE and MAE the more inconsistent the error size. The following is an example from a CAN report,
While these methods have their limitations, they are simple tools for evaluating forecast accuracy that can be used without knowing anything about the forecast except the past values of a forecast.
Finally, even if you know the accuracy of the forecast you should be mindful of the assumption we discussed at the beginning of the post: just because a forecast has been accurate in the past does not mean it will be accurate in the future. Professional forecasters update their methods to try to correct for past errors. However, these corrections may make the forecast less accurate. Also, there is always the possibility of an event occurring that the model producing the forecast cannot anticipate, a black swan event. When this happens, you don’t know how big the error will be. Errors associated with these events are not typical errors, which is what RMSE, MAPE, and MAE try to measure. So, while forecast accuracy can tell us a lot about the past, remember these limitations when using forecasts to predict the future.
To learn more about forecasting, download our eBook, Predictive Analytics: The Future of Business Intelligence.
Interested in seeing how we can help you with forecasting?
Featured Posts – Click the Brain
