Union Pacific Railroad Predictive Analytics

For most, seeing a Union Pacific diesel-electric locomotive painted in the historic Armour Yellow, Signal Red, and Harbor Mist Gray does not bring predictive analytics to mind. However, the largest railroad network in the United States is showing the effectiveness of analyzing non-conventional data forms to increase operational efficiency.
Applications in industries including financial services, insurance, retail, and healthcare are what commonly come to mind when discussing predictive analytics. Thinking outside the box, Union Pacific is currently using bearing acoustic monitors to identify faulty or failing bearings on trains. The acoustic detectors have been used for years, but enough data has now been collected to show the correlation between failing bearings and certain frequency spectrums. This correlation has been applied to real time acoustic readings to help predict bearing failures and ultimately decrease derailments and costly train delays. Download our Case Study on Mechanical Failure and Predictive Analytics(more…)

Forbes: The Age of Big Data / A Looming Talent Gap For Data Scientists / Why Companies Are Spending More on Analytics

The Age of Big Data: “…Big Data has the potential to utterly transform the relationship that individuals have with institutions, customers with companies, patients with the healthcare system, students with universities, and voters with government. And that means once it has fully penetrated society and industry, the Big Data revolution may very well prove a turning point in our economic – and ultimately, cultural – history as great as the electronics revolution. . . perhaps even as great as the first and second Industrial Revolutions.”
–“Why? Because once the relationship of individuals to institutions transforms, the benefits to the individual consumer, citizen, patient and student will be profound.” (Forbes) http://goo.gl/kxAlq
 
How a Looming Talent Gap Will Crush Enterprise Hopes for Big Data: “’A lot of companies don’t know how to find data scientists, and don’t understand data science,’ … ‘These enterprise companies can’t implement a proper data analytical solution because they have no data talent.'”
— “Part of the problem is an overall lack of big data skills in the United States. In May 2011, the McKinsey Global Institute laid out the numbers: ‘By 2018, the United States alone could face a shortage of 140,000 to 190,000 people with deep analytical skills as well as 1.5 million managers and analysts with the know-how to use the analysis of big data to make effective decisions.’” (ReadWriteWeb) http://goo.gl/SQqyS
 
Big Data Security Is Inevitable: “There’s been a fair amount of discussion about the fact that security analytics is becoming a big data problem. … If you think that enterprises recognize these trends, boning up on Hadoop, Cassandra, and NoSQL, and hiring data scientists to tag along with security analysts, think again.  There’s a growing security skills shortage that will preclude these activities before they even start.(Network Worlds) http://goo.gl/tME2c
 
Big Data Holds Big Promise for Government: “Big data has the potential to transform the work of government agencies, unlocking advancements in efficiency, the speed and accuracy of decisions and the capability to forecast, according to a separate report from MeriTalk.”
–“…the Centre for Advanced Spatial Analysis (CASA) at University College London is combining data from London’s Oyster cards – used to pay for public transport – and Twitter messages. Tube-travel patterns are regular: people who enter the system at one station tend to leave it at a particular other one. Twitter messages reveal a city’s structure and its activity.” (Smart Data Collective) http://goo.gl/aqokm
 
Why are companies spending more on analytics despite cutbacks elsewhere? “Analyst Dan Vesset, author of IDC’s “Worldwide Business Analytics Software” report, credits ‘attention-grabbing headlines’ about big data, rather than the data stockpiles themselves, with helping to put business analytics on the agenda of senior executives. Goodnight seems equally dubious, saying big data is the hot new topic ‘because people got tired of talking about the cloud.'” (InformationWeek) http://goo.gl/feQ2o

Analytics Market Grows in 2012

Analytics Market Grows in 2012

“The global market for business analytics software grew roughly 14 percent in 2011, fueled by pervasive hype about ‘big data’ as well as new technological innovations, according to a report unveiled by analyst firm IDC yesterday. Between now and 2016, the business analytics market will have a compound annual growth rate of 9.8 percent, reaching US$50.7 billion, IDC said.” (Global Financial Network) http://goo.gl/HL4qA
— “As part of that overall business analytics segment, the data warehousing platform software portion represented the fastest growth, at 15.2 percent in 2011 compared with 2010. IDC also pegged analytic application growth at 13.3 percent last year from 2010, and BI and analytic tools at 13.2 percent last year from 2010.” (Information Management) http://goo.gl/FTX8Y (more…)

Analytics and the Nimble Corporation

Brian Sommer at ZDNet has an excellent three part blog series about nimble verses the ossified companies and how each will use big data analytics technology. (Mr. Sommer defines ossified companies cease to develop and innovate and becoming stagnant or rigid in their ways.)
Brian says ossified companies could care less about responding to their market, therefore analytics will be a waste of time and money for them. Brian writes, “They are so rigid in their world view, their processes and business practices that they choose to ignore the very suggestions that could save their firms. They’ve not only ossified, they’ve turned deaf, too.”
What are the characteristics of an ossified company? Brian says they lack “leadership, vision, a continuous change capability and a culture that rewards risk-taking and change over risk avoidance and slavish adherence to ever growing obsolete processes.”
On the flip side of the coin, the nimble companies will live and die by analytics technology because responding to market conditions is part of their DNA.

A nimble firm experiments. Thomas Edison tried something like 6000 attempts at creating a long-lasting light bulb. Edison would have never been allowed 1% of those at most companies. A nimble firm can refine their analytics to isolate experimental results from those of other markets. The insights from these experiments will guide the eventual rollout of game-changing new solutions/processes/etc.

A nimble firm has many current hypotheses about the market. They use analytics to test, prove/disprove and refine these.

A nimble firm, and this is most important, can scale fast. When they see a new market opportunity, they test, refine and then use explosive energy to seize the awaiting market opportunity. These firms can exploit a new market opportunity with incredible speed and precision. They are not only capable of change, but, they can change their entire firm almost overnight.

 I highly recommend reading all three posts.
The Ossified Organization Won’t ‘Get’ Analytics (part 1 of 3)
How Tough Will Analytics Be in Ossified Firms? (Part 2 of 3)
Analytics and the Nimble Organization (part 3 of 3)
 
 
 

Is Your Data Valuable?

Consider this, the data you collect about your products could become another profitable revenue stream. Ken Oestreich of Gigaom.com:

When thinking about the value of the data a company collects vs. the traditional value of the product it may produce, collecting and analyzing broad categories of customer + product data is becoming equally — if not more — valuable than the product itself.

Some industry examples:

Grocery and/or consumer retailers keep massive records on purchasing habits of customers, overtly for affinity programs and for making targeted offers. However, this data could be federated with other complementary retailers to establish (geographic, temporal, or correlative) purchasing patterns to increase overall sales per customer. For example, Neilson recently signed an agreement with Walmart where Neilson gets to perform point-of-sale analysis with Walmart data, and (presumably) cross-correlate it with other retailers.

Real estate and geographic data such as from Zillow or Factual can provide core accretion of data value for complementary data-based services. Indeed, such data is available to be crossed and mashed-up for use in healthcare, local government, retail marketing/sales, leisure services, and much more. Consider the value for developing assessing detailed demographics, localized services, etc. However, these business models, both with Zillow and Factual, are of pure data services, rather than a derivative from a “legacy” core business.

So how do you know if your data if valuable? Mr. Oestreich says you should consider the following:

    • Uniqueness – is your data unique to you, and therefore hard to replicate by others?
    • Size/completeness – how physically large is your data? Does it reach back temporally? Does it include deep details and/or trends? To whom would this be valuable, either pre- or post-analysis?
    • Desirability – in addition to uniqueness, would your data be useful/desirable by others in adjacent spaces? How marketable is it?
    • Complementarity – consider how valuable your data is to complement or complete other forms of data, or to build-upon other data sets in a mash-up fashion?
    • Statistical/correlative relevance – can your data be statistically analyzed for patterns? Are there correlations or patters that might be drawn between it, and other external data sets?
    • Long tail relevance – does your data contain elements that lie +/-3 sigma outside the norm? could this “fringe” data be valuable to incrementally increasing sales, or for addressing customer needs outside the core set?
    • Data gravity – this new term, coined by Dave McCrory, speaks to the physical immobility of data, and the tendency/requirement to co-locate other applications and data sets with it. Is your data potentially so large that it might actually attract actual applications onto the platform? Perhaps within a special purpose compute-cloud?

 
You can read the entire post here

Analytics Fights Fires in New York City

When we talk about predictive analytics it’s usually in the context of profitability – where it works wonderfully with products like, ahem…CAN’s very own Pulse. (Sorry, couldn’t help myself.)
But as analytics (or “big data” if you will) becomes more mainstream we find it being used to solve problems for everything from crime fighting, to city planning and even recruiting in Major League Baseball. (more…)

How to Apply Predictive Analytics

Predictive Analytics allows people to make better decisions about how to spend their limited time, energy and money. The potential impact of predictive analytics on business will be similar to the personal computer, relational database and Internet. The power of predictive analytics is that it is a scientific business process improvement method that can be used to model complicated and hard to measure actives, such as why people buy something or which employees are likely to leave. Many business executives understand this potential and are excited about applying predictive analytics to their businesses.
CAN has 4 years of experience helping 200+ companies realize the benefits of predictive analytics. We have developed a 6 stage process for applying predictive analytics to our clients’ businesses that maximizes our clients return on investment, increases their chances for success, and makes sure that the results of our research are applied.
Six Steps to Applying Predictive Analytics
The first stage is to define the company’s mission, vision and values. We want to know why the company was started, and why it exists. We want to know what they want to accomplish in the future. Most importantly we want to know how they do business; what values they have that are unique and perminant even when the strategy changes. This understanding set the priorities and filters that guide future discussions. The second stage is to define the company’s goals. Unlike mission, vision and values, goals have clear beginnings and ends and typically can be accomplish in less than a year. Companies typically have one to three goals. Goals should be in alignment with the company’s vision for the future, and should be accomplished in a way that adheres to the company’s values. The third stage is to define the business question to be answered. The business question is about business process improvement, and should not involve technology or research questions. When answered a business questions should have a noticeable impact on at least one of the three parts of a business; sales, operations and administrative support.
The Business Question
The fourth stage is to determine what resources are available. Resources include political approval, availability of necessary data, and determining research methodology. It is important to note that we only determine research methodology once we have defined the business question. If we don’t have the necessary resources to answer the business question, we go back to stage 3 and try to refine the business question to fit the available resources. It is also during this stage that we determine which of CAN’s resources are best for the client. There are two basic options; custom solution or CAN’s products. If possible we try to answer the business question using one of CAN’s 5 products. This allows us to minimize cost while increasing chances for success. Our 5 products are designed to answer 5 key business questions that the majority of business owners have: 1. Tracker: Who is most likely to purchase my product next? 2. Capture: Where and when should I spend my marketing budget? 3. Pulse: How do I attract and retain my best customers? 4. Beacon: Which employees are most likely to leave and why? 5. Terrain: What sales are likely to be next quarter? If a business question can’t be answered using one of CAN’s products, we offer custom solutions. Many of CAN’s clients leverage our custom solutions to develop a competitive advantage in predictive analytics. When developing custom solutions it is essential that we become apart of our clients team and fully understand their business, goals and resources. Before committing to any custom projects, CAN requires that we build a proof of concept. The purpose of the proof of concept is to make sure that we fully understand what we need to build and that we have all the necessary resources. The fifth stage is to determine how the models will be implemented. While our research is complex, we make sure that our work is easy to understand and use, because that is how it gets implemented. We use 4 methods to implement our research, and often combine multiple methods depending on what the client’s goals are.
Reporting Predictive Analytics
1. Formal Reports help our clients understand the nuances and details of our research. Formal reports are most useful when the results of our research will influence a company’s strategy, will be used by a small and specialized audience, and frequent updates are not required. 2. Marketing Summaries provide our clients with colorful and easy to understand summaries of our research. Many of our clients use these summaries as marketing pieces to communicate quickly with large and unspecialized audiences about research that impact future strategy. Marketing pieces go beyond executive summaries because they can be used be used to inform executives, employees, customers and the community. 3. Dashboards help our clients quickly get the up to date information that need to run their operations. While formal reports and marketing summaries often include data visualizations, dashboards are unique because they can be quickly updated and display key information on a single screen that can be monitored at a glance. Dashboards are typically used by a small and specialized audience that is trained to understand and use the information on the dashboard. Dashboards can also be very useful for sensitive information, because administrators can control access by user on a need to know basis. 4. Workflow Integration provides our clients with the ability to use our research to impact the activities and operations of large number of people through the systems they are currently using. Workflow integration is useful because users don’t have to learn or get in the habit of using a new system. The predictive models and coefficients that CAN develops act as a filter to the current database and either users are presented with familiar data fields or if need new fields. The method that we choose depends on who the audience will be and how they will use the results. The fewer the people that need access to our research the more important security and control becomes. If the use of our research is for strategy development then we typically publish a formal report. However if the use of our research is to optimize operations then we publish it as a dashboard, marketing piece, or integrate it into the software you are currently using. The sixth step is to evaluate the model. As part of developing models we run tests to make sure that they are statistical robust. However, it is important to further evaluate a model before and after we implement it. The first evaluation criteria is does the model answer the intended business question. The second criteria is does the model produce results that reflect reality. While the model might be statistical robust, it is useless if it produces misleading results that the experts in your business know aren’t true. The third evaluation criteria is once implemented does the model produce the expected results. Predictive analytics is a very new field. While the technology is exciting, it is predictive analytics ability to answer hard to answer or previously impossible to answer business questions that is most exciting. What separates CAN from our competition is our focus on making sure that we answer our clients’ business questions, instead of being enamored by the technology. Our hope is that we can help our clients apply predictive analytics to their businesses, and that our 6 stage process helps them maximize their return on investment, increase changes for success, and makes sure that the results of our research are applied.

We are Having a Party!

Contemporary Analysis, Omaha, NE

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.

What We Look for When Hiring People

When hiring new people, we primarily look for four things; People that just build things, use learning as a tool, pursue the truth and are passionate about our vision.
First, it is important that they just build things. We don’t care about people that love to build things. Everyone on our team just builds things. They get an idea for a product, process, business or experience, and they just build it. We are very interested to know what people have built and why. We are looking for their ability to identify a need, scope the solution, and organize the resources necessary to develop the solution. We expect every CAN employee to be an executive, because they are expected to make decisions everyday that impact the future of the organization.
Second, they must use learning as a tool. We don’t care about people who love learning. At CAN the ability to learn almost anything is essential to success. When we get a new client or encounter a new problem, we figure out what needs to be done, learn how to do it, and then do it. Working at CAN would be very frustrating for someone that uses knowledge as a tool. We don’t pay people for what they know, we pay people for what they can learn.
Third, they must pursue the truth. We need people that not only pursue that truth, but embrace it without hiding from uncomfortable facts. Our clients pay us to change their organizations to help them work smarter. Often, CAN’s research uncovers uncomfortable truths about our client’s business, and it is our job to expose that truth. More often than not this is very uncomfortable, and our people must be able to confront these truths while operating with an executive level of consideration.
Fourth, they need to be passionate about our vision. CAN’s vision is to develop simple systems to help all companies work smart.  We do not need technicians that are in love with technology. We need people that see technology as a tool, and are passionate about building simple tools that everyone can use. Also, we need people that are passionate about business.
These criteria apply to every position, and are internal to the culture of CAN.  Having clearing defined what we look for when hiring people allows us to hire people based on their alignment to our culture, instead of their specific skills or education.  While this makes finding the right people very difficult, it has created an environment that is rife with creativity and original ideas.  Everyone is pursuing the same goals with same culture and different skills, experiences and education.
CAN is not for everyone, but we don’t need everyone.

Security

Security is very important to CAN since we work with our clients’ most sensitive information and provide them insights that are essential to the future of their organizations.  Our clients trust us with their most valuable information including business plans, intellectual property, financial and customer data.  We work daily to respect that trust.  The following is an introduction to how CAN maintains the security of all of our systems, sensitive data, and Contemporary Analysis. (more…)

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