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Did you know CAN’s blog is full of sound data science related advice dating back to the beginning of CAN? In case you didn’t, we make it a habit to regularly re-post our favorites. What follows are reasons why you should consider becoming a data scientist. If it grabs you – check out the Omaha Data Science Academy. It might be the first step in your data science career.

Why Become a Data Scientist?

Too few of today’s college students realize they want to be data scientists when they graduate. We believe that data scientists are the future, and that we are on edge of a data science revolution. Therefore, we decided to explain why to become a Data Scientist.

1. As a data scientist, you have incredible access across the business.

Your job of modeling specific business strategies and forecasts requires you to have broad access across your company. People look to you to bridge the gap between business theory and relevant data.

This is a tough role because it requires you to develop and implement a strategy to create consensus in order to implement the results of your work. Since the days of the English Luddites (the anti-technology loom weavers) there have been people who are against technological progress and the efficiency it brings to the economy. The best data scientists will be able to manage the political and social change that comes from their work. Data Science success isn’t only about making work more productive, it is also about helping other people adjust and succeed.

2. Being a Data Scientist is a specialized field.

The requirements to be a data scientist are long, because the decisions they make impact thousands of people. Data scientists usually have a 3.5 GPA or higher. They must have the ability to learn and share several different forms of knowledge, including principles of computer science, high business acumen, and complex math. Learn more about how to become a data scientist.

3. You have the opportunity to work with top level management extremely early in your career.

While this sounds great, it is also challenging. You need to be comfortable giving board room presentations to people who don’t understand what you’re talking about. A specific aspect of your position is to clearly articulate why your results are useful and valid — and do it without math speak. Learn more about presenting business intelligence.  

4. The best data scientists never settle, and question everything.

Whereas a statistician starts with a data set and a problem, a data scientist has a more difficult task. A really great data scientist will constantly ask, “are we solving the right problem?” Often the perceived problem won’t match your data, requiring you to look at everything from a new perspective.

A data scientist spends his energy asking machines questions and then trying to validate the answers, instead of spending energy trying to address the question directly. This requires a different work process, one that requires humility and understanding. Data scientist know while they are the ‘go to’ person in the organization, they don’t have all the answers. However, at the end of the day they are still responsible for finding the answers, which is why they get paid the big bucks. Data Science is a complex science as opposed to a simple science. 

5. You use artificial intelligence to automate the most routine, frustrating jobs known to mankind.

Instead of doing routine tasks, you can be responsible for automating the most tedious aspects of business, while saving your customers money and making enterprise more efficient.

If you are interested in learning more about Data Science and Predictive Analytics, download our free eBook — Predictive Analytics: The Future of Business Intelligence.


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As a predictive analytics team, we at CAN take the science behind Big Data very seriously, but that doesn’t mean that our whole process is centered around the software we create. In fact, we prioritize our relationships with our customers on a human level, and do our best to educate them about what we do best: data. The following article is an educational piece for our customers to learn more about CAN and CAN’s process. 

 

With technology developing so quickly, new ways to implement marketing strategies and more effectively reach consumers are popping up all the time. Predictive analysis is one such technique. Praised for its ability to inform companies of future trends and reveal important information, predictive analysis is growing in popularity, with 87 percent of B2B marketing leaders saying they had already implemented or were planning to implement predictive analytics in the coming 12 months. So what is predictive analysis and how can it benefit you? Let’s check out the details of this new process sweeping its way through the business world.

 

What Is Predictive Analysis?

 

Before fleshing out its benefits, it’s probably best to first explain what predictive analysis is: through data mining, statistics, modeling, machine learning and artificial intelligence, predictive analysis is a process for collecting and analyzing current data. To learn more about how CAN uses predictive analysis, check out our blog post here.

As a result, brands are able to interpret big data and uncover patterns and relationship regarding consumer behavior. For example, the latest mobile technology, such as the Samsung Galaxy S7, has developed sophisticated and compressive methods to retrieve such data from app behavior and mobile activity. With mobile being such a popular device choice for consumers, this is beneficial for retrieving fast and relevant information.

 

How Can Predictive Analysis Benefit Marketing and Sales?

 

  1. More Efficient Customer Acquisition

By providing your sales team with specific data, predictive analysis can allow them to acquire new customers and keep old ones more efficiently and with less cost. What journey do they take to purchase a product? What advertising do they respond to? What is it about your product/service that they enjoy the most? All these questions can be answered by analyzing previous data and drawing conclusions about future activity. This information can then be used to determine which customers to reach out and how best to appeal to them, saving time and money.

 

  1. Determine Up-sell Opportunities

Predictive analysis also assists in drawing conclusions about other aspects of your customers’ buying behavior. Through analysis, brands can better understand what their customers’ needs are and what exactly they’re looking for. This can then be used to tailor the sales and marketing strategy to specific customers.

For example, if you are a fashion brand and have customers who are in need of shoes, it would be inefficient and wasteful to send them an advertisement for a new shoe promotion. Instead, it would be better to send this to customers in need of footwear to maximize on profit.

 

  1. Optimize Marketing Strategy

Not only can predictive analysis benefit brands by helping to find information on customers, it can also help in regards to the market environment. You can learn what time of the year spending peaks, how much people are spending and what they’re spending their money on. This information can assist in the successful execution of marketing strategies by ensuring you are targeting the right people at the right time.

Or you can figure out where to score the most candy on Halloween, like CAN did here. See, predictive analysis can be fun too.

 

Predictive analysis is an increasingly popular method for brands to more effectively initiate sales and marketing strategies. By providing detailed information about market trends and buying behavior, brands can cut costs, boost profit and increase overall efficiency.

 

Hooked on predictive analysis? We’d love to chat with you! Contact Nate Watson via e-mail at nate@canworksmart.com.



CAN’s product roadmap is driven by:

  • Who adopts new technology
  • Why they adopt new technology
  • The hurdles they encounter

 

The adoption of new technology starts with play. Play is inquisitive and experimental. Try something, if you don’t like it: no worries, on to the next thing. The goal is to have a good time.

 

Work is about producing. Doing what you say you will, when you say. Work is about being dependable, known, dedicated. There is nothing inquisitive or experimental with work. Work is about doing what is known to produce value.

 

Value vs. Known

The most common fallacy is that value is what drives business adoption. It doesn’t, don’t act like it does. Known is more important than value, especially if value requires change. Anyone who is currently comfortable will take a bird in the hand vs. venture for two in the bush.

 

The Goal.

How can CAN build a product the allows play, but once familiar transforms work. This is one of the reasons that Twitter — Yammer: a similar service — has been able to gain substantial transaction among professionals for sharing knowledge.

  • Open Source: In the technology community your credibility comes from what you have built. To stay current developers have to build outside of work. They use free open source software — with enterprise support, and once they have gain familiarity it often ends up in their work lives. Play becomes work.
    • Examples: Hadoop, R, Ruby on Rails, AngularJS, Backbone.js.

 

  • That Next-Level: Take technology that people love to use in their personal lives to the next level. No new technology, but technology that is work ready. Yammer is a work ready version of Twitter and Facebook. Microsoft Lync is a work ready version of Microsoft Skype. Windows is a work ready version of Mac OSX.
    • Linux, Apache, Personal Computer, Drones, 3D Printing, iPhone

 

  • Academic Bump: Professors provide advice to students as teachers and professionals as consultants. When possible providing software to professors — free or discounted — can spur adoption in businesses as students get jobs and consulting results are operationalized.
    • Qualtrics, SAS, SPSS,

 

MVP to Maturity:
Technology tends to mature from general to specific applications. Flint Scrapers evolved into a specific application of using sharp edges to process materials, e.g. knives, spears, axes, and cleavers.

 

  • Cutting: Flint Scrapers evolved into a specific application of using sharp edges to process materials, e.g. knives, spears, axes, and cleavers.
  • Digital Screen: A modern example is the splintering of digital screens from a lab tool into the variety of digital screens we have today. Matrix of light bulbs, CRT’s, Plasma, LED, Liquid Crystal.
  • Personal Computer: Even the PC has evolved into a spectrum of diversity. Starting with desktop PCs, and moving to servers, laptops, mobile phones, smart phones, tablets, netbooks, cloud computing, and Internet of Things.

It is impossible to fight the extropy — the trend towards order — of the nature of technium — technology as a biological Kingdom. Technology will always fracture from general to specialized. CAN’s product roadmap leverages the nature of technology instead of fights it.


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Why to become a data scientist

Too few of today’s college students realize they want to be data scientists when they graduate. We believe that data scientists are the future, and that we are on edge of a data science revolution. Therefore, we decided to explain why to become a Data Scientist.

1. As a data scientist, you have incredible access across the business.

Your job of modeling specific business strategies and forecasts requires you to have broad access across your company. People look to you to bridge the gap between business theory and relevant data.

This is a tough role because it requires you to develop and implement a strategy to create consensus in order to implement the results of your work. Since the days of the English Luddites (the anti-technology loom weavers) there have been people who are against technological progress and the efficiency it brings to the economy. The best data scientists will be able to manage the political and social change that comes from their work. Data Science success isn’t only about making work more productive, it is also about helping other people adjust and succeed.

Read more…



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