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Posts Tagged ‘data science’

Data Analysis, Predicting the Weather, and Business Decisions

ThunderstormMany people believe that weather impacts us only when it is severe. However, weather impacts people and businesses in a myriad of other ways.

Some of the ways that weather impacts business are obvious. For example, food prices go up after a rash of bad hail, drought, or extreme heat.

Some of those impacts are less obvious. Retail sales can plummet when the weather is bad because nobody wants to be out in the middle of a storm.

The science of weather prediction itself is getting better. National Geographic outlined NOAA’s improved hurricane forecasting software quite recently.

Business is finding ways to turn improved weather data to its advantage. This CNBC report titled “Big Data Companies Try to Outwit Mother Nature’s Chaos” outlines some of those methods.

“While it’s still virtually impossible to predict an event like [the Oklahoma tornado,] the models and forecasts from big data companies can be extremely valuable to a variety of businesses, ranging from retailers to insurers, as they plan ahead.

Earth Risk, for example, focuses on the energy trading market. By focusing in on probability models for extreme heat and extreme cold it can help investors profit in the futures market.

[John Plavan, co-founder and CEO of Earth Risk Technologies] points to the winter of 2011-12, which many traders in the natural gas market expected to be cold, driving up futures prices. Earth Risk’s models, though, showed that the atmosphere wasn’t setting itself up for a high probability of cold weather, letting clients position themselves to make money when natural gas prices declined.”

Some private weather prediction models may even have an edge on NOAA. For example, IBM’s “Deep Thunder” draws its data from multiple sources, including NOAA itself. It also uses Earth Networks, NASA, and the U.S. Geological Survey.

If weather has an impact on your business it is a good idea to learn more about data mining and its many uses. TMA offers a variety of data analytics training courses to help companies learn how to use these powerful tools. Get started with TMAs’ free webinar today.

‘Big Data’ Is Dead

Style: "Porcelain pastel"Possibly the most overused and the least understood term since ‘cloud computing’, ‘Big Data’ is dead. It is dead because of it’s complete uselessness in the sense that the phrase itself has lost its meaning. In a constantly maturing industry like Data Mining and Analytics, there can be no clear blanket term to replace ‘Big Data’; What was once a blanket term for a collection of information now lacks specificity and no longer applies universally. Data Mining has evolved in a variety of ways, each of which will be further examined in this post.

Firstly, as analytics become more advanced, some important terms are emerging to reference new, narrowly focused, and highly specialized tools and technologies. As identified by, the top items about which you’ll want to be in the know are as follows:

1. Smart Data: As companies needs to more efficiently mine data through solely electronic needs takes precedent in the coming years, one can expect to hear more and more about ‘Smart Data’, data which can be processed without a human mind combing through it by utilizing predictive analytics to anticipate consumer actions. In today’s marketplace, examples such as automated personalizations and recommendations through companies like Amazon, Netflix, and LinkedIn can already be clearly observed.

2. Data Science: A useful term whose popularity has skyrocketed recently, with close to but perhaps not so much overuse as is associated with ‘Big Data’, ‘Data Science’ is meant to refer to a new field which utilizes statistics, machine learning, natural language processing, and computer science to extract meaning from large amounts of data, often with the goal of creating new data products.

3. NewSQL: The scalability of NoSQL combined with the strong ACID guarantees of legacy relational databases offers users new options when dealing with relational data. NoSQL will continue to be valuable for companies who do not require an ACID guarantee, but NewSQL is a solid buzz word of which to be aware and an item whose presence will likely continue to grow.

4. Predictive Analytics: A complicated process which relies on advanced machine learning and statistics to recognize and exploit patterns, predictive analytics is perhaps the root of many of the above mentioned items. Relying on manipulation of historical data to anticipate future actions allows Data Scientists to advise companies and consumers in their best interest. Activities in this arena are applicable in any possible industry and are the driving force behind consumer recommendation services and even fraud detection.

Although the circumstances that gave rise to ‘Big Data’ are still relevant, processes in this arena have progressed, and will continue to progress, beyond the need to store copious amounts of data. New and better systems for processing data will continue to emerge, as will more highly specific terms to describe them.

If you’re interested in learning more about Data Mining’s changing landscape, consider joining The Modeling Agency for a free webinar.  TMA’s training seminars are another, more in-depth way to canvas this growing field and ensure that your company is receiving the maximum benefit from your amassed data.

The Argument for a Data Science Degree

Data science degreeWith the rise in popularity of “data science” as a term and a concept, journalists are increasingly pointing out the lack of a formal data science degree from any university in the country.

This is important because a well-rounded data science degree could produce graduates who are capable of handling all facets of data analytics and data management, rather than the current custom of companies hiring people who may only be skilled in one area of analytics, and training them for the rest.

Recently, Government Technology took the call for a data science degree a step further and outlined 3 key ingredients that such a degree would have to possess to be effective. Writer Tanya Roscorla suggests:

  1. Multi-discipline – the ideal data science education would include mathematics, statistics, computer science and content discipline. This presents a unique challenge for universities, as in order to achieve this mix in the current popular university structure you might have to cross over between several colleges to achieve the well-rounded curriculum.
  2. Graduate-level – undergrads with a focus on one of the four aforementioned disciplines could pursue a graduate data science degree where they’d learn the other three. According to Roscorla’s interview with Jennifer Lewis Priestley, associate professor of statistics and director of the Center for Statistics and Analytical Services at Kennesaw State University, “Young students can’t study all four things at once at an undergraduate level because they don’t have the absorptive capacity to understand all the concepts.”
  3. Research orientation – big data presents big problems, when it comes to analyzing enormous amounts of data and culling the wisdom within it. A data science degree that teaches research would lead to data scientists who can actively seek solutions for performing analytics on rapidly, massively growing data.

For the full article visit Government Technology.

Are you for or against a formal data science degree?

Ready to take your own data analytics education to the next level? Register for a free data mining webinar with The Modeling Agency, or enroll in a comprehensive data mining and predictive analytics training session.

Will Data Mining Be a Savior for Record Labels?

Data Mining and Record LabelsA recent post on The Modeling Agency blog discussed some practical examples of data mining. Now from the headlines comes another example: data mining may equal salvation for struggling record labels.

The Guardian Media Network writer Lucy Fisher explains how record labels are suffering from a lack of CRM, or customer relationship management, and how data mining is the key to developing an essential CRM framework.  She writes:

In the digital world, there’s a need to reach out to millions of music lovers, for whom accessing tracks involves just the click of a button. “There are various social media properties for artists but these don’t represent proper CRM,” he says. “True CRM is where they need to get to. If they don’t own the data and the customer relationship across the various touchpoints, they won’t succeed.”

The acid test, says Uttley, is: do you know the 100,000 biggest fans and do you have their contact details?

Building a CRM through data mining with the amount of data a large record label possesses is no small effort. And restructuring daily operations so that they are data-friendly has proved to be something of a challenge. The music industry is heavily entrenched in gut instinct and decision making based on educated guesswork. Data mining, on the other hand, is a precise science.

But data mining can be used not only to identify brand ambassadors and key influencers, but to form predictive models to estimate future customer behavior. That is, after all, not so far off from the way record labels have traditionally operated – making often major business decisions based on a prediction, however based in intuition, about how customers will react.

The Guardian piece is revealing and intriguing, and worth a read for anyone who is a fan of data science, or just looking to understand data mining through concrete examples of how it can be used in business.

If you think data mining could help your business but you’re not sure where to start, try one of The Modeling Agency’s free webinars. You’ll learn the foundation of successful data mining practices and get your next campaign off on the right foot.