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Posts Tagged ‘predictive analytics’

In a World Where Data Analysis Predicts Blockbusters

ClapperThe New York Times recently ran an article on the ways data mining is being used in Hollywood to predict sales at the box office. It turns out that there are some tangible factors that pop up in movies that do well vs. those that receive two-thumbs-down from moviegoers.

The industry leader in this enterprise is former statistics professor Vinny Bruzzese.

“Demons in horror movies can target people or be summoned,” Mr. Bruzzese said in a gravelly voice, by way of example. “If it’s a targeting demon, you are likely to have higher opening-weekend sales than if it’s summoned, so get rid of that Ouija Board scene.”

“Bowling scenes tend to pop up in films that fizzle…Therefore it’s statistically unwise to include one in your script. A cursed superhero never sells as well as a guardian superhero, one like Superman who acts as a protector.”

The average Hollywood film costs over $60 million to produce. It stands to reason that execs would want to have some guidance about what works and what doesn’t before investing any money into a film.

Bruzzese’s example only helps to demonstrate how data mining can help isolate what customers really like and want. That means that they have a better experience because companies can then deliver without messy guesswork.

Of course, there are some people who fear that this means that we’ll all be stuck in a world of bland products (and movies). But these fears are unfounded. Data mining is only a tool.

There are a lot of different ways to write movies with targeting demons and guardian superheroes, after all.

The Modeling Agency offers companies the training they need to make the most of advances in data mining no matter what their industry is. Start with TMA’s free webinar, and then move on to other data analysis training courses here.

‘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 VentureBeat.com, 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.

An Overview of the Data Mining Process

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How Does Data Mining Work?

Perhaps you have heard of data mining and are wondering how this useful tool could help your business, but you aren’t sure where to start. It’s likely that you have a wealth of data amassed from your various business transactions, but how does the process of mining actionable information from this overwhelming collection of data really work?

Certainly, the ability of a company to collect data about their operations has improved with the growth of technology. With today’s emphasis on digitized processes, and the necessity of a company to maintain reliable historical transaction data, most companies can be described as ‘data-rich’.  However, just as many business are also ‘information-poor.’  They are awash in data, but don’t really understand what all that data is telling them.  The process of data mining allows a company to extract valuable insights and actionable information from data which will ultimately assist and inform important business decisions.

How to Prepare for Data Mining

When one sets out to mine data for useful insights, one must first assess the quality of the available data in the context of business objectives. Hopefully, the data to be mined is comprehensive and thorough; however, analysis can only begin once records have been checked for accuracy and completion and updated as necessary. Once this type of preparation has been done, the result is a clean set of data through which associations and predictive measures can be developed.

But most importantly, an organization should first conduct a comprehensive, methodical assessment to fully itemize and evaluate business objectives, team capability, environmental readiness and data capability.  A separate full article written by TMA’s president and published as the feature in BI Trends & Strategies  focuses on how and why to prepare for predictive modeling, …and why those who jump directly into the data are destined for failure.

Beyond the Basics

Although most organizations can acquire software and start building adequate models, it’s rare that they succeed at the project level.   Training and guidance from highly seasoned data mining consultants goes a far way to avoid starting projects prematurely, ensure that technology investments are right-sized, and establish a solid project definition that ensures a low-risk / high-impact methodical approach to the practice. When a company works with The Modeling Agency, they gain the advantage of the most current practices in data mining, allowing them to extract truly useful and accountable information with which to make informed and anticipatory decisions.

To learn how The Modeling Agency can help your company gain insights that will reduce costs and increase revenue, consider attending one of TMA’s training courses, or register for a free data mining webinar.

Mining Social Media to Track Outbreaks

outbreakWith the instant spread of information through social media, the Center for Disease Control (CDC) now has a new way to track the outbreak and spread of diseases, including this year’s deadly flu epidemic. According to Richard Quartertone, health communication specialist at CDC’s Division of Notifiable Diseases and Healthcare Information, as quoted by FCW:

“The CDC uses a variety of surveillance methods to track the spread of disease. They include longstanding techniques such as monitoring hospital emergency room visits, performing laboratory tests and conducting population surveys. Now, epidemiologists also watch trends in web usage and at social-media sites [like Twitter and Facebook].”

Data mining the information gathered by utilizing predictive tools like Google Flu Trends and MappyHealth, winner of the Department of Health and Human Service’s NowTrending2012 Contest, help the CDC see quickly and accurately when trends begin and how quickly they spread. Information is pulled from social media using keywords including conditions by name, as well as specific symptoms. The end data is compiled into charts, allowing end-users to visually asses trends.

By drawing from colloquial sources and utilizing these predictive tools to support their analysis, the CDC has effectively shaved several weeks off of their reporting time. A typical report that must begin in the local department and gradually be passed up to the federal level may take several weeks. Pulling from real-time tweets may allow our public health systems to be more thorough in warning citizens of the dangers of any particular epidemic before it escalates to catastrophic levels.

How could predictive analytics help your industry alleviate risks? Beyond just mitigating dangers, Anticipating trends can create opportunities for your business as well. If you’re interested in learning more about Data Mining and Analytics, perhaps The Modeling Agency’s free, one-hour webinars are the perfect introduction. For more in-depth training, consider attending our next Training Seminar.

How Data Mining Can Assist Internal Operations

30396724From maintaining morale to identifying the talent that will help the company succeed, Human Resources is the pulse of any business and the key to supporting effective practices. Certainly, happy employees are more efficient, and promoting the strongest performers will plainly help you get ahead, but how can a business owner know for sure which benefits are most appreciated or how well an individual works? Mining your records for proven data is the most reliable answer.

We can all take a lesson from Google, named the ‘Best Place to Work’ by Fortune Magazine for the fourth consecutive year. Not only are Google’s internal processes focused on collecting and utilizing their data to maintain employee satisfaction, but also making sure to keep their best and brightest. From which size plates are preferred in the cafeteria to which retirement plans are most appreciated by employees, Google knows how every decision affects the day-to-day satisfaction of their employees. The same is true for their management decisions; utilizing over 100 different variable to analyze and predict outcomes, Google can be certain that those chosen for promotion to management roles have been carefully assessed and proven to support company growth.

Now, It’s true that Google is a data genius with more resources than the average small or medium-sized business, but are there lessons here that apply across the board? Perhaps to your company? Most assuredly. No matter the size of your infrastructure, mining your data allows you to stay connected with  a no-nonsense view of what’s going on. Data won’t mislead you or your decision making process, so you can clearly observe the practices that really worked.

For more specifics on Google’s internal affairs and why they work so very well, read Business2Community‘s captivating article. If you’re ready to delve into your own data and would like to learn more about The Modeling Agency’s webinars, designed to help you learn the best practices for data mining, read on here.