According to The University of Sydney in Australia, data analysis is taking the opal mining industry into the 21st century. Data analysts are helping miners find promising unexplored areas by targeting the conditions that are optimal for opal mining formations.

“Opal is Australia’s national gemstone, but no new significant opal discoveries have been made since the early 1900s. Most opal exploration is carved out by individual miners digging in desolate areas around old opal fields in the Great Artesian Basin.”

The University of Sydney’s data mining strategy has turned this around. It has already successfully identified lucrative new opportunities, one of which has already been confirmed as a successful mining location.

This information and technique could prove invaluable to companies like Gemfields who are interested in bringing more order to the gem mining industry. Gemfields has expressed a desire to create a more predictable supply of precious gemstones, especially colored varieties like emeralds.

After all, Gemfields would simply need to apply the same technique to the geologic data that is the most conducive to locating emeralds instead of opals.

In fact, this data mining process seems like it would be absolutely instrumental to any attempts to modernize mining, since it reduces the chances that labor costs and other resources would be wasted on places that aren’t going to be productive.

Having the proper training is crucial to making the most of data mining advances, in this or any other industry. That’s why The Modeling Agency offers a free data analysis webinar and other data analysis training courses. Make the most of these exciting new developments!

More and more businesses are coming to understand the value of data mining. Retail stores are particularly poised to reap the benefits of data analysis, as outlined in a recent Forbes.com article called “Can ‘Predictive Analytics’ Help Retailers Dodge a J.C. Penny Style Debacle?”

The title references sweeping changes that J.C. Penny’s former CEO Ron Johnson made in a trial-and-error attempt to increase sales. The attempt flopped.

Yet in the past, trial-and-error were all that retailers really had to guide them through the marketplace.

Admittedly, once-upon-a-time that marketplace moved a lot slower. Retailers weren’t competing with e-commerce. Nor were they dealing with customers who could compare prices by the simple expedient of whipping out their cell phones.

In the Information Age, retailers can really only seize a single edge: customer experience.

“For retailers, competing based on price, location, and even product is no longer sufficient. The way they’ll win is by providing a superior customer experience–which is proven to be an accurate predictor of financial performance.”

So what can data do?

Data can help retailers figure out exactly how to provide that experience.

Good retail data analysis can draw from several sources, including customer surveys, point-of-sale data, social media, and all sales channels both offline and online.

Together, these sources work together to identify what customers want. Thus, retailers can create customer experiences that keep customers coming back for more by predicting the outcome of certain changes.

Furthermore, this data can help identify places where retailers are already turning customers off so that they can correct problems. They can also fill gaps in their inventory so they aren’t leaving money on the table.

Of course, all of these benefits depend on having the proper training to harness and reap these benefits. That’s why The Modeling Agency offers a free data mining webinar and other analytics training courses. If you’re in retail you should consider taking advantage of these powerful tools today so that you can seize the edge you need to stay alive in today’s hyper-connected, hyper-competitive marketplace.

Data mining can save fleet managers millions of dollars, according to The Financial Review. The article demonstrated ways that good data analysis and predictive modeling can help with a fleet manager’s most pressing problems.

For example, fleet managers can use these systems to determine residual values, which helps them make better decisions about purchasing, leasing, or selling their inventory. Residual value calculations are impacted by a number of different factors, and mistakes can cost companies dearly. The ability to get it right has an incredibly positive impact on managing both costs and cash flow. In fact, this value determination is the first place where the savings really start to add up.

Predictive modeling allows managers to adjust for the external factors that impact their business, factors like interest rates and gas prices that are beyond their control. Getting a clear picture of the ways that these issues will impact the fleet’s upcoming needs and challenges allows fleet managers to shield themselves from risk while keeping the fleet functional.

In order to reap these benefits, however, fleet managers need adequate data analysis training. According to Telogis, the leading producer of fleet management software platforms, “analysis paralysis” is one of the top 8 challenges faced by today’s fleet manager. Telogis estimates that fleet managers receive 36 million packets of data every day (assuming a fleet of 50,000 vehicles equipped with GPS devices). This highlights the need for fleet managers to understand how to deal with that data in a meaningful way, one that lets them make the decisions that matter most to them.

The Modeling Agency can help fleet managers make the most of their data. TMA offers comprehensive data analytics training courses which can help companies make the most of their assets while reaping million-dollar savings rewards for their fleets. Start by making the most of this free webinar, designed to help companies get a better understanding of data mining and predictive modeling.

Computers relieve us of so many tedious tasks that it can be tempting to think they may do just about everything for us one day. Any task that relies on formulas to compute or decipher seems to lend itself to technology. However, could a process like data mining one day be handled completely automatically? The short answer is ‘no’.

Although collection of data through electronic means is absolutely viable and, with most big data, absolutely essential, the actual mining of data requires clear direction and individual understanding. For instance, as we know, the very first step in mining data is to determine, not only the challenge or problem to be solved, but most crucially, the question to be answered in order to solve it. This, of course, requires an individual to have a clear business understanding. Oftentimes, determining the question may require a data miner to consult business experts in order to define the clear question.

As described by SmartDataCollective.com, what comes next is “understanding the data, the way they have been collected, their particularities… [and deriving] knowledge useful for preparing the data.” Clearly, as this is a task centered on internalizing data and its absolute meaning, a machine is not up to the task, no matter how advanced or orderly.

Data preparation for mining and analytics can, to a degree, be automated. Even in this case, when raw data is combed through to leave only relevant, useful information, outliers and missing fields could easily stump a computer. Detection of issues may be straight-forward enough, but deciding how to assess and move forward is a task for none but a data scientist. Again, the core to all of the decision making within data mining as a whole is genuine business understanding of the problem at hand. How can we expect a computer to manage this task?

Assuming that “that there are enough data, that the choice of the algorithm is not business dependent (which is usually not the case) and that the evaluation criterion is known,” SmartDataCollective.com tells us, “electing a data mining algorithm and tuning its parameters [through careful testing]  is certainly the task that can be the most easily automated.” What follows, however, is the process of evaluating the results and then implementing a solution to the root question, once again requiring business knowledge and deductive reasoning.

It is clear that data mining in its simplest forms can be automated, but for a business owner who intends to make decisions affecting the future of his or her company, a deeper understanding of what the data means is essential. For an amateur data scientist, this can seem a daunting task. Fortunately, The Modeling Agency offers a free webinar to help you grapple the problem. If you’re ready to dive into this exciting challenge further, perhaps it’s time that you learn about TMA’s intensive Training Seminars.

In a constantly evolving, global, digital marketplace, data mining will be the key to coming out on top. Since today’s economy can’t ignore the constant, surrounding influences of a completely universal market, applying information intelligently allows a business to create a need for their product or service and effectively fill it, whether focusing their efforts locally or worldwide. Each and every business today has the same opportunities to collect and store data as every other. For companies whose business exists mostly, or even solely, on the internet, this store of potential knowledge is, not only broader, but all the more important. The deciding factor in success, then, will be who can apply the collected information with better predictive modeling.

A clear example of this challenge will unfold as we watch the development of businesses like Groupon, whose client base is at the root of its successes. Groupon offers daily online discounts which are highly focused to a client’s georgraphic placement and their interests. At once a novel concept and one which is no longer unique, Groupon’s business model, though sound, will be challenged by imitators, especially larger competitors with vast stores of consumer data to mine, like Amazon. Amazon, a super power in online commerce, has been tracking and storing data on consumer purchase history and even browsing habits for some time. How can a smaller company like Groupon hope to compete? As described in an editorial on the Financial Times, James Bateman writes,

“My feeling is that Groupon is worth the value of its email database, and if it has data on customer spending habits (that is, which promotions they have responded to) then for many businesses this could theoretically be a value acquisition. But trying to compete against Amazon – if the latter takes the daily-deal market seriously – seems doomed to failure. The only consolation that I can offer Groupon at the moment is that Amazon’s focus seems poor.”

Mr. Bateman cites Amazon’s failure to specify more carefully which clients receive which deals as Groupon’s key to survival. Given enough time to grow and cultivate their consumer list, could Groupon compete realistically with a giant corporation like Amazon? Perhaps not, but we can clearly see that it is data mining and predictive modeling of client behaviors that is allowing Groupon to hold on against this giant adversary.

Whether your company is competing in the vast, online marketplace as well, or has a more direct relationship to your clients, data mining can be a key factor in your growth and success as well. Saving you from wasting resources and energy on misplaced or misguided marketing efforts, Data mining can help your business identify new outlets and opportunities.

If you are ready to learn more about how data mining can support your marketing efforts, sign up for one of The Modeling Agency’s free webinars. To gain a broader understanding, TMA also suggests attending their in-depth Training Seminars.