Guidance and results for the data-rich, yet information-poor.

The Data Warehousing Institute invited TMA’s Tony Rathburn to deliver the keynote speech entitled “Enhanced Resource Allocation: Business Use  of Predictive Analytics and Data Mining” at their World Conference in Boston.  Watch the full keynote presentation as Tony emphasizes how most analytic practioners have tunnel vision on the wrong end of the problem.  Tony reveals how data scientists are building more-than-accurate models, but falling short at the project level to arrive at results that are truly actionable, understandable, and measurable.

Watch for Tony’s story of a data mining project gone wrong about halfway through the video. It’s an excellent demonstration of how anyone can develop elegant solutions for all the wrong problems.

AlgorithmPeople often ask about various algorithms during TMA webinars. Often, the question revolves around whether or not TMA covers this or that algorithm in any training course.

But an algorithm is just a tool in a larger tool box, so this question is a little bit like asking a carpenter whether or not he ever uses his hammer, or his screwdriver.

It’s concerning to watch so many people get fixated on this or that analytical method. No algorithm or analysis method can magically solve business problems.

Instead, it’s up to the analyst to solve business problems by learning what the algorithm can do, just as it’s up to the carpenter to learn how to build a house by knowing when it’s time to use the hammer and when it’s time to use the screwdriver.

Otherwise, you’re just throwing data into a system. The system’s going to spit out a result, but it won’t necessarily spit out a useful result.

If you don’t learn how to master your tools, then your tools will master you. When that happens, you will be unable to realize the full potential of data mining and predictive analytics.

Want to ask questions of your own, and get answers from gurus Tony and Scott? Sign up for TMA’s next free webinar and learn how to launch your predictive analytics projects successfully!

It's going to take more than a mess of information to solve real business problems.

It’s going to take more than a mess of information to solve real business problems.

Everybody’s excited about “big data.” Organizations all over the world are leaping on board the Big Data Bandwagon, and are eager to see what it can do.

There’s just one problem. “Big data” isn’t exactly what you think it is. There are a lot of problems with the term, and those problems lead to misconceptions that keep you from making the most of your organization’s data.

“Big Data” is a marketing term.

There’s nothing new about predictive analytics. The science has been around for decades.

Big data has just been the particularly successful marketing term that has catapulted predictive analytics into the consciousness of the mainstream. But sooner or later, “big data” will start to lose steam. What will happen then?

Marketers will still need to sell predictive analytics software. So they’ll make up another term. The new term might be just as successful or it might fall flat, but it will still be a new name for the same science.

You need to know this, because it will keep you from making silly decisions. You will not, for example, leap to buy the most expensive “big data” software package on the market when you know that people have been performing many of the same functions in boring old Microsoft Excel for years and years.

“Big Data” assumes that bigger is better.

In truth, bigger isn’t always better, and “big data” can often mean “having more data than you know what to do with.” Many organizations need to master small data before they start messing around with big data…and many of your insights are going to come from sample sizes that represent only a small portion of the data that your organization has been collecting.

Stop worrying so much about big. It’s more important to shift your mindset about data. Gathering more and more data can’t help you until you’ve started asking the right questions. Namely: what problem are we attempting to solve by delving into this data? Everything else has to flow from that mindset.

Don’t ignore your data. But don’t romanticize it, either.

Yes, your data does have a lot to tell you. TMA wouldn’t be here if it didn’t.

You just can’t afford to get swept up into the hype. Data is just a tool, a tool that you will hopefully use to discover solutions to some of the problems that your organization is facing.

Data, and what you can do with it, is not magic. It is math. And when you recognize that, you can approach it as a mathematician would, which means ignoring all the hype. Instead, get laser focused on creating a data analytics program that will be truly useful to you.

DataPreparationDo you know the difference between a great modeler and a mediocre modeler? Many people assume that modeling greatness is wrapped up in the ability to build a better algorithm.

Building a better algorithm is like building a faster rocket ship. It’s great, as long as you’re pointing the ship in the right direction. Nobody wants to move faster if they’re going in the wrong direction, but this is precisely what happens in many organizations. Data preparation is a big part of keeping that rocket pointed in the right direction.

There are two issues which you might encounter during your data preparation phase.

The issue of low-quality data.

The professionals at TMA are often asked whether predictive analytics is possible with “low quality” data. The short answer is — yes, of course it is. The data is what it is, and a modeler can rarely wait for higher quality data to present itself. Instead, the modeler will have to engage with the data, cleaning it up so that it may be used.

There is, of course, a caveat that you should be aware of. For example, you never want to clean your data to the point where you can’t develop it in a live environment. You should always do your modeling in the environment where the data is expected to perform.

The issue of low-quantity data.

TMA professionals also hear a lot of concerns about the quantity of data that any given organization might possess. There is always a fear that there just won’t be enough data to complete meaningful projects.

Yes, you do need sufficient data to complete your project.

No, this isn’t often a real problem for the modern organization. The typical organization will have far more data than is necessary to complete most projects.

The modern organization usually has more data than it can handle.

It’s all about getting to know your data.

If either of these issues are poised to become a problem you will learn about it during the data prep phase. This phase is all about getting to know the data and its limitations so that the data may be applied to the problem at hand. You can’t skip this step–you must understand what your data can do.

This is yet another reason why you can’t just dump data into a software program or an algorithm if you expect to get good results.

But it’s usually possible to solve the problems inherent in the data. You shouldn’t let the state of the data stop you. You should just accept it as part of the process.

All data is dirty. It’s up to you, as the analyst, to improve it.

presenting data analyticsThe primary purpose of data mining and prescriptive analytics is the creation of actionable insights. But data analytics can’t provide those insights if nobody understands what it says.

That’s why it’s important to consider the best way to present your data analytics. It’s hardly a frivolous question.

However, there is no such thing as “one true data analytics presentation method.”

Everybody processes information differently.

Some people are very talented with numbers, and so want all of their facts lined up in neat, orderly tables. Others are more visual, and need graphs, charts, or other visualizations to really understand what’s going on.

There are two ways to make sure that you are presenting your data in the best possible way.

Method #1: Ask the Audience

One method is to ask the decision makers to whom you will be presenting your results. After all, they will be the ones who need to choose a direction based on the insights you are bringing to them.

Your data mining projects will be proportionately successful to your success at speaking to the organization’s leadership. If management is intimidated or overwhelmed by the information that you are giving to them then they are not going to act on it, which means that you won’t be able to produce the kinds of results you were hoping to achieve.

Method #2: Use Multiple Formats

You won’t always have the luxury of asking your audience what they want. You may also need to present your finding sto multiple stakeholders, all of whom process the information a little differently.

So it may be in your best interest to choose multiple methods for presenting your data. That way, you’ll increase your chances of helping every stakeholder understand what you are trying to get across to them.

It’s all about soft skills.

TMA students are often surprised to hear that “soft skills” are vital to the success of their predictive analytics projects. Many people come to class believing that data mining is primarily about the math.

Nothing could be further from the truth. Navigating organizational politics is vital if you actually want data mining to help your organization.

Want to find out why? Don’t forget to sign up for TMA’s next free webinar: Data Mining, Failure to Launch!