Blog

As we mentioned in a previous blog, Predictive Analytics is More than a Magic Act, one of the impediments to predictive analytics success are company-wide misconceptions. These misconceptions can arise before a predictive analytics roll out even begins.

Predictive analytics certainly has the power to influence major decisions at the top. For instance, if a company is looking into a merger, executives may want to forecast the financial risks involved or evaluate the impact the move could have on consumer demands. Or if decision-makers are contemplating a change in their current business model, predictive analytics can help them identify new opportunities that could come with the change.

The way analytics are described in some marketing and sales circles, it almost seems like you just need to push a button, numbers are spit out, and your company gains this amazing knowledge transfusion to make better decisions. It’s like having a direct link to a magic eight ball that actually works. As James Taylor (yeah, you guessed it, not that James Taylor) explained, there is much more work involved for realizing the value that predictive analytics bring. Companies that experience failures when they start their predictive analytics projects tend to lose sight of the most essential steps.

Taylor—the CEO of Decision Management Solutions—explained how to avoid common pitfalls of predictive analytics in the MIT Sloan Review article, The Four Traps of Predictive Analytics (link to article: http://sloanreview.mit.edu/article/the-four-traps-of-predictive-analytics/).

Predictive maintenance (PM) programs, in asset heavy industries, can generate massive ROIs. These ROIs are delivered through the following benefits:

Try Googling “TM1 Performance problem” and a lot of advice comes up. Some of it contains useful information, for example http://www-01.ibm.com/support/docview.wss?uid=swg21454290.

And then there is the “shot in the dark” stuff like this which is only helpful if your problem happens to have this one cause (and is useless otherwise): http://pic.dhe.ibm.com/infocenter/ctm1/v10r1m0/index.jsp?topic=%2Fcom.ibm.swg.ba.cognos.tm1_api.10.1.0.doc%2Fc_tm1serverperformance_n802b4.html

When Apple and IBM recently announced that they joined forces to align mobile, Big Data, and analytics into a central access point for iPad and iPhone users, it made perfect sense.

With IBM’s surging influence in the analytics and Big Data market space, Apple products serve as the ideal vessels to drive information across different mobile landscapes. Big Blue will be able to tap into a whole new base of users across the globe who have been waiting for the day they can use their iPads and iPhones to access decision-making support capabilities wherever they go.

Specifically, Apple is feeding more analytics and Big Data consumption for a base of mobile users that are soon to become the millennial standard. VMware refers to this group as “anytime, anywhere workers”. These individuals travel more and check their devices in airport terminals and during the cab rides back-and-forth. According to Vodafone, 82% of employees use at least one travel app. They are constantly looking to stay engaged with the information they need to stay productive.

To that end, according to CIO Insight, one of the biggest footholds IBM has gained is with senior decision-makers. These decision-makers—such as CIOs—make up a significant portion of the world-wide mobile user base and are responsible for making purchasing decisions. As a result, there is potential to drive additional iPad and iPhone purchases in enterprise settings.

In a conversation with CNBC, Apple CEO Tim Cook stated, “I think that the people that will really benefit from this are the enterprise customers who can be more productive running their businesses.”

From an industry-based data consumption standpoint, one can see how on-the-go consumers will benefit from unfettered analytics access. For instance, healthcare professionals can use iPads to dissect patient data on different hospital floors, or retailers can use their iPhones to track in-store customer activities.

There will be a rollout of natively built apps for iOS that target other industries such as banking, transportation, and telecommunications. These apps are specifically designed and configured to take advantage of the functionality of Apple devices.

How do you see this move advancing analytics through Apple devices? Let us know your thoughts!  

The impact of a predictive data model largely depends on the software technology behind it. But both the models and software can only be so effective without the proper business purposes established. If the goals of a predictive analysis aren’t established up front, then the technology really can’t drive the results you are looking for. Nate Silver harped on the importance of making sure that the data programs, analytics tools, and goals of the findings should all be intertwined.

“Tools are important and efficient code is important, but at the same time, the attitudes you adopt toward this, and a solid understanding of what your goals are… those are more fundamental issues than which software you’re using.”

Training is the ultimate Catch-22: So much to learn, so little time. The value that training can bring, especially when it is focused and organized, is undeniable. But for users and trainers alike, there is also no denying that training can be viewed as a giant game of Beat the Clock. (For those of you that aren’t up on your game-show trivia, Beat the Clock was a popular show that aired in the 50’s, but I digress...)

The very idea of training immediately brings to mind a to-do list, which includes allocating hours, reserving available physical spaces, and ensuring that everyone’s schedules align. There also needs to be plenty of time for practicing, feedback, and testing. With all the time necessary for planning, how does one predict the effectiveness of the training session’s outcome?

Back in 2012, The Memphis Daily News revealed some noteworthy results of a McKinsey Quarterly survey of 2,207 executives. In this survey, only 28% of participants stated that the quality of strategic decisions was generally good, and 60% thought that bad decisions were about as frequent as good ones. Think about that last stat point for a second. If those bad decisions translate into equally bad outcomes, there’s no telling how many failed projects, failed hires, and failed experiments have occurred, to name a few failures. So what gives?

Believe it or not, there are plenty of biases that get in the way of would-be objective data analysis, and those biases largely impair decision-making. It’s especially a troublesome prospect for business leaders who count on well-founded information.