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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.

Along with trying to find the highest probabilities for particular business outcomes, it is equally important to put your data to the test once it’s ready. In that regard, taking a “trial and error” approach is what Nate Silver recommends to derive the most value from large data sets.

Really, what Silver advocates is bypassing the theory stage, jumping into the heart of advanced analytics and decision-making, and not being afraid of the missteps that could follow. It’s definitely a scary thought. However, without making mistakes, how can a company get better?

Is data the source to find right answers, or is it used to weed out the obvious wrong conclusions? If you believe in the latter point, you are probably on to something. And data and statistics guru Nate Silver would agree. In many instances, there is no magic right answer to a business problem or scenario. There can be many answers that can be construed as being right or justifiable. The goal of any CFO or CIO is to eliminate as many incorrect or unusable data points as possible. For them, it’s like taking an exam with five different answer options, eliminating the clear wrongs, and then potentially deducing the best rights from two or three very viable choices.

Getting back to a basic (yet highly important) point we made in the blog Big Data Perception vs. Reality: Is it Value or Noise?, predictive models and advanced analytics deliver probabilities that can cut down on the frequency of irrelevant data points popping up. Probabilities exist to enhance the chances that a good decision can be made under a certain set of circumstances; not that it will be made. Once decision-makers start blurring the lines between probabilities and certainties, they’re caught flat-footed.

As we discussed in our last blog, big data is highly influential and is certainly changing the decision-making process across the business landscape. More organizations are consuming tools such as Hadoop and YARN to get in on the data-crunching fun. According to a survey that IDC conducted, 32% of businesses have already deployed a Hadoop solution. Meanwhile, 31% said they plan to deploy within the next year. However, for those organizations looking to use Hadoop to run their own version of Moneyball, they may be expecting too much.

Great data points that stand out on their own (out of context) can certainly look impressive and convincing enough for decision-makers to make a bold move. The problem is, in the big data universe, there are plenty of those data points if you look closely enough. Do all of those big data findings equate to prime business opportunities? Nate Silver—one of the foremost statisticians, predictors, and vocal big data experts—says no. There is a need to discern from all of the noise that big data brings and logically assess the information that is in front of you. As Silver puts it, businesses need to stop “cherry-picking the results they want to see.”

So how should businesses go about running the most optimal predictive models and analytics to uncover the truth about their data?

Tom Davenport, A well-known author of various books about the value of analytics shows that businesses see significant competitive advantage as they move from Descriptive Analytics through Predictive Analytics and then finally to Prescriptive Analytics.