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We are entering an age in which corporate predictions and decisions can’t be made without sound analytical backing. Without data credibility, even the most promising ideas and initiatives will be shot down. In an interview with the Harvard Business Review, Tom Davenport (author of the book, Analytics at Work: Smarter Decisions, Better Results) expounded on this point:

Apache Spark is a game changer for big data analytics. In many ways, it will democratize analytics on big data. Spark will do this by making big data analytics accessible to a much larger group of data scientists (and analysts) via a simple programming API and familiar tools such as SQL, Python and R. Being open source, it will also allow many companies to embrace it with a smaller initial capital investment. Spark is also being embraced by IBM and other large analytic software providers as the engine that will drive their big data applications. The primary benefits of Spark are the ability to scale analytics to massive data sets and that Spark is a significantly easier to use platform (than Hadoop + Map Reduce + custom code) that will allow data scientists to be far more productive. Rapid development often results in a faster ROI.

The fear factor stems from the idea that data is being exposed to the public. Also, that providers are the ones in control of the primary source data. Entrusting a cloud vendor with the keys to the data castle is intimidating no matter how you look at it.

Imagine any milestone in your lifetime when fear potentially stood in the way of watershed success. Switching career paths, moving to different parts of the country, or even something as simple as buying a new car. If the fear of change or failure prevented you from making a move, could you imagine what would happen if you stayed stagnant? Chances, are you couldn’t have achieved a certain level of progress.

In the cloud universe, where seemingly every IT function is “as a service” compatible, there are three main cloud service delivery models that businesses leverage every day to run their operations:

One of the advantages of being a Business Analytics consultant, is that you rarely know what a feasible solution looks like until you have taken the time to truly understand your client’s business, strategy and culture. This applies to financial performance management (including financial reporting, planning and customer profitability modeling), just as it does to predictive and prescriptive analytics problems which may not always directly impinge on the Office of Finance.

No review is complete without taking a sneak peek at what’s coming next. In our last blog, we discussed noteworthy Big Data events that stood out in 2014. One of trends that had already been forecasted in projections for 2014 is that Big Data spending will continue to increase. IDC has projected that Big Data will reach $125 billion worldwide in 2015. This growth rate is three times faster than originally anticipated.

In turn, as spending increases, there should be a natural uptick in Big Data deployments. Right now, plenty of people are investing dollars into Big Data solutions, but to this point, deployments have largely been at a standstill.

Whenever New Year celebrations kick start, news stations run their yearly recap montages in a thrilling, cinematic fashion. So while we may lack some of the pyrotechnics of those broadcasts, you can still anticipate a lot of excitement from this 2014 Big Data recap. After all, Big Data is such a hot topic of conversation, even the White House is talking about it – but more on that in a bit.

Back when the year started, we discussed how much organizations were spending on Big Data compared to other IT areas (the tally was $34 million according to Gartner). While we are still waiting on the financial figures to come across to draw a direct comparison, there are some other interesting numbers to chew on.