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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.

TM1 10.2.2's release was, probably not coincidentally, in advance of the IBM Vision 2014 conference in Orlando. Continue reading to find out more about the release.

At QueBIT we like to describe Advanced Analytics as the powerful combination of Predictive Analytics and Prescriptive Analytics. The term Data Mining is still in wide use, however. It can be described as one of the most important methods for doing Predictive Analytics. Tom Khabaza has done more than most in clarifying what is meant by Data Mining, and how to do it well. Much of his great insight is available on the web.

The modern CFO wields significant power and influence around a wide variety of business functions. Whether those functions are regulatory, strategic planning, governance, or beyond, CFOs are under pressure to provide accurate, and timely reporting with the most relevant data from all business departments. Given the ever-increasing risk and liability of manual processes, CFOs must ask themselves, “How can I automate and optimize finance processes while meeting all of my fiduciary responsibilities?”

Throughout all of the back-and-forth that report distribution presents, organizations are wasting precious time, and reports are being delayed. The longer these reports are on the shelf, the less they remain relevant and timely for the decision-making process. What if the finance department has an assembly area where stakeholders could automate and keep track of multiple contributions in multiple formats, including narrative content and commentary?

The introduction of external report management solutions have helped today’s CFOs stem the tide to some degree. Automation has sped up the delivery time on the external end. But the internal end has remained muddied with error-prone, manual process issues. And those pesky security concerns remain deeply-rooted throughout all departmental reporting. New tools allow finance organizations to reduce cost, improve efficiency, enhance security, and reduce risk while ensuring more transparency and collaboration.

In rolling out the Cognos Disclosure Management platform, IBM has significantly closed the gaps in internal and external reporting. This solution is geared to break down silos, allow companies to meet ever-evolving regulatory and statutory mandates, and unify business processes across the enterprise. Now the reports that are seen represent a “single version of the truth” for business leaders and managers. From a collaboration, integration, and security standpoint, this automated solution can tie up all of the loose ends.

What else can Cognos Disclosure Management do for your business?

TM1 Brings Light to Emergent BioSolutions’ Financial Planning and Forecasting

For a lot of finance departments, the thought of sitting down to prepare the annual budget can send shivers down spines. After all, budgeting and planning season can be a messy endeavor, full of tabbed Excel spreadsheets, back-and-forth budget approval requests, and a MASTER budget sheet that needs constant corrections before it is finalized.  All of these tasks are issued manually through email requests and via the physical distribution of documents and reports. And to make matters worse, there has to be swift coordination between all business units, as well as the executive management teams.

What if budgeting didn’t have to be so complicated or error-prone? Emergent BioSolutions—a global specialty pharmaceutical company—found out the night-and-day difference that an automated, integrated budgeting and forecasting solution can make. It represents a complete cultural change for Emergent. This exciting transition meant going from static, once-a-year budgeting and forecasting, to a dynamic, rolling forecast.

According to Felipe Alcorta, Director of Financial Systems & Analysis, “We are constantly on a rolling forecast. 9 months into the year, we’re already 3 quarters of the way to establishing a budget before the planning period actually begins.”

The R programming language is the result of a collaborative effort with contributions from all over the world. Initially written by Robert Gentleman and Ross Ihaka of the University of Auckland in the late 90s, it is popular and in wide use. It has been featured in the New York Times. Even estimates that are several years old have put the number of users above a ¼ million. The current number is certainly much higher. One popular LinkedIn group has 30,000 members. It has been featured in the New York Times. Polls on KDNuggets.com have placed its popularity even higher than the two players that have dominated statistical computing for decades: SPSS Statistics and SAS. The open source nature, and its corresponding price, are extremely attractive to academics and students. Critically, it is also very powerful.

So what’s the catch? Even its fans admit to a learning curve. It is a programming language, so there is no Graphical User Interface to get you quickly up to speed. Software environments have been created to support working in R, and many of them are popular, but nonetheless, there is some effort to be spent on getting started. On the upside, it is universally recognized as having fine graphics capability and if measured solely in terms of sheer volume, no commercial package can compete with the number of algorithms and methods available in R.