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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.

YES! Do you agree? If so, read no further :) !

Even though your smartphone or tablet makes you incredibly productive and keeps you connected to the world, do you still find yourself searching for newer or better apps that will make that experience even better and more productive?  Have you ever come across an app that fundamentally changes the way you do things, but which you never realized you needed?   When we see the benefits of technology in our lives, we naturally want to keep seeking out new ways of enhancing and extending those benefits to other parts of our life.

 My husband and I were out to lunch at our local seafood restaurant the other day.  I overheard the conversation at the table next to me – 2 guys talking about Predictive Analytics!   WOW!  Predictive Analytics is finally lunchtime conversation!

I heard them talk about the businesses collecting massive amounts of “Big Data.”  And how “Correlation Engines” and “Predictive Analytics” could sift through this data to find interesting patterns that could be leveraged.  I also heard mention of Data Scientists, HADOOP, and programming in Mahout.

There are seven learning styles that make it challenging for instructors and design teams to meet the individual learning needs of their students. In order to provide an effective training class, you need to incorporate:

This article will give you a high-level overview of TMQ and what it means for your TM1 Applications. Read on for an introduction to some of the research QueBIT Labs is doing to maximize the benfits of MTQ.