Tim O'Bryan 270001NMX7 email@example.com | | Tags:  businessanalytics businessanalyticstoday provenpractices timobryan | 0 Comments | 978 Visits
Learn the value of the new IBM Cognos Planning 10.1.1 (GA November 22). You will be pleased to learn of this release as it affirms IBM’s continued commitment to ongoing support and value-added enhancements to the IBM Cognos Planning solution. The release fulfills our customers latest requests with:
- features for greater ease and speed;
IBM Cognos Planning v10.1.1 delivers additional functionality for contributors (end users), faster access to data for reporting, an improved installation features, and conformance with IBM Cognos BI version 10.1.1 and Microsoft Excel 2010, and other key solutions.
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Tim O'Bryan 270001NMX7 firstname.lastname@example.org | | Tags:  timobryan provenpractices businessanalyticstoday | 2 Comments | 1,373 Visits
We often hear Business Analytics being so many different things that we feel it’s near impossible to get a handle on what it really is. I’m sure you were just getting used to the idea of what Performance Management is and now we throw Business Analytics into the equation. To make matters worse, there’s a great deal of prognosticators, thought leaders, and industry analysts who still are married to the idea of calling the space Business Intelligence. I thought it might make sense to pass along a simple explanation of each without all of the Big 5 consulting speak that usually goes with it. So, here you are.
Business Intelligence (“The Historian”)
BI is where the historian in all of us comes out. This is where you’re doing rear view mirror analysis, querying, reporting, with enabled “alerts”, real-time monitoring, dashboards, scorecards, and visualization focused on past performance. This is your investigative practice area asking the questions ‘what happened?’ and ‘how are we doing?’ followed by thorough analysis of the detail behind the answers to these questions, i.e why are we on- or off-track?
Performance Management (“The Pragmatist”)
Performance Management builds off of “The Historian” to include the following: planning, budgeting, forecasting, and scenario modeling; customer and product profitability, i.e. profitability modeling and optimization; strategy management; governance, risk, and compliance; and, financial consolidation and external reporting. Performance Management is “the Pragmatist” who looks not only at monitoring and analyzing past performance (“The Historian”) but also wants to then use this past performance to help determine what the future outcomes are expected to be (Think budget/forecast), which is based off of these past results weighed against current conditions and intuitive insight, i.e. the “knowns” and “unknowns” about today and the foreseeable future.
Also included is the practice of governance, risk, and compliance. Of course, there needs to be rigor and accountability around these processes, including stringent compliance controls to meet all regulatory requirements. In addition, risk assessments are a necessary component of performance management should not only your performance assumptions (Think Risk-Adjusted Forecasting) be wrong not to mention other business risk elements of the business including strategic risk, market risk, credit risk, IT risk, operational risk, etc. The practice of governance, risk, and compliance enables customers to identify, manage, monitor and report on risk and compliance initiatives across the enterprise, helping businesses to reduce losses, improve decision-making capabilities about things like resource allocation, and, ultimately, optimize business performance.
Performance Management = [Business Intelligence] + [Planning, Budgeting & Forecasting, Profitability Modeling & Optimization, Governance, Risk, and Compliance, Strategy Management, and Financial Management & Control]
Business Analytics (“The Futurist”)
“The Futurist” looks at everything the “The Pragmatist” does but then runs what’s called Predictive Analytics against the Performance Management data that you already have to uncover unexpected patterns and associations and develop models to guide what should be done next. It turns the human element in planning, budgeting, and forecasting on its head by applying pure user-enabled algorithms and customizable statistical analysis providing you with the data driven answers. More simply, with predictive analytics companies are able to prevent high-value customers from leaving, sell additional services to current customers, develop successful products more efficiently, or identify and minimize fraud and risk. This is all being done by businesses all over the world today. Predictive analytics is just what its name suggests: It’s about giving you the knowledge to predict. [Business Analytics = [Performance Management] + [Predictive Analytics]
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Financial Performance Management for the Empowered CFO (using IBM Cognos TM1, IBM Cognos Controller & IBM Cognos BI for Scorecarding)
Tim O'Bryan 270001NMX7 email@example.com | | Tags:  timobryan businessanalytics businessanalyticstoday provenpractices | 0 Comments | 1,053 Visits
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Delaney Turner 270003RQ8K Delaney.Turner@ca.ibm.com | | Tags:  deloitte ibmbao | 0 Comments | 1,118 Visits
If using analytics in the Office of Finance isn’t particularly new, the kinds of analytics now available to finance professionals most certainly are. Finance still builds budgets and closes the books, but now it’s in areas such as model-based forecasting, advanced fraud detection and portfolio optimization where Finance professionals are finding new sources of value and competitive advantage. Here, I speak to Miles Ewing and Scott Wallace of Deloitte. (Download the podcast version)
Miles is partner in Deloitte’s Finance practice and leads Deloitte’s Integrated performance management practice in the U.S. Scott is a Director in Deloitte’s Risk Information practice and leads the U.S.-Cognos Alliance Relationship.
Analytics can mean different things to different people because you can do so many things with them. Can you explain how Deloitte defines analytics for its clients?
Miles Ewing: Analytics is a very broad term, and from our perspective they’ve been going on since humanity created fire and decided it was warmer to stand next to it than further away from it. But when we think about what’s different today, there are three aspects. First is the fundamental volume of data that’s available today. There will be more information created this year than in the past 5,000 years. Next is the speed at which we can analyze this data. If it took us 10 years to code the genome a decade ago, we can do that it in a week today with our processing power. Third, there’s the reach and breadth of the data. From social networks to sensing technologies there’s a dramatically broader reach.
These combine to give us an enhanced capability to look at both patterns in data and advise on specific individual transaction-level data. Because of this we can make decisions either at a higher level or lower level that we weren’t able to do in the past. And it’s that combined capability and bringing those disciplines to business that is really where Deloitte defines analytics.
Scott Wallace: More tactically speaking, it's really bringing what used to be back-office functions – either with your statisticians and actuaries - into the front office, where Finance professionals can use capabilities to do this analysis on their own. There’s an ability to do more with analytics tactically than before that’s bringing it to life.
Deloitte has different analytical disciplines. Can you provide us with some examples?
Miles Ewing: We break analytics into three areas. The first is core analytics – from basic variance analysis in your budget to the analysis that goes into your external reporting. It’s not just in your traditional FP&A group, but the analytics in your tax department, treasury, investor relations and operations. Companies have been doing that for a long time will continue to do so.
There are two things that are new. The first is where Finance teams are taking advanced analytic methods such as model-based forecasting - algorithmic-based forecasting, advanced fraud detection or portfolio optimization - and bringing those capabilities to their core, either to improve the efficiency and accuracy of these functions, or to add a different way of looking at it and get more bang for their buck on the core analytic side.
The second area is what we would call Finance-supported analytics. And these are areas where Finance is bringing its cross-functional capabilities to the problems faced by other parts of the business, be they in supply chain, procurement, IT or sales and marketing. What we see here is Finance taking a cross-functional view of the situation and coming out to support things like pricing, or vendor spend analysis or technology investment prioritization. These are areas where because of the reach and speed of data, Finance can support decisions at the micro level and provide better, more effective decision-making in those functions in a way that they couldn’t in the past.
Scott Wallace: It’s been core to Finance for a long time to have access and visibility across the organization. The CFO and his or her team need to be aware of what’s happening in other parts of the organization. What you’re seeing with analytics is that coming together and making it more meaningful and more impactful to the organization. Lately we’ve have a lot of requests from our clients asking how to integrate their sales or operational planning with their financial planning. So not only has Finance typically taken a cross-functional view, now there’s a demand pull for that view across organizations because of the capabilities of the tools and the data availability.
What areas of Finance need the most help?
Scott Wallace: As you read the different literature around Finance and analytics from firms like ours and from the academics, they’re really pushing the envelope on how to become a more value-added function using analytics; yet many organizations are still fundamentally trying to fix core processes. I do see a continuing demand and convergence in the area of forecasting. That’s where you’re seeing this convergence of the analytic capabilities and when you think back to what Miles said about the different kinds of analytics, the ability to have insight into other functional information and data, and then how do I move that kind of information into predictive forecasting – identifying those real key drivers of the business across the functions that I can model based on historical data, based on external data, and start to have more confidence in my ability to predict the future financial performance of the company. That’s where we’re asked to provide help.
Miles Ewing: Companies are at very different places. Some are still trying to get the core right and they need to get that set first. Organizations that have been unable to get that core right over the past decade will find it difficult to really advance into that support. They may lack credibility as analytical leaders in their company. Focusing on that core becomes increasingly urgent for them.
Where does the demand for analytics come from? Is it from a CFO setting out a new vision, or does it come from the bottom up? What trends are you seeing?
Scott Wallace: Right now we’re experiencing lot of top-down demand from the CEO and CFO. A lot of it is borne of frustration – despite all the data they have in their ERP and their more advanced operational systems they still don’t feel they’re getting the right levels of transparency and insight. Also, because of the influx of information about analytics and tools and methodologies and success stories they’ve seen, CFOs are really asking themselves how they can continue to grow their relevance within their organizations. They’re really pushing on analytics.
Deloitte has six guiding principles for getting started with analytics. Can you outline them?
Scott Wallace: First off, link your goals and objectives with clear business drivers. If you’re going to use analytics, make sure they tie to your existing strategies or other initiatives you have inside and outside Finance. Ask yourself: What am I really trying to do? What are the competitive differentiators I’m trying to find in my data set?
The second is to know your data. Many of our clients have a good vision. They know what they want to do and how to tie their analytics together, but they run into data issues because the data isn’t in a single location or it’s not clean enough to provide the right insights.
The third is to start simple. Analytics needs to be something that can be accepted by your organization. Pick an area where there’s a need or pent-up demand. Stay focused on that area, get the numbers right and get them delivered properly. Build the confidence within your leadership team that the predictive capabilities and outcomes you’re providing make sense.
The fourth is to leverage existing insights. If you’ve got programs under way – customer analysis programs, working capital analysis programs, for example – look for ways to enhance them using insights you can get from analytics. How can you better project things that are already being looked at by the organization? You’re adding insight to a point of view that’s already being used in the organization.