Ask QueBIT About Analytics

Episode 62: Taking Technology Fandom to the Next Level

Guest: Christoph Hein

Most of us are ambivalent about the technology we use at work. Ideally, we don’t even want to think about it. We know that technology enables business operations to proceed at a speed and scale that would have been unthinkable just a generation ago. In fact, anytime our work-technology attracts attention to itself, it’s usually not for a good reason.

But sometimes a piece of technology gets a foothold somewhere and becomes the basis for a community of passionate fans whose imaginations are sparked by all the creative possibilities it enables.

Christoph Hein, our guest this week, epitomizes this kind of fandom as an active member of the global community of TM1 adherents. TM1, which is now IBM Planning Analytics, is a multi-dimensional database and modeling platform used for many financial and operational planning and reporting applications in companies of all sizes around the world.

Episode 57: How to be a Transformative CFO

Guest: Brian Frohn

Large influential consultancies like McKinsey, Gartner and others have been talking for several years now about how the responsibilities of the Chief Financial Officer (CFO) are expanding to include their companies’ digital activities. This makes sense, since the Office of Finance is the most central of all functions in any business, and embracing digital technology is the only way for Finance to stay connected in an age when data volumes and velocities are exploding.

But just because it makes sense doesn’t mean that it is easy to do. Those who work in Finance have demanding day-jobs with hard deadlines, and little room for error. Surveys of finance professionals regularly show that more time is spent on low-value tasks like data manipulation and validation, leaving very little time for high-value analysis and interpretation. While most people understand that the use of technology is necessary to correct this imbalance, finding a way to break the cycle is extremely difficult.

This week we talked to Brian Frohn, who recently started a new career as an Executive Coach after many years working as a CFO and a CEO. In this conversation, we dug into Brian’s own experiences of how to drive change in an achievable manner, and how he – as a non-technologist who started their career in Public Accounting – was able to introduce technology that transformed how his Finance team did their jobs for the better.

Guest: Bruna Garcia

In 2021 “Supply Chain” went from being an esoteric piece of business jargon, to being a household phrase. We will leave it to the popular press to fan the flames of public anxiety while lambasting industries plagued with Supply Chain problems. Instead, we will dig into how companies plan both demand and supply in order to manage their supply chains, and what challenges they need to overcome to produce and transport their goods in an efficient, timely and profitable manner.

Episode 51: Accelerating down the Home Stretch of xP&A

Guest: Andy Weiss

Episode 49: The Importance of Data Strategy for xP&A

Guest: Mike Burke

At which point does the “data” part of your planning project start to take center-stage? When you go beyond grabbing general ledger trial balances from a single data source, to wanting to truly mine more of your enterprise data for deeper insights, more accurate forecasting, and ultimately better business outcomes.

Everyone knows they have lots of data, but getting to it let alone putting it to work for you can be hard. Half the battle is recognizing the problem, and acknowledging that you can’t wrangle your data into submission without having a data strategy.

Episode 48: Hybrid Cloud for Planning, Reporting and Analysis

Guest: Mike Cowie

It’s the summer of 2021 and IBM has been airing a series of TV commercials on something called “hybrid cloud”. One of them features racing driver Jamie Chadwick asking “why should your business go hybrid?”, and concluding “... so you can focus on, you know, winning!”.

Episode 47: Why xP&A is not just marketing hype

No guest

Extended Planning and Analysis – or xP&A – is having a moment right now. It is the latest piece of jargon being hyped up by analysts like Gartner (who coined it) and planning software vendors like Anaplan, IBM and Workday Adaptive. Even QueBIT is talking about it!

But is it JUST hype? In this week’s podcast episode, we explain why we believe it is more than mere hype, and represents a very real shift in how companies are rethinking their planning and reporting processes in a fast-moving, digital world awash in change and disruption.

The old solution of working late or throwing more bodies at the problem is no longer realistic: it simply cannot scale at the pace the situation requires. We also talk about digital transformation in general, what is pushing companies to make those investments now, and how it is imperative that digital transformation of internal planning, analysis and reporting processes keeps pace with all the other changes. This is what embracing xP&A is all about.

Episode 46: Taking the First Step

Guest: John Sterner

It’s interesting how human beings are used to planning journeys through life – like vacations, going to college, and retirement – but when it comes to buying technology, there can be a tendency to want everything at once.

Perhaps it is because there is “software program” involved? Perhaps software vendors have been too successful in invoking “magic” to overcome natural and healthy skepticism in order to satisfy their quarterly sales quotas? Or perhaps we buyers succumb to feeling intimidated by technology we don’t understand, and hesitate to ask all the reasonable questions, even when given the opportunity?

Either way, often the “safe” option seems to be to do nothing, but this can come at tremendous opportunity cost especially when faced with change and disruption. In this week’s episode we talk to John Sterner about his 25 years helping companies implement technology, and why they often stop short of true digital transformation.

Episode 45: Software Evaluation Principles

Guest: Christine Schoenen

There is so much technology being thrown at us each day that deciding which software tools to invest in is overwhelming. It’s one thing to take a chance on a free app on your phone, but a completely different thing to spend tens of thousands of dollars (or more!) on something like a software platform for analytics. This is where companies turn to trusted experts like QueBIT for guidance. But how do the experts know what to recommend? Don’t they face a similar problem?

In this episode we talk to QueBIT Master Consultant Christine Schoenen about QueBIT’s formal process for evaluating software vendor partnerships. In 2020 Christine led a QueBIT Guild whose mission was to improve our software evaluation processes to increase agility, scalability and quality. The fruits of these efforts are now being put to good use!

Episode 44: The Sad Truth about Data

No guest.

Episode 40: Why Master Data Management Matters

Guest: Clare Hutton

Our clients often ask us whether we “do” Master Data Management (MDM). It’s a good question because Master Data Management is important to DO, but answering the question can feel a bit like tip-toeing through a minefield. This is because the term “Master Data Management” means different things to different people. QueBIT’s answer may be “yes” or “no” depending on what you are thinking of.

This is why, when QueBIT launched its internal Guilds initiative in 2019 as a way to build community through having cross-functional teams explore and “go deep” on different topics that interested them, we selected Master Data Management as a guild topic. The goal was to drill down past the jargon, the hype put out by “MDM” software vendors and QueBIT’s own experiences, to understand the essential business problem that MDM purports to solve.

Episode 39: Diversity, Inclusion … and Transformative Analytics

No guest

Episode 37: Learning about Splunk

Guest: Jim Miller

Episode 36: Analytics Shorts: 3 Stories

Guests: Christine Schoenen, Dan Viterise, Jim Miller

Guest: Gary Quirke 

What do “cash forecasting” and “looking out for bears” have in common? Both these activities have existential consequences. The damage a bear can cause is obviously terrifying to think of, but if your company runs out of cash and you lose your job as a result it can be quite frightening too.

A company with a good model-driven cash forecasting process can anticipate cash problems far enough in advance to take mitigation steps, for example to delay payments, renegotiate terms or cut costs.

If its cash forecasting process is sufficiently detailed and supported by the right technology, a company will easily be able to consider a range of scenarios and prepare different plans ahead of time to be in the best possible position as reality plays out. Conversely, not having this capability is the equivalent of tripping over a bear cub with mama bear in sight.

Guest: AG Tan

Planning, forecasting, reporting and analysis are core business activities served by analytics technology. Many companies are busily re-doing plans and forecasts in response to the 2020 COVID-19 pandemic, to figure out how to stay in business (when demand has plummeted) or how to maximize capacity (when demand has sky-rocketed). At the frontlines of all these efforts are financial analysts working in the corporate finance, treasury and operational planning functions. If they do a good job, better decisions are made, a company survives, jobs are saved, and the economic recovery is hastened.

This is extremely important work that is poorly understood, and little appreciated, by those who don’t do it for a living. Especially poorly understood is the qualitative value of giving financial analysts the right tools to manage data, build models, collaborate and share information. Companies that understand this are flying supersonic jets in comparison with companies that don’t, which are doing the equivalent of riding bicycles.

In this week’s episode we explain the critically important contribution of financial analysts, and point out a huge, neglected business opportunity!

Guests: Justin Croft and James Saronson

When there is so much going on around you that you can’t do anything about, it is helpful to reflect on the things you can control. If your job involves business planning and forecasting, it’s about becoming more comfortable with uncertainty. This can be done by exploring a range of possible scenarios ahead of time, so that you are prepared to act regardless of which scenario plays out.

There is nothing new in this concept: planners explore scenarios all the time.

But the COVID-19 pandemic has drastically altered the context in which planning and forecasting takes place. Most of all, it has compressed the timeline and increased the frequency. Annual budgets and quarterly forecasts will not be any use. Companies need to look at their business assumptions by month, by week or even by day! Analytics technology to support planning and forecasting functions evolve from being a nice-to-have, to being indispensable.

In this week’s podcast episode, Justin Croft and James Saronson – both data scientists – talk about specific ways in which technology can support planning and forecasting processes at times like these.

Episode 32: Embedding AI in FPA Replay

Guest: AG Tan

This episode is a replay of an in-person keynote presentation delivered at IBM’s offices in New York City in November 2019. The purpose of the talk was to demystify the value of Artificial Intelligence (AI) for planning and forecasting use cases in extended Planning and Analysis (xP&A = integrated financial and operational planning and analysis), and to provide practical advice on what it takes to get started. We explained the relationship between AI, Machine Learning and Predictive Analytics, and went on to provide a checklist of considerations to help prepare for a successful predictive demand planning initiative.

Episode 31: Analytics in Uncertain Times: COVID-19 edition

Episode 30: Analytics for Hospitality & Property Management

Guest: Walter Hugo Añez

Customer experience is a hot topic in every business these days, but in the hospitality industry it has always been paramount! When you walk in the doors of a hotel or a resort as a customer, your expectations are already sky-high. No one goes on an expensive vacation “hoping for the best” after all! Meeting these high expectations is the non-negotiable everyday reality of everyone in hospitality. How do they do it – and still make a profit?

In today’s podcast episode, Walter Hugo Añez gives us some insight into how it is done. Superior analytics in the form of planning, reporting and analysis make all the difference regardless of whether you are a brand (such as Marriott, Hilton or Holiday Inn), an investor/owner or a property manager. Daily reporting on occupancy and “on-the-books” reservations enables hotel managers to optimize staffing levels and drive promotional efforts. Cash flow modeling enables investors to project operating cash requirements in the long run. And so on.

We finish up by talking about the limitations of Excel in delivering all this information in a timely manner, and the opportunities that come from investing in a planning software platform. Efficiency opportunities manifest as quicker turnaround times to support better operational decision making. Business opportunities arise from having higher quality, more detailed and more trustworthy data to work with as well as having more time to analyze it and be pro-active in driving strategy. We also touch on the opportunities of deploying predictive and other advanced analytics technologies for demand forecasting and optimizing metrics like capacity and staffing levels.

Episode 29: Predictive Forecasting: Under the Covers

Guest: Scott Mutchler

If you have ever worried that Predictive Forecasting as an application of Artificial Intelligence (AI) and Machine Learning is more hype than reality, then this podcast episode is for you! There is a lot of superficial information being put out to sell us all a gleaming vision of an AI powered future, leaving us with a sense that magic must be involved!

In this episode, QueBIT’s Chief Data Scientist Scott Mutchler confirms that it is not magic, and then goes on to explain – in terms that you do NOT need to be a data scientist to understand – how it all comes together. Among other things, we talk about how there are mathematical techniques to separate effects like seasonality from the underlying trend, and how sales of low velocity items can be modeled, even when there is a shortage of data. From there we move on to talk about the power of applying predictive technology to detailed bottom-up forecasting and the opportunity to break down silos and make more time for strategic analysis by delivering on the promise of a shared forecast for finance and operations.

If nothing else, this webinar will leave you with a better understanding of how predictive forecasting works, and position you (1) to be an educated buyer, and (2) to build a confident business case for embracing it!

Episode 28: The Challenge of Implementing Analytics on a Global Scale

Guest: Dan Viterise

People, especially developers and other technologists like me, tend to view analytics projects through the lens of “the technical challenge”. Even if we know that activities like “requirements gathering” and “getting buy-in” are important, they may still be allocated less time and energy than activities related to system architecture, data wrangling and model building which appear – in the moment – to be the bigger challenges.

In this week’s podcast, Dan Viterise tells the story of a global luxury brand that had the reasonable-sounding idea of taking an analytics system that had been successful for financial planning in one part of the world and rolling it out to other regions. Unfortunately, it did not go well. The pitfall was that the initiative was viewed as a technology rollout, missing the fact that the purpose of the system was to solve a business problem and that the business drivers outside the original region – and therefore the business requirements - were quite different!

There is no question that the causes are much easier to diagnose in hindsight than they were in the moment. Even so, we hope that sharing this story will help someone else about to embark on such a project, even if it is just to ask:

1. Have we really understood the business problem?

2. Have we truly listened? Has there been even a hint of skepticism or concern expressed that we should dig into more?

Episode 27: Analytics in the Health Insurance Industry

Guest: Ryan McKinney

If you heard that profit margins in the health insurance industry can be as low as 3%, would you be surprised? Much as consumers like to complain about health insurers, let’s also give them their due because they are in a difficult business!

In this week’s podcast, Ryan McKInney educates us on how the health insurance industry works. In the simplest terms, it’s about expanding membership, controlling medical costs and managing cash. Complexity comes from all the things that the insurer has limited control over like regulations, unpredictable claim schedules and the increasing cost of medical care.

The good news is that there is a lot of potential for health insurers to deploy analytics technologies across all areas of their businesses and increase efficiency, improve quality of service, boost preventative care and negotiate better rates with providers. In this wide-ranging conversation, we touch on several valuable use cases for analytics, and finish up with some good advice on how a health insurer (or any other business) can get started on their analytics journey!

Episode 26: Trusting the AI Forecast in Finance

Episode 25: IBM Cognos Analytics – a New Paradigm for Business Intelligence

Guest: Gus Slaughter

Back in the early 2000s Cognos was the leader in enterprise business intelligence (BI) platforms. In July 2008 Forrester Research recognized Cognos Business Intelligence as the most modern and scalable BI platform on the market. Cognos BI brought “pixel perfect” push and pull reporting to the enterprise on a robust platform that ensured data security and responsiveness, even with large numbers of users.

Valuable as this is, businesses today crave ever-increasing levels of agility, and the old model of IT-maintained and administered analytics has given way to expectations of self-service convenience. In short: business users want to work directly with their data! New players have entered the BI market catering to these expectations, but finding the right balance between ease-of-use, technical skills and data integrity is not easy.

Episode 24: Planning for Change Management

Guest: Brian Moore

Regardless of whether you like or dislike change, the best way to manage it is to assume there will be disruption! When change comes to a business in the form of a new software solution, we sometimes forget the disruption part because (by definition) a new software solution will be “better” and “make life easier”. If that were not the expectation, we would not be spending the money!

Episode 23 - Predictive Price Optimization

Guest: James Saronson

How to price your product or service is a fundamental question in any business. If you price too high, you may sell less than you might, and if you price too low you may earn less than you might. Either of these scenarios can affect profit margins. To further complicate matters, the “right” answer – or optimal price - may vary depending on where you are selling, and what the business objectives are. For example, if you are launching a product in a new geography and trying to break into a market, you may price more aggressively than somewhere else where you have an established foothold.

Episode 22: A Practical Approach to Innovation in Analytics

Guest: Andreas Kugelmeier, IBM

Innovation is much easier to talk about than to do in practice. Challenges include getting buy-in from groups you depend on but have little leverage over, and resisting the urge to go for a BIG vision. In this episode, Andreas Kugelmeier from the IBM Data and AI Labs, talks about the concept of a Business Analytics Innovation Loop as a practical, low-risk and high-reward approach to effecting innovation. The concept has strong parallels with the notion of agile software development, in which progress is made through a sequence of incremental but valuable steps. In Business Analytics there is an added advantage to taking an incremental approach because each steps brings new insights and understanding to light, which can inform and change subsequent steps for the better!

Episode 21: Everything you always wanted to know about Testing

Guest: Jim Brinnen

When implementing a new analytics system, testing is sometimes treated as a “nice-to-have” rather than a core requirement. While no one says up-front that testing is a low priority, it is not unusual for there to be inadequate dollars, time and planning set aside for testing. While this is true for any software application implementation, it may be particularly true in analytics because analytics projects are often initiated and owned by the Line of Business, rather than IT. This is a good thing because the Business recognizes business value when it sees it (and can thus be a very engaged partner), but issues may arise because the Business typically has limited experience in implementing software systems. At the beginning of the project, the challenges of acquiring data and defining requirements can be overwhelming. It is normal to go through a process of learning that leads to changed expectations and modified scope. Since analytics tools are pitched as business friendly and easy to use, this often leads to pressure to shift consulting costs away from testing towards expanded scope under the assumption that the customer will, by then, have learned enough about the system to take care of testing it themselves.

Episode 20: Data Virtualization

Guest: Keith Hollen

Data Virtualization technologies can be used in a number of different ways, including real-time operational reporting and analytics against multiple source systems, and quick-and-dirty data exploration! As business users increasingly expect to have direct access to data for self-service analytics, data virtualization can be an effective way to make that possible.

Episode 19: Lean Demand Forecasting

Guest: Dan Barrett

Episode 18: The Evolution of Business Intelligence

Guest: Robin Wilkinson

It turns out that Business Intelligence (BI), as a concept, has been around since 1865! Back then it was a manual paper and pencil process, but it still enabled one business to prevail over its competitors by making better data driven decisions. Today we are dealing with massive amounts of data, technology has evolved tremendously, and people have evolved too!

Episode 17: Analytics for Retail Buyers

Guest: Jim Hanifin

As on-line shopping becomes the norm, the traditional retail industry is going through a lot of adjustments. Employing analytics technology to increase operational efficiency and make better decisions faster is an obvious avenue to take.

In this episode we speak with Jim Hanifin who recounts the journey at a retail customer he worked with recently. We go into detail about how their retail buyers transitioned from a weekly paper-based analysis and planning system, to an on-line dashboard that put the data at their fingertips in real time. If

you have ever wondered how retailers get the products you want to buy into stores, you should find this peek under the covers interesting!

Episode 16: How to become an Analytics Expert

Guests: Daniel Williams, Odile Davis Wolfe, Pete Horgan

QueBIT has grown by cross-training business and finance people in analytics technology, and by cross-training engineers and computer scientists in business and finance. Meanwhile analytics experts such as data scientists are at a premium in the marketplace, and many finance and business leaders are recognizing the need for their business and financial analysts to develop more technical skills, so as to be able to service growing demands for data and insights in support of better decision making! Specifically, there is a trend towards the finance function expanding beyond responsibility for financial reporting, financial planning and analysis into a new world that encompasses data and analytics strategy for the entire organization!

Episode 15: A Financial Consolidation is an Analytics project!

Financial Analytics is an important branch of the field of Analytics. It involves data management, modeling and reporting. A Financial Consolidation system is a Financial Analytics project that brings together data from one or more ERP (Enterprise Resource Planning) system together with a financial model with the purpose of producing consolidated financial reports. Not only are these obligatory for regulatory reporting, but they can also be a strategic asset. Shorter month-end closing cycles, coupled with detailed product/segment/regional/entity/etc reporting made available quickly, to front line managers WITH the ability to drill down for more detail in an intuitive and interactive manner … can all translate into business value.

Episode 13: The Evolution of Analytics Solutions

Guest: Gary Corrigan

15 years ago, when Gary Corrigan first sold analytics technologies, customers were looking for technology platforms. Today the landscape has changed, and customers are now looking for solutions to their specific business problems. That’s a significant change! It’s as though customers have gone from wanting to buy a truckload of bricks to shopping for ready-built houses – and Gary has had a front row seat!

In this week’s podcast conversation, Gary articulates very clearly what the value of analytics technologies are to businesses, and how they are not a luxury add-on to an ERP (“Enterprise Resource Planning”) system, but an absolute necessity for any company to survive and thrive in today’s business

environment. Among other things he talks about the importance of listening to and connecting with customer needs, and how it is just as important to figure out early that you are not a good fit, as it is to close a deal. At the end Gary shares valuable practical advice for both sellers and buyers of technology.

In the podcast, Gary references these resources: 

- Lou Diamond’s Thrive Loud podcast 

- Gary Corrigan’s LinkedIn blog post: The Quarter-End Conundrum