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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.

This epsiode is sponsored by QueBIT.

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

Guest: Gary Quirke

QueBIT has clients from all industries, that range in size from smaller and mid-sized businesses to giant global companies. Surveying our client base is like looking at a microcosm of business in general, and so in this week’s podcast episode, we speak with QueBIT CEO Gary Quirke about how the COVID-19 pandemic is affecting our clients, and the ways in which analytics are helping them cope.

Uncertainty is always present in business, but the pandemic amplifies it. Analytics tools, when used well, help by giving you visibility into what’s happening in the business and increasing planning agility. Some of the most important analytics-supported activities for dealing with uncertainty are what-if scenario planning, the ability to compare results for multiple scenarios and cash management and forecasting.

We also went on to talk about what QueBIT is doing to survive, and support our employees and customers through these difficult times.

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.

This podcast is brought to you by QueBIT experts in analytics.  

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!

This podcast is sponsored by QueBIT experts in analytics.  

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?

This Podcast is sponsored by QueBIT experts in analytics.

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!

This episode is brought to you by QueBIT experts in analytics. 

Episode 26: Trusting the AI Forecast in Finance

Guest: Allen Hudson

Many companies are looking for a way to transcend the limitations of Excel for financial and operational planning, reporting and analysis. They need something that will enable them to securely centralize their data and models (so that both numbers and calculations are managed in just one place), collaborate easily, all while still retaining the flexibility to adjust in response to changing business conditions and organizational structures.

Moving away from Excel to a planning software platform is just the first step, but it is an important one. The obvious benefit is that it releases analysts from the drudgery of manipulating data in Excel spreadsheets, and the stress of worrying whether all the reports are correct. They can use their new-found freedom to do more analysis and thinking about the data, and to find ways to add real value to the business. Examples range from understanding the cost structure (and finding savings), to examining sales performance (and driving revenue).

A possible next step, and one that more and more QueBIT customers are interested in, is how to deploy Predictive and Artificial Intelligence technologies to their advantage. In this week’s episode we share the story of a customer we worked with recently to incorporate a computer-generated predictive forecast into their planning processes. Just as the first step reduced drudgery for the analysts responsible for managing and consolidating the planning process, this step can eliminate drudgery for those responsible for doing the forecast, and enable them to focus their attention on areas of strategic and competitive importance to the business!

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.

In this week’s podcast Gus Slaughter, QueBIT Business Intelligence and Data Management sales lead makes the case for how IBM Cognos Analytics introduces a whole new paradigm for self-service BI, by leveraging Artificial Intelligence (AI) to help business users bridge the technology gap in a variety of ways.

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!

When our guest, VP of Consulting Services Brian Moore, leads a project to implement a new financial analytics software solution, he is acutely aware of the problems that can arise when change is imposed on an organization, even if it is ultimately for the better! In this week’s episode he shares his checklist of what you need to take into account, and provides practical advice for managing change successfully.  This episode is sponsored by QueBIT, trusted experts in analytics.

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.

In this episode Principal Data Scientist James Saronson explains how predictive technologies can be leveraged to optimize pricing decisions. He describes it as a human-machine partnership where the machine deals with complexity and volume, but people still get the final say. We also talk about examples and wrap up with advice on how to be successful in implementing a solution of this kind.  This podcast is sponsored by QueBIT the trusted experts in analytics.