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The world of business is always changing. To keep up, companies must embrace new technologies and methods to streamline their processes. One area that can be greatly improved with automation is revenue forecasting. Automated revenue forecasting can reduce the amount of time spent on data gathering and analysis, leading to more accurate predictions and a better bottom line. However, automated forecasting with machine learning (ML) can take things one step further by incorporating past data, trends, and other factors that may not be easily observed or quantified. This leads to even more accurate predictions and a better understanding of the business landscape.

Inclusive cultures strive to make people – all people - feel valued and proud of who they are. Statistically, diversity in the workplace indicates that inclusive companies are more likely to create a workforce that reflects a variety of backgrounds, experiences and needs. Often though, for a number of reasons, there is a demographic that is excluded from these efforts and that is those with disabilities.

It’s not easy being a CIO these days. Not only are you expected to keep the technology infrastructure purring along smoothly (e.g. “let’s all just work from home!”) and safely (e.g. “we can’t afford to be hacked!”) in the face of unrelenting external pressures, but you must also be an active participant in satisfying the digital-transformation needs of the business.

Forecasting is a discipline that reduces companies' exposure to risk. The process of forecasting alerts us to upcoming shortages in supply or demand, allowing us to take precautionary measures before we find ourselves in a negative cash-flow scenario. Forecasting is truly a critical business function within any organization. Automating your forecasting process can help you maximize efficiency while reducing costs and improving accuracy. This article will outline how you can use a variety of tools and approaches to automate various aspects of your forecasting process.

It is no secret that analytics and planning projects are based on data. Typically projects involve wrangling some historical datasets, each with their own peculiarities, and then adding in some projection or prediction. Because data is essential, it is also a common stumbling block as business users consider beginning a new analytics and planning project.

QueBIT routinely works with clients that have either selected a poor-performing planning software tool, or who are suffering from the poor implementation of a good planning software tool. Both of these scenarios-- bad software and a bad implementation-- can dramatically hinder the cross-functional planning process and make improvement efforts less impactful and responsive. The challenge for a given client often then comes in recognizing which scenario applies to the situation at hand. Is this client underperforming because their software isn't a good fit? Or is it really about how the software is set up inside that particular company?

In the two decades since QueBIT was founded, we have often been puzzled by the slow pace of change in enterprise planning and performance management. As recently as 2019 we attended a CFO symposium, only to be surprised at how the case study presentations around revenue and expense planning, headcount and capital planning, and variance and profitability analysis were little different from spreadsheet-centric presentations we had seen ten years before, and how few finance organizations had embraced technology to support data extraction, data governance and modeling their businesses.

Upon releasing the new version of Planning Analytics Workspace (PAW) 2.0.71 SC, IBM has also announced some important information regarding the use the of older versions for their Cloud clients. With the release of PAW version 2.0.56 in October 2020, IBM completely updated the PAW tool with a brand new user experience. Since that point all newer versions of PAW have used the new experience with all versions prior to the 2.0.56 upgrade labeled as Planning Analytics Workspace Classic.

With evolving regulatory requirements, ever growing product lines and no shortage of legal entities, allocations are critical for every insurance organization’s financial reporting process. An effective allocation process always starts with a good design, and in this blog post I will outline several best practices to consider.