IBM returned to Miami this year with a renamed and somewhat-rebranded version of the conference formerly known as IBM Analytics University. The concept of this new IBM Data and AI Forum was structured around IBM’s “AI ladder” and the rungs of that conceptual ladder: Modernize, Collect, Organize, Analyze and Infuse. It should come as no surprise that “AI” was, thus, more prevalent as a topic of discussion.
The ASC 606 revenue recognition rules took effect for most public companies in December 2017. This Accounting Standards Update explains how and when your organization would recognize revenue from contracts with your customers. The guideline helps improve the comparability of revenue recognition practices, provide more useful information to financial statement users, and simplify the preparation of financial statements, for example. While many companies have gone through the trouble to restate actuals to follow the new ASC 606 standards, some are not yet ready to forecast and plan revenue on the same basis. They still need to update their budgeting and forecasting processes to align with the new standards. Here are FIVE STEPS to follow to update your planning processes and model to conform to the ASC 606 standards.
The field of analytics has exploded over the decade as data itself has exploded. From the phone in your pocket to “smart” toilet paper holders which analyze your “technique” and automatically reorder paper when you’re out (I bet you think I’m joking…I’m not), data and analytics inundate our lives. Harnessing the meaningful stuff and letting go of the rest is more difficult and yet more important than ever. Hmm, just re-read that last sentence and that’s a great life lesson, not just a declaration about analytics, isn’t it!
Can we level the playing field and admit off the top that everybody, or more appropriately, every organization plans? This has been a mantra of my colleague A.G. Tan for as long as I’ve had the pleasure of working with her, and it’s no less true today than it was 4 1/2 years ago when I heard it for the first time, and no less true spanning the decades of collective experience at QueBIT Consulting. And while some planning is simple (e.g. single GL, single chart of accounts, single data source) and some complex (e.g. multiple companies, consolidations, multiple GLs/charts of accounts, multiple data sources, etc.) at the end of the day, much of the work is the same, it’s a matter of approach and scale and having the right tools to support the plan.
IBM Planning Analytics (TM1) gives us the ability to quickly and effectively prototype a potential business solution demonstrating to stakeholders and others proposed functionalities and capabilities of a design rather than relying on only discussions and theory.
If you are a long time IBM Planning Analytics (TM1) administrator, you may have developed a fondness through the years for a utility called TM1TOP. TM1TOP would tell you what your TM1 Server was up to, which was especially useful if users called to complain that it was “slow” or “hanging”.
We at QueBIT think of Cognos Analytics as more than just a BI tool, but rather as a platform that contains BI tools. Between the introduction of the Exploration tool and visualization insights in the past year, Cognos Analytics has enabled users to explore, understand, model, visualize, and most importantly - share these findings securely with others. This means that the platform socializes data engineers, data architects, analytics professional, process experts, and consumers by keeping each party involved in the process of making actionable decisions with data.
I’ve got a lot of stuff in my office. Your house is filled with a lot of things you might not need. And then there’s that lot# on your beverage can. But what is a lot and why does it matter? How can something as arbitrary as a “lot” have real importance in our everyday lives? In the English language the word “lot” is used for describing a large quantity of items or some grouping or set of items. In operations we define “lots” as groups of goods received or produced.