TM1 is a powerful modeling and analysis tool. It can handle extremely large amounts of data –
transactional data, revenue and cost on millions of products, workforce planning for thousands of employees worldwide, etc. But that doesn’t mean that all TM1 models are equally robust. Good architecture is necessary to ensure that models perform well, and are easy to use.
Here at QueBIT, we have developed hundreds of solutions for our clients. That’s as expected, it’s our business! We strive to get input from business users prior to implementing their new systems. Standing up a new, automated solution (planning, sales forecasting, or operational) is exciting, and can be transformative for an organization and its employees. We don’t want you to have to re-create the wheel, when implementing a project. If you’re looking at automating a new system, here’s a cheat sheet to help you avoid common pitfalls.
IBM released the latest version of Cognos Analytics (Cognos 11.0.6) on March 17, 2017. The newest version continues to add enhancements to its key functional areas. In this release, IBM focused on making it faster and easier to create dashboards and provided more enhancements to the storytelling capabilities. There are significant enhancements to the mapping capabilities by increasing the resolution of the postal code/area code level and expanding support for up to five levels of administrative boundaries.
Topics: Cognos Analytics 11.0.6
If you’re a TM1 user, you know rules are how you work business logic into your model. You probably calculate things like salaries and sales, and you may even have more complex calculations in your model such as exchange rates and allocations.
It is often difficult to understand the story that a particular set of data is telling without a meaningful visualization. You can examine the numbers, but without plotting a trendline or a histogram, it is almost impossible to extract actionable intelligence from them. With IBM’s Cognos Disclosure Management (CDM), native Excel functionality is the user’s toolbox for creating these data visualizations. However, despite there being a breadth of options at the user’s disposal, some of the more complex visualizations are quite difficult to create in Excel. This may include things like stacked bar charts, mekko charts, bubble charts, among others.
It’s the end of the quarter, and you’re looking to put together your quarterly presentation for the board. You’ve run the data, the numbers look good, and you’ve done your qualitative analysis. There’s only one thing left to do – update the PowerPoint. With 40 slides worth of numbers, charts, and commentary to update, this is actually easier said than done, and will require countless hours of work. So you think to yourself, is there a better way to do this?
Data science has become a key differentiator for many companies. It’s no longer an isolated group of analysts, with little focus on tangible results, building predictive models in a vacuum. Data science has matured to become an integral part of an overall business strategy to leverage the most important corporate asset: data.
For the uninitiated, perhaps a brief primer is in order around the subject of data science. If you’ve only recently heard of the “data scientist” moniker, you’re in pretty good company. According to Google Trends, use of the term was relatively obscure just a few short years ago.