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Lately there’s been quite a bit of talk about how an organization could - or should - implement a data strategy. One insightful example is a recent post from IBM providing a “six-step approach”.

This is the last (for now) in a series of blog posts about whether ChatGPT can help us with Planning Analytics (PA) / TM1, more specifically with Turbo Integrator (TI) script. In the first post in the series, I provided some background on AI technology like ChatGPT and really useful AI tools for programmers, like GitHub Copilot. In the second post in the series, I shared some ways in which ChatGPT is already able to help understand TI script, which could be useful in tasks like system documentation. One other finding in that second blog post is that although ChatGPT does a good job of accurately describing TI scripts, it is not yet capable of helping to suggest ways of optimizing or improving those scripts; it suggests things that are not possible in TI script (like CASE statements) and even makes up functions which don’t exist. As a reminder, all ChatGPT responses in this series are from ChatGPT-3.5, unless otherwise specified.

The integration of Microsoft Excel with Planning Analytics, powered by TM1 Server, remains one of the most powerful pieces in the overall planning, modeling, and other solution capabilities provided by Planning Analytics! In a recent webinar of the same name, which you can find in our QueBIT video catalog, we focused on that integration and made a strong case for the Planning Analytics for Excel add-in as the Excel integration standard-bearer, going forward.

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.

In an earlier post (IBM Planning Analytics Data Modelling with Context) I stated that when modeling data as part of a planning analytics solution design, context clues should be developed, through a process referred to as profiling and then “built in” to the data.

Blog#3: The six best things from an Administrator’s Perspective.

Cognos Planning (formally known as Adaytum Planning) has had a good run, but the state-of-the-art solution, with respect to planning and forecasting systems, has moved on since the early 1990s. Today, we have higher expectations, including:

Blog #2: The six best things from a User’s Perspective.

Cognos Planning (formally known as Adaytum Planning) has served many companies well since the early 1990’s, but is approaching the end of its life from an IBM support perspective, and the product is no longer being further developed by IBM.

The target audience for this blog post is anyone on IBM Cognos TM1 10.2.2, or an older release, who is looking to upgrade to IBM Planning Analytics. TM1 10.2.0 will no longer be supported by IBM after September 2018 (this year) and TM1 10.2.2 will no longer be supported after September 2019.

If you are still on a version of IBM Cognos TM1, it is time to plan your upgrade to its rebranded successor, IBM Planning Analytics.