Dashboarding, reporting, business intelligence, and funky visualisations

Having information in dashboards is important. Having contextualised information in actionable dashboards is important-er.

Looking at the speedometer in your car is an oft-used analogy for reporting on the actual numbers of how your business is performing.

But the speed at which you’re cruising only tells so much and speed loses much of its meaning when presented
just by itself.

What are the speed limits on the road signs (legislation)?
Which country are we driving in (differences in constraints)?
What are the road conditions (environment)?
Are other drivers slowing you down in traffic jams (competition)?
How fast is your vehicle (product)?
Who is driving along with you (customer)?
And how awake/fresh/skilled are you driving that car (lean organisation)?
What speed were you actually planning to go (budget)?
And what speed is your navigation predicting you should be driving (forecast)?
And what do other people think of your driving (social media)?

It’s only when numbers are contextualised that they make sense, and bring real value to the organisation.

Common sense tells us that you should be not only looking at your business, but also how surrounding factors affect it and its performance.

We agree, and to the best of our knowledge, the most efficient way of doing it is with TM1.

Most organisations these days have in place reporting tools or mechanisms in Excel, Business Intelligence tools, ERP reports, but depending on the nature of what you are looking at, the level of detail of these reports will differ in terms of date/time and contents.

Staying on top of what is happening is a necessity to be able to respond to events as they happen. As it is impossible to predict and forecast reality 100%, reactive processes steered by reports have their rightful place in every company’s operations.

In order to make sure these reports are bringing value to their maximum potential, showing context is essential. For example, not just looking at the revenue number, but showing it with the volumes, product mix, year-on-year comparisons, versus budget and forecast. Or even taking it one step further and linking the number to the underlying drivers.

How much was produced, by which factory, with how many production hours? Is the inventory turnover in line with expectations? Or which deviation in the sales forecast is responsible for the overstock of certain raw materials?

Looking at what happened is important, but knowing why is even more valuable.

In the plan and in our reporting we are using drivers to explain the reality. But are those drivers sufficient?

Especially when we are breaking outside of the shell of our own internal data and decision-making, looking at macro-economic trends, weather, competition, customer trends, technology, evolutions in employment regulation etc… there are so many more things to consider.

Through correlation analysis, clustering techniques and data mining, relationships can be discovered between your internal data/actual results and a variety of drivers. How relevant each driver is, is a discovery process.

While Excel has its limitations in terms of planning and reporting, it is in this discipline that its limitations become even more evident.

In this type of setup we tend to see a cycle of progressive insight.

Realising the correlation between certain drivers and the results we are perceiving, can show us that maybe certain drivers aren’t as important as we thought they were, or they have changed over time. Some people call this “AI”, but let’s not do that here – it is nothing new, just becoming more prevalent and easier to use.

Asking the why question, using data and insights, will help you focus on addressing the right problems and set a strategy geared for the future.

There is a goldmine in internal and external data — use it to its full potential and automate the heavy lifting to get to better insights in all aspects of your business.

Linear regression in your Excel Model with TM1 and Python through TM1Py
With TM1 and Python, you can easily calculate the correlation between weather data and sales data in a particular location through a linear regression — R², standard error, and P value. It’s all there.

The specific case of everyone’s favourite finance process:
month-end close.

We spend a lot of time talking about planning and reporting, but let’s not forget everyone’s favourite finance process: month end close.

This is maybe where most of the time in spreadsheets is spent and potentially in the most core financial business processes where accuracy, compliance and collaboration are paramount. 

TM1 has been the unsung hero of month end close processes since the 1980s. 

All of the reporting, analysis, calculations, allocations, reconciliations, and flat-out storing of data and version control for many companies is happening in spreadsheets. And often finance workers do not realise that there is any other way to do this outside of the limited capability and promise from GL and ERP vendors. 

But TM1 fills these gaps perfectly by taking business-critical, collaborative processes of month end living in spreadsheets and putting it into a workflow-managed, central, secure, robust model that allows everyone to collaborate with it and is automatically and reliably updating data from systems of record. And the best is, you still get to use Excel as your user interface to the system. 

Recently products focused just on this seem to have gained in popularity, but TM1 has more than a three decade head start on these tools and a global fanbase to prove that TM1 is *the* month end tool of choice.