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Our Tableau dashboards don’t tell us very much

Our Tableau dashboards don’t tell us very much

Chelsea Wise

Most people have bad dashboards. And if you don’t, then you’re lucky. 

Seeing hundreds of Tableau and Power BI dashboards across Fortune 500 and ASX listed companies, the following is an example of the quality of insights delivered across the business, in this example, an iconic Australian business.

This is a newly implemented Tableau dashboard, delivered in February 2020, cost $250K (yearly licence fee and build cost), and tells the organisation the following:

  • Total sales are down 1000% vs. last year
  • Revenue is also down across most of the key brands/regions
  • Membership revenue is down by 400% vs. last year
  • Number of website visitors is about the same vs. last month
  • Number of calls to the support office is up by 1000% vs. last month
  • Number of instagram followers is down by 20% vs. last month
Google Analytics dashboard showing statistics on the last 6 months

In good times?

In good times, nobody really cares whether the insights process is broken. Most things are up. Some things are down. If performance is down, go ask the BI team for more information. Get a ticket and wait in the queue.

But bad dashboards in hard times?

In hard times, it becomes blatantly obvious that the brief to the BI team to produce “a simple dashboard that covers key KPIs, a bit of google analytics and a bit of social”, does little to help the organisation know what the heck to do in these uncertain times.

We repeat, that was the exact brief:

“To build a simple dashboard that covers key KPIs, a bit of google analytics and a bit of social”.

How does this help in hard times? Who wins in this situation? 

Is the dashboard company happy? 

On one hand, they received $250K in licence fees and set-up costs to implement the system, following the brief. They did their job successfully as briefed. But on the other hand, they don’t want a client to have a poorly designed dashboard, or one that offers little insight in tricky times, such as now.

Is the BI team happy? 

Not at all. Some have already been stood down due to COVID-19. The remaining team members are overwhelmed with requests and can’t keep up with the demands of the business. 

And the rest of the business? Namely, the consumers of this dashboard? 

Challenged to cut costs and invest in growth, told to make decisions today, with little support from the insights team, it’s clear that the so-called ‘simple’ dashboard is not designed to move further into the data, to unlock deeper insights and do this automatically, and in simple english, without the need to ‘get a ticket and wait for the BI team to answer our questions’.


How do you discover insights when times are tough?

So how do you discover which areas to improve or invest in when Finance and BI teams are overwhelmed with request after request

Broadly speaking, there are three ways to discover insights:

  1. The traditional ‘get a ticket and wait in the queue’ approach, relying largely on others (and large budgets) to discover insights.
  2. The ‘export the data into Excel and do-it-yourself’ approach, relying largely on your own abilities to discover insights.
  3. The ‘Hyper Anna’ approach, leveraging the power of AI to automate insight discovery at scale.

In most organisations, options (1) and (2) are the norm. However, in times of a recession, option (3) is critical to service the company to inform tough decisions today. In an upcoming article, we’ll discuss all three methods of discovering insights covering both pros and cons. Watch this space.

So, that’s our thoughts for today. Good luck in your businesses. Stay tuned for more on how to find the key insights during these uncertain economic times. 

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Note

Our recommended reading:

For further reading on the value of dashboards, we recommend this letter to dashboard addicts. Not seeking to look down on dashboards (in fact, he builds them all the time), Jon clearly articulates the place and purpose of dashboards in the process of discovering insights, with the key takeaway being that they shouldn’t be used as a ‘hammer’, namely, not the only tool for insight discovery.

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