Real Time Visual AnalyticsTalking face to face with your data

Visual data analysis lets us use our powerful visual perceptual abilities (we all have this) to quickly and clearly see what our data is telling us

Doing it in real time lets us think our way through the questions and the insights that the data reveals

PS: This is the reading version of this presentation. Never present and speak to something with this much text!

Let’s illustrate this with an example. Suppose you are the sales manager for a hot beverage company. You have data on your sales, expenses and profits across the United States. You know there are some disappointing overall results in several areas, but also that it is not clear what is driving these. But the Board wants to know. Having fun yet?

Here’s part of a set of data, representing sales, expenses and profits for your beverage company.

Would you rather be able to see the trends and relationships in the data right now? Or would you prefer to ship it off to the data analyst and get a technical report later?

* This hypothetical data is supplied by Tableau™ software for product demonstration and education

Really?

What if you could explore your data live with your team, and then take your management, again live, through the findings that you have uncovered? Instead of trying to speak to a report that you didn’t even write.

Here’s a first look at the data, analysed live. This took virtually no time whatsoever, using software capable of instant data visualization. So let’s dig a little deeper – we want to see where these sales are coming from and why some are so high, or low……

(the data graphics in this presentation were produced with Tableau Desktop™)

Again, dragging and dropping in a new variable, product type, we can see that the picture is not so simple … the primary sources of sales vary across the states… it’s a bit messy, so let’s sort on one of the categories….

There we go, sorted instantly. Now we see more clearly some of the key differences in the sales of the product types across the states by simply looking at the shapes of the distributions relative to the sorted one and each other.

So, how’s this related to profitability? Do profits mirror sales?

Not exactly a one to one relationship between profits and sales by state – at least not in some cases.

So there is lots to look at. Suppose we look a little closer at what lies behind these aggregates?

We see lots of things. Highly profitable California is so due to strength in espresso and teas, as is Illinois. We’d have a more profitable operation in Missouri if we didn’t try to sell herbal tea there, nor espresso in New Hampshire. Little profit is made in coffee in Iowa, nor Nevada, which are overall high profit states. And so on…. visual analysis works quickly and powerfully.

We could also visualize this as a table…..

Here, use of colour coded to profits makes the points of interest stand out.

Suppose we are the coffee team. We are surprised by results such as California, where coffee has not been the profit driver.

Here we can produce, visually, the cross tabulation of profits, sales and expenses by state, for the three types of coffee we sell.

For instance, we see clearly that in California, we are only doing well in Columbian coffee, thus reducing “coffee” overall as the profit driver in that state. Expenses are too high relative to sales for Amaretto and Decaf Irish Cream. No need for econometrics here!

Remember, all of this analytical power and clarity has come from data visualizations that can be done on the spot, at the monthly sales meeting, from this unfriendly – looking mess of data (this is just the profit data) below. No need to send away to the data crunching department to find out what’s going on.

Putting key views into a dashboard, where everything can be seen at once allows even more analytical power and clarity. This, again, is done on the spot during visual data analysis.

Perhaps we’d like to see a more direct analysis of the relationship between sales and profit. A quick drag and drop and we have a scatter plot with trend line. It’s a pretty strong relationship, but with a significant set of unprofitable outliers. The points are individual vendors.

To look at this in a bit more detail, we can use a “trellis” or “small multiples” display. We see here, as before, that there is a problem with two of the products in the West. Since we already know that California is where this is happening most, let’s go there…..

With another quick adjustment of the visual, we can see that the relationship of sales to profits, while poor in both cases, is different for the Amaretto and Decaf Irish Cream products – in the latter case, the more is being sold by a vendor the more their profitability falls. These would offer different implications to the product manager as to how to improve these results.

In this demonstration, visual data analysis let us use our own powerful visual perceptual abilities see what the data was saying. And this could have been done live, at the sales meeting. No reams of statistics and charts, no delay.

That’s a better way to work.

Real Time Visual AnalyticsTalking face to face with your data